diff --git a/.rat-excludes b/.rat-excludes
index 2238a5b68e359..ac652ed29fc09 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -30,6 +30,9 @@ spark-env.sh.template
 log4j-defaults.properties
 bootstrap-tooltip.js
 jquery-1.11.1.min.js
+d3.min.js
+dagre-d3.min.js
+graphlib-dot.min.js
 sorttable.js
 vis.min.js
 vis.min.css
@@ -71,5 +74,12 @@ logs
 .*scalastyle-output.xml
 .*dependency-reduced-pom.xml
 known_translations
+json_expectation
+local-1422981759269/*
+local-1422981780767/*
+local-1425081759269/*
+local-1426533911241/*
+local-1426633911242/*
+local-1427397477963/*
 DESCRIPTION
 NAMESPACE
diff --git a/LICENSE b/LICENSE
index 21c42e9a20fa3..b2001f029a4f0 100644
--- a/LICENSE
+++ b/LICENSE
@@ -643,6 +643,36 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 THE SOFTWARE.
 
+========================================================================
+For d3 (core/src/main/resources/org/apache/spark/ui/static/d3.min.js):
+========================================================================
+
+Copyright (c) 2010-2015, Michael Bostock
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* The name Michael Bostock may not be used to endorse or promote products
+  derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL MICHAEL BOSTOCK BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
+EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 ========================================================================
 For Scala Interpreter classes (all .scala files in repl/src/main/scala
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index b59b700af5dc9..841e77e55e0d8 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -167,7 +167,7 @@ setMethod("isLocal",
 setMethod("showDF",
           signature(x = "DataFrame"),
           function(x, numRows = 20) {
-            cat(callJMethod(x@sdf, "showString", numToInt(numRows)), "\n")
+            callJMethod(x@sdf, "showString", numToInt(numRows))
           })
 
 #' show
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index af7a6c582047a..f82e56fdd8278 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -641,7 +641,7 @@ test_that("toJSON() returns an RDD of the correct values", {
 
 test_that("showDF()", {
   df <- jsonFile(sqlCtx, jsonPath)
-  expect_output(showDF(df), "age  name   \nnull Michael\n30   Andy   \n19   Justin ")
+  expect_output(showDF(df), "+----+-------+\n| age|   name|\n+----+-------+\n|null|Michael|\n|  30|   Andy|\n|  19| Justin|\n+----+-------+\n")
 })
 
 test_that("isLocal()", {
diff --git a/core/pom.xml b/core/pom.xml
index 2dfb00d7ecf26..fc42f48973fe9 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -228,6 +228,14 @@
       <artifactId>json4s-jackson_${scala.binary.version}</artifactId>
       <version>3.2.10</version>
     </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.mesos</groupId>
       <artifactId>mesos</artifactId>
@@ -273,7 +281,6 @@
     <dependency>
       <groupId>org.apache.ivy</groupId>
       <artifactId>ivy</artifactId>
-      <version>${ivy.version}</version>
     </dependency>
     <dependency>
       <groupId>oro</groupId>
@@ -362,7 +369,7 @@
     <dependency>
       <groupId>org.spark-project</groupId>
       <artifactId>pyrolite</artifactId>
-      <version>2.0.1</version>
+      <version>4.4</version>
     </dependency>
     <dependency>
       <groupId>net.sf.py4j</groupId>
diff --git a/core/src/main/java/org/apache/spark/JobExecutionStatus.java b/core/src/main/java/org/apache/spark/JobExecutionStatus.java
index 6e161313702bb..0287fb79f8dd2 100644
--- a/core/src/main/java/org/apache/spark/JobExecutionStatus.java
+++ b/core/src/main/java/org/apache/spark/JobExecutionStatus.java
@@ -17,9 +17,15 @@
 
 package org.apache.spark;
 
+import org.apache.spark.util.EnumUtil;
+
 public enum JobExecutionStatus {
   RUNNING,
   SUCCEEDED,
   FAILED,
-  UNKNOWN
+  UNKNOWN;
+
+  public static JobExecutionStatus fromString(String str) {
+    return EnumUtil.parseIgnoreCase(JobExecutionStatus.class, str);
+  }
 }
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java b/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
new file mode 100644
index 0000000000000..8c7dcf776fda8
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/status/api/v1/ApplicationStatus.java
@@ -0,0 +1,30 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.status.api.v1;
+
+import org.apache.spark.util.EnumUtil;
+
+public enum ApplicationStatus {
+  COMPLETED,
+  RUNNING;
+
+  public static ApplicationStatus fromString(String str) {
+    return EnumUtil.parseIgnoreCase(ApplicationStatus.class, str);
+  }
+
+}
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java b/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
new file mode 100644
index 0000000000000..9dbb565aab707
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/status/api/v1/StageStatus.java
@@ -0,0 +1,31 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.status.api.v1;
+
+import org.apache.spark.util.EnumUtil;
+
+public enum StageStatus {
+  ACTIVE,
+  COMPLETE,
+  FAILED,
+  PENDING;
+
+  public static StageStatus fromString(String str) {
+    return EnumUtil.parseIgnoreCase(StageStatus.class, str);
+  }
+}
diff --git a/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java b/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
new file mode 100644
index 0000000000000..f19ed01d5aebf
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/status/api/v1/TaskSorting.java
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.status.api.v1;
+
+import org.apache.spark.util.EnumUtil;
+
+import java.util.HashSet;
+import java.util.Set;
+
+public enum TaskSorting {
+  ID,
+  INCREASING_RUNTIME("runtime"),
+  DECREASING_RUNTIME("-runtime");
+
+  private final Set<String> alternateNames;
+  private TaskSorting(String... names) {
+    alternateNames = new HashSet<String>();
+    for (String n: names) {
+      alternateNames.add(n);
+    }
+  }
+
+  public static TaskSorting fromString(String str) {
+    String lower = str.toLowerCase();
+    for (TaskSorting t: values()) {
+      if (t.alternateNames.contains(lower)) {
+        return t;
+      }
+    }
+    return EnumUtil.parseIgnoreCase(TaskSorting.class, str);
+  }
+
+}
diff --git a/core/src/main/java/org/apache/spark/util/EnumUtil.java b/core/src/main/java/org/apache/spark/util/EnumUtil.java
new file mode 100644
index 0000000000000..c40c7e727613c
--- /dev/null
+++ b/core/src/main/java/org/apache/spark/util/EnumUtil.java
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.util;
+
+import com.google.common.base.Joiner;
+import org.apache.spark.annotation.Private;
+
+@Private
+public class EnumUtil {
+  public static <E extends Enum<E>> E parseIgnoreCase(Class<E> clz, String str) {
+    E[] constants = clz.getEnumConstants();
+    if (str == null) {
+      return null;
+    }
+    for (E e : constants) {
+      if (e.name().equalsIgnoreCase(str)) {
+        return e;
+      }
+    }
+    throw new IllegalArgumentException(
+      String.format("Illegal type='%s'. Supported type values: %s",
+        str, Joiner.on(", ").join(constants)));
+  }
+}
diff --git a/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js b/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js
index 2934181c1006a..acd6096e6743e 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/bootstrap-tooltip.js
@@ -1,9 +1,9 @@
 /* ===========================================================
- * bootstrap-tooltip.js v2.2.2
- * http://twitter.github.com/bootstrap/javascript.html#tooltips
+ * bootstrap-tooltip.js v2.3.2
+ * http://getbootstrap.com/2.3.2/javascript.html#tooltips
  * Inspired by the original jQuery.tipsy by Jason Frame
  * ===========================================================
- * Copyright 2012 Twitter, Inc.
+ * Copyright 2013 Twitter, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -38,19 +38,27 @@
   , init: function (type, element, options) {
       var eventIn
         , eventOut
+        , triggers
+        , trigger
+        , i
 
       this.type = type
       this.$element = $(element)
       this.options = this.getOptions(options)
       this.enabled = true
 
-      if (this.options.trigger == 'click') {
-        this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
-      } else if (this.options.trigger != 'manual') {
-        eventIn = this.options.trigger == 'hover' ? 'mouseenter' : 'focus'
-        eventOut = this.options.trigger == 'hover' ? 'mouseleave' : 'blur'
-        this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
-        this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
+      triggers = this.options.trigger.split(' ')
+
+      for (i = triggers.length; i--;) {
+        trigger = triggers[i]
+        if (trigger == 'click') {
+          this.$element.on('click.' + this.type, this.options.selector, $.proxy(this.toggle, this))
+        } else if (trigger != 'manual') {
+          eventIn = trigger == 'hover' ? 'mouseenter' : 'focus'
+          eventOut = trigger == 'hover' ? 'mouseleave' : 'blur'
+          this.$element.on(eventIn + '.' + this.type, this.options.selector, $.proxy(this.enter, this))
+          this.$element.on(eventOut + '.' + this.type, this.options.selector, $.proxy(this.leave, this))
+        }
       }
 
       this.options.selector ?
@@ -59,7 +67,7 @@
     }
 
   , getOptions: function (options) {
-      options = $.extend({}, $.fn[this.type].defaults, options, this.$element.data())
+      options = $.extend({}, $.fn[this.type].defaults, this.$element.data(), options)
 
       if (options.delay && typeof options.delay == 'number') {
         options.delay = {
@@ -72,7 +80,15 @@
     }
 
   , enter: function (e) {
-      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
+      var defaults = $.fn[this.type].defaults
+        , options = {}
+        , self
+
+      this._options && $.each(this._options, function (key, value) {
+        if (defaults[key] != value) options[key] = value
+      }, this)
+
+      self = $(e.currentTarget)[this.type](options).data(this.type)
 
       if (!self.options.delay || !self.options.delay.show) return self.show()
 
@@ -97,14 +113,16 @@
 
   , show: function () {
       var $tip
-        , inside
         , pos
         , actualWidth
         , actualHeight
         , placement
         , tp
+        , e = $.Event('show')
 
       if (this.hasContent() && this.enabled) {
+        this.$element.trigger(e)
+        if (e.isDefaultPrevented()) return
         $tip = this.tip()
         this.setContent()
 
@@ -116,19 +134,18 @@
           this.options.placement.call(this, $tip[0], this.$element[0]) :
           this.options.placement
 
-        inside = /in/.test(placement)
-
         $tip
           .detach()
           .css({ top: 0, left: 0, display: 'block' })
-          .insertAfter(this.$element)
 
-        pos = this.getPosition(inside)
+        this.options.container ? $tip.appendTo(this.options.container) : $tip.insertAfter(this.$element)
+
+        pos = this.getPosition()
 
         actualWidth = $tip[0].offsetWidth
         actualHeight = $tip[0].offsetHeight
 
-        switch (inside ? placement.split(' ')[1] : placement) {
+        switch (placement) {
           case 'bottom':
             tp = {top: pos.top + pos.height, left: pos.left + pos.width / 2 - actualWidth / 2}
             break
@@ -143,11 +160,56 @@
             break
         }
 
-        $tip
-          .offset(tp)
-          .addClass(placement)
-          .addClass('in')
+        this.applyPlacement(tp, placement)
+        this.$element.trigger('shown')
+      }
+    }
+
+  , applyPlacement: function(offset, placement){
+      var $tip = this.tip()
+        , width = $tip[0].offsetWidth
+        , height = $tip[0].offsetHeight
+        , actualWidth
+        , actualHeight
+        , delta
+        , replace
+
+      $tip
+        .offset(offset)
+        .addClass(placement)
+        .addClass('in')
+
+      actualWidth = $tip[0].offsetWidth
+      actualHeight = $tip[0].offsetHeight
+
+      if (placement == 'top' && actualHeight != height) {
+        offset.top = offset.top + height - actualHeight
+        replace = true
+      }
+
+      if (placement == 'bottom' || placement == 'top') {
+        delta = 0
+
+        if (offset.left < 0){
+          delta = offset.left * -2
+          offset.left = 0
+          $tip.offset(offset)
+          actualWidth = $tip[0].offsetWidth
+          actualHeight = $tip[0].offsetHeight
+        }
+
+        this.replaceArrow(delta - width + actualWidth, actualWidth, 'left')
+      } else {
+        this.replaceArrow(actualHeight - height, actualHeight, 'top')
       }
+
+      if (replace) $tip.offset(offset)
+    }
+
+  , replaceArrow: function(delta, dimension, position){
+      this
+        .arrow()
+        .css(position, delta ? (50 * (1 - delta / dimension) + "%") : '')
     }
 
   , setContent: function () {
@@ -161,6 +223,10 @@
   , hide: function () {
       var that = this
         , $tip = this.tip()
+        , e = $.Event('hide')
+
+      this.$element.trigger(e)
+      if (e.isDefaultPrevented()) return
 
       $tip.removeClass('in')
 
@@ -179,6 +245,8 @@
         removeWithAnimation() :
         $tip.detach()
 
+      this.$element.trigger('hidden')
+
       return this
     }
 
@@ -193,11 +261,12 @@
       return this.getTitle()
     }
 
-  , getPosition: function (inside) {
-      return $.extend({}, (inside ? {top: 0, left: 0} : this.$element.offset()), {
-        width: this.$element[0].offsetWidth
-      , height: this.$element[0].offsetHeight
-      })
+  , getPosition: function () {
+      var el = this.$element[0]
+      return $.extend({}, (typeof el.getBoundingClientRect == 'function') ? el.getBoundingClientRect() : {
+        width: el.offsetWidth
+      , height: el.offsetHeight
+      }, this.$element.offset())
     }
 
   , getTitle: function () {
@@ -215,6 +284,10 @@
       return this.$tip = this.$tip || $(this.options.template)
     }
 
+  , arrow: function(){
+      return this.$arrow = this.$arrow || this.tip().find(".tooltip-arrow")
+    }
+
   , validate: function () {
       if (!this.$element[0].parentNode) {
         this.hide()
@@ -236,8 +309,8 @@
     }
 
   , toggle: function (e) {
-      var self = $(e.currentTarget)[this.type](this._options).data(this.type)
-      self[self.tip().hasClass('in') ? 'hide' : 'show']()
+      var self = e ? $(e.currentTarget)[this.type](this._options).data(this.type) : this
+      self.tip().hasClass('in') ? self.hide() : self.show()
     }
 
   , destroy: function () {
@@ -269,10 +342,11 @@
   , placement: 'top'
   , selector: false
   , template: '<div class="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>'
-  , trigger: 'hover'
+  , trigger: 'hover focus'
   , title: ''
   , delay: 0
   , html: false
+  , container: false
   }
 
 
@@ -285,4 +359,3 @@
   }
 
 }(window.jQuery);
-
diff --git a/core/src/main/resources/org/apache/spark/ui/static/d3.min.js b/core/src/main/resources/org/apache/spark/ui/static/d3.min.js
new file mode 100644
index 0000000000000..30cd292198b91
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/d3.min.js
@@ -0,0 +1,5 @@
+/*v3.5.5*/!function(){function n(n){return n&&(n.ownerDocument||n.document||n).documentElement}function t(n){return n&&(n.ownerDocument&&n.ownerDocument.defaultView||n.document&&n||n.defaultView)}function e(n,t){return t>n?-1:n>t?1:n>=t?0:0/0}function r(n){return null===n?0/0:+n}function u(n){return!isNaN(n)}function i(n){return{left:function(t,e,r,u){for(arguments.length<3&&(r=0),arguments.length<4&&(u=t.length);u>r;){var i=r+u>>>1;n(t[i],e)<0?r=i+1:u=i}return r},right:function(t,e,r,u){for(arguments.length<3&&(r=0),arguments.length<4&&(u=t.length);u>r;){var i=r+u>>>1;n(t[i],e)>0?u=i:r=i+1}return r}}}function o(n){return n.length}function a(n){for(var t=1;n*t%1;)t*=10;return t}function c(n,t){for(var e in t)Object.defineProperty(n.prototype,e,{value:t[e],enumerable:!1})}function l(){this._=Object.create(null)}function s(n){return(n+="")===pa||n[0]===va?va+n:n}function f(n){return(n+="")[0]===va?n.slice(1):n}function h(n){return s(n)in this._}function g(n){return(n=s(n))in 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diff --git a/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js
new file mode 100644
index 0000000000000..6d2da25024a83
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js
@@ -0,0 +1,29 @@
+/*v0.4.3 with 1 additional commit (see http://github.com/andrewor14/dagre-d3)*/(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.dagreD3=f()}})(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){/**
+ * @license
+ * Copyright (c) 2012-2013 Chris Pettitt
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+module.exports={graphlib:require("./lib/graphlib"),dagre:require("./lib/dagre"),intersect:require("./lib/intersect"),render:require("./lib/render"),util:require("./lib/util"),version:require("./lib/version")}},{"./lib/dagre":8,"./lib/graphlib":9,"./lib/intersect":10,"./lib/render":23,"./lib/util":25,"./lib/version":26}],2:[function(require,module,exports){var util=require("./util");module.exports={"default":normal,normal:normal,vee:vee,undirected:undirected};function normal(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function vee(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 0 L 10 5 L 0 10 L 4 5 z").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}function undirected(parent,id,edge,type){var marker=parent.append("marker").attr("id",id).attr("viewBox","0 0 10 10").attr("refX",9).attr("refY",5).attr("markerUnits","strokeWidth").attr("markerWidth",8).attr("markerHeight",6).attr("orient","auto");var path=marker.append("path").attr("d","M 0 5 L 10 5").style("stroke-width",1).style("stroke-dasharray","1,0");util.applyStyle(path,edge[type+"Style"])}},{"./util":25}],3:[function(require,module,exports){var util=require("./util");module.exports=createClusters;function createClusters(selection,g){var clusters=g.nodes().filter(function(v){return util.isSubgraph(g,v)}),svgClusters=selection.selectAll("g.cluster").data(clusters,function(v){return v});svgClusters.enter().append("g").attr("id",function(v){return"cluster_"+v.replace(/^cluster/,"")}).attr("name",function(v){return g.node(v).label}).attr("class","cluster").style("opacity",0).append("rect");util.applyTransition(svgClusters.exit(),g).style("opacity",0).remove();util.applyTransition(svgClusters,g).style("opacity",1);util.applyTransition(svgClusters.selectAll("rect"),g).attr("width",function(v){return g.node(v).width}).attr("height",function(v){return g.node(v).height}).attr("x",function(v){var node=g.node(v);return node.x-node.width/2}).attr("y",function(v){var node=g.node(v);return node.y-node.height/2})}},{"./util":25}],4:[function(require,module,exports){"use strict";var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util"),d3=require("./d3");module.exports=createEdgeLabels;function createEdgeLabels(selection,g){var svgEdgeLabels=selection.selectAll("g.edgeLabel").data(g.edges(),function(e){return util.edgeToId(e)}).classed("update",true);svgEdgeLabels.selectAll("*").remove();svgEdgeLabels.enter().append("g").classed("edgeLabel",true).style("opacity",0);svgEdgeLabels.each(function(e){var edge=g.edge(e),label=addLabel(d3.select(this),g.edge(e),0,0).classed("label",true),bbox=label.node().getBBox();if(edge.labelId){label.attr("id",edge.labelId)}if(!_.has(edge,"width")){edge.width=bbox.width}if(!_.has(edge,"height")){edge.height=bbox.height}});util.applyTransition(svgEdgeLabels.exit(),g).style("opacity",0).remove();return svgEdgeLabels}},{"./d3":7,"./label/add-label":18,"./lodash":20,"./util":25}],5:[function(require,module,exports){"use strict";var _=require("./lodash"),intersectNode=require("./intersect/intersect-node"),util=require("./util"),d3=require("./d3");module.exports=createEdgePaths;function createEdgePaths(selection,g,arrows){var svgPaths=selection.selectAll("g.edgePath").data(g.edges(),function(e){return util.edgeToId(e)}).classed("update",true);enter(svgPaths,g);exit(svgPaths,g);util.applyTransition(svgPaths,g).style("opacity",1);svgPaths.each(function(e){var domEdge=d3.select(this);var edge=g.edge(e);edge.elem=this;if(edge.id){domEdge.attr("id",edge.id)}util.applyClass(domEdge,edge["class"],(domEdge.classed("update")?"update ":"")+"edgePath")});svgPaths.selectAll("path.path").each(function(e){var edge=g.edge(e);edge.arrowheadId=_.uniqueId("arrowhead");var domEdge=d3.select(this).attr("marker-end",function(){return"url(#"+edge.arrowheadId+")"}).style("fill","none");util.applyTransition(domEdge,g).attr("d",function(e){return calcPoints(g,e)});util.applyStyle(domEdge,edge.style)});svgPaths.selectAll("defs *").remove();svgPaths.selectAll("defs").each(function(e){var edge=g.edge(e),arrowhead=arrows[edge.arrowhead];arrowhead(d3.select(this),edge.arrowheadId,edge,"arrowhead")});return svgPaths}function calcPoints(g,e){var edge=g.edge(e),tail=g.node(e.v),head=g.node(e.w),points=edge.points.slice(1,edge.points.length-1);points.unshift(intersectNode(tail,points[0]));points.push(intersectNode(head,points[points.length-1]));return createLine(edge,points)}function createLine(edge,points){var line=d3.svg.line().x(function(d){return d.x}).y(function(d){return d.y});if(_.has(edge,"lineInterpolate")){line.interpolate(edge.lineInterpolate)}if(_.has(edge,"lineTension")){line.tension(Number(edge.lineTension))}return line(points)}function getCoords(elem){var bbox=elem.getBBox(),matrix=elem.getTransformToElement(elem.ownerSVGElement).translate(bbox.width/2,bbox.height/2);return{x:matrix.e,y:matrix.f}}function enter(svgPaths,g){var svgPathsEnter=svgPaths.enter().append("g").attr("class","edgePath").style("opacity",0);svgPathsEnter.append("path").attr("class","path").attr("d",function(e){var edge=g.edge(e),sourceElem=g.node(e.v).elem,points=_.range(edge.points.length).map(function(){return getCoords(sourceElem)});return createLine(edge,points)});svgPathsEnter.append("defs")}function exit(svgPaths,g){var svgPathExit=svgPaths.exit();util.applyTransition(svgPathExit,g).style("opacity",0).remove();util.applyTransition(svgPathExit.select("path.path"),g).attr("d",function(e){var source=g.node(e.v);if(source){var points=_.range(this.pathSegList.length).map(function(){return source});return createLine({},points)}else{return d3.select(this).attr("d")}})}},{"./d3":7,"./intersect/intersect-node":14,"./lodash":20,"./util":25}],6:[function(require,module,exports){"use strict";var _=require("./lodash"),addLabel=require("./label/add-label"),util=require("./util"),d3=require("./d3");module.exports=createNodes;function createNodes(selection,g,shapes){var simpleNodes=g.nodes().filter(function(v){return!util.isSubgraph(g,v)});var svgNodes=selection.selectAll("g.node").data(simpleNodes,function(v){return v}).classed("update",true);svgNodes.selectAll("*").remove();svgNodes.enter().append("g").attr("id",function(v){return"node_"+v}).attr("name",function(v){return g.node(v).label}).attr("class","node").style("opacity",0);svgNodes.each(function(v){var node=g.node(v),thisGroup=d3.select(this),labelGroup=thisGroup.append("g").attr("class","label"),labelDom=addLabel(labelGroup,node),shape=shapes[node.shape],bbox=_.pick(labelDom.node().getBBox(),"width","height");node.elem=this;if(node.id){thisGroup.attr("id",node.id)}if(node.labelId){labelGroup.attr("id",node.labelId)}util.applyClass(thisGroup,node["class"],(thisGroup.classed("update")?"update ":"")+"node");if(_.has(node,"width")){bbox.width=node.width}if(_.has(node,"height")){bbox.height=node.height}bbox.width+=node.paddingLeft+node.paddingRight;bbox.height+=node.paddingTop+node.paddingBottom;labelGroup.attr("transform","translate("+(node.paddingLeft-node.paddingRight)/2+","+(node.paddingTop-node.paddingBottom)/2+")");var shapeSvg=shape(d3.select(this),bbox,node);util.applyStyle(shapeSvg,node.style);var shapeBBox=shapeSvg.node().getBBox();node.width=shapeBBox.width;node.height=shapeBBox.height});util.applyTransition(svgNodes.exit(),g).style("opacity",0).remove();return svgNodes}},{"./d3":7,"./label/add-label":18,"./lodash":20,"./util":25}],7:[function(require,module,exports){module.exports=window.d3},{}],8:[function(require,module,exports){var dagre;if(require){try{dagre=require("dagre")}catch(e){}}if(!dagre){dagre=window.dagre}module.exports=dagre},{dagre:27}],9:[function(require,module,exports){var graphlib;if(require){try{graphlib=require("graphlib")}catch(e){}}if(!graphlib){graphlib=window.graphlib}module.exports=graphlib},{graphlib:57}],10:[function(require,module,exports){module.exports={node:require("./intersect-node"),circle:require("./intersect-circle"),ellipse:require("./intersect-ellipse"),polygon:require("./intersect-polygon"),rect:require("./intersect-rect")}},{"./intersect-circle":11,"./intersect-ellipse":12,"./intersect-node":14,"./intersect-polygon":15,"./intersect-rect":16}],11:[function(require,module,exports){var intersectEllipse=require("./intersect-ellipse");module.exports=intersectCircle;function intersectCircle(node,rx,point){return intersectEllipse(node,rx,rx,point)}},{"./intersect-ellipse":12}],12:[function(require,module,exports){module.exports=intersectEllipse;function intersectEllipse(node,rx,ry,point){var cx=node.x;var cy=node.y;var px=cx-point.x;var py=cy-point.y;var det=Math.sqrt(rx*rx*py*py+ry*ry*px*px);var dx=Math.abs(rx*ry*px/det);if(point.x<cx){dx=-dx}var dy=Math.abs(rx*ry*py/det);if(point.y<cy){dy=-dy}return{x:cx+dx,y:cy+dy}}},{}],13:[function(require,module,exports){module.exports=intersectLine;function intersectLine(p1,p2,q1,q2){var a1,a2,b1,b2,c1,c2;var r1,r2,r3,r4;var denom,offset,num;var x,y;a1=p2.y-p1.y;b1=p1.x-p2.x;c1=p2.x*p1.y-p1.x*p2.y;r3=a1*q1.x+b1*q1.y+c1;r4=a1*q2.x+b1*q2.y+c1;if(r3!==0&&r4!==0&&sameSign(r3,r4)){return}a2=q2.y-q1.y;b2=q1.x-q2.x;c2=q2.x*q1.y-q1.x*q2.y;r1=a2*p1.x+b2*p1.yy+c2;r2=a2*p2.x+b2*p2.y+c2;if(r1!==0&&r2!==0&&sameSign(r1,r2)){return}denom=a1*b2-a2*b1;if(denom===0){return}offset=Math.abs(denom/2);num=b1*c2-b2*c1;x=num<0?(num-offset)/denom:(num+offset)/denom;num=a2*c1-a1*c2;y=num<0?(num-offset)/denom:(num+offset)/denom;return{x:x,y:y}}function sameSign(r1,r2){return r1*r2>0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i<polyPoints.length;i++){var p1=polyPoints[i];var p2=polyPoints[i<polyPoints.length-1?i+1:0];var intersect=intersectLine(node,point,{x:left+p1.x,y:top+p1.y},{x:left+p2.x,y:top+p2.y});if(intersect){intersections.push(intersect)}}if(!intersections.length){console.log("NO INTERSECTION FOUND, RETURN NODE CENTER",node);return node}if(intersections.length>1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distp<distq?-1:distp===distq?0:1})}return intersections[0]}},{"./intersect-line":13}],16:[function(require,module,exports){module.exports=intersectRect;function intersectRect(node,point){var x=node.x;var y=node.y;var dx=point.x-x;var dy=point.y-y;var w=node.width/2;var h=node.height/2;var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;i<lines.length;i++){domNode.append("tspan").attr("xml:space","preserve").attr("dy","1em").attr("x","1").text(lines[i])}util.applyStyle(domNode,node.labelStyle);return domNode}function processEscapeSequences(text){var newText="",escaped=false,ch;for(var i=0;i<text.length;++i){ch=text[i];if(escaped){switch(ch){case"n":newText+="\n";break;default:newText+=ch}escaped=false}else if(ch==="\\"){escaped=true}else{newText+=ch}}return newText}},{"../util":25}],20:[function(require,module,exports){var lodash;if(require){try{lodash=require("lodash")}catch(e){}}if(!lodash){lodash=window._}module.exports=lodash},{lodash:77}],21:[function(require,module,exports){"use strict";var util=require("./util"),d3=require("./d3"),_=require("./lodash");module.exports=positionEdgeLabels;function positionEdgeLabels(selection,g){var created=selection.filter(function(){return!d3.select(this).classed("update")});function translate(e){var edge=g.edge(e);return _.has(edge,"x")?"translate("+edge.x+","+edge.y+")":""}created.attr("transform",translate);util.applyTransition(selection,g).style("opacity",1).attr("transform",translate)}},{"./d3":7,"./lodash":20,"./util":25}],22:[function(require,module,exports){"use strict";var util=require("./util"),d3=require("./d3");module.exports=positionNodes;function positionNodes(selection,g){var created=selection.filter(function(){return!d3.select(this).classed("update")});function translate(v){var node=g.node(v);return"translate("+node.x+","+node.y+")"}created.attr("transform",translate);util.applyTransition(selection,g).style("opacity",1).attr("transform",translate)}},{"./d3":7,"./util":25}],23:[function(require,module,exports){var _=require("./lodash"),layout=require("./dagre").layout;module.exports=render;function render(){var createNodes=require("./create-nodes"),createClusters=require("./create-clusters"),createEdgeLabels=require("./create-edge-labels"),createEdgePaths=require("./create-edge-paths"),positionNodes=require("./position-nodes"),positionEdgeLabels=require("./position-edge-labels"),shapes=require("./shapes"),arrows=require("./arrows");var fn=function(svg,g){preProcessGraph(g);var outputGroup=createOrSelectGroup(svg,"output"),clustersGroup=createOrSelectGroup(outputGroup,"clusters"),edgePathsGroup=createOrSelectGroup(outputGroup,"edgePaths"),edgeLabels=createEdgeLabels(createOrSelectGroup(outputGroup,"edgeLabels"),g),nodes=createNodes(createOrSelectGroup(outputGroup,"nodes"),g,shapes);layout(g);positionNodes(nodes,g);positionEdgeLabels(edgeLabels,g);createEdgePaths(edgePathsGroup,g,arrows);createClusters(clustersGroup,g);postProcessGraph(g)};fn.createNodes=function(value){if(!arguments.length)return createNodes;createNodes=value;return fn};fn.createClusters=function(value){if(!arguments.length)return createClusters;createClusters=value;return fn};fn.createEdgeLabels=function(value){if(!arguments.length)return createEdgeLabels;createEdgeLabels=value;return fn};fn.createEdgePaths=function(value){if(!arguments.length)return createEdgePaths;createEdgePaths=value;return fn};fn.shapes=function(value){if(!arguments.length)return shapes;shapes=value;return fn};fn.arrows=function(value){if(!arguments.length)return arrows;arrows=value;return fn};return fn}var NODE_DEFAULT_ATTRS={paddingLeft:10,paddingRight:10,paddingTop:10,paddingBottom:10,rx:0,ry:0,shape:"rect"};var EDGE_DEFAULT_ATTRS={arrowhead:"normal",lineInterpolate:"linear"};function preProcessGraph(g){g.nodes().forEach(function(v){var node=g.node(v);if(!_.has(node,"label")){node.label=v}if(_.has(node,"paddingX")){_.defaults(node,{paddingLeft:node.paddingX,paddingRight:node.paddingX})}if(_.has(node,"paddingY")){_.defaults(node,{paddingTop:node.paddingY,paddingBottom:node.paddingY})}if(_.has(node,"padding")){_.defaults(node,{paddingLeft:node.padding,paddingRight:node.padding,paddingTop:node.padding,paddingBottom:node.padding})}_.defaults(node,NODE_DEFAULT_ATTRS);_.each(["paddingLeft","paddingRight","paddingTop","paddingBottom"],function(k){node[k]=Number(node[k])});if(_.has(node,"width")){node._prevWidth=node.width}if(_.has(node,"height")){node._prevHeight=node.height}});g.edges().forEach(function(e){var edge=g.edge(e);if(!_.has(edge,"label")){edge.label=""}_.defaults(edge,EDGE_DEFAULT_ATTRS)})}function postProcessGraph(g){_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"_prevWidth")){node.width=node._prevWidth}else{delete node.width}if(_.has(node,"_prevHeight")){node.height=node._prevHeight}else{delete node.height}delete node._prevWidth;delete node._prevHeight})}function createOrSelectGroup(root,name){var selection=root.select("g."+name);if(selection.empty()){selection=root.append("g").attr("class",name)}return selection}},{"./arrows":2,"./create-clusters":3,"./create-edge-labels":4,"./create-edge-paths":5,"./create-nodes":6,"./dagre":8,"./lodash":20,"./position-edge-labels":21,"./position-nodes":22,"./shapes":24}],24:[function(require,module,exports){"use strict";var intersectRect=require("./intersect/intersect-rect"),intersectEllipse=require("./intersect/intersect-ellipse"),intersectCircle=require("./intersect/intersect-circle"),intersectPolygon=require("./intersect/intersect-polygon");module.exports={rect:rect,ellipse:ellipse,circle:circle,diamond:diamond};function rect(parent,bbox,node){var shapeSvg=parent.insert("rect",":first-child").attr("rx",node.rx).attr("ry",node.ry).attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("width",bbox.width).attr("height",bbox.height);node.intersect=function(point){return intersectRect(node,point)};return shapeSvg}function ellipse(parent,bbox,node){var rx=bbox.width/2,ry=bbox.height/2,shapeSvg=parent.insert("ellipse",":first-child").attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("rx",rx).attr("ry",ry);node.intersect=function(point){return intersectEllipse(node,rx,ry,point)};return shapeSvg}function circle(parent,bbox,node){var r=Math.max(bbox.width,bbox.height)/2,shapeSvg=parent.insert("circle",":first-child").attr("x",-bbox.width/2).attr("y",-bbox.height/2).attr("r",r);node.intersect=function(point){return intersectCircle(node,r,point)};return shapeSvg}function diamond(parent,bbox,node){var w=bbox.width*Math.SQRT2/2,h=bbox.height*Math.SQRT2/2,points=[{x:0,y:-h},{x:-w,y:0},{x:0,y:h},{x:w,y:0}],shapeSvg=parent.insert("polygon",":first-child").attr("points",points.map(function(p){return p.x+","+p.y}).join(" "));node.intersect=function(p){return intersectPolygon(node,points,p)};return shapeSvg}},{"./intersect/intersect-circle":11,"./intersect/intersect-ellipse":12,"./intersect/intersect-polygon":15,"./intersect/intersect-rect":16}],25:[function(require,module,exports){var _=require("./lodash");module.exports={isSubgraph:isSubgraph,edgeToId:edgeToId,applyStyle:applyStyle,applyClass:applyClass,applyTransition:applyTransition};function isSubgraph(g,v){return!!g.children(v).length}function edgeToId(e){return escapeId(e.v)+":"+escapeId(e.w)+":"+escapeId(e.name)}var ID_DELIM=/:/g;function escapeId(str){return str?String(str).replace(ID_DELIM,"\\:"):""}function applyStyle(dom,styleFn){if(styleFn){dom.attr("style",styleFn)}}function applyClass(dom,classFn,otherClasses){if(classFn){dom.attr("class",classFn).attr("class",otherClasses+" "+dom.attr("class"))}}function applyTransition(selection,g){var graph=g.graph();if(_.isPlainObject(graph)){var transition=graph.transition;if(_.isFunction(transition)){return transition(selection)}}return selection}},{"./lodash":20}],26:[function(require,module,exports){module.exports="0.4.4-pre"},{}],27:[function(require,module,exports){module.exports={graphlib:require("./lib/graphlib"),layout:require("./lib/layout"),debug:require("./lib/debug"),util:{time:require("./lib/util").time,notime:require("./lib/util").notime},version:require("./lib/version")}},{"./lib/debug":32,"./lib/graphlib":33,"./lib/layout":35,"./lib/util":55,"./lib/version":56}],28:[function(require,module,exports){"use strict";var _=require("./lodash"),greedyFAS=require("./greedy-fas");module.exports={run:run,undo:undo};function run(g){var fas=g.graph().acyclicer==="greedy"?greedyFAS(g,weightFn(g)):dfsFAS(g);_.each(fas,function(e){var label=g.edge(e);g.removeEdge(e);label.forwardName=e.name;label.reversed=true;g.setEdge(e.w,e.v,label,_.uniqueId("rev"))});function weightFn(g){return function(e){return g.edge(e).weight}}}function dfsFAS(g){var fas=[],stack={},visited={};function dfs(v){if(_.has(visited,v)){return}visited[v]=true;stack[v]=true;_.each(g.outEdges(v),function(e){if(_.has(stack,e.w)){fas.push(e)}else{dfs(e.w)}});delete stack[v]}_.each(g.nodes(),dfs);return fas}function undo(g){_.each(g.edges(),function(e){var label=g.edge(e);if(label.reversed){g.removeEdge(e);var forwardName=label.forwardName;delete label.reversed;delete label.forwardName;g.setEdge(e.w,e.v,label,forwardName)}})}},{"./greedy-fas":34,"./lodash":36}],29:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports=addBorderSegments;function addBorderSegments(g){function dfs(v){var children=g.children(v),node=g.node(v);if(children.length){_.each(children,dfs)}if(_.has(node,"minRank")){node.borderLeft=[];node.borderRight=[];for(var rank=node.minRank,maxRank=node.maxRank+1;rank<maxRank;++rank){addBorderNode(g,"borderLeft","_bl",v,node,rank);addBorderNode(g,"borderRight","_br",v,node,rank)}}}_.each(g.children(),dfs)}function addBorderNode(g,prop,prefix,sg,sgNode,rank){var label={width:0,height:0,rank:rank},prev=sgNode[prop][rank-1],curr=util.addDummyNode(g,"border",label,prefix);sgNode[prop][rank]=curr;g.setParent(curr,sg);if(prev){g.setEdge(prev,curr,{weight:1})}}},{"./lodash":36,"./util":55}],30:[function(require,module,exports){"use strict";var _=require("./lodash");module.exports={adjust:adjust,undo:undo};function adjust(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="lr"||rankDir==="rl"){swapWidthHeight(g)}}function undo(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="bt"||rankDir==="rl"){reverseY(g)}if(rankDir==="lr"||rankDir==="rl"){swapXY(g);swapWidthHeight(g)}}function swapWidthHeight(g){_.each(g.nodes(),function(v){swapWidthHeightOne(g.node(v))});_.each(g.edges(),function(e){swapWidthHeightOne(g.edge(e))})}function swapWidthHeightOne(attrs){var w=attrs.width;attrs.width=attrs.height;attrs.height=w}function reverseY(g){_.each(g.nodes(),function(v){reverseYOne(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,reverseYOne);if(_.has(edge,"y")){reverseYOne(edge)}})}function reverseYOne(attrs){attrs.y=-attrs.y}function swapXY(g){_.each(g.nodes(),function(v){swapXYOne(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,swapXYOne);if(_.has(edge,"x")){swapXYOne(edge)}})}function swapXYOne(attrs){var x=attrs.x;attrs.x=attrs.y;attrs.y=x}},{"./lodash":36}],31:[function(require,module,exports){module.exports=List;function List(){var sentinel={};sentinel._next=sentinel._prev=sentinel;this._sentinel=sentinel}List.prototype.dequeue=function(){var sentinel=this._sentinel,entry=sentinel._prev;if(entry!==sentinel){unlink(entry);return entry}};List.prototype.enqueue=function(entry){var sentinel=this._sentinel;if(entry._prev&&entry._next){unlink(entry)}entry._next=sentinel._next;sentinel._next._prev=entry;sentinel._next=entry;entry._prev=sentinel};List.prototype.toString=function(){var strs=[],sentinel=this._sentinel,curr=sentinel._prev;while(curr!==sentinel){strs.push(JSON.stringify(curr,filterOutLinks));curr=curr._prev}return"["+strs.join(", ")+"]"};function unlink(entry){entry._prev._next=entry._next;entry._next._prev=entry._prev;delete entry._next;delete entry._prev}function filterOutLinks(k,v){if(k!=="_next"&&k!=="_prev"){return v}}},{}],32:[function(require,module,exports){var _=require("./lodash"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports={debugOrdering:debugOrdering};function debugOrdering(g){var layerMatrix=util.buildLayerMatrix(g);var h=new Graph({compound:true,multigraph:true}).setGraph({});_.each(g.nodes(),function(v){h.setNode(v,{label:v});h.setParent(v,"layer"+g.node(v).rank)});_.each(g.edges(),function(e){h.setEdge(e.v,e.w,{},e.name)});_.each(layerMatrix,function(layer,i){var layerV="layer"+i;h.setNode(layerV,{rank:"same"});_.reduce(layer,function(u,v){h.setEdge(u,v,{style:"invis"});return v})});return h}},{"./graphlib":33,"./lodash":36,"./util":55}],33:[function(require,module,exports){arguments[4][9][0].apply(exports,arguments)},{dup:9,graphlib:57}],34:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph,List=require("./data/list");module.exports=greedyFAS;var DEFAULT_WEIGHT_FN=_.constant(1);function greedyFAS(g,weightFn){if(g.nodeCount()<=1){return[]}var state=buildState(g,weightFn||DEFAULT_WEIGHT_FN);var results=doGreedyFAS(state.graph,state.buckets,state.zeroIdx);return _.flatten(_.map(results,function(e){return g.outEdges(e.v,e.w)}),true)}function doGreedyFAS(g,buckets,zeroIdx){var results=[],sources=buckets[buckets.length-1],sinks=buckets[0];var entry;while(g.nodeCount()){while(entry=sinks.dequeue()){removeNode(g,buckets,zeroIdx,entry)}while(entry=sources.dequeue()){removeNode(g,buckets,zeroIdx,entry)}if(g.nodeCount()){for(var i=buckets.length-2;i>0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time("  buildLayoutGraph",function(){return buildLayoutGraph(g)});time("  runLayout",function(){runLayout(layoutGraph,time)});time("  updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time("    makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time("    removeSelfEdges",function(){removeSelfEdges(g)});time("    acyclic",function(){acyclic.run(g)});time("    nestingGraph.run",function(){nestingGraph.run(g)});time("    rank",function(){rank(util.asNonCompoundGraph(g))});time("    injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time("    removeEmptyRanks",function(){removeEmptyRanks(g)});time("    nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time("    normalizeRanks",function(){normalizeRanks(g)});time("    assignRankMinMax",function(){assignRankMinMax(g)});time("    removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time("    normalize.run",function(){normalize.run(g)});time("    parentDummyChains",function(){parentDummyChains(g)});time("    addBorderSegments",function(){addBorderSegments(g)});time("    order",function(){order(g)});time("    insertSelfEdges",function(){insertSelfEdges(g)});time("    adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time("    position",function(){position(g)});time("    positionSelfEdges",function(){positionSelfEdges(g)});time("    removeBorderNodes",function(){removeBorderNodes(g)});time("    normalize.undo",function(){normalize.undo(g)});time("    fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time("    undoCoordinateSystem",function(){coordinateSystem.undo(g)});time("    translateGraph",function(){translateGraph(g)});time("    assignNodeIntersects",function(){assignNodeIntersects(g)});time("    reversePoints",function(){reversePointsForReversedEdges(g)});time("    acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.each(inputGraph.nodes(),function(v){var inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();
+
+graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.reversed){edge.points.reverse()}})}function removeBorderNodes(g){_.each(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.each(g.nodes(),function(v){if(g.node(v).dummy==="border"){g.removeNode(v)}})}function removeSelfEdges(g){_.each(g.edges(),function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges){node.selfEdges=[]}node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.each(layers,function(layer){var orderShift=0;_.each(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.each(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){arguments[4][20][0].apply(exports,arguments)},{dup:20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank<wRank;++i,++vRank){edgeLabel.points=[];attrs={width:0,height:0,edgeLabel:edgeLabel,edgeObj:e,rank:vRank};dummy=util.addDummyNode(g,"edge",attrs,"_d");if(vRank===labelRank){attrs.width=edgeLabel.width;attrs.height=edgeLabel.height;attrs.dummy="edge-label";attrs.labelpos=edgeLabel.labelpos}g.setEdge(v,dummy,{weight:edgeLabel.weight},name);if(i===0){g.graph().dummyChains.push(dummy)}v=dummy}g.setEdge(v,w,{weight:edgeLabel.weight},name)}function undo(g){_.each(g.graph().dummyChains,function(v){var node=g.node(v),origLabel=node.edgeLabel,w;g.setEdge(node.edgeObj,origLabel);while(node.dummy){w=g.successors(v)[0];g.removeNode(v);origLabel.points.push({x:node.x,y:node.y});if(node.dummy==="edge-label"){origLabel.x=node.x;origLabel.y=node.y;origLabel.width=node.width;origLabel.height=node.height}v=w;node=g.node(v)}})}},{"./lodash":36,"./util":55}],39:[function(require,module,exports){var _=require("../lodash");module.exports=addSubgraphConstraints;function addSubgraphConstraints(g,cg,vs){var prev={},rootPrev;_.each(vs,function(v){var child=g.parent(v),parent,prevChild;while(child){parent=g.parent(child);if(parent){prevChild=prev[parent];prev[parent]=child}else{prevChild=rootPrev;rootPrev=child}if(prevChild&&prevChild!==child){cg.setEdge(prevChild,child);return}child=parent}})}},{"../lodash":36}],40:[function(require,module,exports){var _=require("../lodash");module.exports=barycenter;function barycenter(g,movable){return _.map(movable,function(v){var inV=g.inEdges(v);if(!inV.length){return{v:v}}else{var result=_.reduce(inV,function(acc,e){var edge=g.edge(e),nodeU=g.node(e.v);return{sum:acc.sum+edge.weight*nodeU.order,weight:acc.weight+edge.weight}},{sum:0,weight:0});return{v:v,barycenter:result.sum/result.weight,weight:result.weight}}})}},{"../lodash":36}],41:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph;module.exports=buildLayerGraph;function buildLayerGraph(g,rank,relationship){var root=createRootNode(g),result=new Graph({compound:true}).setGraph({root:root}).setDefaultNodeLabel(function(v){return g.node(v)});_.each(g.nodes(),function(v){var node=g.node(v),parent=g.parent(v);if(node.rank===rank||node.minRank<=rank&&rank<=node.maxRank){result.setNode(v);result.setParent(v,parent||root);_.each(g[relationship](v),function(e){var u=e.v===v?e.w:e.v,edge=result.edge(u,v),weight=!_.isUndefined(edge)?edge.weight:0;result.setEdge(u,v,{weight:g.edge(e).weight+weight})});if(_.has(node,"minRank")){result.setNode(v,{borderLeft:node.borderLeft[rank],borderRight:node.borderRight[rank]})}}});return result}function createRootNode(g){var v;while(g.hasNode(v=_.uniqueId("_root")));return v}},{"../graphlib":33,"../lodash":36}],42:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=crossCount;function crossCount(g,layering){var cc=0;for(var i=1;i<layering.length;++i){cc+=twoLayerCrossCount(g,layering[i-1],layering[i])}return cc}function twoLayerCrossCount(g,northLayer,southLayer){var southPos=_.zipObject(southLayer,_.map(southLayer,function(v,i){return i}));var southEntries=_.flatten(_.map(northLayer,function(v){return _.chain(g.outEdges(v)).map(function(e){return{pos:southPos[e.w],weight:g.edge(e).weight}}).sortBy("pos").value()}),true);var firstIndex=1;while(firstIndex<southLayer.length)firstIndex<<=1;var treeSize=2*firstIndex-1;firstIndex-=1;var tree=_.map(new Array(treeSize),function(){return 0});var cc=0;_.each(southEntries.forEach(function(entry){var index=entry.pos+firstIndex;tree[index]+=entry.weight;var weightSum=0;while(index>0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc<bestCC){lastBest=0;best=_.cloneDeep(layering);bestCC=cc}}assignOrder(g,best)}function buildLayerGraphs(g,ranks,relationship){return _.map(ranks,function(rank){return buildLayerGraph(g,rank,relationship)})}function sweepLayerGraphs(layerGraphs,biasRight){var cg=new Graph;_.each(layerGraphs,function(lg){var root=lg.graph().root;var sorted=sortSubgraph(lg,root,cg,biasRight);_.each(sorted.vs,function(v,i){lg.node(v).order=i});addSubgraphConstraints(lg,cg,sorted.vs)})}function assignOrder(g,layering){_.each(layering,function(layer){_.each(layer,function(v,i){g.node(v).order=i})})}},{"../graphlib":33,"../lodash":36,"../util":55,"./add-subgraph-constraints":39,"./build-layer-graph":41,"./cross-count":42,"./init-order":44,"./sort-subgraph":46}],44:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=initOrder;function initOrder(g){var visited={},simpleNodes=_.filter(g.nodes(),function(v){return!g.children(v).length}),maxRank=_.max(_.map(simpleNodes,function(v){return g.node(v).rank})),layers=_.map(_.range(maxRank+1),function(){return[]});function dfs(v){if(_.has(visited,v))return;visited[v]=true;var node=g.node(v);layers[node.rank].push(v);_.each(g.successors(v),dfs)}var orderedVs=_.sortBy(simpleNodes,function(v){return g.node(v).rank});_.each(orderedVs,dfs);return layers}},{"../lodash":36}],45:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports=resolveConflicts;function resolveConflicts(entries,cg){var mappedEntries={};_.each(entries,function(entry,i){var tmp=mappedEntries[entry.v]={indegree:0,"in":[],out:[],vs:[entry.v],i:i};if(!_.isUndefined(entry.barycenter)){tmp.barycenter=entry.barycenter;tmp.weight=entry.weight}});_.each(cg.edges(),function(e){var entryV=mappedEntries[e.v],entryW=mappedEntries[e.w];if(!_.isUndefined(entryV)&&!_.isUndefined(entryW)){entryW.indegree++;entryV.out.push(mappedEntries[e.w])}});var sourceSet=_.filter(mappedEntries,function(entry){return!entry.indegree});return doResolveConflicts(sourceSet)}function doResolveConflicts(sourceSet){var entries=[];function handleIn(vEntry){return function(uEntry){if(uEntry.merged){return}if(_.isUndefined(uEntry.barycenter)||_.isUndefined(vEntry.barycenter)||uEntry.barycenter>=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenter<entryW.barycenter){return-1}else if(entryV.barycenter>entryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRank<node.rank){pathIdx++}if(pathV===lca){ascending=false}}if(!ascending){while(pathIdx<path.length-1&&g.node(pathV=path[pathIdx+1]).minRank<=node.rank){pathIdx++}pathV=path[pathIdx]}g.setParent(v,pathV);v=g.successors(v)[0]}})}function findPath(g,postorderNums,v,w){var vPath=[],wPath=[],low=Math.min(postorderNums[v].low,postorderNums[w].low),lim=Math.max(postorderNums[v].lim,postorderNums[w].lim),parent,lca;parent=v;do{parent=g.parent(parent);vPath.push(parent)}while(parent&&(postorderNums[parent].low>low||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPos<k0||k1<uPos)&&!(uLabel.dummy&&g.node(scanNode).dummy)){addConflict(conflicts,u,scanNode)}})});scanPos=i+1;k0=k1}});return layer}_.reduce(layering,visitLayer);return conflicts}function findType2Conflicts(g,layering){var conflicts={};function scan(south,southPos,southEnd,prevNorthBorder,nextNorthBorder){var v;_.each(_.range(southPos,southEnd),function(i){v=south[i];if(g.node(v).dummy){_.each(g.predecessors(v),function(u){var uNode=g.node(u);if(uNode.dummy&&(uNode.order<prevNorthBorder||uNode.order>nextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdx<pos[w]&&!hasConflict(conflicts,v,w)){align[w]=v;align[v]=root[v]=root[w];prevIdx=pos[w]}}}})});return{root:root,align:align}}function horizontalCompaction(g,layering,root,align,reverseSep){var xs={},blockG=buildBlockGraph(g,layering,root,reverseSep);var visited={};function pass1(v){if(!_.has(visited,v)){visited[v]=true;xs[v]=_.reduce(blockG.inEdges(v),function(max,e){pass1(e.v);return Math.max(max,xs[e.v]+blockG.edge(e))},0)}}_.each(blockG.nodes(),pass1);function pass2(v){if(visited[v]!==2){visited[v]++;var min=_.reduce(blockG.outEdges(v),function(min,e){pass2(e.w);return Math.min(min,xs[e.w]-blockG.edge(e))},Number.POSITIVE_INFINITY);if(min!==Number.POSITIVE_INFINITY){xs[v]=Math.max(xs[v],min)}}}_.each(blockG.nodes(),pass2);_.each(align,function(v){xs[v]=xs[root[v]]});return xs}function buildBlockGraph(g,layering,root,reverseSep){var blockGraph=new Graph,graphLabel=g.graph(),sepFn=sep(graphLabel.nodesep,graphLabel.edgesep,reverseSep);_.each(layering,function(layer){var u;_.each(layer,function(v){var vRoot=root[v];blockGraph.setNode(vRoot);if(u){var uRoot=root[u],prevMax=blockGraph.edge(uRoot,vRoot);blockGraph.setEdge(uRoot,vRoot,Math.max(sepFn(g,v,u),prevMax||0))}u=v})});return blockGraph}function findSmallestWidthAlignment(g,xss){return _.min(xss,function(xs){var min=_.min(xs,function(x,v){return x-width(g,v)/2}),max=_.max(xs,function(x,v){return x+width(g,v)/2});return max-min})}function alignCoordinates(xss,alignTo){var alignToMin=_.min(alignTo),alignToMax=_.max(alignTo);_.each(["u","d"],function(vert){_.each(["l","r"],function(horiz){var alignment=vert+horiz,xs=xss[alignment],delta;if(xs===alignTo)return;delta=horiz==="l"?alignToMin-_.min(xs):alignToMax-_.max(xs);if(delta){xss[alignment]=_.mapValues(xs,function(x){return x+delta})}})})}function balance(xss,align){return _.mapValues(xss.ul,function(ignore,v){if(align){return xss[align.toLowerCase()][v]}else{var xs=_.sortBy(_.pluck(xss,v));return(xs[1]+xs[2])/2}})}function positionX(g){var layering=util.buildLayerMatrix(g),conflicts=_.merge(findType1Conflicts(g,layering),findType2Conflicts(g,layering));var xss={},adjustedLayering;_.each(["u","d"],function(vert){adjustedLayering=vert==="u"?layering:_.values(layering).reverse();_.each(["l","r"],function(horiz){if(horiz==="r"){adjustedLayering=_.map(adjustedLayering,function(inner){return _.values(inner).reverse()})}var neighborFn=_.bind(vert==="u"?g.predecessors:g.successors,g);var align=verticalAlignment(g,adjustedLayering,conflicts,neighborFn);var xs=horizontalCompaction(g,adjustedLayering,align.root,align.align,horiz==="r");if(horiz==="r"){xs=_.mapValues(xs,function(x){return-x})}xss[vert+horiz]=xs})});var smallestWidth=findSmallestWidthAlignment(g,xss);alignCoordinates(xss,smallestWidth);return balance(xss,g.graph().align)}function sep(nodeSep,edgeSep,reverseSep){return function(g,v,w){var vLabel=g.node(v),wLabel=g.node(w),sum=0,delta;sum+=vLabel.width/2;if(_.has(vLabel,"labelpos")){switch(vLabel.labelpos.toLowerCase()){case"l":delta=-vLabel.width/2;break;case"r":delta=vLabel.width/2;break}}if(delta){sum+=reverseSep?delta:-delta}delta=0;sum+=(vLabel.dummy?edgeSep:nodeSep)/2;sum+=(wLabel.dummy?edgeSep:nodeSep)/2;sum+=wLabel.width/2;if(_.has(wLabel,"labelpos")){switch(wLabel.labelpos.toLowerCase()){case"l":delta=wLabel.width/2;break;case"r":delta=-wLabel.width/2;break}}if(delta){sum+=reverseSep?delta:-delta}delta=0;return sum}}function width(g,v){return g.node(v).width}},{"../graphlib":33,"../lodash":36,"../util":55}],50:[function(require,module,exports){"use strict";var _=require("../lodash"),util=require("../util"),positionX=require("./bk").positionX;module.exports=position;function position(g){g=util.asNonCompoundGraph(g);positionY(g);_.each(positionX(g),function(x,v){g.node(v).x=x})}function positionY(g){var layering=util.buildLayerMatrix(g),rankSep=g.graph().ranksep,prevY=0;_.each(layering,function(layer){var maxHeight=_.max(_.map(layer,function(v){return g.node(v).height}));_.each(layer,function(v){g.node(v).y=prevY+maxHeight/2});prevY+=maxHeight+rankSep})}},{"../lodash":36,"../util":55,"./bk":49}],51:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,slack=require("./util").slack;module.exports=feasibleTree;function feasibleTree(g){var t=new Graph({directed:false});var start=g.nodes()[0],size=g.nodeCount();t.setNode(start,{});var edge,delta;while(tightTree(t,g)<size){edge=findMinSlackEdge(t,g);delta=t.hasNode(edge.v)?slack(g,edge):-slack(g,edge);shiftRanks(t,g,delta)}return t}function tightTree(t,g){function dfs(v){_.each(g.nodeEdges(v),function(e){var edgeV=e.v,w=v===edgeV?e.w:edgeV;if(!t.hasNode(w)&&!slack(g,e)){t.setNode(w,{});t.setEdge(v,w,{});dfs(w)}})}_.each(t.nodes(),dfs);return t.nodeCount()}function findMinSlackEdge(t,g){return _.min(g.edges(),function(e){if(t.hasNode(e.v)!==t.hasNode(e.w)){return slack(g,e)}})}function shiftRanks(t,g,delta){_.each(t.nodes(),function(v){g.node(v).rank+=delta})}},{"../graphlib":33,"../lodash":36,"./util":54}],52:[function(require,module,exports){"use strict";var rankUtil=require("./util"),longestPath=rankUtil.longestPath,feasibleTree=require("./feasible-tree"),networkSimplex=require("./network-simplex");module.exports=rank;function rank(g){switch(g.graph().ranker){case"network-simplex":networkSimplexRanker(g);break;case"tight-tree":tightTreeRanker(g);break;case"longest-path":longestPathRanker(g);break;default:networkSimplexRanker(g)}}var longestPathRanker=longestPath;function tightTreeRanker(g){longestPath(g);feasibleTree(g)}function networkSimplexRanker(g){networkSimplex(g)}},{"./feasible-tree":51,"./network-simplex":53,"./util":54}],53:[function(require,module,exports){"use strict";var _=require("../lodash"),feasibleTree=require("./feasible-tree"),slack=require("./util").slack,initRank=require("./util").longestPath,preorder=require("../graphlib").alg.preorder,postorder=require("../graphlib").alg.postorder,simplify=require("../util").simplify;module.exports=networkSimplex;networkSimplex.initLowLimValues=initLowLimValues;networkSimplex.initCutValues=initCutValues;networkSimplex.calcCutValue=calcCutValue;networkSimplex.leaveEdge=leaveEdge;networkSimplex.enterEdge=enterEdge;networkSimplex.exchangeEdges=exchangeEdges;function networkSimplex(g){g=simplify(g);initRank(g);var t=feasibleTree(g);initLowLimValues(t);initCutValues(t,g);var e,f;while(e=leaveEdge(t)){f=enterEdge(t,g,e);exchangeEdges(t,g,e,f)}}function initCutValues(t,g){var vs=postorder(t,t.nodes());vs=vs.slice(0,vs.length-1);_.each(vs,function(v){assignCutValue(t,g,v)})}function assignCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent;t.edge(child,parent).cutvalue=calcCutValue(t,g,child)}function calcCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent,childIsTail=true,graphEdge=g.edge(child,parent),cutValue=0;if(!graphEdge){childIsTail=false;graphEdge=g.edge(parent,child)}cutValue=graphEdge.weight;_.each(g.nodeEdges(child),function(e){var isOutEdge=e.v===child,other=isOutEdge?e.w:e.v;if(other!==parent){var pointsToHead=isOutEdge===childIsTail,otherWeight=g.edge(e).weight;cutValue+=pointsToHead?otherWeight:-otherWeight;if(isTreeEdge(t,child,other)){var otherCutValue=t.edge(child,other).cutvalue;cutValue+=pointsToHead?-otherCutValue:otherCutValue}}});return cutValue}function initLowLimValues(tree,root){if(arguments.length<2){root=tree.nodes()[0]}dfsAssignLowLim(tree,{},1,root)}function dfsAssignLowLim(tree,visited,nextLim,v,parent){var low=nextLim,label=tree.node(v);visited[v]=true;_.each(tree.neighbors(v),function(w){if(!_.has(visited,w)){nextLim=dfsAssignLowLim(tree,visited,nextLim,w,v)}});label.low=low;label.lim=nextLim++;if(parent){label.parent=parent}else{delete label.parent}return nextLim}function leaveEdge(tree){return _.find(tree.edges(),function(e){return tree.edge(e).cutvalue<0})}function enterEdge(t,g,edge){var v=edge.v,w=edge.w;if(!g.hasEdge(v,w)){v=edge.w;w=edge.v}var vLabel=t.node(v),wLabel=t.node(w),tailLabel=vLabel,flip=false;if(vLabel.lim>wLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){
+var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance<wEntry.distance){wEntry.distance=distance;wEntry.predecessor=v;pq.decrease(w,distance)}};g.nodes().forEach(function(v){var distance=v===source?0:Number.POSITIVE_INFINITY;results[v]={distance:distance};pq.add(v,distance)});while(pq.size()>0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance<ij.distance){ij.distance=altDistance;ij.predecessor=kj.predecessor}})})});return results}},{"../lodash":75}],64:[function(require,module,exports){module.exports={components:require("./components"),dijkstra:require("./dijkstra"),dijkstraAll:require("./dijkstra-all"),findCycles:require("./find-cycles"),floydWarshall:require("./floyd-warshall"),isAcyclic:require("./is-acyclic"),postorder:require("./postorder"),preorder:require("./preorder"),prim:require("./prim"),tarjan:require("./tarjan"),topsort:require("./topsort")}},{"./components":58,"./dijkstra":61,"./dijkstra-all":60,"./find-cycles":62,"./floyd-warshall":63,"./is-acyclic":65,"./postorder":66,"./preorder":67,"./prim":68,"./tarjan":69,"./topsort":70}],65:[function(require,module,exports){var topsort=require("./topsort");module.exports=isAcyclic;function isAcyclic(g){try{topsort(g)}catch(e){if(e instanceof topsort.CycleException){return false}throw e}return true}},{"./topsort":70}],66:[function(require,module,exports){var dfs=require("./dfs");module.exports=postorder;function postorder(g,vs){return dfs(g,vs,"post")}},{"./dfs":59}],67:[function(require,module,exports){var dfs=require("./dfs");module.exports=preorder;function preorder(g,vs){return dfs(g,vs,"pre")}},{"./dfs":59}],68:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graph"),PriorityQueue=require("../data/priority-queue");module.exports=prim;function prim(g,weightFunc){var result=new Graph,parents={},pq=new PriorityQueue,v;function updateNeighbors(edge){var w=edge.v===v?edge.w:edge.v,pri=pq.priority(w);if(pri!==undefined){var edgeWeight=weightFunc(edge);if(edgeWeight<pri){parents[w]=v;pq.decrease(w,edgeWeight)}}}if(g.nodeCount()===0){return result}_.each(g.nodes(),function(v){pq.add(v,Number.POSITIVE_INFINITY);result.setNode(v)});pq.decrease(g.nodes()[0],0);var init=false;while(pq.size()>0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return 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"+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l<arr.length){largest=arr[l].priority<arr[largest].priority?l:largest;if(r<arr.length){largest=arr[r].priority<arr[largest].priority?r:largest}if(largest!==i){this._swap(i,largest);this._heapify(largest)}}};PriorityQueue.prototype._decrease=function(index){var arr=this._arr;var priority=arr[index].priority;var parent;while(index!==0){parent=index>>1;if(arr[parent].priority<priority){break}this._swap(index,parent);index=parent}};PriorityQueue.prototype._swap=function(i,j){var arr=this._arr;var keyIndices=this._keyIndices;var origArrI=arr[i];var origArrJ=arr[j];arr[i]=origArrJ;arr[j]=origArrI;keyIndices[origArrJ.key]=i;keyIndices[origArrI.key]=j}},{"../lodash":75}],72:[function(require,module,exports){"use strict";var _=require("./lodash");module.exports=Graph;var DEFAULT_EDGE_NAME="\x00",GRAPH_NODE="\x00",EDGE_KEY_DELIM="
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diff --git a/core/src/main/resources/org/apache/spark/ui/static/graphlib-dot.min.js b/core/src/main/resources/org/apache/spark/ui/static/graphlib-dot.min.js
new file mode 100644
index 0000000000000..037316f4e4f38
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/graphlib-dot.min.js
@@ -0,0 +1,4 @@
+/*v0.5.2*/(function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){(function(global){graphlibDot=require("./index");graphlibDot.graphlib=require("graphlib");global.graphlibDot=graphlibDot}).call(this,typeof global!=="undefined"?global:typeof self!=="undefined"?self:typeof window!=="undefined"?window:{})},{"./index":2,graphlib:9}],2:[function(require,module,exports){var read=require("./lib/read-one"),readMany=require("./lib/read-many"),write=require("./lib/write-one"),version=require("./lib/version");module.exports={read:read,readMany:readMany,write:write,version:version,type:"dot",buffer:false}},{"./lib/read-many":5,"./lib/read-one":6,"./lib/version":7,"./lib/write-one":8}],3:[function(require,module,exports){"use strict";var _=require("lodash"),Graph=require("graphlib").Graph;module.exports=buildGraph;function buildGraph(parseTree){var isDirected=parseTree.type!=="graph",isMultigraph=!parseTree.strict,defaultStack=[{node:{},edge:{}}],g=new Graph({directed:isDirected,multigraph:isMultigraph,compound:true});g.setGraph({});_.each(parseTree.stmts,function(stmt){handleStmt(g,stmt,defaultStack)});return g}function handleStmt(g,stmt,defaultStack,sg){switch(stmt.type){case"node":handleNodeStmt(g,stmt,defaultStack,sg);break;case"edge":handleEdgeStmt(g,stmt,defaultStack,sg);break;case"subgraph":handleSubgraphStmt(g,stmt,defaultStack,sg);break;case"attr":handleAttrStmt(g,stmt,defaultStack);break;case"inlineAttr":handleInlineAttrsStmt(g,stmt,defaultStack,sg);break}}function handleNodeStmt(g,stmt,defaultStack,sg){var v=stmt.id,attrs=stmt.attrs;maybeCreateNode(g,v,defaultStack,sg);_.merge(g.node(v),attrs)}function handleEdgeStmt(g,stmt,defaultStack,sg){var attrs=stmt.attrs,prev,curr;_.each(stmt.elems,function(elem){handleStmt(g,elem,defaultStack,sg);switch(elem.type){case"node":curr=[elem.id];break;case"subgraph":curr=collectNodeIds(elem);break}_.each(prev,function(v){_.each(curr,function(w){var name;if(g.hasEdge(v,w)&&g.isMultigraph()){name=_.uniqueId("edge")}if(!g.hasEdge(v,w,name)){g.setEdge(v,w,_.clone(_.last(defaultStack).edge),name)}_.merge(g.edge(v,w,name),attrs)})});prev=curr})}function handleSubgraphStmt(g,stmt,defaultStack,sg){var id=stmt.id;if(id===undefined){id=generateSubgraphId(g)}defaultStack.push(_.clone(_.last(defaultStack)));maybeCreateNode(g,id,defaultStack,sg);_.each(stmt.stmts,function(s){handleStmt(g,s,defaultStack,id)});if(!g.children(id).length){g.removeNode(id)}defaultStack.pop()}function handleAttrStmt(g,stmt,defaultStack){_.merge(_.last(defaultStack)[stmt.attrType],stmt.attrs)}function handleInlineAttrsStmt(g,stmt,defaultStack,sg){_.merge(sg?g.node(sg):g.graph(),stmt.attrs)}function generateSubgraphId(g){var id;do{id=_.uniqueId("sg")}while(g.hasNode(id));return id}function maybeCreateNode(g,v,defaultStack,sg){if(!g.hasNode(v)){g.setNode(v,_.clone(_.last(defaultStack).node));g.setParent(v,sg)}}function collectNodeIds(stmt){var ids={},stack=[],curr;var push=stack.push.bind(stack);push(stmt);while(stack.length){curr=stack.pop();switch(curr.type){case"node":ids[curr.id]=true;break;case"edge":_.each(curr.elems,push);break;case"subgraph":_.each(curr.stmts,push);break}}return _.keys(ids)}},{graphlib:9,lodash:28}],4:[function(require,module,exports){module.exports=function(){function peg$subclass(child,parent){function ctor(){this.constructor=child}ctor.prototype=parent.prototype;child.prototype=new ctor}function SyntaxError(message,expected,found,offset,line,column){this.message=message;this.expected=expected;this.found=found;this.offset=offset;this.line=line;this.column=column;this.name="SyntaxError"}peg$subclass(SyntaxError,Error);function parse(input){var options=arguments.length>1?arguments[1]:{},peg$FAILED={},peg$startRuleFunctions={start:peg$parsestart,graphStmt:peg$parsegraphStmt},peg$startRuleFunction=peg$parsestart,peg$c0=[],peg$c1=peg$FAILED,peg$c2=null,peg$c3="{",peg$c4={type:"literal",value:"{",description:'"{"'},peg$c5="}",peg$c6={type:"literal",value:"}",description:'"}"'},peg$c7=function(strict,type,id,stmts){return{type:type,id:id,strict:strict!==null,stmts:stmts}},peg$c8=";",peg$c9={type:"literal",value:";",description:'";"'},peg$c10=function(first,rest){var result=[first];for(var i=0;i<rest.length;++i){result.push(rest[i][1])}return result},peg$c11=function(type,attrs){return{type:"attr",attrType:type,attrs:attrs||{}}},peg$c12="=",peg$c13={type:"literal",value:"=",description:'"="'},peg$c14=function(k,v){var attrs={};attrs[k]=v;return{type:"inlineAttr",attrs:attrs}},peg$c15=function(id,attrs){return{type:"node",id:id,attrs:attrs||{}}},peg$c16=function(lhs,rhs,attrs){var elems=[lhs];for(var i=0;i<rhs.length;++i){elems.push(rhs[i])}return{type:"edge",elems:elems,attrs:attrs||{}}},peg$c17=function(id,stmts){id=id&&id[2]||[];return{type:"subgraph",id:id[0],stmts:stmts}},peg$c18=function(first,rest){var result=first;for(var i=0;i<rest.length;++i){_.merge(result,rest[i][1])}return result},peg$c19="[",peg$c20={type:"literal",value:"[",description:'"["'},peg$c21="]",peg$c22={type:"literal",value:"]",description:'"]"'},peg$c23=function(aList){return aList},peg$c24=",",peg$c25={type:"literal",value:",",description:'","'},peg$c26=function(first,rest){var result=first;for(var i=0;i<rest.length;++i){_.merge(result,rest[i][3])}return result},peg$c27="--",peg$c28={type:"literal",value:"--",description:'"--"'},peg$c29=function(){return directed},peg$c30=void 0,peg$c31="->",peg$c32={type:"literal",value:"->",description:'"->"'},peg$c33=function(rhs,rest){var result=[rhs];if(rest){for(var i=0;i<rest.length;++i){result.push(rest[i])}}return result},peg$c34=function(k,v){var result={};result[k]=v[3];return result},peg$c35=function(id){return{type:"node",id:id,attrs:{}}},peg$c36=function(id){return id},peg$c37=":",peg$c38={type:"literal",value:":",description:'":"'},peg$c39="ne",peg$c40={type:"literal",value:"ne",description:'"ne"'},peg$c41="se",peg$c42={type:"literal",value:"se",description:'"se"'},peg$c43="sw",peg$c44={type:"literal",value:"sw",description:'"sw"'},peg$c45="nw",peg$c46={type:"literal",value:"nw",description:'"nw"'},peg$c47="n",peg$c48={type:"literal",value:"n",description:'"n"'},peg$c49="e",peg$c50={type:"literal",value:"e",description:'"e"'},peg$c51="s",peg$c52={type:"literal",value:"s",description:'"s"'},peg$c53="w",peg$c54={type:"literal",value:"w",description:'"w"'},peg$c55="c",peg$c56={type:"literal",value:"c",description:'"c"'},peg$c57="_",peg$c58={type:"literal",value:"_",description:'"_"'},peg$c59={type:"other",description:"identifier"},peg$c60=/^[a-zA-Z\u0200-\u0377_]/,peg$c61={type:"class",value:"[a-zA-Z\\u0200-\\u0377_]",description:"[a-zA-Z\\u0200-\\u0377_]"},peg$c62=/^[a-zA-Z\u0200-\u0377_0-9]/,peg$c63={type:"class",value:"[a-zA-Z\\u0200-\\u0377_0-9]",description:"[a-zA-Z\\u0200-\\u0377_0-9]"},peg$c64=function(fst,rest){return fst+rest.join("")},peg$c65="-",peg$c66={type:"literal",value:"-",description:'"-"'},peg$c67=".",peg$c68={type:"literal",value:".",description:'"."'},peg$c69=/^[0-9]/,peg$c70={type:"class",value:"[0-9]",description:"[0-9]"},peg$c71=function(sign,dot,after){return(sign||"")+dot+after.join("")},peg$c72=function(sign,before,after){return(sign||"")+before.join("")+(after?after[0]:"")+(after?after[1].join(""):"")},peg$c73='"',peg$c74={type:"literal",value:'"',description:'"\\""'},peg$c75='\\"',peg$c76={type:"literal",value:'\\"',description:'"\\\\\\""'},peg$c77=function(){return'"'},peg$c78="\\",peg$c79={type:"literal",value:"\\",description:'"\\\\"'},peg$c80=/^[^"]/,peg$c81={type:"class",value:'[^"]',description:'[^"]'},peg$c82=function(ch){return"\\"+ch},peg$c83=function(id){return id.join("")},peg$c84="node",peg$c85={type:"literal",value:"node",description:'"node"'},peg$c86=function(k){return k.toLowerCase()},peg$c87="edge",peg$c88={type:"literal",value:"edge",description:'"edge"'},peg$c89="graph",peg$c90={type:"literal",value:"graph",description:'"graph"'},peg$c91="digraph",peg$c92={type:"literal",value:"digraph",description:'"digraph"'},peg$c93="subgraph",peg$c94={type:"literal",value:"subgraph",description:'"subgraph"'},peg$c95="strict",peg$c96={type:"literal",value:"strict",description:'"strict"'},peg$c97=function(graph){directed=graph==="digraph";return graph},peg$c98={type:"other",description:"whitespace"},peg$c99=/^[ \t\r\n]/,peg$c100={type:"class",value:"[ \\t\\r\\n]",description:"[ \\t\\r\\n]"},peg$c101={type:"other",description:"comment"},peg$c102="//",peg$c103={type:"literal",value:"//",description:'"//"'},peg$c104=/^[^\n]/,peg$c105={type:"class",value:"[^\\n]",description:"[^\\n]"},peg$c106="/*",peg$c107={type:"literal",value:"/*",description:'"/*"'},peg$c108="*/",peg$c109={type:"literal",value:"*/",description:'"*/"'},peg$c110={type:"any",description:"any character"},peg$currPos=0,peg$reportedPos=0,peg$cachedPos=0,peg$cachedPosDetails={line:1,column:1,seenCR:false},peg$maxFailPos=0,peg$maxFailExpected=[],peg$silentFails=0,peg$result;if("startRule"in options){if(!(options.startRule in peg$startRuleFunctions)){throw new Error("Can't start parsing from rule \""+options.startRule+'".')}peg$startRuleFunction=peg$startRuleFunctions[options.startRule]}function text(){return input.substring(peg$reportedPos,peg$currPos)}function offset(){return peg$reportedPos}function line(){return peg$computePosDetails(peg$reportedPos).line}function column(){return peg$computePosDetails(peg$reportedPos).column}function expected(description){throw peg$buildException(null,[{type:"other",description:description}],peg$reportedPos)}function error(message){throw peg$buildException(message,null,peg$reportedPos)}function peg$computePosDetails(pos){function advance(details,startPos,endPos){var p,ch;for(p=startPos;p<endPos;p++){ch=input.charAt(p);if(ch==="\n"){if(!details.seenCR){details.line++}details.column=1;details.seenCR=false}else if(ch==="\r"||ch==="\u2028"||ch==="\u2029"){details.line++;details.column=1;details.seenCR=true}else{details.column++;details.seenCR=false}}}if(peg$cachedPos!==pos){if(peg$cachedPos>pos){peg$cachedPos=0;peg$cachedPosDetails={line:1,column:1,seenCR:false}}advance(peg$cachedPosDetails,peg$cachedPos,pos);peg$cachedPos=pos}return peg$cachedPosDetails}function peg$fail(expected){if(peg$currPos<peg$maxFailPos){return}if(peg$currPos>peg$maxFailPos){peg$maxFailPos=peg$currPos;peg$maxFailExpected=[]}peg$maxFailExpected.push(expected)}function peg$buildException(message,expected,pos){function cleanupExpected(expected){var i=1;expected.sort(function(a,b){if(a.description<b.description){return-1}else if(a.description>b.description){return 1}else{return 0}});while(i<expected.length){if(expected[i-1]===expected[i]){expected.splice(i,1)}else{i++}}}function buildMessage(expected,found){function stringEscape(s){function hex(ch){return ch.charCodeAt(0).toString(16).toUpperCase()}return s.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\x08/g,"\\b").replace(/\t/g,"\\t").replace(/\n/g,"\\n").replace(/\f/g,"\\f").replace(/\r/g,"\\r").replace(/[\x00-\x07\x0B\x0E\x0F]/g,function(ch){return"\\x0"+hex(ch)}).replace(/[\x10-\x1F\x80-\xFF]/g,function(ch){return"\\x"+hex(ch)}).replace(/[\u0180-\u0FFF]/g,function(ch){return"\\u0"+hex(ch)}).replace(/[\u1080-\uFFFF]/g,function(ch){return"\\u"+hex(ch)})}var expectedDescs=new Array(expected.length),expectedDesc,foundDesc,i;for(i=0;i<expected.length;i++){expectedDescs[i]=expected[i].description}expectedDesc=expected.length>1?expectedDescs.slice(0,-1).join(", ")+" or "+expectedDescs[expected.length-1]:expectedDescs[0];foundDesc=found?'"'+stringEscape(found)+'"':"end of input";return"Expected "+expectedDesc+" but "+foundDesc+" found."}var posDetails=peg$computePosDetails(pos),found=pos<input.length?input.charAt(pos):null;if(expected!==null){cleanupExpected(expected)}return new SyntaxError(message!==null?message:buildMessage(expected,found),expected,found,pos,posDetails.line,posDetails.column)}function peg$parsestart(){var s0,s1;s0=[];s1=peg$parsegraphStmt();if(s1!==peg$FAILED){while(s1!==peg$FAILED){s0.push(s1);s1=peg$parsegraphStmt()}}else{s0=peg$c1}return s0}function peg$parsegraphStmt(){var s0,s1,s2,s3,s4,s5,s6,s7,s8,s9,s10,s11,s12,s13;s0=peg$currPos;s1=[];s2=peg$parse_();while(s2!==peg$FAILED){s1.push(s2);s2=peg$parse_()}if(s1!==peg$FAILED){s2=peg$currPos;s3=peg$parsestrict();if(s3!==peg$FAILED){s4=peg$parse_();if(s4!==peg$FAILED){s3=[s3,s4];s2=s3}else{peg$currPos=s2;s2=peg$c1}}else{peg$currPos=s2;s2=peg$c1}if(s2===peg$FAILED){s2=peg$c2}if(s2!==peg$FAILED){s3=peg$parsegraphType();if(s3!==peg$FAILED){s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$parseid();if(s5===peg$FAILED){s5=peg$c2}if(s5!==peg$FAILED){s6=[];s7=peg$parse_();while(s7!==peg$FAILED){s6.push(s7);s7=peg$parse_()}if(s6!==peg$FAILED){if(input.charCodeAt(peg$currPos)===123){s7=peg$c3;peg$currPos++}else{s7=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c4)}}if(s7!==peg$FAILED){s8=[];s9=peg$parse_();while(s9!==peg$FAILED){s8.push(s9);s9=peg$parse_()}if(s8!==peg$FAILED){s9=peg$parsestmtList();if(s9===peg$FAILED){s9=peg$c2}if(s9!==peg$FAILED){s10=[];s11=peg$parse_();while(s11!==peg$FAILED){s10.push(s11);s11=peg$parse_()}if(s10!==peg$FAILED){if(input.charCodeAt(peg$currPos)===125){s11=peg$c5;peg$currPos++}else{s11=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c6)}}if(s11!==peg$FAILED){s12=[];s13=peg$parse_();while(s13!==peg$FAILED){s12.push(s13);s13=peg$parse_()}if(s12!==peg$FAILED){peg$reportedPos=s0;s1=peg$c7(s2,s3,s5,s9);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parsestmtList(){var s0,s1,s2,s3,s4,s5,s6,s7,s8,s9;s0=peg$currPos;s1=peg$parsestmt();if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){if(input.charCodeAt(peg$currPos)===59){s3=peg$c8;peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c9)}}if(s3===peg$FAILED){s3=peg$c2}if(s3!==peg$FAILED){s4=[];s5=peg$currPos;s6=[];s7=peg$parse_();while(s7!==peg$FAILED){s6.push(s7);s7=peg$parse_()}if(s6!==peg$FAILED){s7=peg$parsestmt();if(s7!==peg$FAILED){s8=[];s9=peg$parse_();while(s9!==peg$FAILED){s8.push(s9);s9=peg$parse_()}if(s8!==peg$FAILED){if(input.charCodeAt(peg$currPos)===59){s9=peg$c8;peg$currPos++}else{s9=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c9)}}if(s9===peg$FAILED){s9=peg$c2}if(s9!==peg$FAILED){s6=[s6,s7,s8,s9];s5=s6}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}while(s5!==peg$FAILED){s4.push(s5);s5=peg$currPos;s6=[];s7=peg$parse_();while(s7!==peg$FAILED){s6.push(s7);s7=peg$parse_()}if(s6!==peg$FAILED){s7=peg$parsestmt();if(s7!==peg$FAILED){s8=[];s9=peg$parse_();while(s9!==peg$FAILED){s8.push(s9);s9=peg$parse_()}if(s8!==peg$FAILED){if(input.charCodeAt(peg$currPos)===59){s9=peg$c8;peg$currPos++}else{s9=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c9)}}if(s9===peg$FAILED){s9=peg$c2}if(s9!==peg$FAILED){s6=[s6,s7,s8,s9];s5=s6}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}}if(s4!==peg$FAILED){peg$reportedPos=s0;s1=peg$c10(s1,s4);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parsestmt(){var s0;s0=peg$parseattrStmt();if(s0===peg$FAILED){s0=peg$parseedgeStmt();if(s0===peg$FAILED){s0=peg$parsesubgraphStmt();if(s0===peg$FAILED){s0=peg$parseinlineAttrStmt();if(s0===peg$FAILED){s0=peg$parsenodeStmt()}}}}return s0}function peg$parseattrStmt(){var s0,s1,s2,s3;s0=peg$currPos;s1=peg$parsegraph();if(s1===peg$FAILED){s1=peg$parsenode();if(s1===peg$FAILED){s1=peg$parseedge()}}if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parseattrList();if(s3!==peg$FAILED){peg$reportedPos=s0;s1=peg$c11(s1,s3);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseinlineAttrStmt(){var s0,s1,s2,s3,s4,s5;s0=peg$currPos;s1=peg$parseid();if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){if(input.charCodeAt(peg$currPos)===61){s3=peg$c12;peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c13)}}if(s3!==peg$FAILED){s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$parseid();if(s5!==peg$FAILED){peg$reportedPos=s0;s1=peg$c14(s1,s5);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parsenodeStmt(){var s0,s1,s2,s3;s0=peg$currPos;s1=peg$parsenodeId();if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parseattrList();if(s3===peg$FAILED){s3=peg$c2}if(s3!==peg$FAILED){peg$reportedPos=s0;s1=peg$c15(s1,s3);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseedgeStmt(){var s0,s1,s2,s3,s4,s5;s0=peg$currPos;s1=peg$parsenodeIdOrSubgraph();if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parseedgeRHS();if(s3!==peg$FAILED){s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$parseattrList();if(s5===peg$FAILED){s5=peg$c2}if(s5!==peg$FAILED){peg$reportedPos=s0;s1=peg$c16(s1,s3,s5);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parsesubgraphStmt(){var s0,s1,s2,s3,s4,s5,s6,s7;s0=peg$currPos;s1=peg$currPos;s2=peg$parsesubgraph();if(s2!==peg$FAILED){s3=[];s4=peg$parse_();while(s4!==peg$FAILED){s3.push(s4);s4=peg$parse_()}if(s3!==peg$FAILED){s4=peg$currPos;s5=peg$parseid();if(s5!==peg$FAILED){s6=[];s7=peg$parse_();while(s7!==peg$FAILED){s6.push(s7);s7=peg$parse_()}if(s6!==peg$FAILED){s5=[s5,s6];s4=s5}else{peg$currPos=s4;s4=peg$c1}}else{peg$currPos=s4;s4=peg$c1}if(s4===peg$FAILED){s4=peg$c2}if(s4!==peg$FAILED){s2=[s2,s3,s4];s1=s2}else{peg$currPos=s1;s1=peg$c1}}else{peg$currPos=s1;s1=peg$c1}}else{peg$currPos=s1;s1=peg$c1}if(s1===peg$FAILED){s1=peg$c2}if(s1!==peg$FAILED){if(input.charCodeAt(peg$currPos)===123){s2=peg$c3;peg$currPos++}else{s2=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c4)}}if(s2!==peg$FAILED){s3=[];s4=peg$parse_();while(s4!==peg$FAILED){s3.push(s4);s4=peg$parse_()}if(s3!==peg$FAILED){s4=peg$parsestmtList();if(s4===peg$FAILED){s4=peg$c2}if(s4!==peg$FAILED){s5=[];s6=peg$parse_();while(s6!==peg$FAILED){s5.push(s6);s6=peg$parse_()}if(s5!==peg$FAILED){if(input.charCodeAt(peg$currPos)===125){s6=peg$c5;peg$currPos++}else{s6=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c6)}}if(s6!==peg$FAILED){peg$reportedPos=s0;s1=peg$c17(s1,s4);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseattrList(){var s0,s1,s2,s3,s4,s5;s0=peg$currPos;s1=peg$parseattrListBlock();if(s1!==peg$FAILED){s2=[];s3=peg$currPos;s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$parseattrListBlock();if(s5!==peg$FAILED){s4=[s4,s5];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}while(s3!==peg$FAILED){s2.push(s3);s3=peg$currPos;s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$parseattrListBlock();if(s5!==peg$FAILED){s4=[s4,s5];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}if(s2!==peg$FAILED){peg$reportedPos=s0;s1=peg$c18(s1,s2);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseattrListBlock(){var s0,s1,s2,s3,s4,s5;s0=peg$currPos;if(input.charCodeAt(peg$currPos)===91){s1=peg$c19;peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c20)}}if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parseaList();if(s3===peg$FAILED){s3=peg$c2}if(s3!==peg$FAILED){s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){if(input.charCodeAt(peg$currPos)===93){s5=peg$c21;peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c22)}}if(s5!==peg$FAILED){peg$reportedPos=s0;s1=peg$c23(s3);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseaList(){var s0,s1,s2,s3,s4,s5,s6,s7;s0=peg$currPos;s1=peg$parseidDef();if(s1!==peg$FAILED){s2=[];s3=peg$currPos;s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){if(input.charCodeAt(peg$currPos)===44){s5=peg$c24;peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c25)}}if(s5===peg$FAILED){s5=peg$c2}if(s5!==peg$FAILED){s6=[];s7=peg$parse_();while(s7!==peg$FAILED){s6.push(s7);s7=peg$parse_()}if(s6!==peg$FAILED){s7=peg$parseidDef();if(s7!==peg$FAILED){s4=[s4,s5,s6,s7];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}while(s3!==peg$FAILED){s2.push(s3);s3=peg$currPos;s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){if(input.charCodeAt(peg$currPos)===44){s5=peg$c24;peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c25)}}if(s5===peg$FAILED){s5=peg$c2}if(s5!==peg$FAILED){s6=[];s7=peg$parse_();while(s7!==peg$FAILED){s6.push(s7);s7=peg$parse_()}if(s6!==peg$FAILED){s7=peg$parseidDef();if(s7!==peg$FAILED){s4=[s4,s5,s6,s7];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}if(s2!==peg$FAILED){peg$reportedPos=s0;s1=peg$c26(s1,s2);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseedgeRHS(){var s0,s1,s2,s3,s4,s5;s0=peg$currPos;s1=peg$currPos;if(input.substr(peg$currPos,2)===peg$c27){s2=peg$c27;peg$currPos+=2}else{s2=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c28)}}if(s2!==peg$FAILED){peg$reportedPos=peg$currPos;s3=peg$c29();if(s3){s3=peg$c1}else{s3=peg$c30}if(s3!==peg$FAILED){s2=[s2,s3];s1=s2}else{peg$currPos=s1;s1=peg$c1}}else{peg$currPos=s1;s1=peg$c1}if(s1===peg$FAILED){s1=peg$currPos;if(input.substr(peg$currPos,2)===peg$c31){s2=peg$c31;peg$currPos+=2}else{s2=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c32)}}if(s2!==peg$FAILED){peg$reportedPos=peg$currPos;s3=peg$c29();if(s3){s3=peg$c30}else{s3=peg$c1}if(s3!==peg$FAILED){s2=[s2,s3];s1=s2}else{peg$currPos=s1;s1=peg$c1}}else{peg$currPos=s1;s1=peg$c1}}if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parsenodeIdOrSubgraph();if(s3!==peg$FAILED){s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$parseedgeRHS();if(s5===peg$FAILED){s5=peg$c2}if(s5!==peg$FAILED){peg$reportedPos=s0;s1=peg$c33(s3,s5);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseidDef(){var s0,s1,s2,s3,s4,s5,s6;s0=peg$currPos;s1=peg$parseid();if(s1!==peg$FAILED){s2=peg$currPos;s3=[];s4=peg$parse_();while(s4!==peg$FAILED){s3.push(s4);s4=peg$parse_()}if(s3!==peg$FAILED){if(input.charCodeAt(peg$currPos)===61){s4=peg$c12;peg$currPos++}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c13)}}if(s4!==peg$FAILED){s5=[];s6=peg$parse_();while(s6!==peg$FAILED){s5.push(s6);s6=peg$parse_()}if(s5!==peg$FAILED){s6=peg$parseid();if(s6!==peg$FAILED){s3=[s3,s4,s5,s6];s2=s3}else{peg$currPos=s2;s2=peg$c1}}else{peg$currPos=s2;s2=peg$c1}}else{peg$currPos=s2;s2=peg$c1}}else{peg$currPos=s2;s2=peg$c1}if(s2===peg$FAILED){s2=peg$c2}if(s2!==peg$FAILED){peg$reportedPos=s0;s1=peg$c34(s1,s2);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parsenodeIdOrSubgraph(){var s0,s1;s0=peg$parsesubgraphStmt();if(s0===peg$FAILED){s0=peg$currPos;s1=peg$parsenodeId();if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c35(s1)}s0=s1}return s0}function peg$parsenodeId(){var s0,s1,s2,s3;s0=peg$currPos;s1=peg$parseid();if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parseport();if(s3===peg$FAILED){s3=peg$c2}if(s3!==peg$FAILED){peg$reportedPos=s0;s1=peg$c36(s1);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parseport(){var s0,s1,s2,s3,s4,s5,s6,s7,s8;s0=peg$currPos;if(input.charCodeAt(peg$currPos)===58){s1=peg$c37;peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c38)}}if(s1!==peg$FAILED){s2=[];s3=peg$parse_();while(s3!==peg$FAILED){s2.push(s3);s3=peg$parse_()}if(s2!==peg$FAILED){s3=peg$parseid();if(s3!==peg$FAILED){s4=[];s5=peg$parse_();while(s5!==peg$FAILED){s4.push(s5);s5=peg$parse_()}if(s4!==peg$FAILED){s5=peg$currPos;if(input.charCodeAt(peg$currPos)===58){s6=peg$c37;peg$currPos++}else{s6=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c38)}}if(s6!==peg$FAILED){s7=[];s8=peg$parse_();while(s8!==peg$FAILED){s7.push(s8);s8=peg$parse_()}if(s7!==peg$FAILED){s8=peg$parsecompassPt();if(s8!==peg$FAILED){s6=[s6,s7,s8];s5=s6}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}}else{peg$currPos=s5;s5=peg$c1}if(s5===peg$FAILED){s5=peg$c2}if(s5!==peg$FAILED){s1=[s1,s2,s3,s4,s5];s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}return s0}function peg$parsecompassPt(){var s0;if(input.substr(peg$currPos,2)===peg$c39){s0=peg$c39;peg$currPos+=2}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c40)}}if(s0===peg$FAILED){if(input.substr(peg$currPos,2)===peg$c41){s0=peg$c41;peg$currPos+=2}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c42)}}if(s0===peg$FAILED){if(input.substr(peg$currPos,2)===peg$c43){s0=peg$c43;peg$currPos+=2}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c44)}}if(s0===peg$FAILED){if(input.substr(peg$currPos,2)===peg$c45){s0=peg$c45;peg$currPos+=2}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c46)}}if(s0===peg$FAILED){if(input.charCodeAt(peg$currPos)===110){s0=peg$c47;peg$currPos++}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c48)}}if(s0===peg$FAILED){if(input.charCodeAt(peg$currPos)===101){s0=peg$c49;peg$currPos++}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c50)}}if(s0===peg$FAILED){if(input.charCodeAt(peg$currPos)===115){s0=peg$c51;peg$currPos++}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c52)}}if(s0===peg$FAILED){if(input.charCodeAt(peg$currPos)===119){s0=peg$c53;peg$currPos++}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c54)}}if(s0===peg$FAILED){if(input.charCodeAt(peg$currPos)===99){s0=peg$c55;peg$currPos++}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c56)}}if(s0===peg$FAILED){if(input.charCodeAt(peg$currPos)===95){s0=peg$c57;peg$currPos++}else{s0=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c58)}}}}}}}}}}}return s0}function peg$parseid(){var s0,s1,s2,s3,s4,s5,s6;peg$silentFails++;s0=peg$currPos;if(peg$c60.test(input.charAt(peg$currPos))){s1=input.charAt(peg$currPos);peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c61)}}if(s1!==peg$FAILED){s2=[];if(peg$c62.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c63)}}while(s3!==peg$FAILED){s2.push(s3);if(peg$c62.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c63)}}}if(s2!==peg$FAILED){peg$reportedPos=s0;s1=peg$c64(s1,s2);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}if(s0===peg$FAILED){s0=peg$currPos;if(input.charCodeAt(peg$currPos)===45){s1=peg$c65;peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c66)}}if(s1===peg$FAILED){s1=peg$c2}if(s1!==peg$FAILED){if(input.charCodeAt(peg$currPos)===46){s2=peg$c67;peg$currPos++}else{s2=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c68)}}if(s2!==peg$FAILED){s3=[];if(peg$c69.test(input.charAt(peg$currPos))){s4=input.charAt(peg$currPos);peg$currPos++}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c70)}}if(s4!==peg$FAILED){while(s4!==peg$FAILED){s3.push(s4);if(peg$c69.test(input.charAt(peg$currPos))){s4=input.charAt(peg$currPos);peg$currPos++}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c70)}}}}else{s3=peg$c1}if(s3!==peg$FAILED){peg$reportedPos=s0;s1=peg$c71(s1,s2,s3);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}if(s0===peg$FAILED){s0=peg$currPos;if(input.charCodeAt(peg$currPos)===45){s1=peg$c65;peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c66)}}if(s1===peg$FAILED){s1=peg$c2}if(s1!==peg$FAILED){s2=[];if(peg$c69.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c70)}}if(s3!==peg$FAILED){while(s3!==peg$FAILED){s2.push(s3);if(peg$c69.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c70)}}}}else{s2=peg$c1}if(s2!==peg$FAILED){s3=peg$currPos;if(input.charCodeAt(peg$currPos)===46){s4=peg$c67;peg$currPos++}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c68)}}if(s4!==peg$FAILED){s5=[];if(peg$c69.test(input.charAt(peg$currPos))){s6=input.charAt(peg$currPos);peg$currPos++}else{s6=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c70)}}while(s6!==peg$FAILED){s5.push(s6);if(peg$c69.test(input.charAt(peg$currPos))){s6=input.charAt(peg$currPos);peg$currPos++}else{s6=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c70)}}}if(s5!==peg$FAILED){s4=[s4,s5];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}if(s3===peg$FAILED){s3=peg$c2}if(s3!==peg$FAILED){peg$reportedPos=s0;s1=peg$c72(s1,s2,s3);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}if(s0===peg$FAILED){s0=peg$currPos;if(input.charCodeAt(peg$currPos)===34){s1=peg$c73;peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c74)}}if(s1!==peg$FAILED){s2=[];s3=peg$currPos;if(input.substr(peg$currPos,2)===peg$c75){s4=peg$c75;peg$currPos+=2}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c76)}}if(s4!==peg$FAILED){peg$reportedPos=s3;s4=peg$c77()}s3=s4;if(s3===peg$FAILED){s3=peg$currPos;if(input.charCodeAt(peg$currPos)===92){s4=peg$c78;peg$currPos++}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c79)}}if(s4!==peg$FAILED){if(peg$c80.test(input.charAt(peg$currPos))){s5=input.charAt(peg$currPos);peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c81)}}if(s5!==peg$FAILED){peg$reportedPos=s3;s4=peg$c82(s5);s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}if(s3===peg$FAILED){if(peg$c80.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);
+peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c81)}}}}while(s3!==peg$FAILED){s2.push(s3);s3=peg$currPos;if(input.substr(peg$currPos,2)===peg$c75){s4=peg$c75;peg$currPos+=2}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c76)}}if(s4!==peg$FAILED){peg$reportedPos=s3;s4=peg$c77()}s3=s4;if(s3===peg$FAILED){s3=peg$currPos;if(input.charCodeAt(peg$currPos)===92){s4=peg$c78;peg$currPos++}else{s4=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c79)}}if(s4!==peg$FAILED){if(peg$c80.test(input.charAt(peg$currPos))){s5=input.charAt(peg$currPos);peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c81)}}if(s5!==peg$FAILED){peg$reportedPos=s3;s4=peg$c82(s5);s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}if(s3===peg$FAILED){if(peg$c80.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c81)}}}}}if(s2!==peg$FAILED){if(input.charCodeAt(peg$currPos)===34){s3=peg$c73;peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c74)}}if(s3!==peg$FAILED){peg$reportedPos=s0;s1=peg$c83(s2);s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}}}peg$silentFails--;if(s0===peg$FAILED){s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c59)}}return s0}function peg$parsenode(){var s0,s1;s0=peg$currPos;if(input.substr(peg$currPos,4).toLowerCase()===peg$c84){s1=input.substr(peg$currPos,4);peg$currPos+=4}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c85)}}if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c86(s1)}s0=s1;return s0}function peg$parseedge(){var s0,s1;s0=peg$currPos;if(input.substr(peg$currPos,4).toLowerCase()===peg$c87){s1=input.substr(peg$currPos,4);peg$currPos+=4}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c88)}}if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c86(s1)}s0=s1;return s0}function peg$parsegraph(){var s0,s1;s0=peg$currPos;if(input.substr(peg$currPos,5).toLowerCase()===peg$c89){s1=input.substr(peg$currPos,5);peg$currPos+=5}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c90)}}if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c86(s1)}s0=s1;return s0}function peg$parsedigraph(){var s0,s1;s0=peg$currPos;if(input.substr(peg$currPos,7).toLowerCase()===peg$c91){s1=input.substr(peg$currPos,7);peg$currPos+=7}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c92)}}if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c86(s1)}s0=s1;return s0}function peg$parsesubgraph(){var s0,s1;s0=peg$currPos;if(input.substr(peg$currPos,8).toLowerCase()===peg$c93){s1=input.substr(peg$currPos,8);peg$currPos+=8}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c94)}}if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c86(s1)}s0=s1;return s0}function peg$parsestrict(){var s0,s1;s0=peg$currPos;if(input.substr(peg$currPos,6).toLowerCase()===peg$c95){s1=input.substr(peg$currPos,6);peg$currPos+=6}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c96)}}if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c86(s1)}s0=s1;return s0}function peg$parsegraphType(){var s0,s1;s0=peg$parsegraph();if(s0===peg$FAILED){s0=peg$currPos;s1=peg$parsedigraph();if(s1!==peg$FAILED){peg$reportedPos=s0;s1=peg$c97(s1)}s0=s1}return s0}function peg$parsewhitespace(){var s0,s1;peg$silentFails++;s0=[];if(peg$c99.test(input.charAt(peg$currPos))){s1=input.charAt(peg$currPos);peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c100)}}if(s1!==peg$FAILED){while(s1!==peg$FAILED){s0.push(s1);if(peg$c99.test(input.charAt(peg$currPos))){s1=input.charAt(peg$currPos);peg$currPos++}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c100)}}}}else{s0=peg$c1}peg$silentFails--;if(s0===peg$FAILED){s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c98)}}return s0}function peg$parsecomment(){var s0,s1,s2,s3,s4,s5;peg$silentFails++;s0=peg$currPos;if(input.substr(peg$currPos,2)===peg$c102){s1=peg$c102;peg$currPos+=2}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c103)}}if(s1!==peg$FAILED){s2=[];if(peg$c104.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c105)}}while(s3!==peg$FAILED){s2.push(s3);if(peg$c104.test(input.charAt(peg$currPos))){s3=input.charAt(peg$currPos);peg$currPos++}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c105)}}}if(s2!==peg$FAILED){s1=[s1,s2];s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}if(s0===peg$FAILED){s0=peg$currPos;if(input.substr(peg$currPos,2)===peg$c106){s1=peg$c106;peg$currPos+=2}else{s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c107)}}if(s1!==peg$FAILED){s2=[];s3=peg$currPos;s4=peg$currPos;peg$silentFails++;if(input.substr(peg$currPos,2)===peg$c108){s5=peg$c108;peg$currPos+=2}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c109)}}peg$silentFails--;if(s5===peg$FAILED){s4=peg$c30}else{peg$currPos=s4;s4=peg$c1}if(s4!==peg$FAILED){if(input.length>peg$currPos){s5=input.charAt(peg$currPos);peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c110)}}if(s5!==peg$FAILED){s4=[s4,s5];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}while(s3!==peg$FAILED){s2.push(s3);s3=peg$currPos;s4=peg$currPos;peg$silentFails++;if(input.substr(peg$currPos,2)===peg$c108){s5=peg$c108;peg$currPos+=2}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c109)}}peg$silentFails--;if(s5===peg$FAILED){s4=peg$c30}else{peg$currPos=s4;s4=peg$c1}if(s4!==peg$FAILED){if(input.length>peg$currPos){s5=input.charAt(peg$currPos);peg$currPos++}else{s5=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c110)}}if(s5!==peg$FAILED){s4=[s4,s5];s3=s4}else{peg$currPos=s3;s3=peg$c1}}else{peg$currPos=s3;s3=peg$c1}}if(s2!==peg$FAILED){if(input.substr(peg$currPos,2)===peg$c108){s3=peg$c108;peg$currPos+=2}else{s3=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c109)}}if(s3!==peg$FAILED){s1=[s1,s2,s3];s0=s1}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}else{peg$currPos=s0;s0=peg$c1}}peg$silentFails--;if(s0===peg$FAILED){s1=peg$FAILED;if(peg$silentFails===0){peg$fail(peg$c101)}}return s0}function peg$parse_(){var s0;s0=peg$parsewhitespace();if(s0===peg$FAILED){s0=peg$parsecomment()}return s0}var _=require("lodash");var directed;peg$result=peg$startRuleFunction();if(peg$result!==peg$FAILED&&peg$currPos===input.length){return peg$result}else{if(peg$result!==peg$FAILED&&peg$currPos<input.length){peg$fail({type:"end",description:"end of input"})}throw peg$buildException(null,peg$maxFailExpected,peg$maxFailPos)}}return{SyntaxError:SyntaxError,parse:parse}}()},{lodash:28}],5:[function(require,module,exports){var _=require("lodash"),grammar=require("./dot-grammar"),buildGraph=require("./build-graph");module.exports=function readMany(str){var parseTree=grammar.parse(str);return _.map(parseTree,buildGraph)}},{"./build-graph":3,"./dot-grammar":4,lodash:28}],6:[function(require,module,exports){var grammar=require("./dot-grammar"),buildGraph=require("./build-graph");module.exports=function readOne(str){var parseTree=grammar.parse(str,{startRule:"graphStmt"});return buildGraph(parseTree)}},{"./build-graph":3,"./dot-grammar":4}],7:[function(require,module,exports){module.exports="0.5.2"},{}],8:[function(require,module,exports){var _=require("lodash");module.exports=writeOne;var UNESCAPED_ID_PATTERN=/^[a-zA-Z\200-\377_][a-zA-Z\200-\377_0-9]*$/;function writeOne(g){var ec=g.isDirected()?"->":"--",writer=new Writer;if(!g.isMultigraph()){writer.write("strict ")}writer.writeLine((g.isDirected()?"digraph":"graph")+" {");writer.indent();var graphAttrs=g.graph();if(_.isObject(graphAttrs)){_.each(graphAttrs,function(v,k){writer.writeLine(id(k)+"="+id(v)+";")})}writeSubgraph(g,undefined,writer);g.edges().forEach(function(edge){writeEdge(g,edge,ec,writer)});writer.unindent();writer.writeLine("}");return writer.toString()}function writeSubgraph(g,v,writer){var children=g.isCompound()?g.children(v):g.nodes();_.each(children,function(w){if(!g.isCompound()||!g.children(w).length){writeNode(g,w,writer)}else{writer.writeLine("subgraph "+id(w)+" {");writer.indent();if(_.isObject(g.node(w))){_.map(g.node(w),function(val,key){writer.writeLine(id(key)+"="+id(val)+";")})}writeSubgraph(g,w,writer);writer.unindent();writer.writeLine("}")}})}function writeNode(g,v,writer){writer.write(id(v));writeAttrs(g.node(v),writer);writer.writeLine()}function writeEdge(g,edge,ec,writer){var v=edge.v,w=edge.w,attrs=g.edge(edge);writer.write(id(v)+" "+ec+" "+id(w));writeAttrs(attrs,writer);writer.writeLine()}function writeAttrs(attrs,writer){if(_.isObject(attrs)){var attrStrs=_.map(attrs,function(val,key){return id(key)+"="+id(val)});if(attrStrs.length){writer.write(" ["+attrStrs.join(",")+"]")}}}function id(obj){if(typeof obj==="number"||obj.toString().match(UNESCAPED_ID_PATTERN)){return obj}return'"'+obj.toString().replace(/"/g,'\\"')+'"'}function Writer(){this._indent="";this._content="";this._shouldIndent=true}Writer.prototype.INDENT="  ";Writer.prototype.indent=function(){this._indent+=this.INDENT};Writer.prototype.unindent=function(){this._indent=this._indent.slice(this.INDENT.length)};Writer.prototype.writeLine=function(line){this.write((line||"")+"\n");this._shouldIndent=true};Writer.prototype.write=function(str){if(this._shouldIndent){this._shouldIndent=false;this._content+=this._indent}this._content+=str};Writer.prototype.toString=function(){return this._content}},{lodash:28}],9:[function(require,module,exports){var _=require("lodash");module.exports=_.clone(require("./lib"));module.exports.json=require("./lib/json");module.exports.alg=require("./lib/alg")},{"./lib":25,"./lib/alg":16,"./lib/json":26,lodash:28}],10:[function(require,module,exports){var _=require("lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{lodash:28}],11:[function(require,module,exports){var _=require("lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{lodash:28}],12:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"./dijkstra":13,lodash:28}],13:[function(require,module,exports){var _=require("lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance<wEntry.distance){wEntry.distance=distance;wEntry.predecessor=v;pq.decrease(w,distance)}};g.nodes().forEach(function(v){var distance=v===source?0:Number.POSITIVE_INFINITY;results[v]={distance:distance};pq.add(v,distance)});while(pq.size()>0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":23,lodash:28}],14:[function(require,module,exports){var _=require("lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"./tarjan":21,lodash:28}],15:[function(require,module,exports){var _=require("lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance<ij.distance){ij.distance=altDistance;ij.predecessor=kj.predecessor}})})});return results}},{lodash:28}],16:[function(require,module,exports){module.exports={components:require("./components"),dijkstra:require("./dijkstra"),dijkstraAll:require("./dijkstra-all"),findCycles:require("./find-cycles"),floydWarshall:require("./floyd-warshall"),isAcyclic:require("./is-acyclic"),postorder:require("./postorder"),preorder:require("./preorder"),prim:require("./prim"),tarjan:require("./tarjan"),topsort:require("./topsort")}},{"./components":10,"./dijkstra":13,"./dijkstra-all":12,"./find-cycles":14,"./floyd-warshall":15,"./is-acyclic":17,"./postorder":18,"./preorder":19,"./prim":20,"./tarjan":21,"./topsort":22}],17:[function(require,module,exports){var topsort=require("./topsort");module.exports=isAcyclic;function isAcyclic(g){try{topsort(g)}catch(e){if(e instanceof topsort.CycleException){return false}throw e}return true}},{"./topsort":22}],18:[function(require,module,exports){var dfs=require("./dfs");module.exports=postorder;function postorder(g,vs){return dfs(g,vs,"post")}},{"./dfs":11}],19:[function(require,module,exports){var dfs=require("./dfs");module.exports=preorder;function preorder(g,vs){return dfs(g,vs,"pre")}},{"./dfs":11}],20:[function(require,module,exports){var _=require("lodash"),Graph=require("../graph"),PriorityQueue=require("../data/priority-queue");module.exports=prim;function prim(g,weightFunc){var result=new Graph,parents={},pq=new PriorityQueue,v;function updateNeighbors(edge){var w=edge.v===v?edge.w:edge.v,pri=pq.priority(w);if(pri!==undefined){var edgeWeight=weightFunc(edge);if(edgeWeight<pri){parents[w]=v;pq.decrease(w,edgeWeight)}}}if(g.nodeCount()===0){return result}_.each(g.nodes(),function(v){pq.add(v,Number.POSITIVE_INFINITY);result.setNode(v)});pq.decrease(g.nodes()[0],0);var init=false;while(pq.size()>0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return 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"+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l<arr.length){largest=arr[l].priority<arr[largest].priority?l:largest;if(r<arr.length){largest=arr[r].priority<arr[largest].priority?r:largest}if(largest!==i){this._swap(i,largest);this._heapify(largest)}}};PriorityQueue.prototype._decrease=function(index){var arr=this._arr;var priority=arr[index].priority;var parent;while(index!==0){parent=index>>1;if(arr[parent].priority<priority){break}this._swap(index,parent);index=parent}};PriorityQueue.prototype._swap=function(i,j){var arr=this._arr;var keyIndices=this._keyIndices;var origArrI=arr[i];var origArrJ=arr[j];arr[i]=origArrJ;arr[j]=origArrI;keyIndices[origArrJ.key]=i;keyIndices[origArrI.key]=j}},{lodash:28}],24:[function(require,module,exports){"use strict";var _=require("lodash");module.exports=Graph;var DEFAULT_EDGE_NAME="\x00",GRAPH_NODE="\x00",EDGE_KEY_DELIM="
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Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE){return parent}}};Graph.prototype.children=function(v){if(_.isUndefined(v)){v=GRAPH_NODE}if(this._isCompound){var children=this._children[v];if(children){return _.keys(children)}}else if(v===GRAPH_NODE){return this.nodes()}else if(this.hasNode(v)){return[]}};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV){return 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e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,e)){if(valueSpecified){this._edgeLabels[e]=value}return this}if(!_.isUndefined(name)&&!this._isMultigraph){throw new Error("Cannot set a named edge when isMultigraph = false")}this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var edgeObj=edgeArgsToObj(this._isDirected,v,w,name);v=edgeObj.v;w=edgeObj.w;Object.freeze(edgeObj);this._edgeObjs[e]=edgeObj;incrementOrInitEntry(this._preds[w],v);incrementOrInitEntry(this._sucs[v],w);this._in[w][e]=edgeObj;this._out[v][e]=edgeObj;this._edgeCount++;return this};Graph.prototype.edge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return this._edgeLabels[e]};Graph.prototype.hasEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return 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diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
new file mode 100644
index 0000000000000..99b029477c5ff
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -0,0 +1,392 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/*
+ * This file contains the logic to render the RDD DAG visualization in the UI.
+ *
+ * This DAG describes the relationships between
+ *   (1) an RDD and its dependencies,
+ *   (2) an RDD and its operation scopes, and
+ *   (3) an RDD's operation scopes and the stage / job hierarchy
+ *
+ * An operation scope is a general, named code block representing an operation
+ * that instantiates RDDs (e.g. filter, textFile, reduceByKey). An operation
+ * scope can be nested inside of other scopes if the corresponding RDD operation
+ * invokes other such operations (for more detail, see o.a.s.rdd.operationScope).
+ *
+ * A stage may include one or more operation scopes if the RDD operations are
+ * streamlined into one stage (e.g. rdd.map(...).filter(...).flatMap(...)).
+ * On the flip side, an operation scope may also include one or many stages,
+ * or even jobs if the RDD operation is higher level than Spark's scheduling
+ * primitives (e.g. take, any SQL query).
+ *
+ * In the visualization, an RDD is expressed as a node, and its dependencies
+ * as directed edges (from parent to child). operation scopes, stages, and
+ * jobs are expressed as clusters that may contain one or many nodes. These
+ * clusters may be nested inside of each other in the scenarios described
+ * above.
+ *
+ * The visualization is rendered in an SVG contained in "div#dag-viz-graph",
+ * and its input data is expected to be populated in "div#dag-viz-metadata"
+ * by Spark's UI code. This is currently used only on the stage page and on
+ * the job page.
+ *
+ * This requires jQuery, d3, and dagre-d3. Note that we use a custom release
+ * of dagre-d3 (http://github.com/andrewor14/dagre-d3) for some specific
+ * functionality. For more detail, please track the changes in that project
+ * since it was forked (commit 101503833a8ce5fe369547f6addf3e71172ce10b).
+ */
+
+var VizConstants = {
+  rddColor: "#444444",
+  rddCachedColor: "#FF0000",
+  rddOperationColor: "#AADFFF",
+  stageColor: "#FFDDEE",
+  clusterLabelColor: "#888888",
+  edgeColor: "#444444",
+  edgeWidth: "1.5px",
+  svgMarginX: 0,
+  svgMarginY: 20,
+  stageSep: 50,
+  graphPrefix: "graph_",
+  nodePrefix: "node_",
+  stagePrefix: "stage_",
+  clusterPrefix: "cluster_",
+  stageClusterPrefix: "cluster_stage_"
+};
+
+// Helper d3 accessors for the elements that contain our graph and its metadata
+function graphContainer() { return d3.select("#dag-viz-graph"); }
+function metadataContainer() { return d3.select("#dag-viz-metadata"); }
+
+/*
+ * Show or hide the RDD DAG visualization.
+ * The graph is only rendered the first time this is called.
+ */
+function toggleDagViz(forJob) {
+  var arrowSelector = ".expand-dag-viz-arrow";
+  $(arrowSelector).toggleClass('arrow-closed');
+  $(arrowSelector).toggleClass('arrow-open');
+  var shouldShow = $(arrowSelector).hasClass("arrow-open");
+  if (shouldShow) {
+    var shouldRender = graphContainer().select("svg").empty();
+    if (shouldRender) {
+      renderDagViz(forJob);
+    }
+    graphContainer().style("display", "block");
+  } else {
+    // Save the graph for later so we don't have to render it again
+    graphContainer().style("display", "none");
+  }
+}
+
+/*
+ * Render the RDD DAG visualization.
+ *
+ * Input DOM hierarchy:
+ *   div#dag-viz-metadata >
+ *   div.stage-metadata >
+ *   div.[dot-file | incoming-edge | outgoing-edge]
+ *
+ * Output DOM hierarchy:
+ *   div#dag-viz-graph >
+ *   svg >
+ *   g#cluster_stage_[stageId]
+ *
+ * Note that the input metadata is populated by o.a.s.ui.UIUtils.showDagViz.
+ * Any changes in the input format here must be reflected there.
+ */
+function renderDagViz(forJob) {
+
+  // If there is not a dot file to render, fail fast and report error
+  if (metadataContainer().empty()) {
+    graphContainer().append("div").text(
+      "No visualization information available for this " + (forJob ? "job" : "stage"));
+    return;
+  }
+
+  var svg = graphContainer().append("svg");
+  if (forJob) {
+    renderDagVizForJob(svg);
+  } else {
+    renderDagVizForStage(svg);
+  }
+
+  // Find cached RDDs
+  metadataContainer().selectAll(".cached-rdd").each(function(v) {
+    var nodeId = VizConstants.nodePrefix + d3.select(this).text();
+    graphContainer().selectAll("#" + nodeId).classed("cached", true);
+  });
+
+  // Set the appropriate SVG dimensions to ensure that all elements are displayed
+  var boundingBox = svg.node().getBBox();
+  svg.style("width", (boundingBox.width + VizConstants.svgMarginX) + "px");
+  svg.style("height", (boundingBox.height + VizConstants.svgMarginY) + "px");
+
+  // Add labels to clusters because dagre-d3 doesn't do this for us
+  svg.selectAll("g.cluster rect").each(function() {
+    var rect = d3.select(this);
+    var cluster = d3.select(this.parentNode);
+    // Shift the boxes up a little to make room for the labels
+    rect.attr("y", toFloat(rect.attr("y")) - 10);
+    rect.attr("height", toFloat(rect.attr("height")) + 10);
+    var labelX = toFloat(rect.attr("x")) + toFloat(rect.attr("width")) - 5;
+    var labelY = toFloat(rect.attr("y")) + 15;
+    var labelText = cluster.attr("name").replace(VizConstants.clusterPrefix, "");
+    cluster.append("text")
+      .attr("x", labelX)
+      .attr("y", labelY)
+      .attr("text-anchor", "end")
+      .text(labelText);
+  });
+
+  // We have shifted a few elements upwards, so we should fix the SVG views
+  var startX = -VizConstants.svgMarginX;
+  var startY = -VizConstants.svgMarginY;
+  var endX = toFloat(svg.style("width")) + VizConstants.svgMarginX;
+  var endY = toFloat(svg.style("height")) + VizConstants.svgMarginY;
+  var newViewBox = startX + " " + startY + " " + endX + " " + endY;
+  svg.attr("viewBox", newViewBox);
+
+  // Lastly, apply some custom style to the DAG
+  styleDagViz(forJob);
+}
+
+/* Render the RDD DAG visualization for a stage. */
+function renderDagVizForStage(svgContainer) {
+  var metadata = metadataContainer().select(".stage-metadata");
+  var dot = metadata.select(".dot-file").text();
+  var containerId = VizConstants.graphPrefix + metadata.attr("stageId");
+  var container = svgContainer.append("g").attr("id", containerId);
+  renderDot(dot, container);
+}
+
+/*
+ * Render the RDD DAG visualization for a job.
+ *
+ * Due to limitations in dagre-d3, each stage is rendered independently so that
+ * we have more control on how to position them. Unfortunately, this means we
+ * cannot rely on dagre-d3 to render edges that cross stages and must render
+ * these manually on our own.
+ */
+function renderDagVizForJob(svgContainer) {
+  var crossStageEdges = [];
+
+  metadataContainer().selectAll(".stage-metadata").each(function(d, i) {
+    var metadata = d3.select(this);
+    var dot = metadata.select(".dot-file").text();
+    var stageId = metadata.attr("stageId");
+    var containerId = VizConstants.graphPrefix + stageId;
+    // TODO: handle stage attempts
+    var stageLink =
+      "/stages/stage/?id=" + stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0";
+    var container = svgContainer
+      .append("a").attr("xlink:href", stageLink)
+      .append("g").attr("id", containerId);
+    // Now we need to shift the container for this stage so it doesn't overlap
+    // with existing ones. We do not need to do this for the first stage.
+    if (i > 0) {
+      // Take into account the position and width of the last stage's container
+      var existingStages = stageClusters();
+      if (!existingStages.empty()) {
+        var lastStage = existingStages[0].pop();
+        var lastStageId = d3.select(lastStage).attr("id");
+        var lastStageWidth = toFloat(d3.select("#" + lastStageId + " rect").attr("width"));
+        var lastStagePosition = getAbsolutePosition(lastStageId);
+        var offset = lastStagePosition.x + lastStageWidth + VizConstants.stageSep;
+        container.attr("transform", "translate(" + offset + ", 0)");
+      }
+    }
+    renderDot(dot, container);
+    // If there are any incoming edges into this graph, keep track of them to render
+    // them separately later. Note that we cannot draw them now because we need to
+    // put these edges in a separate container that is on top of all stage graphs.
+    metadata.selectAll(".incoming-edge").each(function(v) {
+      var edge = d3.select(this).text().split(","); // e.g. 3,4 => [3, 4]
+      crossStageEdges.push(edge);
+    });
+  });
+
+  // Draw edges that cross stages
+  if (crossStageEdges.length > 0) {
+    var container = svgContainer.append("g").attr("id", "cross-stage-edges");
+    for (var i = 0; i < crossStageEdges.length; i++) {
+      var fromRDDId = crossStageEdges[i][0];
+      var toRDDId = crossStageEdges[i][1];
+      connectRDDs(fromRDDId, toRDDId, container);
+    }
+  }
+}
+
+/* Render the dot file as an SVG in the given container. */
+function renderDot(dot, container) {
+  var escaped_dot = dot
+    .replace(/&lt;/g, "<")
+    .replace(/&gt;/g, ">")
+    .replace(/&quot;/g, "\"");
+  var g = graphlibDot.read(escaped_dot);
+  var renderer = new dagreD3.render();
+  renderer(container, g);
+}
+
+/* Style the visualization we just rendered. */
+function styleDagViz(forJob) {
+  graphContainer().selectAll("svg g.cluster rect")
+    .style("fill", "white")
+    .style("stroke", VizConstants.rddOperationColor)
+    .style("stroke-width", "4px")
+    .style("stroke-opacity", "0.5");
+  graphContainer().selectAll("svg g.cluster text")
+    .attr("fill", VizConstants.clusterLabelColor)
+    .attr("font-size", "11px");
+  graphContainer().selectAll("svg path")
+    .style("stroke", VizConstants.edgeColor)
+    .style("stroke-width", VizConstants.edgeWidth);
+  stageClusters()
+    .select("rect")
+    .style("stroke", VizConstants.stageColor)
+    .style("strokeWidth", "6px");
+
+  // Put an arrow at the end of every edge
+  // We need to do this because we manually render some edges ourselves
+  // For these edges, we borrow the arrow marker generated by dagre-d3
+  var dagreD3Marker = graphContainer().select("svg g.edgePaths marker").node();
+  graphContainer().select("svg")
+    .append(function() { return dagreD3Marker.cloneNode(true); })
+    .attr("id", "marker-arrow")
+    .select("path")
+    .attr("fill", VizConstants.edgeColor)
+    .attr("strokeWidth", "0px");
+  graphContainer().selectAll("svg g > path").attr("marker-end", "url(#marker-arrow)");
+  graphContainer().selectAll("svg g.edgePaths def").remove(); // We no longer need these
+
+  // Apply any job or stage specific styles
+  if (forJob) {
+    styleDagVizForJob();
+  } else {
+    styleDagVizForStage();
+  }
+}
+
+/* Apply job-page-specific style to the visualization. */
+function styleDagVizForJob() {
+  graphContainer().selectAll("svg g.node circle")
+    .style("fill", VizConstants.rddColor);
+  // TODO: add a legend to explain what a highlighted dot means
+  graphContainer().selectAll("svg g.cached circle")
+    .style("fill", VizConstants.rddCachedColor);
+  graphContainer().selectAll("svg g#cross-stage-edges path")
+    .style("fill", "none");
+}
+
+/* Apply stage-page-specific style to the visualization. */
+function styleDagVizForStage() {
+  graphContainer().selectAll("svg g.node rect")
+    .style("fill", "none")
+    .style("stroke", VizConstants.rddColor)
+    .style("stroke-width", "2px")
+    .attr("rx", "5") // round corners
+    .attr("ry", "5");
+    // TODO: add a legend to explain what a highlighted RDD means
+  graphContainer().selectAll("svg g.cached rect")
+    .style("stroke", VizConstants.rddCachedColor);
+  graphContainer().selectAll("svg g.node g.label text tspan")
+    .style("fill", VizConstants.rddColor);
+}
+
+/*
+ * (Job page only) Helper method to compute the absolute
+ * position of the group element identified by the given ID.
+ */
+function getAbsolutePosition(groupId) {
+  var obj = d3.select("#" + groupId).filter("g");
+  var _x = 0, _y = 0;
+  while (!obj.empty()) {
+    var transformText = obj.attr("transform");
+    var translate = d3.transform(transformText).translate
+    _x += translate[0];
+    _y += translate[1];
+    obj = d3.select(obj.node().parentNode).filter("g")
+  }
+  return { x: _x, y: _y };
+}
+
+/* (Job page only) Connect two RDD nodes with a curved edge. */
+function connectRDDs(fromRDDId, toRDDId, container) {
+  var fromNodeId = VizConstants.nodePrefix + fromRDDId;
+  var toNodeId = VizConstants.nodePrefix + toRDDId
+  var fromPos = getAbsolutePosition(fromNodeId);
+  var toPos = getAbsolutePosition(toNodeId);
+
+  // On the job page, RDDs are rendered as dots (circles). When rendering the path,
+  // we need to account for the radii of these circles. Otherwise the arrow heads
+  // will bleed into the circle itself.
+  var delta = toFloat(graphContainer()
+    .select("g.node#" + toNodeId)
+    .select("circle")
+    .attr("r"));
+  if (fromPos.x < toPos.x) {
+    fromPos.x += delta;
+    toPos.x -= delta;
+  } else if (fromPos.x > toPos.x) {
+    fromPos.x -= delta;
+    toPos.x += delta;
+  }
+
+  if (fromPos.y == toPos.y) {
+    // If they are on the same rank, curve the middle part of the edge
+    // upward a little to avoid interference with things in between
+    // e.g.       _______
+    //      _____/       \_____
+    var points = [
+      [fromPos.x, fromPos.y],
+      [fromPos.x + (toPos.x - fromPos.x) * 0.2, fromPos.y],
+      [fromPos.x + (toPos.x - fromPos.x) * 0.3, fromPos.y - 20],
+      [fromPos.x + (toPos.x - fromPos.x) * 0.7, fromPos.y - 20],
+      [fromPos.x + (toPos.x - fromPos.x) * 0.8, toPos.y],
+      [toPos.x, toPos.y]
+    ];
+  } else {
+    // Otherwise, draw a curved edge that flattens out on both ends
+    // e.g.       _____
+    //           /
+    //          |
+    //    _____/
+    var points = [
+      [fromPos.x, fromPos.y],
+      [fromPos.x + (toPos.x - fromPos.x) * 0.4, fromPos.y],
+      [fromPos.x + (toPos.x - fromPos.x) * 0.6, toPos.y],
+      [toPos.x, toPos.y]
+    ];
+  }
+
+  var line = d3.svg.line().interpolate("basis");
+  container.append("path").datum(points).attr("d", line);
+}
+
+/* Helper d3 accessor to clusters that represent stages. */
+function stageClusters() {
+  return graphContainer().selectAll("g.cluster").filter(function() {
+    return d3.select(this).attr("id").indexOf(VizConstants.stageClusterPrefix) > -1;
+  });
+}
+
+/* Helper method to convert attributes to numeric values. */
+function toFloat(f) {
+  return parseFloat(f.replace(/px$/, ""));
+}
+
diff --git a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css
new file mode 100644
index 0000000000000..5da9d631ad124
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.css
@@ -0,0 +1,58 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+.graph {
+  font: 10px sans-serif;
+}
+
+.axis path, .axis line {
+  fill: none;
+  stroke: gray;
+  shape-rendering: crispEdges;
+}
+
+.axis text {
+  fill: gray;
+}
+
+.tooltip-inner {
+  max-width: 500px !important; // Make sure we only have one line tooltip
+}
+
+.line {
+  fill: none;
+  stroke: #0088cc;
+  stroke-width: 1.5px;
+}
+
+.bar rect {
+  fill: #0088cc;
+  shape-rendering: crispEdges;
+}
+
+.bar rect:hover {
+  fill: #00c2ff;
+}
+
+.timeline {
+  width: 500px;
+}
+
+.histogram {
+  width: auto;
+}
diff --git a/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js
new file mode 100644
index 0000000000000..a4e03b156f13e
--- /dev/null
+++ b/core/src/main/resources/org/apache/spark/ui/static/streaming-page.js
@@ -0,0 +1,274 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+// timeFormat: StreamingPage.scala will generate a global "timeFormat" dictionary to store the time
+// and its formatted string. Because we cannot specify a timezone in JavaScript, to make sure the
+// server and client use the same timezone, we use the "timeFormat" dictionary to format all time
+// values used in the graphs.
+
+// A global margin left for all timeline graphs. It will be set in "registerTimeline". This will be
+// used to align all timeline graphs.
+var maxMarginLeftForTimeline = 0;
+
+// The max X values for all histograms. It will be set in "registerHistogram".
+var maxXForHistogram = 0;
+
+var histogramBinCount = 10;
+var yValueFormat = d3.format(",.2f");
+
+// Show a tooltip "text" for "node"
+function showBootstrapTooltip(node, text) {
+    $(node).tooltip({title: text, trigger: "manual", container: "body"});
+    $(node).tooltip("show");
+}
+
+// Hide the tooltip for "node"
+function hideBootstrapTooltip(node) {
+    $(node).tooltip("destroy");
+}
+
+// Register a timeline graph. All timeline graphs should be register before calling any
+// "drawTimeline" so that we can determine the max margin left for all timeline graphs.
+function registerTimeline(minY, maxY) {
+    var numOfChars = yValueFormat(maxY).length;
+    // A least width for "maxY" in the graph
+    var pxForMaxY = numOfChars * 8 + 10;
+    // Make sure we have enough space to show the ticks in the y axis of timeline
+    maxMarginLeftForTimeline = pxForMaxY > maxMarginLeftForTimeline? pxForMaxY : maxMarginLeftForTimeline;
+}
+
+// Register a histogram graph. All histogram graphs should be register before calling any
+// "drawHistogram" so that we can determine the max X value for histograms.
+function registerHistogram(values, minY, maxY) {
+    var data = d3.layout.histogram().range([minY, maxY]).bins(histogramBinCount)(values);
+    // d.x is the y values while d.y is the x values
+    var maxX = d3.max(data, function(d) { return d.y; });
+    maxXForHistogram = maxX > maxXForHistogram ? maxX : maxXForHistogram;
+}
+
+// Draw a line between (x1, y1) and (x2, y2)
+function drawLine(svg, xFunc, yFunc, x1, y1, x2, y2) {
+    var line = d3.svg.line()
+        .x(function(d) { return xFunc(d.x); })
+        .y(function(d) { return yFunc(d.y); });
+    var data = [{x: x1, y: y1}, {x: x2, y: y2}];
+    svg.append("path")
+        .datum(data)
+        .style("stroke-dasharray", ("6, 6"))
+        .style("stroke", "lightblue")
+        .attr("class", "line")
+        .attr("d", line);
+}
+
+/**
+ * @param id the `id` used in the html `div` tag
+ * @param data the data for the timeline graph
+ * @param minX the min value of X axis
+ * @param maxX the max value of X axis
+ * @param minY the min value of Y axis
+ * @param maxY the max value of Y axis
+ * @param unitY the unit of Y axis
+ * @param batchInterval if "batchInterval" is specified, we will draw a line for "batchInterval" in the graph
+ */
+function drawTimeline(id, data, minX, maxX, minY, maxY, unitY, batchInterval) {
+    // Hide the right border of "<td>". We cannot use "css" directly, or "sorttable.js" will override them.
+    d3.select(d3.select(id).node().parentNode)
+        .style("padding", "8px 0 8px 8px")
+        .style("border-right", "0px solid white");
+
+    var margin = {top: 20, right: 27, bottom: 30, left: maxMarginLeftForTimeline};
+    var width = 500 - margin.left - margin.right;
+    var height = 150 - margin.top - margin.bottom;
+
+    var x = d3.scale.linear().domain([minX, maxX]).range([0, width]);
+    var y = d3.scale.linear().domain([minY, maxY]).range([height, 0]);
+
+    var xAxis = d3.svg.axis().scale(x).orient("bottom").tickFormat(function(d) { return timeFormat[d]; });
+    var formatYValue = d3.format(",.2f");
+    var yAxis = d3.svg.axis().scale(y).orient("left").ticks(5).tickFormat(formatYValue);
+
+    var line = d3.svg.line()
+        .x(function(d) { return x(d.x); })
+        .y(function(d) { return y(d.y); });
+
+    var svg = d3.select(id).append("svg")
+        .attr("width", width + margin.left + margin.right)
+        .attr("height", height + margin.top + margin.bottom)
+        .append("g")
+            .attr("transform", "translate(" + margin.left + "," + margin.top + ")");
+
+    // Only show the first and last time in the graph
+    xAxis.tickValues(x.domain());
+
+    svg.append("g")
+        .attr("class", "x axis")
+        .attr("transform", "translate(0," + height + ")")
+        .call(xAxis)
+
+    svg.append("g")
+        .attr("class", "y axis")
+        .call(yAxis)
+        .append("text")
+            .attr("transform", "translate(0," + (-3) + ")")
+            .text(unitY);
+
+
+    if (batchInterval && batchInterval <= maxY) {
+        drawLine(svg, x, y, minX, batchInterval, maxX, batchInterval);
+    }
+
+    svg.append("path")
+        .datum(data)
+        .attr("class", "line")
+        .attr("d", line);
+
+    // Add points to the line. However, we make it invisible at first. But when the user moves mouse
+    // over a point, it will be displayed with its detail.
+    svg.selectAll(".point")
+        .data(data)
+        .enter().append("circle")
+            .attr("stroke", "white") // white and opacity = 0 make it invisible
+            .attr("fill", "white")
+            .attr("opacity", "0")
+            .attr("cx", function(d) { return x(d.x); })
+            .attr("cy", function(d) { return y(d.y); })
+            .attr("r", function(d) { return 3; })
+            .on('mouseover', function(d) {
+                var tip = formatYValue(d.y) + " " + unitY + " at " + timeFormat[d.x];
+                showBootstrapTooltip(d3.select(this).node(), tip);
+                // show the point
+                d3.select(this)
+                    .attr("stroke", "steelblue")
+                    .attr("fill", "steelblue")
+                    .attr("opacity", "1");
+            })
+            .on('mouseout',  function() {
+                hideBootstrapTooltip(d3.select(this).node());
+                // hide the point
+                d3.select(this)
+                    .attr("stroke", "white")
+                    .attr("fill", "white")
+                    .attr("opacity", "0");
+            })
+            .on("click", function(d) {
+                window.location.href = "batch/?id=" + d.x;
+            });
+}
+
+/**
+ * @param id the `id` used in the html `div` tag
+ * @param values the data for the histogram graph
+ * @param minY the min value of Y axis
+ * @param maxY the max value of Y axis
+ * @param unitY the unit of Y axis
+ * @param batchInterval if "batchInterval" is specified, we will draw a line for "batchInterval" in the graph
+ */
+function drawHistogram(id, values, minY, maxY, unitY, batchInterval) {
+    // Hide the left border of "<td>". We cannot use "css" directly, or "sorttable.js" will override them.
+    d3.select(d3.select(id).node().parentNode)
+        .style("padding", "8px 8px 8px 0")
+        .style("border-left", "0px solid white");
+
+    var margin = {top: 20, right: 30, bottom: 30, left: 10};
+    var width = 300 - margin.left - margin.right;
+    var height = 150 - margin.top - margin.bottom;
+
+    var x = d3.scale.linear().domain([0, maxXForHistogram]).range([0, width]);
+    var y = d3.scale.linear().domain([minY, maxY]).range([height, 0]);
+
+    var xAxis = d3.svg.axis().scale(x).orient("top").ticks(5);
+    var yAxis = d3.svg.axis().scale(y).orient("left").ticks(0).tickFormat(function(d) { return ""; });
+
+    var data = d3.layout.histogram().range([minY, maxY]).bins(histogramBinCount)(values);
+
+    var svg = d3.select(id).append("svg")
+        .attr("width", width + margin.left + margin.right)
+        .attr("height", height + margin.top + margin.bottom)
+        .append("g")
+            .attr("transform", "translate(" + margin.left + "," + margin.top + ")");
+
+    if (batchInterval && batchInterval <= maxY) {
+        drawLine(svg, x, y, 0, batchInterval, maxXForHistogram, batchInterval);
+    }
+
+    svg.append("g")
+        .attr("class", "x axis")
+        .call(xAxis)
+
+    svg.append("g")
+        .attr("class", "y axis")
+        .call(yAxis)
+
+    var bar = svg.selectAll(".bar")
+        .data(data)
+        .enter()
+        .append("g")
+            .attr("transform", function(d) { return "translate(0," + (y(d.x) - height + y(d.dx))  + ")";})
+            .attr("class", "bar").append("rect")
+            .attr("width", function(d) { return x(d.y); })
+            .attr("height", function(d) { return height - y(d.dx); })
+            .on('mouseover', function(d) {
+                var percent = yValueFormat(d.y * 100.0 / values.length) + "%";
+                var tip = d.y + " batches (" + percent + ") between " + yValueFormat(d.x) + " and " + yValueFormat(d.x + d.dx) + " " + unitY;
+                showBootstrapTooltip(d3.select(this).node(), tip);
+            })
+            .on('mouseout',  function() {
+                hideBootstrapTooltip(d3.select(this).node());
+            });
+
+    if (batchInterval && batchInterval <= maxY) {
+        // Add the "stable" text to the graph below the batch interval line.
+        var stableXOffset = x(maxXForHistogram) - 20;
+        var stableYOffset = y(batchInterval) + 15;
+        svg.append("text")
+            .style("fill", "lightblue")
+            .attr("class", "stable-text")
+            .attr("text-anchor", "middle")
+            .attr("transform", "translate(" + stableXOffset + "," + stableYOffset + ")")
+            .text("stable")
+            .on('mouseover', function(d) {
+              var tip = "Processing Time <= Batch Interval (" + yValueFormat(batchInterval) +" " + unitY +")";
+              showBootstrapTooltip(d3.select(this).node(), tip);
+            })
+            .on('mouseout',  function() {
+              hideBootstrapTooltip(d3.select(this).node());
+            });
+    }
+}
+
+$(function() {
+    function getParameterFromURL(param)
+    {
+        var parameters = window.location.search.substring(1); // Remove "?"
+        var keyValues = parameters.split('&');
+        for (var i = 0; i < keyValues.length; i++)
+        {
+            var paramKeyValue = keyValues[i].split('=');
+            if (paramKeyValue[0] == param)
+            {
+                return paramKeyValue[1];
+            }
+        }
+    }
+
+    if (getParameterFromURL("show-streams-detail") == "true") {
+        // Show the details for all InputDStream
+        $('#inputs-table').toggle('collapsed');
+        $('#triangle').html('&#9660;');
+    }
+});
diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 4910744d1d790..669ad48937c05 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -145,7 +145,7 @@ pre {
   border: none;
 }
 
-span.expand-additional-metrics {
+span.expand-additional-metrics, span.expand-dag-viz {
   cursor: pointer;
 }
 
diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
index 228d9149df2a2..66bda68088502 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
@@ -21,7 +21,10 @@ import java.util.concurrent.TimeUnit
 
 import scala.collection.mutable
 
+import com.codahale.metrics.{Gauge, MetricRegistry}
+
 import org.apache.spark.scheduler._
+import org.apache.spark.metrics.source.Source
 import org.apache.spark.util.{ThreadUtils, Clock, SystemClock, Utils}
 
 /**
@@ -144,6 +147,9 @@ private[spark] class ExecutorAllocationManager(
   private val executor =
     ThreadUtils.newDaemonSingleThreadScheduledExecutor("spark-dynamic-executor-allocation")
 
+  // Metric source for ExecutorAllocationManager to expose internal status to MetricsSystem.
+  val executorAllocationManagerSource = new ExecutorAllocationManagerSource
+
   /**
    * Verify that the settings specified through the config are valid.
    * If not, throw an appropriate exception.
@@ -579,6 +585,29 @@ private[spark] class ExecutorAllocationManager(
     }
   }
 
+  /**
+   * Metric source for ExecutorAllocationManager to expose its internal executor allocation
+   * status to MetricsSystem.
+   * Note: These metrics heavily rely on the internal implementation of
+   * ExecutorAllocationManager, metrics or value of metrics will be changed when internal
+   * implementation is changed, so these metrics are not stable across Spark version.
+   */
+  private[spark] class ExecutorAllocationManagerSource extends Source {
+    val sourceName = "ExecutorAllocationManager"
+    val metricRegistry = new MetricRegistry()
+
+    private def registerGauge[T](name: String, value: => T, defaultValue: T): Unit = {
+      metricRegistry.register(MetricRegistry.name("executors", name), new Gauge[T] {
+        override def getValue: T = synchronized { Option(value).getOrElse(defaultValue) }
+      })
+    }
+
+    registerGauge("numberExecutorsToAdd", numExecutorsToAdd, 0)
+    registerGauge("numberExecutorsPendingToRemove", executorsPendingToRemove.size, 0)
+    registerGauge("numberAllExecutors", executorIds.size, 0)
+    registerGauge("numberTargetExecutors", numExecutorsTarget, 0)
+    registerGauge("numberMaxNeededExecutors", maxNumExecutorsNeeded(), 0)
+  }
 }
 
 private object ExecutorAllocationManager {
diff --git a/core/src/main/scala/org/apache/spark/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/SizeEstimator.scala
new file mode 100644
index 0000000000000..54fc3a856adfa
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/SizeEstimator.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in
+ * memory-aware caches.
+ *
+ * Based on the following JavaWorld article:
+ * http://www.javaworld.com/javaworld/javaqa/2003-12/02-qa-1226-sizeof.html
+ */
+@DeveloperApi
+object SizeEstimator {
+  /**
+   * :: DeveloperApi ::
+   * Estimate the number of bytes that the given object takes up on the JVM heap. The estimate
+   * includes space taken up by objects referenced by the given object, their references, and so on
+   * and so forth.
+   *
+   * This is useful for determining the amount of heap space a broadcast variable will occupy on
+   * each executor or the amount of space each object will take when caching objects in
+   * deserialized form. This is not the same as the serialized size of the object, which will
+   * typically be much smaller.
+   */
+  @DeveloperApi
+  def estimate(obj: AnyRef): Long = org.apache.spark.util.SizeEstimator.estimate(obj)
+}
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 4ef90546a2452..2ca6882c8d890 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -428,7 +428,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _ui =
       if (conf.getBoolean("spark.ui.enabled", true)) {
         Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener,
-          _env.securityManager,appName))
+          _env.securityManager,appName, startTime = startTime))
       } else {
         // For tests, do not enable the UI
         None
@@ -537,6 +537,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     _taskScheduler.postStartHook()
     _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler))
     _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager))
+    _executorAllocationManager.foreach { e =>
+      _env.metricsSystem.registerSource(e.executorAllocationManagerSource)
+    }
 
     // Make sure the context is stopped if the user forgets about it. This avoids leaving
     // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
@@ -659,6 +662,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null)
   }
 
+  /**
+   * Execute a block of code in a scope such that all new RDDs created in this body will
+   * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}.
+   *
+   * Note: Return statements are NOT allowed in the given body.
+   */
+  private def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body)
+
   // Methods for creating RDDs
 
   /** Distribute a local Scala collection to form an RDD.
@@ -669,7 +680,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @note avoid using `parallelize(Seq())` to create an empty `RDD`. Consider `emptyRDD` for an
    * RDD with no partitions, or `parallelize(Seq[T]())` for an RDD of `T` with empty partitions.
    */
-  def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = {
+  def parallelize[T: ClassTag](
+      seq: Seq[T],
+      numSlices: Int = defaultParallelism): RDD[T] = withScope {
     assertNotStopped()
     new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]())
   }
@@ -678,14 +691,16 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    *
    * This method is identical to `parallelize`.
    */
-  def makeRDD[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = {
+  def makeRDD[T: ClassTag](
+      seq: Seq[T],
+      numSlices: Int = defaultParallelism): RDD[T] = withScope {
     parallelize(seq, numSlices)
   }
 
   /** Distribute a local Scala collection to form an RDD, with one or more
     * location preferences (hostnames of Spark nodes) for each object.
     * Create a new partition for each collection item. */
-  def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = {
+  def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = withScope {
     assertNotStopped()
     val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap
     new ParallelCollectionRDD[T](this, seq.map(_._1), seq.size, indexToPrefs)
@@ -695,10 +710,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * Read a text file from HDFS, a local file system (available on all nodes), or any
    * Hadoop-supported file system URI, and return it as an RDD of Strings.
    */
-  def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = {
+  def textFile(
+      path: String,
+      minPartitions: Int = defaultMinPartitions): RDD[String] = withScope {
     assertNotStopped()
     hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text],
-      minPartitions).map(pair => pair._2.toString).setName(path)
+      minPartitions).map(pair => pair._2.toString)
   }
 
   /**
@@ -728,8 +745,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    *
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
-  def wholeTextFiles(path: String, minPartitions: Int = defaultMinPartitions):
-  RDD[(String, String)] = {
+  def wholeTextFiles(
+      path: String,
+      minPartitions: Int = defaultMinPartitions): RDD[(String, String)] = withScope {
     assertNotStopped()
     val job = new NewHadoopJob(hadoopConfiguration)
     // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking
@@ -776,8 +794,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @note Small files are preferred; very large files may cause bad performance.
    */
   @Experimental
-  def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions):
-      RDD[(String, PortableDataStream)] = {
+  def binaryFiles(
+      path: String,
+      minPartitions: Int = defaultMinPartitions): RDD[(String, PortableDataStream)] = withScope {
     assertNotStopped()
     val job = new NewHadoopJob(hadoopConfiguration)
     // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking
@@ -806,8 +825,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @return An RDD of data with values, represented as byte arrays
    */
   @Experimental
-  def binaryRecords(path: String, recordLength: Int, conf: Configuration = hadoopConfiguration)
-      : RDD[Array[Byte]] = {
+  def binaryRecords(
+      path: String,
+      recordLength: Int,
+      conf: Configuration = hadoopConfiguration): RDD[Array[Byte]] = withScope {
     assertNotStopped()
     conf.setInt(FixedLengthBinaryInputFormat.RECORD_LENGTH_PROPERTY, recordLength)
     val br = newAPIHadoopFile[LongWritable, BytesWritable, FixedLengthBinaryInputFormat](path,
@@ -848,8 +869,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       inputFormatClass: Class[_ <: InputFormat[K, V]],
       keyClass: Class[K],
       valueClass: Class[V],
-      minPartitions: Int = defaultMinPartitions
-      ): RDD[(K, V)] = {
+      minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope {
     assertNotStopped()
     // Add necessary security credentials to the JobConf before broadcasting it.
     SparkHadoopUtil.get.addCredentials(conf)
@@ -869,8 +889,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       inputFormatClass: Class[_ <: InputFormat[K, V]],
       keyClass: Class[K],
       valueClass: Class[V],
-      minPartitions: Int = defaultMinPartitions
-      ): RDD[(K, V)] = {
+      minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope {
     assertNotStopped()
     // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it.
     val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration))
@@ -901,7 +920,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    */
   def hadoopFile[K, V, F <: InputFormat[K, V]]
       (path: String, minPartitions: Int)
-      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = {
+      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope {
     hadoopFile(path,
       fm.runtimeClass.asInstanceOf[Class[F]],
       km.runtimeClass.asInstanceOf[Class[K]],
@@ -924,13 +943,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * copy them using a `map` function.
    */
   def hadoopFile[K, V, F <: InputFormat[K, V]](path: String)
-      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] =
+      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope {
     hadoopFile[K, V, F](path, defaultMinPartitions)
+  }
 
   /** Get an RDD for a Hadoop file with an arbitrary new API InputFormat. */
   def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]]
       (path: String)
-      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = {
+      (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope {
     newAPIHadoopFile(
       path,
       fm.runtimeClass.asInstanceOf[Class[F]],
@@ -953,7 +973,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       fClass: Class[F],
       kClass: Class[K],
       vClass: Class[V],
-      conf: Configuration = hadoopConfiguration): RDD[(K, V)] = {
+      conf: Configuration = hadoopConfiguration): RDD[(K, V)] = withScope {
     assertNotStopped()
     // The call to new NewHadoopJob automatically adds security credentials to conf,
     // so we don't need to explicitly add them ourselves
@@ -987,7 +1007,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       conf: Configuration = hadoopConfiguration,
       fClass: Class[F],
       kClass: Class[K],
-      vClass: Class[V]): RDD[(K, V)] = {
+      vClass: Class[V]): RDD[(K, V)] = withScope {
     assertNotStopped()
     // Add necessary security credentials to the JobConf. Required to access secure HDFS.
     val jconf = new JobConf(conf)
@@ -1007,7 +1027,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       keyClass: Class[K],
       valueClass: Class[V],
       minPartitions: Int
-      ): RDD[(K, V)] = {
+      ): RDD[(K, V)] = withScope {
     assertNotStopped()
     val inputFormatClass = classOf[SequenceFileInputFormat[K, V]]
     hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions)
@@ -1021,7 +1041,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first
     * copy them using a `map` function.
     * */
-  def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): RDD[(K, V)] = {
+  def sequenceFile[K, V](
+      path: String,
+      keyClass: Class[K],
+      valueClass: Class[V]): RDD[(K, V)] = withScope {
     assertNotStopped()
     sequenceFile(path, keyClass, valueClass, defaultMinPartitions)
   }
@@ -1051,16 +1074,17 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    def sequenceFile[K, V]
        (path: String, minPartitions: Int = defaultMinPartitions)
        (implicit km: ClassTag[K], vm: ClassTag[V],
-        kcf: () => WritableConverter[K], vcf: () => WritableConverter[V])
-      : RDD[(K, V)] = {
-    assertNotStopped()
-    val kc = kcf()
-    val vc = vcf()
-    val format = classOf[SequenceFileInputFormat[Writable, Writable]]
-    val writables = hadoopFile(path, format,
+        kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]): RDD[(K, V)] = {
+    withScope {
+      assertNotStopped()
+      val kc = kcf()
+      val vc = vcf()
+      val format = classOf[SequenceFileInputFormat[Writable, Writable]]
+      val writables = hadoopFile(path, format,
         kc.writableClass(km).asInstanceOf[Class[Writable]],
         vc.writableClass(vm).asInstanceOf[Class[Writable]], minPartitions)
-    writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) }
+      writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) }
+    }
   }
 
   /**
@@ -1073,21 +1097,18 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    */
   def objectFile[T: ClassTag](
       path: String,
-      minPartitions: Int = defaultMinPartitions
-      ): RDD[T] = {
+      minPartitions: Int = defaultMinPartitions): RDD[T] = withScope {
     assertNotStopped()
     sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions)
       .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader))
   }
 
-  protected[spark] def checkpointFile[T: ClassTag](
-      path: String
-    ): RDD[T] = {
+  protected[spark] def checkpointFile[T: ClassTag](path: String): RDD[T] = withScope {
     new CheckpointRDD[T](this, path)
   }
 
   /** Build the union of a list of RDDs. */
-  def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = {
+  def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = withScope {
     val partitioners = rdds.flatMap(_.partitioner).toSet
     if (rdds.forall(_.partitioner.isDefined) && partitioners.size == 1) {
       new PartitionerAwareUnionRDD(this, rdds)
@@ -1097,8 +1118,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   }
 
   /** Build the union of a list of RDDs passed as variable-length arguments. */
-  def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] =
+  def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = withScope {
     union(Seq(first) ++ rest)
+  }
 
   /** Get an RDD that has no partitions or elements. */
   def emptyRDD[T: ClassTag]: EmptyRDD[T] = new EmptyRDD[T](this)
@@ -1657,7 +1679,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       partitions: Seq[Int],
       allowLocal: Boolean
       ): Array[U] = {
-    runJob(rdd, (context: TaskContext, iter: Iterator[T]) => func(iter), partitions, allowLocal)
+    val cleanedFunc = clean(func)
+    runJob(rdd, (ctx: TaskContext, it: Iterator[T]) => cleanedFunc(it), partitions, allowLocal)
   }
 
   /**
@@ -1711,7 +1734,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     val callSite = getCallSite
     logInfo("Starting job: " + callSite.shortForm)
     val start = System.nanoTime
-    val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout,
+    val cleanedFunc = clean(func)
+    val result = dagScheduler.runApproximateJob(rdd, cleanedFunc, evaluator, callSite, timeout,
       localProperties.get)
     logInfo(
       "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s")
@@ -2060,10 +2084,10 @@ object SparkContext extends Logging {
   }
 
   private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description"
-
   private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id"
-
   private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel"
+  private[spark] val RDD_SCOPE_KEY = "spark.rdd.scope"
+  private[spark] val RDD_SCOPE_NO_OVERRIDE_KEY = "spark.rdd.scope.noOverride"
 
   /**
    * Executor id for the driver.  In earlier versions of Spark, this was `<driver>`, but this was
diff --git a/core/src/main/scala/org/apache/spark/annotation/Private.java b/core/src/main/scala/org/apache/spark/annotation/Private.java
new file mode 100644
index 0000000000000..9082fcf0c84bc
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/annotation/Private.java
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.annotation;
+
+import java.lang.annotation.ElementType;
+import java.lang.annotation.Retention;
+import java.lang.annotation.RetentionPolicy;
+import java.lang.annotation.Target;
+
+/**
+ * A class that is considered private to the internals of Spark -- there is a high-likelihood
+ * they will be changed in future versions of Spark.
+ *
+ * This should be used only when the standard Scala / Java means of protecting classes are
+ * insufficient.  In particular, Java has no equivalent of private[spark], so we use this annotation
+ * in its place.
+ *
+ * NOTE: If there exists a Scaladoc comment that immediately precedes this annotation, the first
+ * line of the comment must be ":: Private ::" with no trailing blank line. This is because
+ * of the known issue that Scaladoc displays only either the annotation or the comment, whichever
+ * comes first.
+ */
+@Retention(RetentionPolicy.RUNTIME)
+@Target({ElementType.TYPE, ElementType.FIELD, ElementType.METHOD, ElementType.PARAMETER,
+        ElementType.CONSTRUCTOR, ElementType.LOCAL_VARIABLE, ElementType.PACKAGE})
+public @interface Private {}
diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
index 257491e90dd66..1f1debcf84ad4 100644
--- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -56,16 +56,13 @@ private[spark] object SerDeUtil extends Logging {
     //    {'\0', 0, 0, 0} /* Sentinel */
     //  };
     // TODO: support Py_UNICODE with 2 bytes
-    // FIXME: unpickle array of float is wrong in Pyrolite, so we reverse the
-    // machine code for float/double here to workaround it.
-    // we should fix this after Pyrolite fix them
     val machineCodes: Map[Char, Int] = if (ByteOrder.nativeOrder().equals(ByteOrder.BIG_ENDIAN)) {
       Map('c' -> 1, 'B' -> 0, 'b' -> 1, 'H' -> 3, 'h' -> 5, 'I' -> 7, 'i' -> 9,
-        'L' -> 11, 'l' -> 13, 'f' -> 14, 'd' -> 16, 'u' -> 21
+        'L' -> 11, 'l' -> 13, 'f' -> 15, 'd' -> 17, 'u' -> 21
       )
     } else {
       Map('c' -> 1, 'B' -> 0, 'b' -> 1, 'H' -> 2, 'h' -> 4, 'I' -> 6, 'i' -> 8,
-        'L' -> 10, 'l' -> 12, 'f' -> 15, 'd' -> 17, 'u' -> 20
+        'L' -> 10, 'l' -> 12, 'f' -> 14, 'd' -> 16, 'u' -> 20
       )
     }
     override def construct(args: Array[Object]): Object = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 42b5d41b7b526..8a0327984e195 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -701,7 +701,7 @@ object SparkSubmit {
 }
 
 /** Provides utility functions to be used inside SparkSubmit. */
-private[deploy] object SparkSubmitUtils {
+private[spark] object SparkSubmitUtils {
 
   // Exposed for testing
   var printStream = SparkSubmit.printStream
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
index 6a5011af17458..298a8201960d1 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.history
 
 import org.apache.spark.ui.SparkUI
 
-private[history] case class ApplicationAttemptInfo(
+private[spark] case class ApplicationAttemptInfo(
     attemptId: Option[String],
     startTime: Long,
     endTime: Long,
@@ -27,7 +27,7 @@ private[history] case class ApplicationAttemptInfo(
     sparkUser: String,
     completed: Boolean = false)
 
-private[history] case class ApplicationHistoryInfo(
+private[spark] case class ApplicationHistoryInfo(
     id: String,
     name: String,
     attempts: List[ApplicationAttemptInfo])
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index 993763f3aa092..45c2be34c8680 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -17,23 +17,21 @@
 
 package org.apache.spark.deploy.history
 
-import java.io.{IOException, BufferedInputStream, FileNotFoundException, InputStream}
+import java.io.{BufferedInputStream, FileNotFoundException, IOException, InputStream}
 import java.util.concurrent.{ExecutorService, Executors, TimeUnit}
 
 import scala.collection.mutable
-import scala.concurrent.duration.Duration
 
-import com.google.common.util.concurrent.ThreadFactoryBuilder
-
-import com.google.common.util.concurrent.MoreExecutors
-import org.apache.hadoop.fs.permission.AccessControlException
+import com.google.common.util.concurrent.{MoreExecutors, ThreadFactoryBuilder}
 import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.fs.permission.AccessControlException
+
+import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.SparkUI
 import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils}
-import org.apache.spark.{Logging, SecurityManager, SparkConf}
 
 /**
  * A class that provides application history from event logs stored in the file system.
@@ -151,7 +149,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
             val conf = this.conf.clone()
             val appSecManager = new SecurityManager(conf)
             SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId,
-              HistoryServer.getAttemptURI(appId, attempt.attemptId))
+              HistoryServer.getAttemptURI(appId, attempt.attemptId), attempt.startTime)
             // Do not call ui.bind() to avoid creating a new server for each application
           }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index 754c8e9b6668b..50522e69dc519 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -25,6 +25,7 @@ import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.status.api.v1.{ApplicationInfo, ApplicationsListResource, JsonRootResource, UIRoot}
 import org.apache.spark.ui.{SparkUI, UIUtils, WebUI}
 import org.apache.spark.ui.JettyUtils._
 import org.apache.spark.util.{SignalLogger, Utils}
@@ -45,7 +46,7 @@ class HistoryServer(
     provider: ApplicationHistoryProvider,
     securityManager: SecurityManager,
     port: Int)
-  extends WebUI(securityManager, port, conf) with Logging {
+  extends WebUI(securityManager, port, conf) with Logging with UIRoot {
 
   // How many applications to retain
   private val retainedApplications = conf.getInt("spark.history.retainedApplications", 50)
@@ -56,7 +57,7 @@ class HistoryServer(
       require(parts.length == 1 || parts.length == 2, s"Invalid app key $key")
       val ui = provider
         .getAppUI(parts(0), if (parts.length > 1) Some(parts(1)) else None)
-        .getOrElse(throw new NoSuchElementException())
+        .getOrElse(throw new NoSuchElementException(s"no app with key $key"))
       attachSparkUI(ui)
       ui
     }
@@ -113,6 +114,10 @@ class HistoryServer(
     }
   }
 
+  def getSparkUI(appKey: String): Option[SparkUI] = {
+    Option(appCache.get(appKey))
+  }
+
   initialize()
 
   /**
@@ -123,6 +128,9 @@ class HistoryServer(
    */
   def initialize() {
     attachPage(new HistoryPage(this))
+
+    attachHandler(JsonRootResource.getJsonServlet(this))
+
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
 
     val contextHandler = new ServletContextHandler
@@ -160,7 +168,13 @@ class HistoryServer(
    *
    * @return List of all known applications.
    */
-  def getApplicationList(): Iterable[ApplicationHistoryInfo] = provider.getListing()
+  def getApplicationList(): Iterable[ApplicationHistoryInfo] = {
+    provider.getListing()
+  }
+
+  def getApplicationInfoList: Iterator[ApplicationInfo] = {
+    getApplicationList().iterator.map(ApplicationsListResource.appHistoryInfoToPublicAppInfo)
+  }
 
   /**
    * Returns the provider configuration to show in the listing page.
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
index 85e1e83c6f5b7..aa54ed9360f36 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala
@@ -27,7 +27,7 @@ import org.apache.spark.deploy.ApplicationDescription
 import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.util.Utils
 
-private[deploy] class ApplicationInfo(
+private[spark] class ApplicationInfo(
     val startTime: Long,
     val id: String,
     val desc: ApplicationDescription,
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index d68bc08fd4c08..7938b77a69ed5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -769,9 +769,9 @@ private[master] class Master(
 
   /**
    * Rebuild a new SparkUI from the given application's event logs.
-   * Return whether this is successful.
+   * Return the UI if successful, else None
    */
-  private def rebuildSparkUI(app: ApplicationInfo): Boolean = {
+  private[master] def rebuildSparkUI(app: ApplicationInfo): Option[SparkUI] = {
     val appName = app.desc.name
     val notFoundBasePath = HistoryServer.UI_PATH_PREFIX + "/not-found"
     try {
@@ -779,7 +779,7 @@ private[master] class Master(
         .getOrElse {
           // Event logging is not enabled for this application
           app.desc.appUiUrl = notFoundBasePath
-          return false
+          return None
         }
       
       val eventLogFilePrefix = EventLoggingListener.getLogPath(
@@ -802,7 +802,7 @@ private[master] class Master(
       val logInput = EventLoggingListener.openEventLog(new Path(eventLogFile), fs)
       val replayBus = new ReplayListenerBus()
       val ui = SparkUI.createHistoryUI(new SparkConf, replayBus, new SecurityManager(conf),
-        appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}")
+        appName + status, HistoryServer.UI_PATH_PREFIX + s"/${app.id}", app.startTime)
       val maybeTruncated = eventLogFile.endsWith(EventLoggingListener.IN_PROGRESS)
       try {
         replayBus.replay(logInput, eventLogFile, maybeTruncated)
@@ -813,7 +813,7 @@ private[master] class Master(
       webUi.attachSparkUI(ui)
       // Application UI is successfully rebuilt, so link the Master UI to it
       app.desc.appUiUrl = ui.basePath
-      true
+      Some(ui)
     } catch {
       case fnf: FileNotFoundException =>
         // Event logging is enabled for this application, but no event logs are found
@@ -823,7 +823,7 @@ private[master] class Master(
         msg += " Did you specify the correct logging directory?"
         msg = URLEncoder.encode(msg, "UTF-8")
         app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&title=$title"
-        false
+        None
       case e: Exception =>
         // Relay exception message to application UI page
         val title = s"Application history load error (${app.id})"
@@ -832,7 +832,7 @@ private[master] class Master(
         logError(msg, e)
         msg = URLEncoder.encode(msg, "UTF-8")
         app.desc.appUiUrl = notFoundBasePath + s"?msg=$msg&exception=$exception&title=$title"
-        false
+        None
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
index 101bc66c35d0c..e28e7e379ac91 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala
@@ -21,10 +21,7 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.json4s.JValue
-import org.json4s.JsonAST.JNothing
-
-import org.apache.spark.deploy.{ExecutorState, JsonProtocol}
+import org.apache.spark.deploy.ExecutorState
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState}
 import org.apache.spark.deploy.master.ExecutorDesc
 import org.apache.spark.ui.{UIUtils, WebUIPage}
@@ -34,20 +31,6 @@ private[ui] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app")
 
   private val master = parent.masterEndpointRef
 
-  /** Executor details for a particular application */
-  override def renderJson(request: HttpServletRequest): JValue = {
-    val appId = request.getParameter("appId")
-    val state = master.askWithRetry[MasterStateResponse](RequestMasterState)
-    val app = state.activeApps.find(_.id == appId).getOrElse({
-      state.completedApps.find(_.id == appId).getOrElse(null)
-    })
-    if (app == null) {
-      JNothing
-    } else {
-      JsonProtocol.writeApplicationInfo(app)
-    }
-  }
-
   /** Executor details for a particular application */
   def render(request: HttpServletRequest): Seq[Node] = {
     val appId = request.getParameter("appId")
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
index 47ac996f5ef7c..1da69b764de01 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala
@@ -32,9 +32,12 @@ import org.apache.spark.util.Utils
 private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
   private val master = parent.masterEndpointRef
 
+  def getMasterState: MasterStateResponse = {
+    master.askWithRetry[MasterStateResponse](RequestMasterState)
+  }
+
   override def renderJson(request: HttpServletRequest): JValue = {
-    val state = master.askWithRetry[MasterStateResponse](RequestMasterState)
-    JsonProtocol.writeMasterState(state)
+    JsonProtocol.writeMasterState(getMasterState)
   }
 
   def handleAppKillRequest(request: HttpServletRequest): Unit = {
@@ -66,7 +69,7 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") {
 
   /** Index view listing applications and executors */
   def render(request: HttpServletRequest): Seq[Node] = {
-    val state = master.askWithRetry[MasterStateResponse](RequestMasterState)
+    val state = getMasterState
 
     val workerHeaders = Seq("Worker Id", "Address", "State", "Cores", "Memory")
     val workers = state.workers.sortBy(_.id)
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
index d78eb1763ede4..bdc05fbceb4df 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala
@@ -19,6 +19,7 @@ package org.apache.spark.deploy.master.ui
 
 import org.apache.spark.Logging
 import org.apache.spark.deploy.master.Master
+import org.apache.spark.status.api.v1.{ApplicationsListResource, ApplicationInfo, JsonRootResource, UIRoot}
 import org.apache.spark.ui.{SparkUI, WebUI}
 import org.apache.spark.ui.JettyUtils._
 
@@ -27,11 +28,14 @@ import org.apache.spark.ui.JettyUtils._
  */
 private[master]
 class MasterWebUI(val master: Master, requestedPort: Int)
-  extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging {
+  extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging
+  with UIRoot {
 
   val masterEndpointRef = master.self
   val killEnabled = master.conf.getBoolean("spark.ui.killEnabled", true)
 
+  val masterPage = new MasterPage(this)
+
   initialize()
 
   /** Initialize all components of the server. */
@@ -41,6 +45,7 @@ class MasterWebUI(val master: Master, requestedPort: Int)
     attachPage(new HistoryNotFoundPage(this))
     attachPage(masterPage)
     attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static"))
+    attachHandler(JsonRootResource.getJsonServlet(this))
     attachHandler(createRedirectHandler(
       "/app/kill", "/", masterPage.handleAppKillRequest, httpMethods = Set("POST")))
     attachHandler(createRedirectHandler(
@@ -58,6 +63,23 @@ class MasterWebUI(val master: Master, requestedPort: Int)
     assert(serverInfo.isDefined, "Master UI must be bound to a server before detaching SparkUIs")
     ui.getHandlers.foreach(detachHandler)
   }
+
+  def getApplicationInfoList: Iterator[ApplicationInfo] = {
+    val state = masterPage.getMasterState
+    val activeApps = state.activeApps.sortBy(_.startTime).reverse
+    val completedApps = state.completedApps.sortBy(_.endTime).reverse
+    activeApps.iterator.map { ApplicationsListResource.convertApplicationInfo(_, false) } ++
+      completedApps.iterator.map { ApplicationsListResource.convertApplicationInfo(_, true) }
+  }
+
+  def getSparkUI(appId: String): Option[SparkUI] = {
+    val state = masterPage.getMasterState
+    val activeApps = state.activeApps.sortBy(_.startTime).reverse
+    val completedApps = state.completedApps.sortBy(_.endTime).reverse
+    (activeApps ++ completedApps).find { _.id == appId }.flatMap {
+      master.rebuildSparkUI
+    }
+  }
 }
 
 private[master] object MasterWebUI {
diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala
index 3406a7e97e368..ec185340c3a2d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala
@@ -33,7 +33,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
   /**
    * Returns a future for counting the number of elements in the RDD.
    */
-  def countAsync(): FutureAction[Long] = {
+  def countAsync(): FutureAction[Long] = self.withScope {
     val totalCount = new AtomicLong
     self.context.submitJob(
       self,
@@ -53,7 +53,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
   /**
    * Returns a future for retrieving all elements of this RDD.
    */
-  def collectAsync(): FutureAction[Seq[T]] = {
+  def collectAsync(): FutureAction[Seq[T]] = self.withScope {
     val results = new Array[Array[T]](self.partitions.length)
     self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length),
       (index, data) => results(index) = data, results.flatten.toSeq)
@@ -62,7 +62,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
   /**
    * Returns a future for retrieving the first num elements of the RDD.
    */
-  def takeAsync(num: Int): FutureAction[Seq[T]] = {
+  def takeAsync(num: Int): FutureAction[Seq[T]] = self.withScope {
     val f = new ComplexFutureAction[Seq[T]]
 
     f.run {
@@ -109,7 +109,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
   /**
    * Applies a function f to all elements of this RDD.
    */
-  def foreachAsync(f: T => Unit): FutureAction[Unit] = {
+  def foreachAsync(f: T => Unit): FutureAction[Unit] = self.withScope {
     val cleanF = self.context.clean(f)
     self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length),
       (index, data) => Unit, Unit)
@@ -118,7 +118,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
   /**
    * Applies a function f to each partition of this RDD.
    */
-  def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = {
+  def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = self.withScope {
     self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length),
       (index, data) => Unit, Unit)
   }
diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
index 71578d1210fde..922030263756b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
@@ -31,7 +31,7 @@ private[spark]
 class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds: Array[BlockId])
   extends RDD[T](sc, Nil) {
 
-  @transient lazy val locations_ = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
+  @transient lazy val _locations = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
   @volatile private var _isValid = true
 
   override def getPartitions: Array[Partition] = {
@@ -54,7 +54,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds
 
   override def getPreferredLocations(split: Partition): Seq[String] = {
     assertValid()
-    locations_(split.asInstanceOf[BlockRDDPartition].blockId)
+    _locations(split.asInstanceOf[BlockRDDPartition].blockId)
   }
 
   /**
@@ -79,14 +79,14 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds
 
   /** Check if this BlockRDD is valid. If not valid, exception is thrown. */
   private[spark] def assertValid() {
-    if (!_isValid) {
+    if (!isValid) {
       throw new SparkException(
         "Attempted to use %s after its blocks have been removed!".format(toString))
     }
   }
 
   protected def getBlockIdLocations(): Map[BlockId, Seq[String]] = {
-    locations_
+    _locations
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
index 843a893235e56..926bce6f15a2a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala
@@ -30,7 +30,7 @@ import org.apache.spark.util.StatCounter
  */
 class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
   /** Add up the elements in this RDD. */
-  def sum(): Double = {
+  def sum(): Double = self.withScope {
     self.fold(0.0)(_ + _)
   }
 
@@ -38,37 +38,49 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and
    * count of the RDD's elements in one operation.
    */
-  def stats(): StatCounter = {
+  def stats(): StatCounter = self.withScope {
     self.mapPartitions(nums => Iterator(StatCounter(nums))).reduce((a, b) => a.merge(b))
   }
 
   /** Compute the mean of this RDD's elements. */
-  def mean(): Double = stats().mean
+  def mean(): Double = self.withScope {
+    stats().mean
+  }
 
   /** Compute the variance of this RDD's elements. */
-  def variance(): Double = stats().variance
+  def variance(): Double = self.withScope {
+    stats().variance
+  }
 
   /** Compute the standard deviation of this RDD's elements. */
-  def stdev(): Double = stats().stdev
+  def stdev(): Double = self.withScope {
+    stats().stdev
+  }
 
   /**
    * Compute the sample standard deviation of this RDD's elements (which corrects for bias in
    * estimating the standard deviation by dividing by N-1 instead of N).
    */
-  def sampleStdev(): Double = stats().sampleStdev
+  def sampleStdev(): Double = self.withScope {
+    stats().sampleStdev
+  }
 
   /**
    * Compute the sample variance of this RDD's elements (which corrects for bias in
    * estimating the variance by dividing by N-1 instead of N).
    */
-  def sampleVariance(): Double = stats().sampleVariance
+  def sampleVariance(): Double = self.withScope {
+    stats().sampleVariance
+  }
 
   /**
    * :: Experimental ::
    * Approximate operation to return the mean within a timeout.
    */
   @Experimental
-  def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
+  def meanApprox(
+      timeout: Long,
+      confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
     val evaluator = new MeanEvaluator(self.partitions.length, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
@@ -79,7 +91,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * Approximate operation to return the sum within a timeout.
    */
   @Experimental
-  def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
+  def sumApprox(
+      timeout: Long,
+      confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope {
     val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns)
     val evaluator = new SumEvaluator(self.partitions.length, confidence)
     self.context.runApproximateJob(self, processPartition, evaluator, timeout)
@@ -93,7 +107,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * If the RDD contains infinity, NaN throws an exception
    * If the elements in RDD do not vary (max == min) always returns a single bucket.
    */
-  def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = {
+  def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = self.withScope {
     // Scala's built-in range has issues. See #SI-8782
     def customRange(min: Double, max: Double, steps: Int): IndexedSeq[Double] = {
       val span = max - min
@@ -140,7 +154,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable {
    * the maximum value of the last position and all NaN entries will be counted
    * in that bucket.
    */
-  def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long] = {
+  def histogram(
+      buckets: Array[Double],
+      evenBuckets: Boolean = false): Array[Long] = self.withScope {
     if (buckets.length < 2) {
       throw new IllegalArgumentException("buckets array must have at least two elements")
     }
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index f77abac42b623..2cefe63d44b20 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -99,7 +99,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp
  */
 @DeveloperApi
 class HadoopRDD[K, V](
-    sc: SparkContext,
+    @transient sc: SparkContext,
     broadcastedConf: Broadcast[SerializableWritable[Configuration]],
     initLocalJobConfFuncOpt: Option[JobConf => Unit],
     inputFormatClass: Class[_ <: InputFormat[K, V]],
@@ -108,6 +108,10 @@ class HadoopRDD[K, V](
     minPartitions: Int)
   extends RDD[(K, V)](sc, Nil) with Logging {
 
+  if (initLocalJobConfFuncOpt.isDefined) {
+    sc.clean(initLocalJobConfFuncOpt.get)
+  }
+
   def this(
       sc: SparkContext,
       conf: JobConf,
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index 6afe50161dacd..d71bb63000904 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -57,7 +57,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    */
   // TODO: this currently doesn't work on P other than Tuple2!
   def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length)
-      : RDD[(K, V)] =
+      : RDD[(K, V)] = self.withScope
   {
     val part = new RangePartitioner(numPartitions, self, ascending)
     new ShuffledRDD[K, V, V](self, part)
@@ -71,7 +71,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    * This is more efficient than calling `repartition` and then sorting within each partition
    * because it can push the sorting down into the shuffle machinery.
    */
-  def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = {
+  def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = self.withScope {
     new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering)
   }
 
@@ -81,7 +81,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
    * performed efficiently by only scanning the partitions that might contain matching elements.
    * Otherwise, a standard `filter` is applied to all partitions.
    */
-  def filterByRange(lower: K, upper: K): RDD[P] = {
+  def filterByRange(lower: K, upper: K): RDD[P] = self.withScope {
 
     def inRange(k: K): Boolean = ordering.gteq(k, lower) && ordering.lteq(k, upper)
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 05351ba4ff76b..a6d5d2c94e17f 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -29,7 +29,7 @@ import scala.util.DynamicVariable
 
 import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
 import org.apache.hadoop.conf.{Configurable, Configuration}
-import org.apache.hadoop.fs.{FileSystem, Path}
+import org.apache.hadoop.fs.FileSystem
 import org.apache.hadoop.io.SequenceFile.CompressionType
 import org.apache.hadoop.io.compress.CompressionCodec
 import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat}
@@ -75,7 +75,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       mergeCombiners: (C, C) => C,
       partitioner: Partitioner,
       mapSideCombine: Boolean = true,
-      serializer: Serializer = null): RDD[(K, C)] = {
+      serializer: Serializer = null): RDD[(K, C)] = self.withScope {
     require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
     if (keyClass.isArray) {
       if (mapSideCombine) {
@@ -108,7 +108,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   def combineByKey[C](createCombiner: V => C,
       mergeValue: (C, V) => C,
       mergeCombiners: (C, C) => C,
-      numPartitions: Int): RDD[(K, C)] = {
+      numPartitions: Int): RDD[(K, C)] = self.withScope {
     combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
   }
 
@@ -122,7 +122,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * instead of creating a new U.
    */
   def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U,
-      combOp: (U, U) => U): RDD[(K, U)] = {
+      combOp: (U, U) => U): RDD[(K, U)] = self.withScope {
     // Serialize the zero value to a byte array so that we can get a new clone of it on each key
     val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue)
     val zeroArray = new Array[Byte](zeroBuffer.limit)
@@ -131,7 +131,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     lazy val cachedSerializer = SparkEnv.get.serializer.newInstance()
     val createZero = () => cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray))
 
-    combineByKey[U]((v: V) => seqOp(createZero(), v), seqOp, combOp, partitioner)
+    // We will clean the combiner closure later in `combineByKey`
+    val cleanedSeqOp = self.context.clean(seqOp)
+    combineByKey[U]((v: V) => cleanedSeqOp(createZero(), v), cleanedSeqOp, combOp, partitioner)
   }
 
   /**
@@ -144,7 +146,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * instead of creating a new U.
    */
   def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U,
-      combOp: (U, U) => U): RDD[(K, U)] = {
+      combOp: (U, U) => U): RDD[(K, U)] = self.withScope {
     aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp)
   }
 
@@ -158,7 +160,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * instead of creating a new U.
    */
   def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U,
-      combOp: (U, U) => U): RDD[(K, U)] = {
+      combOp: (U, U) => U): RDD[(K, U)] = self.withScope {
     aggregateByKey(zeroValue, defaultPartitioner(self))(seqOp, combOp)
   }
 
@@ -167,7 +169,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * may be added to the result an arbitrary number of times, and must not change the result
    * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
    */
-  def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = {
+  def foldByKey(
+      zeroValue: V,
+      partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = self.withScope {
     // Serialize the zero value to a byte array so that we can get a new clone of it on each key
     val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue)
     val zeroArray = new Array[Byte](zeroBuffer.limit)
@@ -177,7 +181,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     lazy val cachedSerializer = SparkEnv.get.serializer.newInstance()
     val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray))
 
-    combineByKey[V]((v: V) => func(createZero(), v), func, func, partitioner)
+    val cleanedFunc = self.context.clean(func)
+    combineByKey[V]((v: V) => cleanedFunc(createZero(), v), cleanedFunc, cleanedFunc, partitioner)
   }
 
   /**
@@ -185,7 +190,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * may be added to the result an arbitrary number of times, and must not change the result
    * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
    */
-  def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = {
+  def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = self.withScope {
     foldByKey(zeroValue, new HashPartitioner(numPartitions))(func)
   }
 
@@ -194,7 +199,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * may be added to the result an arbitrary number of times, and must not change the result
    * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.).
    */
-  def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = {
+  def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = self.withScope {
     foldByKey(zeroValue, defaultPartitioner(self))(func)
   }
 
@@ -213,7 +218,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    */
   def sampleByKey(withReplacement: Boolean,
       fractions: Map[K, Double],
-      seed: Long = Utils.random.nextLong): RDD[(K, V)] = {
+      seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope {
 
     require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.")
 
@@ -242,9 +247,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * @return RDD containing the sampled subset
    */
   @Experimental
-  def sampleByKeyExact(withReplacement: Boolean,
+  def sampleByKeyExact(
+      withReplacement: Boolean,
       fractions: Map[K, Double],
-      seed: Long = Utils.random.nextLong): RDD[(K, V)] = {
+      seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope {
 
     require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.")
 
@@ -261,7 +267,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * the merging locally on each mapper before sending results to a reducer, similarly to a
    * "combiner" in MapReduce.
    */
-  def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = {
+  def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope {
     combineByKey[V]((v: V) => v, func, func, partitioner)
   }
 
@@ -270,7 +276,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * the merging locally on each mapper before sending results to a reducer, similarly to a
    * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions.
    */
-  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = {
+  def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = self.withScope {
     reduceByKey(new HashPartitioner(numPartitions), func)
   }
 
@@ -280,7 +286,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/
    * parallelism level.
    */
-  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = {
+  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = self.withScope {
     reduceByKey(defaultPartitioner(self), func)
   }
 
@@ -289,7 +295,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * immediately to the master as a Map. This will also perform the merging locally on each mapper
    * before sending results to a reducer, similarly to a "combiner" in MapReduce.
    */
-  def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = {
+  def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = self.withScope {
 
     if (keyClass.isArray) {
       throw new SparkException("reduceByKeyLocally() does not support array keys")
@@ -317,7 +323,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
   /** Alias for reduceByKeyLocally */
   @deprecated("Use reduceByKeyLocally", "1.0.0")
-  def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = reduceByKeyLocally(func)
+  def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = self.withScope {
+    reduceByKeyLocally(func)
+  }
 
   /** 
    * Count the number of elements for each key, collecting the results to a local Map.
@@ -327,7 +335,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which
    * returns an RDD[T, Long] instead of a map.
    */
-  def countByKey(): Map[K, Long] = self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap
+  def countByKey(): Map[K, Long] = self.withScope {
+    self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap
+  }
 
   /**
    * :: Experimental ::
@@ -336,7 +346,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    */
   @Experimental
   def countByKeyApprox(timeout: Long, confidence: Double = 0.95)
-      : PartialResult[Map[K, BoundedDouble]] = {
+      : PartialResult[Map[K, BoundedDouble]] = self.withScope {
     self.map(_._1).countByValueApprox(timeout, confidence)
   }
 
@@ -360,7 +370,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * @param partitioner Partitioner to use for the resulting RDD.
    */
   @Experimental
-  def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner): RDD[(K, Long)] = {
+  def countApproxDistinctByKey(
+      p: Int,
+      sp: Int,
+      partitioner: Partitioner): RDD[(K, Long)] = self.withScope {
     require(p >= 4, s"p ($p) must be >= 4")
     require(sp <= 32, s"sp ($sp) must be <= 32")
     require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
@@ -392,7 +405,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    *                   It must be greater than 0.000017.
    * @param partitioner partitioner of the resulting RDD
    */
-  def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = {
+  def countApproxDistinctByKey(
+      relativeSD: Double,
+      partitioner: Partitioner): RDD[(K, Long)] = self.withScope {
     require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017")
     val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
     assert(p <= 32)
@@ -410,7 +425,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    *                   It must be greater than 0.000017.
    * @param numPartitions number of partitions of the resulting RDD
    */
-  def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)] = {
+  def countApproxDistinctByKey(
+      relativeSD: Double,
+      numPartitions: Int): RDD[(K, Long)] = self.withScope {
     countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions))
   }
 
@@ -424,7 +441,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
    *                   It must be greater than 0.000017.
    */
-  def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = {
+  def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = self.withScope {
     countApproxDistinctByKey(relativeSD, defaultPartitioner(self))
   }
 
@@ -441,7 +458,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any
    * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
    */
-  def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = {
+  def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope {
     // groupByKey shouldn't use map side combine because map side combine does not
     // reduce the amount of data shuffled and requires all map side data be inserted
     // into a hash table, leading to more objects in the old gen.
@@ -465,14 +482,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any
    * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].
    */
-  def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = {
+  def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = self.withScope {
     groupByKey(new HashPartitioner(numPartitions))
   }
 
   /**
    * Return a copy of the RDD partitioned using the specified partitioner.
    */
-  def partitionBy(partitioner: Partitioner): RDD[(K, V)] = {
+  def partitionBy(partitioner: Partitioner): RDD[(K, V)] = self.withScope {
     if (keyClass.isArray && partitioner.isInstanceOf[HashPartitioner]) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
@@ -488,7 +505,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
    * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD.
    */
-  def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = {
+  def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = self.withScope {
     this.cogroup(other, partitioner).flatMapValues( pair =>
       for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w)
     )
@@ -500,7 +517,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to
    * partition the output RDD.
    */
-  def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = {
+  def leftOuterJoin[W](
+      other: RDD[(K, W)],
+      partitioner: Partitioner): RDD[(K, (V, Option[W]))] = self.withScope {
     this.cogroup(other, partitioner).flatMapValues { pair =>
       if (pair._2.isEmpty) {
         pair._1.iterator.map(v => (v, None))
@@ -517,7 +536,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * partition the output RDD.
    */
   def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
-      : RDD[(K, (Option[V], W))] = {
+      : RDD[(K, (Option[V], W))] = self.withScope {
     this.cogroup(other, partitioner).flatMapValues { pair =>
       if (pair._1.isEmpty) {
         pair._2.iterator.map(w => (None, w))
@@ -536,7 +555,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * in `this` have key k. Uses the given Partitioner to partition the output RDD.
    */
   def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
-      : RDD[(K, (Option[V], Option[W]))] = {
+      : RDD[(K, (Option[V], Option[W]))] = self.withScope {
     this.cogroup(other, partitioner).flatMapValues {
       case (vs, Seq()) => vs.iterator.map(v => (Some(v), None))
       case (Seq(), ws) => ws.iterator.map(w => (None, Some(w)))
@@ -549,7 +568,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * existing partitioner/parallelism level.
    */
   def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C)
-    : RDD[(K, C)] = {
+    : RDD[(K, C)] = self.withScope {
     combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
   }
 
@@ -563,7 +582,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
    * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
-  def groupByKey(): RDD[(K, Iterable[V])] = {
+  def groupByKey(): RDD[(K, Iterable[V])] = self.withScope {
     groupByKey(defaultPartitioner(self))
   }
 
@@ -572,7 +591,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
    * (k, v2) is in `other`. Performs a hash join across the cluster.
    */
-  def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = {
+  def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = self.withScope {
     join(other, defaultPartitioner(self, other))
   }
 
@@ -581,7 +600,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
    * (k, v2) is in `other`. Performs a hash join across the cluster.
    */
-  def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = {
+  def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = self.withScope {
     join(other, new HashPartitioner(numPartitions))
   }
 
@@ -591,7 +610,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
    * using the existing partitioner/parallelism level.
    */
-  def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = {
+  def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = self.withScope {
     leftOuterJoin(other, defaultPartitioner(self, other))
   }
 
@@ -601,7 +620,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output
    * into `numPartitions` partitions.
    */
-  def leftOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, Option[W]))] = {
+  def leftOuterJoin[W](
+      other: RDD[(K, W)],
+      numPartitions: Int): RDD[(K, (V, Option[W]))] = self.withScope {
     leftOuterJoin(other, new HashPartitioner(numPartitions))
   }
 
@@ -611,7 +632,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
    * RDD using the existing partitioner/parallelism level.
    */
-  def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = {
+  def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = self.withScope {
     rightOuterJoin(other, defaultPartitioner(self, other))
   }
 
@@ -621,7 +642,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting
    * RDD into the given number of partitions.
    */
-  def rightOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], W))] = {
+  def rightOuterJoin[W](
+      other: RDD[(K, W)],
+      numPartitions: Int): RDD[(K, (Option[V], W))] = self.withScope {
     rightOuterJoin(other, new HashPartitioner(numPartitions))
   }
 
@@ -634,7 +657,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/
    * parallelism level.
    */
-  def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = {
+  def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = self.withScope {
     fullOuterJoin(other, defaultPartitioner(self, other))
   }
 
@@ -646,7 +669,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
    * in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions.
    */
-  def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = {
+  def fullOuterJoin[W](
+      other: RDD[(K, W)],
+      numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = self.withScope {
     fullOuterJoin(other, new HashPartitioner(numPartitions))
   }
 
@@ -656,7 +681,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only
    *          one value per key is preserved in the map returned)
    */
-  def collectAsMap(): Map[K, V] = {
+  def collectAsMap(): Map[K, V] = self.withScope {
     val data = self.collect()
     val map = new mutable.HashMap[K, V]
     map.sizeHint(data.length)
@@ -668,7 +693,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Pass each value in the key-value pair RDD through a map function without changing the keys;
    * this also retains the original RDD's partitioning.
    */
-  def mapValues[U](f: V => U): RDD[(K, U)] = {
+  def mapValues[U](f: V => U): RDD[(K, U)] = self.withScope {
     val cleanF = self.context.clean(f)
     new MapPartitionsRDD[(K, U), (K, V)](self,
       (context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) },
@@ -679,7 +704,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Pass each value in the key-value pair RDD through a flatMap function without changing the
    * keys; this also retains the original RDD's partitioning.
    */
-  def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = {
+  def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = self.withScope {
     val cleanF = self.context.clean(f)
     new MapPartitionsRDD[(K, U), (K, V)](self,
       (context, pid, iter) => iter.flatMap { case (k, v) =>
@@ -697,7 +722,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       other2: RDD[(K, W2)],
       other3: RDD[(K, W3)],
       partitioner: Partitioner)
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
     if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
@@ -715,7 +740,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * list of values for that key in `this` as well as `other`.
    */
   def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner)
-      : RDD[(K, (Iterable[V], Iterable[W]))]  = {
+      : RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
     if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
@@ -730,7 +755,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * tuple with the list of values for that key in `this`, `other1` and `other2`.
    */
   def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner)
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
     if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
       throw new SparkException("Default partitioner cannot partition array keys.")
     }
@@ -748,7 +773,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * for that key in `this`, `other1`, `other2` and `other3`.
    */
   def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
     cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
   }
 
@@ -756,7 +781,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
    */
-  def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = {
+  def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
     cogroup(other, defaultPartitioner(self, other))
   }
 
@@ -765,7 +790,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * tuple with the list of values for that key in `this`, `other1` and `other2`.
    */
   def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)])
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
     cogroup(other1, other2, defaultPartitioner(self, other1, other2))
   }
 
@@ -773,7 +798,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
    * list of values for that key in `this` as well as `other`.
    */
-  def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = {
+  def cogroup[W](
+      other: RDD[(K, W)],
+      numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
     cogroup(other, new HashPartitioner(numPartitions))
   }
 
@@ -782,7 +809,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * tuple with the list of values for that key in `this`, `other1` and `other2`.
    */
   def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int)
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
     cogroup(other1, other2, new HashPartitioner(numPartitions))
   }
 
@@ -795,24 +822,24 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       other2: RDD[(K, W2)],
       other3: RDD[(K, W3)],
       numPartitions: Int)
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
     cogroup(other1, other2, other3, new HashPartitioner(numPartitions))
   }
 
   /** Alias for cogroup. */
-  def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = {
+  def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
     cogroup(other, defaultPartitioner(self, other))
   }
 
   /** Alias for cogroup. */
   def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)])
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope {
     cogroup(other1, other2, defaultPartitioner(self, other1, other2))
   }
 
   /** Alias for cogroup. */
   def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)])
-      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = {
+      : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope {
     cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3))
   }
 
@@ -822,22 +849,27 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
    * RDD will be <= us.
    */
-  def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] =
+  def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = self.withScope {
     subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length)))
+  }
 
   /** Return an RDD with the pairs from `this` whose keys are not in `other`. */
-  def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] =
+  def subtractByKey[W: ClassTag](
+      other: RDD[(K, W)],
+      numPartitions: Int): RDD[(K, V)] = self.withScope {
     subtractByKey(other, new HashPartitioner(numPartitions))
+  }
 
   /** Return an RDD with the pairs from `this` whose keys are not in `other`. */
-  def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] =
+  def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = self.withScope {
     new SubtractedRDD[K, V, W](self, other, p)
+  }
 
   /**
    * Return the list of values in the RDD for key `key`. This operation is done efficiently if the
    * RDD has a known partitioner by only searching the partition that the key maps to.
    */
-  def lookup(key: K): Seq[V] = {
+  def lookup(key: K): Seq[V] = self.withScope {
     self.partitioner match {
       case Some(p) =>
         val index = p.getPartition(key)
@@ -859,7 +891,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class
    * supporting the key and value types K and V in this RDD.
    */
-  def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) {
+  def saveAsHadoopFile[F <: OutputFormat[K, V]](
+      path: String)(implicit fm: ClassTag[F]): Unit = self.withScope {
     saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]])
   }
 
@@ -869,7 +902,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * supplied codec.
    */
   def saveAsHadoopFile[F <: OutputFormat[K, V]](
-      path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) {
+      path: String,
+      codec: Class[_ <: CompressionCodec])(implicit fm: ClassTag[F]): Unit = self.withScope {
     val runtimeClass = fm.runtimeClass
     saveAsHadoopFile(path, keyClass, valueClass, runtimeClass.asInstanceOf[Class[F]], codec)
   }
@@ -878,7 +912,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat`
    * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD.
    */
-  def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) {
+  def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](
+      path: String)(implicit fm: ClassTag[F]): Unit = self.withScope {
     saveAsNewAPIHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]])
   }
 
@@ -891,8 +926,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[_ <: NewOutputFormat[_, _]],
-      conf: Configuration = self.context.hadoopConfiguration)
-  {
+      conf: Configuration = self.context.hadoopConfiguration): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
     val hadoopConf = conf
     val job = new NewAPIHadoopJob(hadoopConf)
@@ -912,7 +946,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       keyClass: Class[_],
       valueClass: Class[_],
       outputFormatClass: Class[_ <: OutputFormat[_, _]],
-      codec: Class[_ <: CompressionCodec]) {
+      codec: Class[_ <: CompressionCodec]): Unit = self.withScope {
     saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass,
       new JobConf(self.context.hadoopConfiguration), Some(codec))
   }
@@ -927,7 +961,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       valueClass: Class[_],
       outputFormatClass: Class[_ <: OutputFormat[_, _]],
       conf: JobConf = new JobConf(self.context.hadoopConfiguration),
-      codec: Option[Class[_ <: CompressionCodec]] = None) {
+      codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
     val hadoopConf = conf
     hadoopConf.setOutputKeyClass(keyClass)
@@ -960,7 +994,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * output paths required (e.g. a table name to write to) in the same way as it would be
    * configured for a Hadoop MapReduce job.
    */
-  def saveAsNewAPIHadoopDataset(conf: Configuration) {
+  def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
     val hadoopConf = conf
     val job = new NewAPIHadoopJob(hadoopConf)
@@ -1027,7 +1061,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop
    * MapReduce job.
    */
-  def saveAsHadoopDataset(conf: JobConf) {
+  def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
     val hadoopConf = conf
     val wrappedConf = new SerializableWritable(hadoopConf)
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 31c07c73fe07b..b3b60578c92e8 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -25,7 +25,7 @@ import scala.language.implicitConversions
 import scala.reflect.{classTag, ClassTag}
 
 import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus
-import org.apache.hadoop.io.{Writable, BytesWritable, NullWritable, Text}
+import org.apache.hadoop.io.{BytesWritable, NullWritable, Text}
 import org.apache.hadoop.io.compress.CompressionCodec
 import org.apache.hadoop.mapred.TextOutputFormat
 
@@ -277,12 +277,20 @@ abstract class RDD[T: ClassTag](
     if (isCheckpointed) firstParent[T].iterator(split, context) else compute(split, context)
   }
 
+  /**
+   * Execute a block of code in a scope such that all new RDDs created in this body will
+   * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}.
+   *
+   * Note: Return statements are NOT allowed in the given body.
+   */
+  private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](sc)(body)
+
   // Transformations (return a new RDD)
 
   /**
    * Return a new RDD by applying a function to all elements of this RDD.
    */
-  def map[U: ClassTag](f: T => U): RDD[U] = {
+  def map[U: ClassTag](f: T => U): RDD[U] = withScope {
     val cleanF = sc.clean(f)
     new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF))
   }
@@ -291,7 +299,7 @@ abstract class RDD[T: ClassTag](
    *  Return a new RDD by first applying a function to all elements of this
    *  RDD, and then flattening the results.
    */
-  def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = {
+  def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = withScope {
     val cleanF = sc.clean(f)
     new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.flatMap(cleanF))
   }
@@ -299,7 +307,7 @@ abstract class RDD[T: ClassTag](
   /**
    * Return a new RDD containing only the elements that satisfy a predicate.
    */
-  def filter(f: T => Boolean): RDD[T] = {
+  def filter(f: T => Boolean): RDD[T] = withScope {
     val cleanF = sc.clean(f)
     new MapPartitionsRDD[T, T](
       this,
@@ -310,13 +318,16 @@ abstract class RDD[T: ClassTag](
   /**
    * Return a new RDD containing the distinct elements in this RDD.
    */
-  def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] =
+  def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
     map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1)
+  }
 
   /**
    * Return a new RDD containing the distinct elements in this RDD.
    */
-  def distinct(): RDD[T] = distinct(partitions.length)
+  def distinct(): RDD[T] = withScope {
+    distinct(partitions.length)
+  }
 
   /**
    * Return a new RDD that has exactly numPartitions partitions.
@@ -327,7 +338,7 @@ abstract class RDD[T: ClassTag](
    * If you are decreasing the number of partitions in this RDD, consider using `coalesce`,
    * which can avoid performing a shuffle.
    */
-  def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = {
+  def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
     coalesce(numPartitions, shuffle = true)
   }
 
@@ -352,7 +363,7 @@ abstract class RDD[T: ClassTag](
    * data distributed using a hash partitioner.
    */
   def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null)
-      : RDD[T] = {
+      : RDD[T] = withScope {
     if (shuffle) {
       /** Distributes elements evenly across output partitions, starting from a random partition. */
       val distributePartition = (index: Int, items: Iterator[T]) => {
@@ -377,16 +388,17 @@ abstract class RDD[T: ClassTag](
 
   /**
    * Return a sampled subset of this RDD.
-   * 
+   *
    * @param withReplacement can elements be sampled multiple times (replaced when sampled out)
    * @param fraction expected size of the sample as a fraction of this RDD's size
    *  without replacement: probability that each element is chosen; fraction must be [0, 1]
    *  with replacement: expected number of times each element is chosen; fraction must be >= 0
    * @param seed seed for the random number generator
    */
-  def sample(withReplacement: Boolean,
+  def sample(
+      withReplacement: Boolean,
       fraction: Double,
-      seed: Long = Utils.random.nextLong): RDD[T] = {
+      seed: Long = Utils.random.nextLong): RDD[T] = withScope {
     require(fraction >= 0.0, "Negative fraction value: " + fraction)
     if (withReplacement) {
       new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed)
@@ -403,7 +415,9 @@ abstract class RDD[T: ClassTag](
    *
    * @return split RDDs in an array
    */
-  def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[T]] = {
+  def randomSplit(
+      weights: Array[Double],
+      seed: Long = Utils.random.nextLong): Array[RDD[T]] = withScope {
     val sum = weights.sum
     val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _)
     normalizedCumWeights.sliding(2).map { x =>
@@ -435,7 +449,9 @@ abstract class RDD[T: ClassTag](
    * @param seed seed for the random number generator
    * @return sample of specified size in an array
    */
-  def takeSample(withReplacement: Boolean,
+  // TODO: rewrite this without return statements so we can wrap it in a scope
+  def takeSample(
+      withReplacement: Boolean,
       num: Int,
       seed: Long = Utils.random.nextLong): Array[T] = {
     val numStDev =  10.0
@@ -483,7 +499,7 @@ abstract class RDD[T: ClassTag](
    * Return the union of this RDD and another one. Any identical elements will appear multiple
    * times (use `.distinct()` to eliminate them).
    */
-  def union(other: RDD[T]): RDD[T] = {
+  def union(other: RDD[T]): RDD[T] = withScope {
     if (partitioner.isDefined && other.partitioner == partitioner) {
       new PartitionerAwareUnionRDD(sc, Array(this, other))
     } else {
@@ -495,7 +511,9 @@ abstract class RDD[T: ClassTag](
    * Return the union of this RDD and another one. Any identical elements will appear multiple
    * times (use `.distinct()` to eliminate them).
    */
-  def ++(other: RDD[T]): RDD[T] = this.union(other)
+  def ++(other: RDD[T]): RDD[T] = withScope {
+    this.union(other)
+  }
 
   /**
    * Return this RDD sorted by the given key function.
@@ -504,10 +522,11 @@ abstract class RDD[T: ClassTag](
       f: (T) => K,
       ascending: Boolean = true,
       numPartitions: Int = this.partitions.length)
-      (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] =
+      (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope {
     this.keyBy[K](f)
         .sortByKey(ascending, numPartitions)
         .values
+  }
 
   /**
    * Return the intersection of this RDD and another one. The output will not contain any duplicate
@@ -515,7 +534,7 @@ abstract class RDD[T: ClassTag](
    *
    * Note that this method performs a shuffle internally.
    */
-  def intersection(other: RDD[T]): RDD[T] = {
+  def intersection(other: RDD[T]): RDD[T] = withScope {
     this.map(v => (v, null)).cogroup(other.map(v => (v, null)))
         .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty }
         .keys
@@ -529,8 +548,9 @@ abstract class RDD[T: ClassTag](
    *
    * @param partitioner Partitioner to use for the resulting RDD
    */
-  def intersection(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null)
-      : RDD[T] = {
+  def intersection(
+      other: RDD[T],
+      partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
     this.map(v => (v, null)).cogroup(other.map(v => (v, null)), partitioner)
         .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty }
         .keys
@@ -544,16 +564,14 @@ abstract class RDD[T: ClassTag](
    *
    * @param numPartitions How many partitions to use in the resulting RDD
    */
-  def intersection(other: RDD[T], numPartitions: Int): RDD[T] = {
-    this.map(v => (v, null)).cogroup(other.map(v => (v, null)), new HashPartitioner(numPartitions))
-        .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty }
-        .keys
+  def intersection(other: RDD[T], numPartitions: Int): RDD[T] = withScope {
+    intersection(other, new HashPartitioner(numPartitions))
   }
 
   /**
    * Return an RDD created by coalescing all elements within each partition into an array.
    */
-  def glom(): RDD[Array[T]] = {
+  def glom(): RDD[Array[T]] = withScope {
     new MapPartitionsRDD[Array[T], T](this, (context, pid, iter) => Iterator(iter.toArray))
   }
 
@@ -561,7 +579,9 @@ abstract class RDD[T: ClassTag](
    * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
    * elements (a, b) where a is in `this` and b is in `other`.
    */
-  def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other)
+  def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope {
+    new CartesianRDD(sc, this, other)
+  }
 
   /**
    * Return an RDD of grouped items. Each group consists of a key and a sequence of elements
@@ -572,8 +592,9 @@ abstract class RDD[T: ClassTag](
    * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
    * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
-  def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] =
+  def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope {
     groupBy[K](f, defaultPartitioner(this))
+  }
 
   /**
    * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements
@@ -584,8 +605,11 @@ abstract class RDD[T: ClassTag](
    * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]
    * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
-  def groupBy[K](f: T => K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] =
+  def groupBy[K](
+      f: T => K,
+      numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope {
     groupBy(f, new HashPartitioner(numPartitions))
+  }
 
   /**
    * Return an RDD of grouped items. Each group consists of a key and a sequence of elements
@@ -597,7 +621,7 @@ abstract class RDD[T: ClassTag](
    * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.
    */
   def groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null)
-      : RDD[(K, Iterable[T])] = {
+      : RDD[(K, Iterable[T])] = withScope {
     val cleanF = sc.clean(f)
     this.map(t => (cleanF(t), t)).groupByKey(p)
   }
@@ -605,13 +629,16 @@ abstract class RDD[T: ClassTag](
   /**
    * Return an RDD created by piping elements to a forked external process.
    */
-  def pipe(command: String): RDD[String] = new PipedRDD(this, command)
+  def pipe(command: String): RDD[String] = withScope {
+    new PipedRDD(this, command)
+  }
 
   /**
    * Return an RDD created by piping elements to a forked external process.
    */
-  def pipe(command: String, env: Map[String, String]): RDD[String] =
+  def pipe(command: String, env: Map[String, String]): RDD[String] = withScope {
     new PipedRDD(this, command, env)
+  }
 
   /**
    * Return an RDD created by piping elements to a forked external process.
@@ -619,7 +646,7 @@ abstract class RDD[T: ClassTag](
    *
    * @param command command to run in forked process.
    * @param env environment variables to set.
-   * @param printPipeContext Before piping elements, this function is called as an oppotunity
+   * @param printPipeContext Before piping elements, this function is called as an opportunity
    *                         to pipe context data. Print line function (like out.println) will be
    *                         passed as printPipeContext's parameter.
    * @param printRDDElement Use this function to customize how to pipe elements. This function
@@ -637,7 +664,7 @@ abstract class RDD[T: ClassTag](
       env: Map[String, String] = Map(),
       printPipeContext: (String => Unit) => Unit = null,
       printRDDElement: (T, String => Unit) => Unit = null,
-      separateWorkingDir: Boolean = false): RDD[String] = {
+      separateWorkingDir: Boolean = false): RDD[String] = withScope {
     new PipedRDD(this, command, env,
       if (printPipeContext ne null) sc.clean(printPipeContext) else null,
       if (printRDDElement ne null) sc.clean(printRDDElement) else null,
@@ -651,9 +678,13 @@ abstract class RDD[T: ClassTag](
    * should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
    */
   def mapPartitions[U: ClassTag](
-      f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = {
-    val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(iter)
-    new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning)
+      f: Iterator[T] => Iterator[U],
+      preservesPartitioning: Boolean = false): RDD[U] = withScope {
+    val cleanedF = sc.clean(f)
+    new MapPartitionsRDD(
+      this,
+      (context: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(iter),
+      preservesPartitioning)
   }
 
   /**
@@ -664,9 +695,13 @@ abstract class RDD[T: ClassTag](
    * should be `false` unless this is a pair RDD and the input function doesn't modify the keys.
    */
   def mapPartitionsWithIndex[U: ClassTag](
-      f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = {
-    val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter)
-    new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning)
+      f: (Int, Iterator[T]) => Iterator[U],
+      preservesPartitioning: Boolean = false): RDD[U] = withScope {
+    val cleanedF = sc.clean(f)
+    new MapPartitionsRDD(
+      this,
+      (context: TaskContext, index: Int, iter: Iterator[T]) => cleanedF(index, iter),
+      preservesPartitioning)
   }
 
   /**
@@ -681,7 +716,7 @@ abstract class RDD[T: ClassTag](
   @deprecated("use TaskContext.get", "1.2.0")
   def mapPartitionsWithContext[U: ClassTag](
       f: (TaskContext, Iterator[T]) => Iterator[U],
-      preservesPartitioning: Boolean = false): RDD[U] = {
+      preservesPartitioning: Boolean = false): RDD[U] = withScope {
     val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(context, iter)
     new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning)
   }
@@ -692,7 +727,8 @@ abstract class RDD[T: ClassTag](
    */
   @deprecated("use mapPartitionsWithIndex", "0.7.0")
   def mapPartitionsWithSplit[U: ClassTag](
-      f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = {
+      f: (Int, Iterator[T]) => Iterator[U],
+      preservesPartitioning: Boolean = false): RDD[U] = withScope {
     mapPartitionsWithIndex(f, preservesPartitioning)
   }
 
@@ -704,7 +740,7 @@ abstract class RDD[T: ClassTag](
   @deprecated("use mapPartitionsWithIndex", "1.0.0")
   def mapWith[A, U: ClassTag]
       (constructA: Int => A, preservesPartitioning: Boolean = false)
-      (f: (T, A) => U): RDD[U] = {
+      (f: (T, A) => U): RDD[U] = withScope {
     mapPartitionsWithIndex((index, iter) => {
       val a = constructA(index)
       iter.map(t => f(t, a))
@@ -719,7 +755,7 @@ abstract class RDD[T: ClassTag](
   @deprecated("use mapPartitionsWithIndex and flatMap", "1.0.0")
   def flatMapWith[A, U: ClassTag]
       (constructA: Int => A, preservesPartitioning: Boolean = false)
-      (f: (T, A) => Seq[U]): RDD[U] = {
+      (f: (T, A) => Seq[U]): RDD[U] = withScope {
     mapPartitionsWithIndex((index, iter) => {
       val a = constructA(index)
       iter.flatMap(t => f(t, a))
@@ -732,11 +768,11 @@ abstract class RDD[T: ClassTag](
    * partition with the index of that partition.
    */
   @deprecated("use mapPartitionsWithIndex and foreach", "1.0.0")
-  def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit) {
+  def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit): Unit = withScope {
     mapPartitionsWithIndex { (index, iter) =>
       val a = constructA(index)
       iter.map(t => {f(t, a); t})
-    }.foreach(_ => {})
+    }
   }
 
   /**
@@ -745,7 +781,7 @@ abstract class RDD[T: ClassTag](
    * partition with the index of that partition.
    */
   @deprecated("use mapPartitionsWithIndex and filter", "1.0.0")
-  def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = {
+  def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = withScope {
     mapPartitionsWithIndex((index, iter) => {
       val a = constructA(index)
       iter.filter(t => p(t, a))
@@ -758,7 +794,7 @@ abstract class RDD[T: ClassTag](
    * partitions* and the *same number of elements in each partition* (e.g. one was made through
    * a map on the other).
    */
-  def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = {
+  def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope {
     zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) =>
       new Iterator[(T, U)] {
         def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match {
@@ -780,33 +816,39 @@ abstract class RDD[T: ClassTag](
    */
   def zipPartitions[B: ClassTag, V: ClassTag]
       (rdd2: RDD[B], preservesPartitioning: Boolean)
-      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] =
+      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope {
     new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, preservesPartitioning)
+  }
 
   def zipPartitions[B: ClassTag, V: ClassTag]
       (rdd2: RDD[B])
-      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] =
-    new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, false)
+      (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope {
+    zipPartitions(rdd2, preservesPartitioning = false)(f)
+  }
 
   def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag]
       (rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean)
-      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] =
+      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope {
     new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning)
+  }
 
   def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag]
       (rdd2: RDD[B], rdd3: RDD[C])
-      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] =
-    new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, false)
+      (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope {
+    zipPartitions(rdd2, rdd3, preservesPartitioning = false)(f)
+  }
 
   def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]
       (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean)
-      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] =
+      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope {
     new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning)
+  }
 
   def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag]
       (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D])
-      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] =
-    new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, false)
+      (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope {
+    zipPartitions(rdd2, rdd3, rdd4, preservesPartitioning = false)(f)
+  }
 
 
   // Actions (launch a job to return a value to the user program)
@@ -814,7 +856,7 @@ abstract class RDD[T: ClassTag](
   /**
    * Applies a function f to all elements of this RDD.
    */
-  def foreach(f: T => Unit) {
+  def foreach(f: T => Unit): Unit = withScope {
     val cleanF = sc.clean(f)
     sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF))
   }
@@ -822,7 +864,7 @@ abstract class RDD[T: ClassTag](
   /**
    * Applies a function f to each partition of this RDD.
    */
-  def foreachPartition(f: Iterator[T] => Unit) {
+  def foreachPartition(f: Iterator[T] => Unit): Unit = withScope {
     val cleanF = sc.clean(f)
     sc.runJob(this, (iter: Iterator[T]) => cleanF(iter))
   }
@@ -830,7 +872,7 @@ abstract class RDD[T: ClassTag](
   /**
    * Return an array that contains all of the elements in this RDD.
    */
-  def collect(): Array[T] = {
+  def collect(): Array[T] = withScope {
     val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
     Array.concat(results: _*)
   }
@@ -840,7 +882,7 @@ abstract class RDD[T: ClassTag](
    *
    * The iterator will consume as much memory as the largest partition in this RDD.
    */
-  def toLocalIterator: Iterator[T] = {
+  def toLocalIterator: Iterator[T] = withScope {
     def collectPartition(p: Int): Array[T] = {
       sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal = false).head
     }
@@ -851,12 +893,14 @@ abstract class RDD[T: ClassTag](
    * Return an array that contains all of the elements in this RDD.
    */
   @deprecated("use collect", "1.0.0")
-  def toArray(): Array[T] = collect()
+  def toArray(): Array[T] = withScope {
+    collect()
+  }
 
   /**
    * Return an RDD that contains all matching values by applying `f`.
    */
-  def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = {
+  def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = withScope {
     filter(f.isDefinedAt).map(f)
   }
 
@@ -866,19 +910,23 @@ abstract class RDD[T: ClassTag](
    * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting
    * RDD will be &lt;= us.
    */
-  def subtract(other: RDD[T]): RDD[T] =
+  def subtract(other: RDD[T]): RDD[T] = withScope {
     subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length)))
+  }
 
   /**
    * Return an RDD with the elements from `this` that are not in `other`.
    */
-  def subtract(other: RDD[T], numPartitions: Int): RDD[T] =
+  def subtract(other: RDD[T], numPartitions: Int): RDD[T] = withScope {
     subtract(other, new HashPartitioner(numPartitions))
+  }
 
   /**
    * Return an RDD with the elements from `this` that are not in `other`.
    */
-  def subtract(other: RDD[T], p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = {
+  def subtract(
+      other: RDD[T],
+      p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope {
     if (partitioner == Some(p)) {
       // Our partitioner knows how to handle T (which, since we have a partitioner, is
       // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples
@@ -900,7 +948,7 @@ abstract class RDD[T: ClassTag](
    * Reduces the elements of this RDD using the specified commutative and
    * associative binary operator.
    */
-  def reduce(f: (T, T) => T): T = {
+  def reduce(f: (T, T) => T): T = withScope {
     val cleanF = sc.clean(f)
     val reducePartition: Iterator[T] => Option[T] = iter => {
       if (iter.hasNext) {
@@ -929,7 +977,7 @@ abstract class RDD[T: ClassTag](
    * @param depth suggested depth of the tree (default: 2)
    * @see [[org.apache.spark.rdd.RDD#reduce]]
    */
-  def treeReduce(f: (T, T) => T, depth: Int = 2): T = {
+  def treeReduce(f: (T, T) => T, depth: Int = 2): T = withScope {
     require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
     val cleanF = context.clean(f)
     val reducePartition: Iterator[T] => Option[T] = iter => {
@@ -961,7 +1009,7 @@ abstract class RDD[T: ClassTag](
    * modify t1 and return it as its result value to avoid object allocation; however, it should not
    * modify t2.
    */
-  def fold(zeroValue: T)(op: (T, T) => T): T = {
+  def fold(zeroValue: T)(op: (T, T) => T): T = withScope {
     // Clone the zero value since we will also be serializing it as part of tasks
     var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance())
     val cleanOp = sc.clean(op)
@@ -979,7 +1027,7 @@ abstract class RDD[T: ClassTag](
    * allowed to modify and return their first argument instead of creating a new U to avoid memory
    * allocation.
    */
-  def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = {
+  def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope {
     // Clone the zero value since we will also be serializing it as part of tasks
     var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance())
     val cleanSeqOp = sc.clean(seqOp)
@@ -999,26 +1047,29 @@ abstract class RDD[T: ClassTag](
   def treeAggregate[U: ClassTag](zeroValue: U)(
       seqOp: (U, T) => U,
       combOp: (U, U) => U,
-      depth: Int = 2): U = {
+      depth: Int = 2): U = withScope {
     require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.")
     if (partitions.length == 0) {
-      return Utils.clone(zeroValue, context.env.closureSerializer.newInstance())
-    }
-    val cleanSeqOp = context.clean(seqOp)
-    val cleanCombOp = context.clean(combOp)
-    val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
-    var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it)))
-    var numPartitions = partiallyAggregated.partitions.length
-    val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
-    // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation.
-    while (numPartitions > scale + numPartitions / scale) {
-      numPartitions /= scale
-      val curNumPartitions = numPartitions
-      partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) =>
-        iter.map((i % curNumPartitions, _))
-      }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values
+      Utils.clone(zeroValue, context.env.closureSerializer.newInstance())
+    } else {
+      val cleanSeqOp = context.clean(seqOp)
+      val cleanCombOp = context.clean(combOp)
+      val aggregatePartition =
+        (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp)
+      var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it)))
+      var numPartitions = partiallyAggregated.partitions.length
+      val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2)
+      // If creating an extra level doesn't help reduce
+      // the wall-clock time, we stop tree aggregation.
+      while (numPartitions > scale + numPartitions / scale) {
+        numPartitions /= scale
+        val curNumPartitions = numPartitions
+        partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex {
+          (i, iter) => iter.map((i % curNumPartitions, _))
+        }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values
+      }
+      partiallyAggregated.reduce(cleanCombOp)
     }
-    partiallyAggregated.reduce(cleanCombOp)
   }
 
   /**
@@ -1032,7 +1083,9 @@ abstract class RDD[T: ClassTag](
    * within a timeout, even if not all tasks have finished.
    */
   @Experimental
-  def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = {
+  def countApprox(
+      timeout: Long,
+      confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope {
     val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) =>
       var result = 0L
       while (iter.hasNext) {
@@ -1053,7 +1106,7 @@ abstract class RDD[T: ClassTag](
    * To handle very large results, consider using rdd.map(x =&gt; (x, 1L)).reduceByKey(_ + _), which
    * returns an RDD[T, Long] instead of a map.
    */
-  def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = {
+  def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = withScope {
     map(value => (value, null)).countByKey()
   }
 
@@ -1064,8 +1117,7 @@ abstract class RDD[T: ClassTag](
   @Experimental
   def countByValueApprox(timeout: Long, confidence: Double = 0.95)
       (implicit ord: Ordering[T] = null)
-      : PartialResult[Map[T, BoundedDouble]] =
-  {
+      : PartialResult[Map[T, BoundedDouble]] = withScope {
     if (elementClassTag.runtimeClass.isArray) {
       throw new SparkException("countByValueApprox() does not support arrays")
     }
@@ -1098,7 +1150,7 @@ abstract class RDD[T: ClassTag](
    *           If `sp` equals 0, the sparse representation is skipped.
    */
   @Experimental
-  def countApproxDistinct(p: Int, sp: Int): Long = {
+  def countApproxDistinct(p: Int, sp: Int): Long = withScope {
     require(p >= 4, s"p ($p) must be at least 4")
     require(sp <= 32, s"sp ($sp) cannot be greater than 32")
     require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)")
@@ -1124,7 +1176,7 @@ abstract class RDD[T: ClassTag](
    * @param relativeSD Relative accuracy. Smaller values create counters that require more space.
    *                   It must be greater than 0.000017.
    */
-  def countApproxDistinct(relativeSD: Double = 0.05): Long = {
+  def countApproxDistinct(relativeSD: Double = 0.05): Long = withScope {
     val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt
     countApproxDistinct(p, 0)
   }
@@ -1142,7 +1194,9 @@ abstract class RDD[T: ClassTag](
    * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee
    * the same index assignments, you should sort the RDD with sortByKey() or save it to a file.
    */
-  def zipWithIndex(): RDD[(T, Long)] = new ZippedWithIndexRDD(this)
+  def zipWithIndex(): RDD[(T, Long)] = withScope {
+    new ZippedWithIndexRDD(this)
+  }
 
   /**
    * Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k,
@@ -1154,7 +1208,7 @@ abstract class RDD[T: ClassTag](
    * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee
    * the same index assignments, you should sort the RDD with sortByKey() or save it to a file.
    */
-  def zipWithUniqueId(): RDD[(T, Long)] = {
+  def zipWithUniqueId(): RDD[(T, Long)] = withScope {
     val n = this.partitions.length.toLong
     this.mapPartitionsWithIndex { case (k, iter) =>
       iter.zipWithIndex.map { case (item, i) =>
@@ -1171,48 +1225,50 @@ abstract class RDD[T: ClassTag](
    * @note due to complications in the internal implementation, this method will raise
    * an exception if called on an RDD of `Nothing` or `Null`.
    */
-  def take(num: Int): Array[T] = {
+  def take(num: Int): Array[T] = withScope {
     if (num == 0) {
-      return new Array[T](0)
-    }
-
-    val buf = new ArrayBuffer[T]
-    val totalParts = this.partitions.length
-    var partsScanned = 0
-    while (buf.size < num && partsScanned < totalParts) {
-      // The number of partitions to try in this iteration. It is ok for this number to be
-      // greater than totalParts because we actually cap it at totalParts in runJob.
-      var numPartsToTry = 1
-      if (partsScanned > 0) {
-        // If we didn't find any rows after the previous iteration, quadruple and retry. Otherwise,
-        // interpolate the number of partitions we need to try, but overestimate it by 50%.
-        // We also cap the estimation in the end.
-        if (buf.size == 0) {
-          numPartsToTry = partsScanned * 4
-        } else {
-          // the left side of max is >=1 whenever partsScanned >= 2
-          numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1)
-          numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) 
+      new Array[T](0)
+    } else {
+      val buf = new ArrayBuffer[T]
+      val totalParts = this.partitions.length
+      var partsScanned = 0
+      while (buf.size < num && partsScanned < totalParts) {
+        // The number of partitions to try in this iteration. It is ok for this number to be
+        // greater than totalParts because we actually cap it at totalParts in runJob.
+        var numPartsToTry = 1
+        if (partsScanned > 0) {
+          // If we didn't find any rows after the previous iteration, quadruple and retry.
+          // Otherwise, interpolate the number of partitions we need to try, but overestimate
+          // it by 50%. We also cap the estimation in the end.
+          if (buf.size == 0) {
+            numPartsToTry = partsScanned * 4
+          } else {
+            // the left side of max is >=1 whenever partsScanned >= 2
+            numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1)
+            numPartsToTry = Math.min(numPartsToTry, partsScanned * 4)
+          }
         }
-      }
 
-      val left = num - buf.size
-      val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts)
-      val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true)
+        val left = num - buf.size
+        val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts)
+        val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true)
 
-      res.foreach(buf ++= _.take(num - buf.size))
-      partsScanned += numPartsToTry
-    }
+        res.foreach(buf ++= _.take(num - buf.size))
+        partsScanned += numPartsToTry
+      }
 
-    buf.toArray
+      buf.toArray
+    }
   }
 
   /**
    * Return the first element in this RDD.
    */
-  def first(): T = take(1) match {
-    case Array(t) => t
-    case _ => throw new UnsupportedOperationException("empty collection")
+  def first(): T = withScope {
+    take(1) match {
+      case Array(t) => t
+      case _ => throw new UnsupportedOperationException("empty collection")
+    }
   }
 
   /**
@@ -1230,7 +1286,9 @@ abstract class RDD[T: ClassTag](
    * @param ord the implicit ordering for T
    * @return an array of top elements
    */
-  def top(num: Int)(implicit ord: Ordering[T]): Array[T] = takeOrdered(num)(ord.reverse)
+  def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
+    takeOrdered(num)(ord.reverse)
+  }
 
   /**
    * Returns the first k (smallest) elements from this RDD as defined by the specified
@@ -1248,7 +1306,7 @@ abstract class RDD[T: ClassTag](
    * @param ord the implicit ordering for T
    * @return an array of top elements
    */
-  def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = {
+  def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
     if (num == 0) {
       Array.empty
     } else {
@@ -1273,13 +1331,17 @@ abstract class RDD[T: ClassTag](
    * Returns the max of this RDD as defined by the implicit Ordering[T].
    * @return the maximum element of the RDD
    * */
-  def max()(implicit ord: Ordering[T]): T = this.reduce(ord.max)
+  def max()(implicit ord: Ordering[T]): T = withScope {
+    this.reduce(ord.max)
+  }
 
   /**
    * Returns the min of this RDD as defined by the implicit Ordering[T].
    * @return the minimum element of the RDD
    * */
-  def min()(implicit ord: Ordering[T]): T = this.reduce(ord.min)
+  def min()(implicit ord: Ordering[T]): T = withScope {
+    this.reduce(ord.min)
+  }
 
   /**
    * @note due to complications in the internal implementation, this method will raise an
@@ -1289,12 +1351,14 @@ abstract class RDD[T: ClassTag](
    * @return true if and only if the RDD contains no elements at all. Note that an RDD
    *         may be empty even when it has at least 1 partition.
    */
-  def isEmpty(): Boolean = partitions.length == 0 || take(1).length == 0
+  def isEmpty(): Boolean = withScope {
+    partitions.length == 0 || take(1).length == 0
+  }
 
   /**
    * Save this RDD as a text file, using string representations of elements.
    */
-  def saveAsTextFile(path: String) {
+  def saveAsTextFile(path: String): Unit = withScope {
     // https://issues.apache.org/jira/browse/SPARK-2075
     //
     // NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit
@@ -1321,7 +1385,7 @@ abstract class RDD[T: ClassTag](
   /**
    * Save this RDD as a compressed text file, using string representations of elements.
    */
-  def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) {
+  def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit = withScope {
     // https://issues.apache.org/jira/browse/SPARK-2075
     val nullWritableClassTag = implicitly[ClassTag[NullWritable]]
     val textClassTag = implicitly[ClassTag[Text]]
@@ -1339,7 +1403,7 @@ abstract class RDD[T: ClassTag](
   /**
    * Save this RDD as a SequenceFile of serialized objects.
    */
-  def saveAsObjectFile(path: String) {
+  def saveAsObjectFile(path: String): Unit = withScope {
     this.mapPartitions(iter => iter.grouped(10).map(_.toArray))
       .map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x))))
       .saveAsSequenceFile(path)
@@ -1348,12 +1412,13 @@ abstract class RDD[T: ClassTag](
   /**
    * Creates tuples of the elements in this RDD by applying `f`.
    */
-  def keyBy[K](f: T => K): RDD[(K, T)] = {
-    map(x => (f(x), x))
+  def keyBy[K](f: T => K): RDD[(K, T)] = withScope {
+    val cleanedF = sc.clean(f)
+    map(x => (cleanedF(x), x))
   }
 
   /** A private method for tests, to look at the contents of each partition */
-  private[spark] def collectPartitions(): Array[Array[T]] = {
+  private[spark] def collectPartitions(): Array[Array[T]] = withScope {
     sc.runJob(this, (iter: Iterator[T]) => iter.toArray)
   }
 
@@ -1392,6 +1457,17 @@ abstract class RDD[T: ClassTag](
   /** User code that created this RDD (e.g. `textFile`, `parallelize`). */
   @transient private[spark] val creationSite = sc.getCallSite()
 
+  /**
+   * The scope associated with the operation that created this RDD.
+   *
+   * This is more flexible than the call site and can be defined hierarchically. For more
+   * detail, see the documentation of {{RDDOperationScope}}. This scope is not defined if the
+   * user instantiates this RDD himself without using any Spark operations.
+   */
+  @transient private[spark] val scope: Option[RDDOperationScope] = {
+    Option(sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)).map(RDDOperationScope.fromJson)
+  }
+
   private[spark] def getCreationSite: String = Option(creationSite).map(_.shortForm).getOrElse("")
 
   private[spark] def elementClassTag: ClassTag[T] = classTag[T]
@@ -1470,7 +1546,7 @@ abstract class RDD[T: ClassTag](
   /** A description of this RDD and its recursive dependencies for debugging. */
   def toDebugString: String = {
     // Get a debug description of an rdd without its children
-    def debugSelf (rdd: RDD[_]): Seq[String] = {
+    def debugSelf(rdd: RDD[_]): Seq[String] = {
       import Utils.bytesToString
 
       val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else ""
@@ -1527,10 +1603,11 @@ abstract class RDD[T: ClassTag](
         case (desc: String, _) => s"$nextPrefix$desc"
       } ++ debugChildren(rdd, nextPrefix)
     }
-    def debugString(rdd: RDD[_],
-                    prefix: String = "",
-                    isShuffle: Boolean = true,
-                    isLastChild: Boolean = false): Seq[String] = {
+    def debugString(
+        rdd: RDD[_],
+        prefix: String = "",
+        isShuffle: Boolean = true,
+        isLastChild: Boolean = false): Seq[String] = {
       if (isShuffle) {
         shuffleDebugString(rdd, prefix, isLastChild)
       } else {
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
new file mode 100644
index 0000000000000..537b56b49f866
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
@@ -0,0 +1,137 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import java.util.concurrent.atomic.AtomicInteger
+
+import com.fasterxml.jackson.annotation.{JsonIgnore, JsonInclude, JsonPropertyOrder}
+import com.fasterxml.jackson.annotation.JsonInclude.Include
+import com.fasterxml.jackson.databind.ObjectMapper
+import com.fasterxml.jackson.module.scala.DefaultScalaModule
+
+import org.apache.spark.SparkContext
+
+/**
+ * A general, named code block representing an operation that instantiates RDDs.
+ *
+ * All RDDs instantiated in the corresponding code block will store a pointer to this object.
+ * Examples include, but will not be limited to, existing RDD operations, such as textFile,
+ * reduceByKey, and treeAggregate.
+ *
+ * An operation scope may be nested in other scopes. For instance, a SQL query may enclose
+ * scopes associated with the public RDD APIs it uses under the hood.
+ *
+ * There is no particular relationship between an operation scope and a stage or a job.
+ * A scope may live inside one stage (e.g. map) or span across multiple jobs (e.g. take).
+ */
+@JsonInclude(Include.NON_NULL)
+@JsonPropertyOrder(Array("id", "name", "parent"))
+private[spark] class RDDOperationScope(
+    val name: String,
+    val parent: Option[RDDOperationScope] = None) {
+
+  val id: Int = RDDOperationScope.nextScopeId()
+
+  def toJson: String = {
+    RDDOperationScope.jsonMapper.writeValueAsString(this)
+  }
+
+  /**
+   * Return a list of scopes that this scope is a part of, including this scope itself.
+   * The result is ordered from the outermost scope (eldest ancestor) to this scope.
+   */
+  @JsonIgnore
+  def getAllScopes: Seq[RDDOperationScope] = {
+    parent.map(_.getAllScopes).getOrElse(Seq.empty) ++ Seq(this)
+  }
+
+  override def equals(other: Any): Boolean = {
+    other match {
+      case s: RDDOperationScope =>
+        id == s.id && name == s.name && parent == s.parent
+      case _ => false
+    }
+  }
+
+  override def toString: String = toJson
+}
+
+/**
+ * A collection of utility methods to construct a hierarchical representation of RDD scopes.
+ * An RDD scope tracks the series of operations that created a given RDD.
+ */
+private[spark] object RDDOperationScope {
+  private val jsonMapper = new ObjectMapper().registerModule(DefaultScalaModule)
+  private val scopeCounter = new AtomicInteger(0)
+
+  def fromJson(s: String): RDDOperationScope = {
+    jsonMapper.readValue(s, classOf[RDDOperationScope])
+  }
+
+  /** Return a globally unique operation scope ID. */
+  def nextScopeId(): Int = scopeCounter.getAndIncrement
+
+  /**
+   * Execute the given body such that all RDDs created in this body will have the same scope.
+   * The name of the scope will be the name of the method that immediately encloses this one.
+   *
+   * Note: Return statements are NOT allowed in body.
+   */
+  private[spark] def withScope[T](
+      sc: SparkContext,
+      allowNesting: Boolean = false)(body: => T): T = {
+    val callerMethodName = Thread.currentThread.getStackTrace()(3).getMethodName
+    withScope[T](sc, callerMethodName, allowNesting)(body)
+  }
+
+  /**
+   * Execute the given body such that all RDDs created in this body will have the same scope.
+   *
+   * If nesting is allowed, this concatenates the previous scope with the new one in a way that
+   * signifies the hierarchy. Otherwise, if nesting is not allowed, then any children calls to
+   * this method executed in the body will have no effect.
+   *
+   * Note: Return statements are NOT allowed in body.
+   */
+  private[spark] def withScope[T](
+      sc: SparkContext,
+      name: String,
+      allowNesting: Boolean = false)(body: => T): T = {
+    // Save the old scope to restore it later
+    val scopeKey = SparkContext.RDD_SCOPE_KEY
+    val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY
+    val oldScopeJson = sc.getLocalProperty(scopeKey)
+    val oldScope = Option(oldScopeJson).map(RDDOperationScope.fromJson)
+    val oldNoOverride = sc.getLocalProperty(noOverrideKey)
+    try {
+      // Set the scope only if the higher level caller allows us to do so
+      if (sc.getLocalProperty(noOverrideKey) == null) {
+        sc.setLocalProperty(scopeKey, new RDDOperationScope(name, oldScope).toJson)
+      }
+      // Optionally disallow the child body to override our scope
+      if (!allowNesting) {
+        sc.setLocalProperty(noOverrideKey, "true")
+      }
+      body
+    } finally {
+      // Remember to restore any state that was modified before exiting
+      sc.setLocalProperty(scopeKey, oldScopeJson)
+      sc.setLocalProperty(noOverrideKey, oldNoOverride)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala
index 059f8963691f0..3dfcf67f0eb66 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala
@@ -85,7 +85,9 @@ class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag
    * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported
    * file system.
    */
-  def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) {
+  def saveAsSequenceFile(
+      path: String,
+      codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope {
     def anyToWritable[U <% Writable](u: U): Writable = u
 
     // TODO We cannot force the return type of `anyToWritable` be same as keyWritableClass and
diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
index cf3db0b94a0b3..e439d2a7e1229 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala
@@ -33,6 +33,7 @@ class StageInfo(
     val name: String,
     val numTasks: Int,
     val rddInfos: Seq[RDDInfo],
+    val parentIds: Seq[Int],
     val details: String) {
   /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */
   var submissionTime: Option[Long] = None
@@ -78,6 +79,7 @@ private[spark] object StageInfo {
       stage.name,
       numTasks.getOrElse(stage.numTasks),
       rddInfos,
+      stage.parents.map(_.id),
       stage.details)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
new file mode 100644
index 0000000000000..5783df5d8220c
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllJobsResource.scala
@@ -0,0 +1,98 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import java.util.{Arrays, Date, List => JList}
+import javax.ws.rs._
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.JobExecutionStatus
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.JobProgressListener
+import org.apache.spark.ui.jobs.UIData.JobUIData
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class AllJobsResource(ui: SparkUI) {
+
+  @GET
+  def jobsList(@QueryParam("status") statuses: JList[JobExecutionStatus]): Seq[JobData] = {
+    val statusToJobs: Seq[(JobExecutionStatus, Seq[JobUIData])] =
+      AllJobsResource.getStatusToJobs(ui)
+    val adjStatuses: JList[JobExecutionStatus] = {
+      if (statuses.isEmpty) {
+        Arrays.asList(JobExecutionStatus.values(): _*)
+      } else {
+        statuses
+      }
+    }
+    val jobInfos = for {
+      (status, jobs) <- statusToJobs
+      job <- jobs if adjStatuses.contains(status)
+    } yield {
+      AllJobsResource.convertJobData(job, ui.jobProgressListener, false)
+    }
+    jobInfos.sortBy{- _.jobId}
+  }
+
+}
+
+private[v1] object AllJobsResource {
+
+  def getStatusToJobs(ui: SparkUI): Seq[(JobExecutionStatus, Seq[JobUIData])] = {
+    val statusToJobs = ui.jobProgressListener.synchronized {
+      Seq(
+        JobExecutionStatus.RUNNING -> ui.jobProgressListener.activeJobs.values.toSeq,
+        JobExecutionStatus.SUCCEEDED -> ui.jobProgressListener.completedJobs.toSeq,
+        JobExecutionStatus.FAILED -> ui.jobProgressListener.failedJobs.reverse.toSeq
+      )
+    }
+    statusToJobs
+  }
+
+  def convertJobData(
+      job: JobUIData,
+      listener: JobProgressListener,
+      includeStageDetails: Boolean): JobData = {
+    listener.synchronized {
+      val lastStageInfo = listener.stageIdToInfo.get(job.stageIds.max)
+      val lastStageData = lastStageInfo.flatMap { s =>
+        listener.stageIdToData.get((s.stageId, s.attemptId))
+      }
+      val lastStageName = lastStageInfo.map { _.name }.getOrElse("(Unknown Stage Name)")
+      val lastStageDescription = lastStageData.flatMap { _.description }
+      new JobData(
+        jobId = job.jobId,
+        name = lastStageName,
+        description = lastStageDescription,
+        submissionTime = job.submissionTime.map{new Date(_)},
+        completionTime = job.completionTime.map{new Date(_)},
+        stageIds = job.stageIds,
+        jobGroup = job.jobGroup,
+        status = job.status,
+        numTasks = job.numTasks,
+        numActiveTasks = job.numActiveTasks,
+        numCompletedTasks = job.numCompletedTasks,
+        numSkippedTasks = job.numCompletedTasks,
+        numFailedTasks = job.numFailedTasks,
+        numActiveStages = job.numActiveStages,
+        numCompletedStages = job.completedStageIndices.size,
+        numSkippedStages = job.numSkippedStages,
+        numFailedStages = job.numFailedStages
+      )
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
new file mode 100644
index 0000000000000..645ede26a0879
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllRDDResource.scala
@@ -0,0 +1,104 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs.{GET, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.storage.{RDDInfo, StorageStatus, StorageUtils}
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.storage.StorageListener
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class AllRDDResource(ui: SparkUI) {
+
+  @GET
+  def rddList(): Seq[RDDStorageInfo] = {
+    val storageStatusList = ui.storageListener.storageStatusList
+    val rddInfos = ui.storageListener.rddInfoList
+    rddInfos.map{rddInfo =>
+      AllRDDResource.getRDDStorageInfo(rddInfo.id, rddInfo, storageStatusList,
+        includeDetails = false)
+    }
+  }
+
+}
+
+private[spark] object AllRDDResource {
+
+  def getRDDStorageInfo(
+      rddId: Int,
+      listener: StorageListener,
+      includeDetails: Boolean): Option[RDDStorageInfo] = {
+    val storageStatusList = listener.storageStatusList
+    listener.rddInfoList.find { _.id == rddId }.map { rddInfo =>
+      getRDDStorageInfo(rddId, rddInfo, storageStatusList, includeDetails)
+    }
+  }
+
+  def getRDDStorageInfo(
+      rddId: Int,
+      rddInfo: RDDInfo,
+      storageStatusList: Seq[StorageStatus],
+      includeDetails: Boolean): RDDStorageInfo = {
+    val workers = storageStatusList.map { (rddId, _) }
+    val blockLocations = StorageUtils.getRddBlockLocations(rddId, storageStatusList)
+    val blocks = storageStatusList
+      .flatMap { _.rddBlocksById(rddId) }
+      .sortWith { _._1.name < _._1.name }
+      .map { case (blockId, status) =>
+        (blockId, status, blockLocations.get(blockId).getOrElse(Seq[String]("Unknown")))
+      }
+
+    val dataDistribution = if (includeDetails) {
+      Some(storageStatusList.map { status =>
+        new RDDDataDistribution(
+          address = status.blockManagerId.hostPort,
+          memoryUsed = status.memUsedByRdd(rddId),
+          memoryRemaining = status.memRemaining,
+          diskUsed = status.diskUsedByRdd(rddId)
+        ) } )
+    } else {
+      None
+    }
+    val partitions = if (includeDetails) {
+      Some(blocks.map { case (id, block, locations) =>
+        new RDDPartitionInfo(
+          blockName = id.name,
+          storageLevel = block.storageLevel.description,
+          memoryUsed = block.memSize,
+          diskUsed = block.diskSize,
+          executors = locations
+        )
+      } )
+    } else {
+      None
+    }
+
+    new RDDStorageInfo(
+      id = rddId,
+      name = rddInfo.name,
+      numPartitions = rddInfo.numPartitions,
+      numCachedPartitions = rddInfo.numCachedPartitions,
+      storageLevel = rddInfo.storageLevel.description,
+      memoryUsed = rddInfo.memSize,
+      diskUsed = rddInfo.diskSize,
+      dataDistribution = dataDistribution,
+      partitions = partitions
+    )
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
new file mode 100644
index 0000000000000..50608588f09ae
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/AllStagesResource.scala
@@ -0,0 +1,309 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import java.util.{Arrays, Date, List => JList}
+import javax.ws.rs.{GET, PathParam, Produces, QueryParam}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.executor.{InputMetrics => InternalInputMetrics, OutputMetrics => InternalOutputMetrics, ShuffleReadMetrics => InternalShuffleReadMetrics, ShuffleWriteMetrics => InternalShuffleWriteMetrics, TaskMetrics => InternalTaskMetrics}
+import org.apache.spark.scheduler.{AccumulableInfo => InternalAccumulableInfo, StageInfo}
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.UIData.{StageUIData, TaskUIData}
+import org.apache.spark.util.Distribution
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class AllStagesResource(ui: SparkUI) {
+
+  @GET
+  def stageList(@QueryParam("status") statuses: JList[StageStatus]): Seq[StageData] = {
+    val listener = ui.jobProgressListener
+    val stageAndStatus = AllStagesResource.stagesAndStatus(ui)
+    val adjStatuses = {
+      if (statuses.isEmpty()) {
+        Arrays.asList(StageStatus.values(): _*)
+      } else {
+        statuses
+      }
+    }
+    for {
+      (status, stageList) <- stageAndStatus
+      stageInfo: StageInfo <- stageList if adjStatuses.contains(status)
+      stageUiData: StageUIData <- listener.synchronized {
+        listener.stageIdToData.get((stageInfo.stageId, stageInfo.attemptId))
+      }
+    } yield {
+      AllStagesResource.stageUiToStageData(status, stageInfo, stageUiData, includeDetails = false)
+    }
+  }
+}
+
+private[v1] object AllStagesResource {
+  def stageUiToStageData(
+      status: StageStatus,
+      stageInfo: StageInfo,
+      stageUiData: StageUIData,
+      includeDetails: Boolean): StageData = {
+
+    val taskData = if (includeDetails) {
+      Some(stageUiData.taskData.map { case (k, v) => k -> convertTaskData(v) } )
+    } else {
+      None
+    }
+    val executorSummary = if (includeDetails) {
+      Some(stageUiData.executorSummary.map { case (k, summary) =>
+        k -> new ExecutorStageSummary(
+          taskTime = summary.taskTime,
+          failedTasks = summary.failedTasks,
+          succeededTasks = summary.succeededTasks,
+          inputBytes = summary.inputBytes,
+          outputBytes = summary.outputBytes,
+          shuffleRead = summary.shuffleRead,
+          shuffleWrite = summary.shuffleWrite,
+          memoryBytesSpilled = summary.memoryBytesSpilled,
+          diskBytesSpilled = summary.diskBytesSpilled
+        )
+      })
+    } else {
+      None
+    }
+
+    val accumulableInfo = stageUiData.accumulables.values.map { convertAccumulableInfo }.toSeq
+
+    new StageData(
+      status = status,
+      stageId = stageInfo.stageId,
+      attemptId = stageInfo.attemptId,
+      numActiveTasks = stageUiData.numActiveTasks,
+      numCompleteTasks = stageUiData.numCompleteTasks,
+      numFailedTasks = stageUiData.numFailedTasks,
+      executorRunTime = stageUiData.executorRunTime,
+      inputBytes = stageUiData.inputBytes,
+      inputRecords = stageUiData.inputRecords,
+      outputBytes = stageUiData.outputBytes,
+      outputRecords = stageUiData.outputRecords,
+      shuffleReadBytes = stageUiData.shuffleReadTotalBytes,
+      shuffleReadRecords = stageUiData.shuffleReadRecords,
+      shuffleWriteBytes = stageUiData.shuffleWriteBytes,
+      shuffleWriteRecords = stageUiData.shuffleWriteRecords,
+      memoryBytesSpilled = stageUiData.memoryBytesSpilled,
+      diskBytesSpilled = stageUiData.diskBytesSpilled,
+      schedulingPool = stageUiData.schedulingPool,
+      name = stageInfo.name,
+      details = stageInfo.details,
+      accumulatorUpdates = accumulableInfo,
+      tasks = taskData,
+      executorSummary = executorSummary
+    )
+  }
+
+  def stagesAndStatus(ui: SparkUI): Seq[(StageStatus, Seq[StageInfo])] = {
+    val listener = ui.jobProgressListener
+    listener.synchronized {
+      Seq(
+        StageStatus.ACTIVE -> listener.activeStages.values.toSeq,
+        StageStatus.COMPLETE -> listener.completedStages.reverse.toSeq,
+        StageStatus.FAILED -> listener.failedStages.reverse.toSeq,
+        StageStatus.PENDING -> listener.pendingStages.values.toSeq
+      )
+    }
+  }
+
+  def convertTaskData(uiData: TaskUIData): TaskData = {
+    new TaskData(
+      taskId = uiData.taskInfo.taskId,
+      index = uiData.taskInfo.index,
+      attempt = uiData.taskInfo.attempt,
+      launchTime = new Date(uiData.taskInfo.launchTime),
+      executorId = uiData.taskInfo.executorId,
+      host = uiData.taskInfo.host,
+      taskLocality = uiData.taskInfo.taskLocality.toString(),
+      speculative = uiData.taskInfo.speculative,
+      accumulatorUpdates = uiData.taskInfo.accumulables.map { convertAccumulableInfo },
+      errorMessage = uiData.errorMessage,
+      taskMetrics = uiData.taskMetrics.map { convertUiTaskMetrics }
+    )
+  }
+
+  def taskMetricDistributions(
+      allTaskData: Iterable[TaskUIData],
+      quantiles: Array[Double]): TaskMetricDistributions = {
+
+    val rawMetrics = allTaskData.flatMap{_.taskMetrics}.toSeq
+
+    def metricQuantiles(f: InternalTaskMetrics => Double): IndexedSeq[Double] =
+      Distribution(rawMetrics.map { d => f(d) }).get.getQuantiles(quantiles)
+
+    // We need to do a lot of similar munging to nested metrics here.  For each one,
+    // we want (a) extract the values for nested metrics (b) make a distribution for each metric
+    // (c) shove the distribution into the right field in our return type and (d) only return
+    // a result if the option is defined for any of the tasks.  MetricHelper is a little util
+    // to make it a little easier to deal w/ all of the nested options.  Mostly it lets us just
+    // implement one "build" method, which just builds the quantiles for each field.
+
+    val inputMetrics: Option[InputMetricDistributions] =
+      new MetricHelper[InternalInputMetrics, InputMetricDistributions](rawMetrics, quantiles) {
+        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalInputMetrics] = {
+          raw.inputMetrics
+        }
+
+        def build: InputMetricDistributions = new InputMetricDistributions(
+          bytesRead = submetricQuantiles(_.bytesRead),
+          recordsRead = submetricQuantiles(_.recordsRead)
+        )
+      }.metricOption
+
+    val outputMetrics: Option[OutputMetricDistributions] =
+      new MetricHelper[InternalOutputMetrics, OutputMetricDistributions](rawMetrics, quantiles) {
+        def getSubmetrics(raw:InternalTaskMetrics): Option[InternalOutputMetrics] = {
+          raw.outputMetrics
+        }
+        def build: OutputMetricDistributions = new OutputMetricDistributions(
+          bytesWritten = submetricQuantiles(_.bytesWritten),
+          recordsWritten = submetricQuantiles(_.recordsWritten)
+        )
+      }.metricOption
+
+    val shuffleReadMetrics: Option[ShuffleReadMetricDistributions] =
+      new MetricHelper[InternalShuffleReadMetrics, ShuffleReadMetricDistributions](rawMetrics,
+        quantiles) {
+        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalShuffleReadMetrics] = {
+          raw.shuffleReadMetrics
+        }
+        def build: ShuffleReadMetricDistributions = new ShuffleReadMetricDistributions(
+          readBytes = submetricQuantiles(_.totalBytesRead),
+          readRecords = submetricQuantiles(_.recordsRead),
+          remoteBytesRead = submetricQuantiles(_.remoteBytesRead),
+          remoteBlocksFetched = submetricQuantiles(_.remoteBlocksFetched),
+          localBlocksFetched = submetricQuantiles(_.localBlocksFetched),
+          totalBlocksFetched = submetricQuantiles(_.totalBlocksFetched),
+          fetchWaitTime = submetricQuantiles(_.fetchWaitTime)
+        )
+      }.metricOption
+
+    val shuffleWriteMetrics: Option[ShuffleWriteMetricDistributions] =
+      new MetricHelper[InternalShuffleWriteMetrics, ShuffleWriteMetricDistributions](rawMetrics,
+        quantiles) {
+        def getSubmetrics(raw: InternalTaskMetrics): Option[InternalShuffleWriteMetrics] = {
+          raw.shuffleWriteMetrics
+        }
+        def build: ShuffleWriteMetricDistributions = new ShuffleWriteMetricDistributions(
+          writeBytes = submetricQuantiles(_.shuffleBytesWritten),
+          writeRecords = submetricQuantiles(_.shuffleRecordsWritten),
+          writeTime = submetricQuantiles(_.shuffleWriteTime)
+        )
+      }.metricOption
+
+    new TaskMetricDistributions(
+      quantiles = quantiles,
+      executorDeserializeTime = metricQuantiles(_.executorDeserializeTime),
+      executorRunTime = metricQuantiles(_.executorRunTime),
+      resultSize = metricQuantiles(_.resultSize),
+      jvmGcTime = metricQuantiles(_.jvmGCTime),
+      resultSerializationTime = metricQuantiles(_.resultSerializationTime),
+      memoryBytesSpilled = metricQuantiles(_.memoryBytesSpilled),
+      diskBytesSpilled = metricQuantiles(_.diskBytesSpilled),
+      inputMetrics = inputMetrics,
+      outputMetrics = outputMetrics,
+      shuffleReadMetrics = shuffleReadMetrics,
+      shuffleWriteMetrics = shuffleWriteMetrics
+    )
+  }
+
+  def convertAccumulableInfo(acc: InternalAccumulableInfo): AccumulableInfo = {
+    new AccumulableInfo(acc.id, acc.name, acc.update, acc.value)
+  }
+
+  def convertUiTaskMetrics(internal: InternalTaskMetrics): TaskMetrics = {
+    new TaskMetrics(
+      executorDeserializeTime = internal.executorDeserializeTime,
+      executorRunTime = internal.executorRunTime,
+      resultSize = internal.resultSize,
+      jvmGcTime = internal.jvmGCTime,
+      resultSerializationTime = internal.resultSerializationTime,
+      memoryBytesSpilled = internal.memoryBytesSpilled,
+      diskBytesSpilled = internal.diskBytesSpilled,
+      inputMetrics = internal.inputMetrics.map { convertInputMetrics },
+      outputMetrics = Option(internal.outputMetrics).flatten.map { convertOutputMetrics },
+      shuffleReadMetrics = internal.shuffleReadMetrics.map { convertShuffleReadMetrics },
+      shuffleWriteMetrics = internal.shuffleWriteMetrics.map { convertShuffleWriteMetrics }
+    )
+  }
+
+  def convertInputMetrics(internal: InternalInputMetrics): InputMetrics = {
+    new InputMetrics(
+      bytesRead = internal.bytesRead,
+      recordsRead = internal.recordsRead
+    )
+  }
+
+  def convertOutputMetrics(internal: InternalOutputMetrics): OutputMetrics = {
+    new OutputMetrics(
+      bytesWritten = internal.bytesWritten,
+      recordsWritten = internal.recordsWritten
+    )
+  }
+
+  def convertShuffleReadMetrics(internal: InternalShuffleReadMetrics): ShuffleReadMetrics = {
+    new ShuffleReadMetrics(
+      remoteBlocksFetched = internal.remoteBlocksFetched,
+      localBlocksFetched = internal.localBlocksFetched,
+      fetchWaitTime = internal.fetchWaitTime,
+      remoteBytesRead = internal.remoteBytesRead,
+      totalBlocksFetched = internal.totalBlocksFetched,
+      recordsRead = internal.recordsRead
+    )
+  }
+
+  def convertShuffleWriteMetrics(internal: InternalShuffleWriteMetrics): ShuffleWriteMetrics = {
+    new ShuffleWriteMetrics(
+      bytesWritten = internal.shuffleBytesWritten,
+      writeTime = internal.shuffleWriteTime,
+      recordsWritten = internal.shuffleRecordsWritten
+    )
+  }
+}
+
+/**
+ * Helper for getting distributions from nested metric types.  Many of the metrics we want are
+ * contained in options inside TaskMetrics (eg., ShuffleWriteMetrics). This makes it easy to handle
+ * the options (returning None if the metrics are all empty), and extract the quantiles for each
+ * metric.  After creating an instance, call metricOption to get the result type.
+ */
+private[v1] abstract class MetricHelper[I,O](
+    rawMetrics: Seq[InternalTaskMetrics],
+    quantiles: Array[Double]) {
+
+  def getSubmetrics(raw: InternalTaskMetrics): Option[I]
+
+  def build: O
+
+  val data: Seq[I] = rawMetrics.flatMap(getSubmetrics)
+
+  /** applies the given function to all input metrics, and returns the quantiles */
+  def submetricQuantiles(f: I => Double): IndexedSeq[Double] = {
+    Distribution(data.map { d => f(d) }).get.getQuantiles(quantiles)
+  }
+
+  def metricOption: Option[O] = {
+    if (data.isEmpty) {
+      None
+    } else {
+      Some(build)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
new file mode 100644
index 0000000000000..17b521f3e1d41
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/ApplicationListResource.scala
@@ -0,0 +1,94 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import java.util.{Arrays, Date, List => JList}
+import javax.ws.rs.{DefaultValue, GET, Produces, QueryParam}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.deploy.history.ApplicationHistoryInfo
+import org.apache.spark.deploy.master.{ApplicationInfo => InternalApplicationInfo}
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class ApplicationListResource(uiRoot: UIRoot) {
+
+  @GET
+  def appList(
+      @QueryParam("status") status: JList[ApplicationStatus],
+      @DefaultValue("2010-01-01") @QueryParam("minDate") minDate: SimpleDateParam,
+      @DefaultValue("3000-01-01") @QueryParam("maxDate") maxDate: SimpleDateParam)
+  : Iterator[ApplicationInfo] = {
+    val allApps = uiRoot.getApplicationInfoList
+    val adjStatus = {
+      if (status.isEmpty) {
+        Arrays.asList(ApplicationStatus.values(): _*)
+      } else {
+        status
+      }
+    }
+    val includeCompleted = adjStatus.contains(ApplicationStatus.COMPLETED)
+    val includeRunning = adjStatus.contains(ApplicationStatus.RUNNING)
+    allApps.filter { app =>
+      val anyRunning = app.attempts.exists(!_.completed)
+      // if any attempt is still running, we consider the app to also still be running
+      val statusOk = (!anyRunning && includeCompleted) ||
+        (anyRunning && includeRunning)
+      // keep the app if *any* attempts fall in the right time window
+      val dateOk = app.attempts.exists { attempt =>
+        attempt.startTime.getTime >= minDate.timestamp &&
+          attempt.startTime.getTime <= maxDate.timestamp
+      }
+      statusOk && dateOk
+    }
+  }
+}
+
+private[spark] object ApplicationsListResource {
+  def appHistoryInfoToPublicAppInfo(app: ApplicationHistoryInfo): ApplicationInfo = {
+    new ApplicationInfo(
+      id = app.id,
+      name = app.name,
+      attempts = app.attempts.map { internalAttemptInfo =>
+        new ApplicationAttemptInfo(
+          attemptId = internalAttemptInfo.attemptId,
+          startTime = new Date(internalAttemptInfo.startTime),
+          endTime = new Date(internalAttemptInfo.endTime),
+          sparkUser = internalAttemptInfo.sparkUser,
+          completed = internalAttemptInfo.completed
+        )
+      }
+    )
+  }
+
+  def convertApplicationInfo(
+      internal: InternalApplicationInfo,
+      completed: Boolean): ApplicationInfo = {
+    // standalone application info always has just one attempt
+    new ApplicationInfo(
+      id = internal.id,
+      name = internal.desc.name,
+      attempts = Seq(new ApplicationAttemptInfo(
+        attemptId = None,
+        startTime = new Date(internal.startTime),
+        endTime = new Date(internal.endTime),
+        sparkUser = internal.desc.user,
+        completed = completed
+      ))
+    )
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
new file mode 100644
index 0000000000000..8ad4656b4dada
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/ExecutorListResource.scala
@@ -0,0 +1,36 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs.{GET, PathParam, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.exec.ExecutorsPage
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class ExecutorListResource(ui: SparkUI) {
+
+  @GET
+  def executorList(): Seq[ExecutorSummary] = {
+    val listener = ui.executorsListener
+    val storageStatusList = listener.storageStatusList
+    (0 until storageStatusList.size).map { statusId =>
+      ExecutorsPage.getExecInfo(listener, statusId)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala b/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
new file mode 100644
index 0000000000000..202a5191ad57d
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/JacksonMessageWriter.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import java.io.OutputStream
+import java.lang.annotation.Annotation
+import java.lang.reflect.Type
+import java.text.SimpleDateFormat
+import java.util.{Calendar, SimpleTimeZone}
+import javax.ws.rs.Produces
+import javax.ws.rs.core.{MediaType, MultivaluedMap}
+import javax.ws.rs.ext.{MessageBodyWriter, Provider}
+
+import com.fasterxml.jackson.annotation.JsonInclude
+import com.fasterxml.jackson.databind.{ObjectMapper, SerializationFeature}
+
+/**
+ * This class converts the POJO metric responses into json, using jackson.
+ *
+ * This doesn't follow the standard jersey-jackson plugin options, because we want to stick
+ * with an old version of jersey (since we have it from yarn anyway) and don't want to pull in lots
+ * of dependencies from a new plugin.
+ *
+ * Note that jersey automatically discovers this class based on its package and its annotations.
+ */
+@Provider
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class JacksonMessageWriter extends MessageBodyWriter[Object]{
+
+  val mapper = new ObjectMapper() {
+    override def writeValueAsString(t: Any): String = {
+      super.writeValueAsString(t)
+    }
+  }
+  mapper.registerModule(com.fasterxml.jackson.module.scala.DefaultScalaModule)
+  mapper.enable(SerializationFeature.INDENT_OUTPUT)
+  mapper.setSerializationInclusion(JsonInclude.Include.NON_NULL)
+  mapper.setDateFormat(JacksonMessageWriter.makeISODateFormat)
+
+  override def isWriteable(
+      aClass: Class[_],
+      `type`: Type,
+      annotations: Array[Annotation],
+      mediaType: MediaType): Boolean = {
+      true
+  }
+
+  override def writeTo(
+      t: Object,
+      aClass: Class[_],
+      `type`: Type,
+      annotations: Array[Annotation],
+      mediaType: MediaType,
+      multivaluedMap: MultivaluedMap[String, AnyRef],
+      outputStream: OutputStream): Unit = {
+    t match {
+      case ErrorWrapper(err) => outputStream.write(err.getBytes("utf-8"))
+      case _ => mapper.writeValue(outputStream, t)
+    }
+  }
+
+  override def getSize(
+      t: Object,
+      aClass: Class[_],
+      `type`: Type,
+      annotations: Array[Annotation],
+      mediaType: MediaType): Long = {
+    -1L
+  }
+}
+
+private[spark] object JacksonMessageWriter {
+  def makeISODateFormat: SimpleDateFormat = {
+    val iso8601 = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSS'GMT'")
+    val cal = Calendar.getInstance(new SimpleTimeZone(0, "GMT"))
+    iso8601.setCalendar(cal)
+    iso8601
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
new file mode 100644
index 0000000000000..c3ec45f54681b
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/JsonRootResource.scala
@@ -0,0 +1,255 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.servlet.ServletContext
+import javax.ws.rs._
+import javax.ws.rs.core.{Context, Response}
+
+import com.sun.jersey.api.core.ResourceConfig
+import com.sun.jersey.spi.container.servlet.ServletContainer
+import org.eclipse.jetty.server.handler.ContextHandler
+import org.eclipse.jetty.servlet.{ServletContextHandler, ServletHolder}
+
+import org.apache.spark.SecurityManager
+import org.apache.spark.ui.SparkUI
+
+/**
+ * Main entry point for serving spark application metrics as json, using JAX-RS.
+ *
+ * Each resource should have endpoints that return **public** classes defined in api.scala.  Mima
+ * binary compatibility checks ensure that we don't inadvertently make changes that break the api.
+ * The returned objects are automatically converted to json by jackson with JacksonMessageWriter.
+ * In addition, there are a number of tests in HistoryServerSuite that compare the json to "golden
+ * files".  Any changes and additions should be reflected there as well -- see the notes in
+ * HistoryServerSuite.
+ */
+@Path("/v1")
+private[v1] class JsonRootResource extends UIRootFromServletContext {
+
+  @Path("applications")
+  def getApplicationList(): ApplicationListResource = {
+    new ApplicationListResource(uiRoot)
+  }
+
+  @Path("applications/{appId}")
+  def getApplication(): OneApplicationResource = {
+    new OneApplicationResource(uiRoot)
+  }
+
+  @Path("applications/{appId}/{attemptId}/jobs")
+  def getJobs(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): AllJobsResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new AllJobsResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/jobs")
+  def getJobs(@PathParam("appId") appId: String): AllJobsResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new AllJobsResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/jobs/{jobId: \\d+}")
+  def getJob(@PathParam("appId") appId: String): OneJobResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new OneJobResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/jobs/{jobId: \\d+}")
+  def getJob(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): OneJobResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new OneJobResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/executors")
+  def getExecutors(@PathParam("appId") appId: String): ExecutorListResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new ExecutorListResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/executors")
+  def getExecutors(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): ExecutorListResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new ExecutorListResource(ui)
+    }
+  }
+
+
+  @Path("applications/{appId}/stages")
+  def getStages(@PathParam("appId") appId: String): AllStagesResource= {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new AllStagesResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/stages")
+  def getStages(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): AllStagesResource= {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new AllStagesResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/stages/{stageId: \\d+}")
+  def getStage(@PathParam("appId") appId: String): OneStageResource= {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new OneStageResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/stages/{stageId: \\d+}")
+  def getStage(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): OneStageResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new OneStageResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/storage/rdd")
+  def getRdds(@PathParam("appId") appId: String): AllRDDResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new AllRDDResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/storage/rdd")
+  def getRdds(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): AllRDDResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new AllRDDResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/storage/rdd/{rddId: \\d+}")
+  def getRdd(@PathParam("appId") appId: String): OneRDDResource = {
+    uiRoot.withSparkUI(appId, None) { ui =>
+      new OneRDDResource(ui)
+    }
+  }
+
+  @Path("applications/{appId}/{attemptId}/storage/rdd/{rddId: \\d+}")
+  def getRdd(
+      @PathParam("appId") appId: String,
+      @PathParam("attemptId") attemptId: String): OneRDDResource = {
+    uiRoot.withSparkUI(appId, Some(attemptId)) { ui =>
+      new OneRDDResource(ui)
+    }
+  }
+
+}
+
+private[spark] object JsonRootResource {
+
+  def getJsonServlet(uiRoot: UIRoot): ServletContextHandler = {
+    val jerseyContext = new ServletContextHandler(ServletContextHandler.NO_SESSIONS)
+    jerseyContext.setContextPath("/json")
+    val holder:ServletHolder = new ServletHolder(classOf[ServletContainer])
+    holder.setInitParameter("com.sun.jersey.config.property.resourceConfigClass",
+      "com.sun.jersey.api.core.PackagesResourceConfig")
+    holder.setInitParameter("com.sun.jersey.config.property.packages",
+      "org.apache.spark.status.api.v1")
+    holder.setInitParameter(ResourceConfig.PROPERTY_CONTAINER_REQUEST_FILTERS,
+      classOf[SecurityFilter].getCanonicalName)
+    UIRootFromServletContext.setUiRoot(jerseyContext, uiRoot)
+    jerseyContext.addServlet(holder, "/*")
+    jerseyContext
+  }
+}
+
+/**
+ * This trait is shared by the all the root containers for application UI information --
+ * the HistoryServer, the Master UI, and the application UI.  This provides the common
+ * interface needed for them all to expose application info as json.
+ */
+private[spark] trait UIRoot {
+  def getSparkUI(appKey: String): Option[SparkUI]
+  def getApplicationInfoList: Iterator[ApplicationInfo]
+
+  /**
+   * Get the spark UI with the given appID, and apply a function
+   * to it.  If there is no such app, throw an appropriate exception
+   */
+  def withSparkUI[T](appId: String, attemptId: Option[String])(f: SparkUI => T): T = {
+    val appKey = attemptId.map(appId + "/" + _).getOrElse(appId)
+    getSparkUI(appKey) match {
+      case Some(ui) =>
+        f(ui)
+      case None => throw new NotFoundException("no such app: " + appId)
+    }
+  }
+  def securityManager: SecurityManager
+}
+
+private[v1] object UIRootFromServletContext {
+
+  private val attribute = getClass.getCanonicalName
+
+  def setUiRoot(contextHandler: ContextHandler, uiRoot: UIRoot): Unit = {
+    contextHandler.setAttribute(attribute, uiRoot)
+  }
+
+  def getUiRoot(context: ServletContext): UIRoot = {
+    context.getAttribute(attribute).asInstanceOf[UIRoot]
+  }
+}
+
+private[v1] trait UIRootFromServletContext {
+  @Context
+  var servletContext: ServletContext = _
+
+  def uiRoot: UIRoot = UIRootFromServletContext.getUiRoot(servletContext)
+}
+
+private[v1] class NotFoundException(msg: String) extends WebApplicationException(
+  new NoSuchElementException(msg),
+    Response
+      .status(Response.Status.NOT_FOUND)
+      .entity(ErrorWrapper(msg))
+      .build()
+)
+
+private[v1] class BadParameterException(msg: String) extends WebApplicationException(
+  new IllegalArgumentException(msg),
+  Response
+    .status(Response.Status.BAD_REQUEST)
+    .entity(ErrorWrapper(msg))
+    .build()
+) {
+  def this(param: String, exp: String, actual: String) = {
+    this(raw"""Bad value for parameter "$param".  Expected a $exp, got "$actual"""")
+  }
+}
+
+/**
+ * Signal to JacksonMessageWriter to not convert the message into json (which would result in an
+ * extra set of quotes).
+ */
+private[v1] case class ErrorWrapper(s: String)
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
new file mode 100644
index 0000000000000..b5ef72649e295
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneApplicationResource.scala
@@ -0,0 +1,31 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs.core.MediaType
+import javax.ws.rs.{Produces, PathParam, GET}
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneApplicationResource(uiRoot: UIRoot) {
+
+  @GET
+  def getApp(@PathParam("appId") appId: String): ApplicationInfo = {
+    val apps = uiRoot.getApplicationInfoList.find { _.id == appId }
+    apps.getOrElse(throw new NotFoundException("unknown app: " + appId))
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
new file mode 100644
index 0000000000000..6d8a60d480aed
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneJobResource.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs.{PathParam, GET, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.JobExecutionStatus
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.UIData.JobUIData
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneJobResource(ui: SparkUI) {
+
+  @GET
+  def oneJob(@PathParam("jobId") jobId: Int): JobData = {
+    val statusToJobs: Seq[(JobExecutionStatus, Seq[JobUIData])] =
+      AllJobsResource.getStatusToJobs(ui)
+    val jobOpt = statusToJobs.map {_._2} .flatten.find { jobInfo => jobInfo.jobId == jobId}
+    jobOpt.map { job =>
+      AllJobsResource.convertJobData(job, ui.jobProgressListener, false)
+    }.getOrElse {
+      throw new NotFoundException("unknown job: " + jobId)
+    }
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
new file mode 100644
index 0000000000000..07b224fac4786
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneRDDResource.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs.{PathParam, GET, Produces}
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.ui.SparkUI
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneRDDResource(ui: SparkUI) {
+
+  @GET
+  def rddData(@PathParam("rddId") rddId: Int): RDDStorageInfo  = {
+    AllRDDResource.getRDDStorageInfo(rddId, ui.storageListener, true).getOrElse(
+      throw new NotFoundException(s"no rdd found w/ id $rddId")
+    )
+  }
+
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
new file mode 100644
index 0000000000000..fd24aea63a8a1
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/OneStageResource.scala
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs._
+import javax.ws.rs.core.MediaType
+
+import org.apache.spark.SparkException
+import org.apache.spark.scheduler.StageInfo
+import org.apache.spark.status.api.v1.StageStatus._
+import org.apache.spark.status.api.v1.TaskSorting._
+import org.apache.spark.ui.SparkUI
+import org.apache.spark.ui.jobs.JobProgressListener
+import org.apache.spark.ui.jobs.UIData.StageUIData
+
+@Produces(Array(MediaType.APPLICATION_JSON))
+private[v1] class OneStageResource(ui: SparkUI) {
+
+  @GET
+  @Path("")
+  def stageData(@PathParam("stageId") stageId: Int): Seq[StageData] = {
+    withStage(stageId){ stageAttempts =>
+      stageAttempts.map { stage =>
+        AllStagesResource.stageUiToStageData(stage.status, stage.info, stage.ui,
+          includeDetails = true)
+      }
+    }
+  }
+
+  @GET
+  @Path("/{stageAttemptId: \\d+}")
+  def oneAttemptData(
+      @PathParam("stageId") stageId: Int,
+      @PathParam("stageAttemptId") stageAttemptId: Int): StageData = {
+    withStageAttempt(stageId, stageAttemptId) { stage =>
+      AllStagesResource.stageUiToStageData(stage.status, stage.info, stage.ui,
+        includeDetails = true)
+    }
+  }
+
+  @GET
+  @Path("/{stageAttemptId: \\d+}/taskSummary")
+  def taskSummary(
+      @PathParam("stageId") stageId: Int,
+      @PathParam("stageAttemptId") stageAttemptId: Int,
+      @DefaultValue("0.05,0.25,0.5,0.75,0.95") @QueryParam("quantiles") quantileString: String)
+  : TaskMetricDistributions = {
+    withStageAttempt(stageId, stageAttemptId) { stage =>
+      val quantiles = quantileString.split(",").map { s =>
+        try {
+          s.toDouble
+        } catch {
+          case nfe: NumberFormatException =>
+            throw new BadParameterException("quantiles", "double", s)
+        }
+      }
+      AllStagesResource.taskMetricDistributions(stage.ui.taskData.values, quantiles)
+    }
+  }
+
+  @GET
+  @Path("/{stageAttemptId: \\d+}/taskList")
+  def taskList(
+      @PathParam("stageId") stageId: Int,
+      @PathParam("stageAttemptId") stageAttemptId: Int,
+      @DefaultValue("0") @QueryParam("offset") offset: Int,
+      @DefaultValue("20") @QueryParam("length") length: Int,
+      @DefaultValue("ID") @QueryParam("sortBy") sortBy: TaskSorting): Seq[TaskData] = {
+    withStageAttempt(stageId, stageAttemptId) { stage =>
+      val tasks = stage.ui.taskData.values.map{AllStagesResource.convertTaskData}.toIndexedSeq
+        .sorted(OneStageResource.ordering(sortBy))
+      tasks.slice(offset, offset + length)  
+    }
+  }
+
+  private case class StageStatusInfoUi(status: StageStatus, info: StageInfo, ui: StageUIData)
+
+  private def withStage[T](stageId: Int)(f: Seq[StageStatusInfoUi] => T): T = {
+    val stageAttempts = findStageStatusUIData(ui.jobProgressListener, stageId)
+    if (stageAttempts.isEmpty) {
+      throw new NotFoundException("unknown stage: " + stageId)
+    } else {
+      f(stageAttempts)
+    }
+  }
+
+  private def findStageStatusUIData(
+      listener: JobProgressListener,
+      stageId: Int): Seq[StageStatusInfoUi] = {
+    listener.synchronized {
+      def getStatusInfoUi(status: StageStatus, infos: Seq[StageInfo]): Seq[StageStatusInfoUi] = {
+        infos.filter { _.stageId == stageId }.map { info =>
+          val ui = listener.stageIdToData.getOrElse((info.stageId, info.attemptId),
+            // this is an internal error -- we should always have uiData
+            throw new SparkException(
+              s"no stage ui data found for stage: ${info.stageId}:${info.attemptId}")
+          )
+          StageStatusInfoUi(status, info, ui)
+        }
+      }
+      getStatusInfoUi(ACTIVE, listener.activeStages.values.toSeq) ++
+        getStatusInfoUi(COMPLETE, listener.completedStages) ++
+        getStatusInfoUi(FAILED, listener.failedStages) ++
+        getStatusInfoUi(PENDING, listener.pendingStages.values.toSeq)
+    }
+  }
+
+  private def withStageAttempt[T](
+      stageId: Int,
+      stageAttemptId: Int)
+      (f: StageStatusInfoUi => T): T = {
+    withStage(stageId) { attempts =>
+        val oneAttempt = attempts.find { stage => stage.info.attemptId == stageAttemptId }
+        oneAttempt match {
+          case Some(stage) =>
+            f(stage)
+          case None =>
+            val stageAttempts = attempts.map { _.info.attemptId }
+            throw new NotFoundException(s"unknown attempt for stage $stageId.  " +
+              s"Found attempts: ${stageAttempts.mkString("[", ",", "]")}")
+        }
+    }
+  }
+}
+
+object OneStageResource {
+  def ordering(taskSorting: TaskSorting): Ordering[TaskData] = {
+    val extractor: (TaskData => Long) = td =>
+      taskSorting match {
+        case ID => td.taskId
+        case INCREASING_RUNTIME => td.taskMetrics.map{_.executorRunTime}.getOrElse(-1L)
+        case DECREASING_RUNTIME => -td.taskMetrics.map{_.executorRunTime}.getOrElse(-1L)
+      }
+    Ordering.by(extractor)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
new file mode 100644
index 0000000000000..95fbd96ade5ab
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/SecurityFilter.scala
@@ -0,0 +1,38 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import javax.ws.rs.WebApplicationException
+import javax.ws.rs.core.Response
+
+import com.sun.jersey.spi.container.{ContainerRequest, ContainerRequestFilter}
+
+private[v1] class SecurityFilter extends ContainerRequestFilter with UIRootFromServletContext {
+  def filter(req: ContainerRequest): ContainerRequest = {
+    val user = Option(req.getUserPrincipal).map { _.getName }.orNull
+    if (uiRoot.securityManager.checkUIViewPermissions(user)) {
+      req
+    } else {
+      throw new WebApplicationException(
+        Response
+          .status(Response.Status.FORBIDDEN)
+          .entity(raw"""user "$user"is not authorized""")
+          .build()
+      )
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
new file mode 100644
index 0000000000000..cee29786c3019
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/SimpleDateParam.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import java.text.SimpleDateFormat
+import java.util.TimeZone
+import javax.ws.rs.WebApplicationException
+import javax.ws.rs.core.Response
+import javax.ws.rs.core.Response.Status
+
+import scala.util.Try
+
+private[v1] class SimpleDateParam(val originalValue: String) {
+  val timestamp: Long = {
+    SimpleDateParam.formats.collectFirst {
+      case fmt if Try(fmt.parse(originalValue)).isSuccess =>
+        fmt.parse(originalValue).getTime()
+    }.getOrElse(
+      throw new WebApplicationException(
+        Response
+          .status(Status.BAD_REQUEST)
+          .entity("Couldn't parse date: " + originalValue)
+          .build()
+      )
+    )
+  }
+}
+
+private[v1] object SimpleDateParam {
+
+  val formats: Seq[SimpleDateFormat] = {
+
+    val gmtDay = new SimpleDateFormat("yyyy-MM-dd")
+    gmtDay.setTimeZone(TimeZone.getTimeZone("GMT"))
+
+    Seq(
+      new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSz"),
+      gmtDay
+    )
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/status/api/v1/api.scala b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala
new file mode 100644
index 0000000000000..ef3c8570d8186
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/status/api/v1/api.scala
@@ -0,0 +1,228 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import java.util.Date
+
+import scala.collection.Map
+
+import org.apache.spark.JobExecutionStatus
+
+class ApplicationInfo private[spark](
+    val id: String,
+    val name: String,
+    val attempts: Seq[ApplicationAttemptInfo])
+
+class ApplicationAttemptInfo private[spark](
+    val attemptId: Option[String],
+    val startTime: Date,
+    val endTime: Date,
+    val sparkUser: String,
+    val completed: Boolean = false)
+
+class ExecutorStageSummary private[spark](
+    val taskTime : Long,
+    val failedTasks : Int,
+    val succeededTasks : Int,
+    val inputBytes : Long,
+    val outputBytes : Long,
+    val shuffleRead : Long,
+    val shuffleWrite : Long,
+    val memoryBytesSpilled : Long,
+    val diskBytesSpilled : Long)
+
+class ExecutorSummary private[spark](
+    val id: String,
+    val hostPort: String,
+    val rddBlocks: Int,
+    val memoryUsed: Long,
+    val diskUsed: Long,
+    val activeTasks: Int,
+    val failedTasks: Int,
+    val completedTasks: Int,
+    val totalTasks: Int,
+    val totalDuration: Long,
+    val totalInputBytes: Long,
+    val totalShuffleRead: Long,
+    val totalShuffleWrite: Long,
+    val maxMemory: Long,
+    val executorLogs: Map[String, String])
+
+class JobData private[spark](
+    val jobId: Int,
+    val name: String,
+    val description: Option[String],
+    val submissionTime: Option[Date],
+    val completionTime: Option[Date],
+    val stageIds: Seq[Int],
+    val jobGroup: Option[String],
+    val status: JobExecutionStatus,
+    val numTasks: Int,
+    val numActiveTasks: Int,
+    val numCompletedTasks: Int,
+    val numSkippedTasks: Int,
+    val numFailedTasks: Int,
+    val numActiveStages: Int,
+    val numCompletedStages: Int,
+    val numSkippedStages: Int,
+    val numFailedStages: Int)
+
+// Q: should Tachyon size go in here as well?  currently the UI only shows it on the overall storage
+// page ... does anybody pay attention to it?
+class RDDStorageInfo private[spark](
+    val id: Int,
+    val name: String,
+    val numPartitions: Int,
+    val numCachedPartitions: Int,
+    val storageLevel: String,
+    val memoryUsed: Long,
+    val diskUsed: Long,
+    val dataDistribution: Option[Seq[RDDDataDistribution]],
+    val partitions: Option[Seq[RDDPartitionInfo]])
+
+class RDDDataDistribution private[spark](
+    val address: String,
+    val memoryUsed: Long,
+    val memoryRemaining: Long,
+    val diskUsed: Long)
+
+class RDDPartitionInfo private[spark](
+    val blockName: String,
+    val storageLevel: String,
+    val memoryUsed: Long,
+    val diskUsed: Long,
+    val executors: Seq[String])
+
+class StageData private[spark](
+    val status: StageStatus,
+    val stageId: Int,
+    val attemptId: Int,
+    val numActiveTasks: Int ,
+    val numCompleteTasks: Int,
+    val numFailedTasks: Int,
+
+    val executorRunTime: Long,
+
+    val inputBytes: Long,
+    val inputRecords: Long,
+    val outputBytes: Long,
+    val outputRecords: Long,
+    val shuffleReadBytes: Long,
+    val shuffleReadRecords: Long,
+    val shuffleWriteBytes: Long,
+    val shuffleWriteRecords: Long,
+    val memoryBytesSpilled: Long,
+    val diskBytesSpilled: Long,
+
+    val name: String,
+    val details: String,
+    val schedulingPool: String,
+
+    val accumulatorUpdates: Seq[AccumulableInfo],
+    val tasks: Option[Map[Long, TaskData]],
+    val executorSummary:Option[Map[String,ExecutorStageSummary]])
+
+class TaskData private[spark](
+    val taskId: Long,
+    val index: Int,
+    val attempt: Int,
+    val launchTime: Date,
+    val executorId: String,
+    val host: String,
+    val taskLocality: String,
+    val speculative: Boolean,
+    val accumulatorUpdates: Seq[AccumulableInfo],
+    val errorMessage: Option[String] = None,
+    val taskMetrics: Option[TaskMetrics] = None)
+
+class TaskMetrics private[spark](
+    val executorDeserializeTime: Long,
+    val executorRunTime: Long,
+    val resultSize: Long,
+    val jvmGcTime: Long,
+    val resultSerializationTime: Long,
+    val memoryBytesSpilled: Long,
+    val diskBytesSpilled: Long,
+    val inputMetrics: Option[InputMetrics],
+    val outputMetrics: Option[OutputMetrics],
+    val shuffleReadMetrics: Option[ShuffleReadMetrics],
+    val shuffleWriteMetrics: Option[ShuffleWriteMetrics])
+
+class InputMetrics private[spark](
+    val bytesRead: Long,
+    val recordsRead: Long)
+
+class OutputMetrics private[spark](
+    val bytesWritten: Long,
+    val recordsWritten: Long)
+
+class ShuffleReadMetrics private[spark](
+    val remoteBlocksFetched: Int,
+    val localBlocksFetched: Int,
+    val fetchWaitTime: Long,
+    val remoteBytesRead: Long,
+    val totalBlocksFetched: Int,
+    val recordsRead: Long)
+
+class ShuffleWriteMetrics private[spark](
+    val bytesWritten: Long,
+    val writeTime: Long,
+    val recordsWritten: Long)
+
+class TaskMetricDistributions private[spark](
+    val quantiles: IndexedSeq[Double],
+
+    val executorDeserializeTime: IndexedSeq[Double],
+    val executorRunTime: IndexedSeq[Double],
+    val resultSize: IndexedSeq[Double],
+    val jvmGcTime: IndexedSeq[Double],
+    val resultSerializationTime: IndexedSeq[Double],
+    val memoryBytesSpilled: IndexedSeq[Double],
+    val diskBytesSpilled: IndexedSeq[Double],
+
+    val inputMetrics: Option[InputMetricDistributions],
+    val outputMetrics: Option[OutputMetricDistributions],
+    val shuffleReadMetrics: Option[ShuffleReadMetricDistributions],
+    val shuffleWriteMetrics: Option[ShuffleWriteMetricDistributions])
+
+class InputMetricDistributions private[spark](
+    val bytesRead: IndexedSeq[Double],
+    val recordsRead: IndexedSeq[Double])
+
+class OutputMetricDistributions private[spark](
+    val bytesWritten: IndexedSeq[Double],
+    val recordsWritten: IndexedSeq[Double])
+
+class ShuffleReadMetricDistributions private[spark](
+    val readBytes: IndexedSeq[Double],
+    val readRecords: IndexedSeq[Double],
+    val remoteBlocksFetched: IndexedSeq[Double],
+    val localBlocksFetched: IndexedSeq[Double],
+    val fetchWaitTime: IndexedSeq[Double],
+    val remoteBytesRead: IndexedSeq[Double],
+    val totalBlocksFetched: IndexedSeq[Double])
+
+class ShuffleWriteMetricDistributions private[spark](
+    val writeBytes: IndexedSeq[Double],
+    val writeRecords: IndexedSeq[Double],
+    val writeTime: IndexedSeq[Double])
+
+class AccumulableInfo private[spark](
+    val id: Long,
+    val name: String,
+    val update: Option[String],
+    val value: String)
diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala
index ad53a3edc7cc1..96062626b5045 100644
--- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala
+++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.storage
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{RDDOperationScope, RDD}
 import org.apache.spark.util.Utils
 
 @DeveloperApi
@@ -26,7 +26,9 @@ class RDDInfo(
     val id: Int,
     val name: String,
     val numPartitions: Int,
-    var storageLevel: StorageLevel)
+    var storageLevel: StorageLevel,
+    val parentIds: Seq[Int],
+    val scope: Option[RDDOperationScope] = None)
   extends Ordered[RDDInfo] {
 
   var numCachedPartitions = 0
@@ -52,7 +54,8 @@ class RDDInfo(
 
 private[spark] object RDDInfo {
   def fromRdd(rdd: RDD[_]): RDDInfo = {
-    val rddName = Option(rdd.name).getOrElse(rdd.id.toString)
-    new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel)
+    val rddName = Option(rdd.name).getOrElse(Utils.getFormattedClassName(rdd))
+    val parentIds = rdd.dependencies.map(_.rdd.id)
+    new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel, parentIds, rdd.scope)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
index 7d75929b96f75..ec711480ebf30 100644
--- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
+++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala
@@ -25,13 +25,17 @@ import org.apache.spark.scheduler._
 /**
  * :: DeveloperApi ::
  * A SparkListener that maintains executor storage status.
+ *
+ * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
 class StorageStatusListener extends SparkListener {
   // This maintains only blocks that are cached (i.e. storage level is not StorageLevel.NONE)
   private[storage] val executorIdToStorageStatus = mutable.Map[String, StorageStatus]()
 
-  def storageStatusList: Seq[StorageStatus] = executorIdToStorageStatus.values.toSeq
+  def storageStatusList: Seq[StorageStatus] = synchronized {
+    executorIdToStorageStatus.values.toSeq
+  }
 
   /** Update storage status list to reflect updated block statuses */
   private def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) {
diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
index 06fce86bd38d2..bfe4a180e8a6f 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.ui
 
+import java.util.Date
+
+import org.apache.spark.status.api.v1.{ApplicationAttemptInfo, ApplicationInfo, JsonRootResource, UIRoot}
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext}
 import org.apache.spark.scheduler._
 import org.apache.spark.storage.StorageStatusListener
@@ -25,6 +28,7 @@ import org.apache.spark.ui.env.{EnvironmentListener, EnvironmentTab}
 import org.apache.spark.ui.exec.{ExecutorsListener, ExecutorsTab}
 import org.apache.spark.ui.jobs.{JobsTab, JobProgressListener, StagesTab}
 import org.apache.spark.ui.storage.{StorageListener, StorageTab}
+import org.apache.spark.ui.scope.RDDOperationGraphListener
 
 /**
  * Top level user interface for a Spark application.
@@ -32,29 +36,35 @@ import org.apache.spark.ui.storage.{StorageListener, StorageTab}
 private[spark] class SparkUI private (
     val sc: Option[SparkContext],
     val conf: SparkConf,
-    val securityManager: SecurityManager,
+    securityManager: SecurityManager,
     val environmentListener: EnvironmentListener,
     val storageStatusListener: StorageStatusListener,
     val executorsListener: ExecutorsListener,
     val jobProgressListener: JobProgressListener,
     val storageListener: StorageListener,
+    val operationGraphListener: RDDOperationGraphListener,
     var appName: String,
-    val basePath: String)
+    val basePath: String,
+    val startTime: Long)
   extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI")
-  with Logging {
+  with Logging
+  with UIRoot {
 
   val killEnabled = sc.map(_.conf.getBoolean("spark.ui.killEnabled", true)).getOrElse(false)
 
+
+  val stagesTab = new StagesTab(this)
+
   /** Initialize all components of the server. */
   def initialize() {
     attachTab(new JobsTab(this))
-    val stagesTab = new StagesTab(this)
     attachTab(stagesTab)
     attachTab(new StorageTab(this))
     attachTab(new EnvironmentTab(this))
     attachTab(new ExecutorsTab(this))
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
     attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath))
+    attachHandler(JsonRootResource.getJsonServlet(this))
     // This should be POST only, but, the YARN AM proxy won't proxy POSTs
     attachHandler(createRedirectHandler(
       "/stages/stage/kill", "/stages", stagesTab.handleKillRequest,
@@ -81,6 +91,24 @@ private[spark] class SparkUI private (
   private[spark] def appUIHostPort = publicHostName + ":" + boundPort
 
   private[spark] def appUIAddress = s"http://$appUIHostPort"
+
+  def getSparkUI(appId: String): Option[SparkUI] = {
+    if (appId == appName) Some(this) else None
+  }
+
+  def getApplicationInfoList: Iterator[ApplicationInfo] = {
+    Iterator(new ApplicationInfo(
+      id = appName,
+      name = appName,
+      attempts = Seq(new ApplicationAttemptInfo(
+        attemptId = None,
+        startTime = new Date(startTime),
+        endTime = new Date(-1),
+        sparkUser = "",
+        completed = false
+      ))
+    ))
+  }
 }
 
 private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String)
@@ -93,6 +121,9 @@ private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String)
 private[spark] object SparkUI {
   val DEFAULT_PORT = 4040
   val STATIC_RESOURCE_DIR = "org/apache/spark/ui/static"
+  val DEFAULT_POOL_NAME = "default"
+  val DEFAULT_RETAINED_STAGES = 1000
+  val DEFAULT_RETAINED_JOBS = 1000
 
   def getUIPort(conf: SparkConf): Int = {
     conf.getInt("spark.ui.port", SparkUI.DEFAULT_PORT)
@@ -104,9 +135,10 @@ private[spark] object SparkUI {
       listenerBus: SparkListenerBus,
       jobProgressListener: JobProgressListener,
       securityManager: SecurityManager,
-      appName: String): SparkUI =  {
+      appName: String,
+      startTime: Long): SparkUI =  {
     create(Some(sc), conf, listenerBus, securityManager, appName,
-      jobProgressListener = Some(jobProgressListener))
+      jobProgressListener = Some(jobProgressListener), startTime = startTime)
   }
 
   def createHistoryUI(
@@ -114,8 +146,9 @@ private[spark] object SparkUI {
       listenerBus: SparkListenerBus,
       securityManager: SecurityManager,
       appName: String,
-      basePath: String): SparkUI = {
-    create(None, conf, listenerBus, securityManager, appName, basePath)
+      basePath: String,
+      startTime: Long): SparkUI = {
+    create(None, conf, listenerBus, securityManager, appName, basePath, startTime = startTime)
   }
 
   /**
@@ -132,7 +165,8 @@ private[spark] object SparkUI {
       securityManager: SecurityManager,
       appName: String,
       basePath: String = "",
-      jobProgressListener: Option[JobProgressListener] = None): SparkUI = {
+      jobProgressListener: Option[JobProgressListener] = None,
+      startTime: Long): SparkUI = {
 
     val _jobProgressListener: JobProgressListener = jobProgressListener.getOrElse {
       val listener = new JobProgressListener(conf)
@@ -144,13 +178,16 @@ private[spark] object SparkUI {
     val storageStatusListener = new StorageStatusListener
     val executorsListener = new ExecutorsListener(storageStatusListener)
     val storageListener = new StorageListener(storageStatusListener)
+    val operationGraphListener = new RDDOperationGraphListener(conf)
 
     listenerBus.addListener(environmentListener)
     listenerBus.addListener(storageStatusListener)
     listenerBus.addListener(executorsListener)
     listenerBus.addListener(storageListener)
+    listenerBus.addListener(operationGraphListener)
 
     new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener,
-      executorsListener, _jobProgressListener, storageListener, appName, basePath)
+      executorsListener, _jobProgressListener, storageListener, operationGraphListener,
+      appName, basePath, startTime)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 395af2ea30b9d..2f3fb181e4026 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -23,6 +23,7 @@ import java.util.{Locale, Date}
 import scala.xml.{Node, Text}
 
 import org.apache.spark.Logging
+import org.apache.spark.ui.scope.RDDOperationGraph
 
 /** Utility functions for generating XML pages with spark content. */
 private[spark] object UIUtils extends Logging {
@@ -172,13 +173,21 @@ private[spark] object UIUtils extends Logging {
     <script src={prependBaseUri("/static/timeline-view.js")}></script>
   }
 
+  def vizHeaderNodes: Seq[Node] = {
+    <script src={prependBaseUri("/static/d3.min.js")}></script>
+    <script src={prependBaseUri("/static/dagre-d3.min.js")}></script>
+    <script src={prependBaseUri("/static/graphlib-dot.min.js")}></script>
+    <script src={prependBaseUri("/static/spark-dag-viz.js")}></script>
+  }
+
   /** Returns a spark page with correctly formatted headers */
   def headerSparkPage(
       title: String,
       content: => Seq[Node],
       activeTab: SparkUITab,
       refreshInterval: Option[Int] = None,
-      helpText: Option[String] = None): Seq[Node] = {
+      helpText: Option[String] = None,
+      showVisualization: Boolean = false): Seq[Node] = {
 
     val appName = activeTab.appName
     val shortAppName = if (appName.length < 36) appName else appName.take(32) + "..."
@@ -196,6 +205,7 @@ private[spark] object UIUtils extends Logging {
     <html>
       <head>
         {commonHeaderNodes}
+        {if (showVisualization) vizHeaderNodes else Seq.empty}
         <title>{appName} - {title}</title>
       </head>
       <body>
@@ -320,4 +330,47 @@ private[spark] object UIUtils extends Logging {
       <div class="bar bar-running" style={startWidth}></div>
     </div>
   }
+
+  /** Return a "DAG visualization" DOM element that expands into a visualization for a stage. */
+  def showDagVizForStage(stageId: Int, graph: Option[RDDOperationGraph]): Seq[Node] = {
+    showDagViz(graph.toSeq, forJob = false)
+  }
+
+  /** Return a "DAG visualization" DOM element that expands into a visualization for a job. */
+  def showDagVizForJob(jobId: Int, graphs: Seq[RDDOperationGraph]): Seq[Node] = {
+    showDagViz(graphs, forJob = true)
+  }
+
+  /**
+   * Return a "DAG visualization" DOM element that expands into a visualization on the UI.
+   *
+   * This populates metadata necessary for generating the visualization on the front-end in
+   * a format that is expected by spark-dag-viz.js. Any changes in the format here must be
+   * reflected there.
+   */
+  private def showDagViz(graphs: Seq[RDDOperationGraph], forJob: Boolean): Seq[Node] = {
+    <div>
+      <span class="expand-dag-viz" onclick={s"toggleDagViz($forJob);"}>
+        <span class="expand-dag-viz-arrow arrow-closed"></span>
+        <strong>DAG visualization</strong>
+      </span>
+      <div id="dag-viz-graph"></div>
+      <div id="dag-viz-metadata">
+        {
+          graphs.map { g =>
+            <div class="stage-metadata" stageId={g.rootCluster.id} style="display:none">
+              <div class="dot-file">{RDDOperationGraph.makeDotFile(g, forJob)}</div>
+              { g.incomingEdges.map { e => <div class="incoming-edge">{e.fromId},{e.toId}</div> } }
+              { g.outgoingEdges.map { e => <div class="outgoing-edge">{e.fromId},{e.toId}</div> } }
+              {
+                g.rootCluster.getAllNodes.filter(_.cached).map { n =>
+                  <div class="cached-rdd">{n.id}</div>
+                }
+              }
+            </div>
+          }
+        }
+      </div>
+    </div>
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
index f9860d1a5ce76..384f2ad26e281 100644
--- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala
@@ -37,7 +37,7 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf}
  * pages. The use of tabs is optional, however; a WebUI may choose to include pages directly.
  */
 private[spark] abstract class WebUI(
-    securityManager: SecurityManager,
+    val securityManager: SecurityManager,
     port: Int,
     conf: SparkConf,
     basePath: String = "",
@@ -77,15 +77,9 @@ private[spark] abstract class WebUI(
     val pagePath = "/" + page.prefix
     val renderHandler = createServletHandler(pagePath,
       (request: HttpServletRequest) => page.render(request), securityManager, basePath)
-    val renderJsonHandler = createServletHandler(pagePath.stripSuffix("/") + "/json",
-      (request: HttpServletRequest) => page.renderJson(request), securityManager, basePath)
     attachHandler(renderHandler)
-    attachHandler(renderJsonHandler)
     pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())
       .append(renderHandler)
-    pageToHandlers.getOrElseUpdate(page, ArrayBuffer[ServletContextHandler]())
-      .append(renderJsonHandler)
-    
   }
 
   /** Attach a handler to this UI. */
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
index 956608d7c0cbe..b247e4cdc3bd4 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsPage.scala
@@ -22,11 +22,11 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
+import org.apache.spark.status.api.v1.ExecutorSummary
 import org.apache.spark.ui.{ToolTips, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
-/** Summary information about an executor to display in the UI. */
-// Needs to be private[ui] because of a false positive MiMa failure.
+// This isn't even used anymore -- but we need to keep it b/c of a MiMa false positive
 private[ui] case class ExecutorSummaryInfo(
     id: String,
     hostPort: String,
@@ -44,6 +44,7 @@ private[ui] case class ExecutorSummaryInfo(
     maxMemory: Long,
     executorLogs: Map[String, String])
 
+
 private[ui] class ExecutorsPage(
     parent: ExecutorsTab,
     threadDumpEnabled: Boolean)
@@ -55,7 +56,8 @@ private[ui] class ExecutorsPage(
     val maxMem = storageStatusList.map(_.maxMem).sum
     val memUsed = storageStatusList.map(_.memUsed).sum
     val diskUsed = storageStatusList.map(_.diskUsed).sum
-    val execInfo = for (statusId <- 0 until storageStatusList.size) yield getExecInfo(statusId)
+    val execInfo = for (statusId <- 0 until storageStatusList.size) yield
+      ExecutorsPage.getExecInfo(listener, statusId)
     val execInfoSorted = execInfo.sortBy(_.id)
     val logsExist = execInfo.filter(_.executorLogs.nonEmpty).nonEmpty
 
@@ -111,7 +113,7 @@ private[ui] class ExecutorsPage(
   }
 
   /** Render an HTML row representing an executor */
-  private def execRow(info: ExecutorSummaryInfo, logsExist: Boolean): Seq[Node] = {
+  private def execRow(info: ExecutorSummary, logsExist: Boolean): Seq[Node] = {
     val maximumMemory = info.maxMemory
     val memoryUsed = info.memoryUsed
     val diskUsed = info.diskUsed
@@ -170,8 +172,11 @@ private[ui] class ExecutorsPage(
     </tr>
   }
 
+}
+
+private[spark] object ExecutorsPage {
   /** Represent an executor's info as a map given a storage status index */
-  private def getExecInfo(statusId: Int): ExecutorSummaryInfo = {
+  def getExecInfo(listener: ExecutorsListener, statusId: Int): ExecutorSummary = {
     val status = listener.storageStatusList(statusId)
     val execId = status.blockManagerId.executorId
     val hostPort = status.blockManagerId.hostPort
@@ -189,7 +194,7 @@ private[ui] class ExecutorsPage(
     val totalShuffleWrite = listener.executorToShuffleWrite.getOrElse(execId, 0L)
     val executorLogs = listener.executorToLogUrls.getOrElse(execId, Map.empty)
 
-    new ExecutorSummaryInfo(
+    new ExecutorSummary(
       execId,
       hostPort,
       rddBlocks,
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
index a7ea12b1655fe..09323d1d80ad6 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala
@@ -179,7 +179,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
 
     <span class="expand-application-timeline">
       <span class="expand-application-timeline-arrow arrow-closed"></span>
-      <strong>Event Timeline</strong>
+      <strong>Event timeline</strong>
     </span> ++
     <div id="application-timeline" class="collapsed">
       <div class="control-panel">
@@ -271,6 +271,12 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
       val shouldShowCompletedJobs = completedJobs.nonEmpty
       val shouldShowFailedJobs = failedJobs.nonEmpty
 
+      val completedJobNumStr = if (completedJobs.size == listener.numCompletedJobs) {
+        s"${completedJobs.size}"
+      } else {
+        s"${listener.numCompletedJobs}, only showing ${completedJobs.size}"
+      }
+
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
@@ -295,9 +301,9 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
             }
             {
               if (shouldShowCompletedJobs) {
-                <li>
+                <li id="completed-summary">
                   <a href="#completed"><strong>Completed Jobs:</strong></a>
-                  {completedJobs.size}
+                  {completedJobNumStr}
                 </li>
               }
             }
@@ -305,7 +311,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
               if (shouldShowFailedJobs) {
                 <li>
                   <a href="#failed"><strong>Failed Jobs:</strong></a>
-                  {failedJobs.size}
+                  {listener.numFailedJobs}
                 </li>
               }
             }
@@ -322,7 +328,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
           activeJobsTable
       }
       if (shouldShowCompletedJobs) {
-        content ++= <h4 id="completed">Completed Jobs ({completedJobs.size})</h4> ++
+        content ++= <h4 id="completed">Completed Jobs ({completedJobNumStr})</h4> ++
           completedJobsTable
       }
       if (shouldShowFailedJobs) {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
index 527f960af2dfc..a37f739ab9c66 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllStagesPage.scala
@@ -27,7 +27,7 @@ import org.apache.spark.ui.{WebUIPage, UIUtils}
 /** Page showing list of all ongoing and recently finished stages and pools */
 private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
   private val sc = parent.sc
-  private val listener = parent.listener
+  private val listener = parent.progressListener
   private def isFairScheduler = parent.isFairScheduler
 
   def render(request: HttpServletRequest): Seq[Node] = {
@@ -42,18 +42,18 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
 
       val activeStagesTable =
         new StageTableBase(activeStages.sortBy(_.submissionTime).reverse,
-          parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler,
+          parent.basePath, parent.progressListener, isFairScheduler = parent.isFairScheduler,
           killEnabled = parent.killEnabled)
       val pendingStagesTable =
         new StageTableBase(pendingStages.sortBy(_.submissionTime).reverse,
-          parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler,
+          parent.basePath, parent.progressListener, isFairScheduler = parent.isFairScheduler,
           killEnabled = false)
       val completedStagesTable =
         new StageTableBase(completedStages.sortBy(_.submissionTime).reverse, parent.basePath,
-          parent.listener, isFairScheduler = parent.isFairScheduler, killEnabled = false)
+          parent.progressListener, isFairScheduler = parent.isFairScheduler, killEnabled = false)
       val failedStagesTable =
         new FailedStageTable(failedStages.sortBy(_.submissionTime).reverse, parent.basePath,
-          parent.listener, isFairScheduler = parent.isFairScheduler)
+          parent.progressListener, isFairScheduler = parent.isFairScheduler)
 
       // For now, pool information is only accessible in live UIs
       val pools = sc.map(_.getAllPools).getOrElse(Seq.empty[Schedulable])
@@ -64,6 +64,12 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
       val shouldShowCompletedStages = completedStages.nonEmpty
       val shouldShowFailedStages = failedStages.nonEmpty
 
+      val completedStageNumStr = if (numCompletedStages == completedStages.size) {
+        s"$numCompletedStages"
+      } else {
+        s"$numCompletedStages, only showing ${completedStages.size}"
+      }
+
       val summary: NodeSeq =
         <div>
           <ul class="unstyled">
@@ -98,9 +104,9 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
             }
             {
               if (shouldShowCompletedStages) {
-                <li>
+                <li id="completed-summary">
                   <a href="#completed"><strong>Completed Stages:</strong></a>
-                  {numCompletedStages}
+                  {completedStageNumStr}
                 </li>
               }
             }
@@ -132,7 +138,7 @@ private[ui] class AllStagesPage(parent: StagesTab) extends WebUIPage("") {
         pendingStagesTable.toNodeSeq
       }
       if (shouldShowCompletedStages) {
-        content ++= <h4 id="completed">Completed Stages ({numCompletedStages})</h4> ++
+        content ++= <h4 id="completed">Completed Stages ({completedStageNumStr})</h4> ++
         completedStagesTable.toNodeSeq
       }
       if (shouldShowFailedStages) {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
index 1f8536d1b7195..d5cdbfac104f8 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/ExecutorTable.scala
@@ -26,7 +26,7 @@ import org.apache.spark.util.Utils
 
 /** Stage summary grouped by executors. */
 private[ui] class ExecutorTable(stageId: Int, stageAttemptId: Int, parent: StagesTab) {
-  private val listener = parent.listener
+  private val listener = parent.progressListener
 
   def toNodeSeq: Seq[Node] = {
     listener.synchronized {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
index dd968e124738e..7163217e1fed0 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala
@@ -31,6 +31,7 @@ import org.apache.spark.ui.jobs.UIData.ExecutorUIData
 
 /** Page showing statistics and stage list for a given job */
 private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
+
   private val STAGES_LEGEND =
     <div class="legend-area"><svg width="150px" height="85px">
       <rect class="completed-stage-legend"
@@ -133,7 +134,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
     events.toSeq
   }
 
-  private def  makeTimeline(
+  private def makeTimeline(
       stages: Seq[StageInfo],
       executors: HashMap[String, ExecutorUIData],
       appStartTime: Long): Seq[Node] = {
@@ -160,7 +161,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
 
     <span class="expand-job-timeline">
       <span class="expand-job-timeline-arrow arrow-closed"></span>
-      <strong>Event Timeline</strong>
+      <strong>Event timeline</strong>
     </span> ++
     <div id="job-timeline" class="collapsed">
       <div class="control-panel">
@@ -186,7 +187,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
       val jobDataOption = listener.jobIdToData.get(jobId)
       if (jobDataOption.isEmpty) {
         val content =
-          <div>
+          <div id="no-info">
             <p>No information to display for job {jobId}</p>
           </div>
         return UIUtils.headerSparkPage(
@@ -198,7 +199,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
         // This could be empty if the JobProgressListener hasn't received information about the
         // stage or if the stage information has been garbage collected
         listener.stageIdToInfo.getOrElse(stageId,
-          new StageInfo(stageId, 0, "Unknown", 0, Seq.empty, "Unknown"))
+          new StageInfo(stageId, 0, "Unknown", 0, Seq.empty, Seq.empty, "Unknown"))
       }
 
       val activeStages = Buffer[StageInfo]()
@@ -303,9 +304,14 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
       var content = summary
       val appStartTime = listener.startTime
       val executorListener = parent.executorListener
+      val operationGraphListener = parent.operationGraphListener
+
       content ++= makeTimeline(activeStages ++ completedStages ++ failedStages,
           executorListener.executorIdToData, appStartTime)
 
+      content ++= UIUtils.showDagVizForJob(
+        jobId, operationGraphListener.getOperationGraphForJob(jobId))
+
       if (shouldShowActiveStages) {
         content ++= <h4 id="active">Active Stages ({activeStages.size})</h4> ++
           activeStagesTable.toNodeSeq
@@ -326,7 +332,7 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
         content ++= <h4 id ="failed">Failed Stages ({failedStages.size})</h4> ++
           failedStagesTable.toNodeSeq
       }
-      UIUtils.headerSparkPage(s"Details for Job $jobId", content, parent)
+      UIUtils.headerSparkPage(s"Details for Job $jobId", content, parent, showVisualization = true)
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
index d6d716de25920..246e191d64776 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala
@@ -25,6 +25,7 @@ import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.BlockManagerId
+import org.apache.spark.ui.SparkUI
 import org.apache.spark.ui.jobs.UIData._
 
 /**
@@ -38,8 +39,6 @@ import org.apache.spark.ui.jobs.UIData._
 @DeveloperApi
 class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
 
-  import JobProgressListener._
-
   // Define a handful of type aliases so that data structures' types can serve as documentation.
   // These type aliases are public because they're used in the types of public fields:
 
@@ -75,6 +74,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   // JobProgressListener's retention limits.
   var numCompletedStages = 0
   var numFailedStages = 0
+  var numCompletedJobs = 0
+  var numFailedJobs = 0
 
   // Misc:
   val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]()
@@ -86,8 +87,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
   // To limit the total memory usage of JobProgressListener, we only track information for a fixed
   // number of non-active jobs and stages (there is no limit for active jobs and stages):
 
-  val retainedStages = conf.getInt("spark.ui.retainedStages", DEFAULT_RETAINED_STAGES)
-  val retainedJobs = conf.getInt("spark.ui.retainedJobs", DEFAULT_RETAINED_JOBS)
+  val retainedStages = conf.getInt("spark.ui.retainedStages", SparkUI.DEFAULT_RETAINED_STAGES)
+  val retainedJobs = conf.getInt("spark.ui.retainedJobs", SparkUI.DEFAULT_RETAINED_JOBS)
 
   // We can test for memory leaks by ensuring that collections that track non-active jobs and
   // stages do not grow without bound and that collections for active jobs/stages eventually become
@@ -218,10 +219,12 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
         completedJobs += jobData
         trimJobsIfNecessary(completedJobs)
         jobData.status = JobExecutionStatus.SUCCEEDED
+        numCompletedJobs += 1
       case JobFailed(exception) =>
         failedJobs += jobData
         trimJobsIfNecessary(failedJobs)
         jobData.status = JobExecutionStatus.FAILED
+        numFailedJobs += 1
     }
     for (stageId <- jobData.stageIds) {
       stageIdToActiveJobIds.get(stageId).foreach { jobsUsingStage =>
@@ -288,8 +291,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
     activeStages(stage.stageId) = stage
     pendingStages.remove(stage.stageId)
     val poolName = Option(stageSubmitted.properties).map {
-      p => p.getProperty("spark.scheduler.pool", DEFAULT_POOL_NAME)
-    }.getOrElse(DEFAULT_POOL_NAME)
+      p => p.getProperty("spark.scheduler.pool", SparkUI.DEFAULT_POOL_NAME)
+    }.getOrElse(SparkUI.DEFAULT_POOL_NAME)
 
     stageIdToInfo(stage.stageId) = stage
     val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), new StageUIData)
@@ -524,9 +527,3 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
     startTime = appStarted.time
   }
 }
-
-private object JobProgressListener {
-  val DEFAULT_POOL_NAME = "default"
-  val DEFAULT_RETAINED_STAGES = 1000
-  val DEFAULT_RETAINED_JOBS = 1000
-}
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala
index 342787f1a3c2d..77ca60b000a9b 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala
@@ -24,10 +24,12 @@ import org.apache.spark.ui.{SparkUI, SparkUITab}
 private[ui] class JobsTab(parent: SparkUI) extends SparkUITab(parent, "jobs") {
   val sc = parent.sc
   val killEnabled = parent.killEnabled
-  def isFairScheduler: Boolean =
-    jobProgresslistener.schedulingMode.exists(_ == SchedulingMode.FAIR)
   val jobProgresslistener = parent.jobProgressListener
   val executorListener = parent.executorsListener
+  val operationGraphListener = parent.operationGraphListener
+
+  def isFairScheduler: Boolean =
+    jobProgresslistener.schedulingMode.exists(_ == SchedulingMode.FAIR)
 
   attachPage(new AllJobsPage(this))
   attachPage(new JobPage(this))
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala
index f47cdc935e539..f3e0b38523f32 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolPage.scala
@@ -21,13 +21,13 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.scheduler.{Schedulable, StageInfo}
+import org.apache.spark.scheduler.StageInfo
 import org.apache.spark.ui.{WebUIPage, UIUtils}
 
 /** Page showing specific pool details */
 private[ui] class PoolPage(parent: StagesTab) extends WebUIPage("pool") {
   private val sc = parent.sc
-  private val listener = parent.listener
+  private val listener = parent.progressListener
 
   def render(request: HttpServletRequest): Seq[Node] = {
     listener.synchronized {
@@ -40,7 +40,7 @@ private[ui] class PoolPage(parent: StagesTab) extends WebUIPage("pool") {
         case None => Seq[StageInfo]()
       }
       val activeStagesTable = new StageTableBase(activeStages.sortBy(_.submissionTime).reverse,
-        parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler,
+        parent.basePath, parent.progressListener, isFairScheduler = parent.isFairScheduler,
         killEnabled = parent.killEnabled)
 
       // For now, pool information is only accessible in live UIs
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala
index df1899e7a9b84..9ba2af54dacf4 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/PoolTable.scala
@@ -25,7 +25,7 @@ import org.apache.spark.ui.UIUtils
 
 /** Table showing list of pools */
 private[ui] class PoolTable(pools: Seq[Schedulable], parent: StagesTab) {
-  private val listener = parent.listener
+  private val listener = parent.progressListener
 
   def toNodeSeq: Seq[Node] = {
     listener.synchronized {
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 797c9404bc449..89d175b06b947 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -25,17 +25,19 @@ import scala.xml.{Elem, Node, Unparsed}
 import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 import org.apache.spark.ui.{ToolTips, WebUIPage, UIUtils}
 import org.apache.spark.ui.jobs.UIData._
+import org.apache.spark.ui.scope.RDDOperationGraph
 import org.apache.spark.util.{Utils, Distribution}
-import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 
 /** Page showing statistics and task list for a given stage */
 private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
-  private val listener = parent.listener
+  private val progressListener = parent.progressListener
+  private val operationGraphListener = parent.operationGraphListener
 
   def render(request: HttpServletRequest): Seq[Node] = {
-    listener.synchronized {
+    progressListener.synchronized {
       val parameterId = request.getParameter("id")
       require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
 
@@ -44,23 +46,31 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
 
       val stageId = parameterId.toInt
       val stageAttemptId = parameterAttempt.toInt
-      val stageDataOption = listener.stageIdToData.get((stageId, stageAttemptId))
+      val stageDataOption = progressListener.stageIdToData.get((stageId, stageAttemptId))
 
-      if (stageDataOption.isEmpty || stageDataOption.get.taskData.isEmpty) {
+      val stageHeader = s"Details for Stage $stageId (Attempt $stageAttemptId)"
+      if (stageDataOption.isEmpty) {
+        val content =
+          <div id="no-info">
+            <p>No information to display for Stage {stageId} (Attempt {stageAttemptId})</p>
+          </div>
+        return UIUtils.headerSparkPage(stageHeader, content, parent)
+
+      }
+      if (stageDataOption.get.taskData.isEmpty) {
         val content =
           <div>
             <h4>Summary Metrics</h4> No tasks have started yet
             <h4>Tasks</h4> No tasks have started yet
           </div>
-        return UIUtils.headerSparkPage(
-          s"Details for Stage $stageId (Attempt $stageAttemptId)", content, parent)
+        return UIUtils.headerSparkPage(stageHeader, content, parent)
       }
 
       val stageData = stageDataOption.get
       val tasks = stageData.taskData.values.toSeq.sortBy(_.taskInfo.launchTime)
 
       val numCompleted = tasks.count(_.taskInfo.finished)
-      val accumulables = listener.stageIdToData((stageId, stageAttemptId)).accumulables
+      val accumulables = progressListener.stageIdToData((stageId, stageAttemptId)).accumulables
       val hasAccumulators = accumulables.size > 0
 
       val summary =
@@ -169,6 +179,9 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
           </div>
         </div>
 
+      val dagViz = UIUtils.showDagVizForStage(
+        stageId, operationGraphListener.getOperationGraphForStage(stageId))
+
       val accumulableHeaders: Seq[String] = Seq("Accumulable", "Value")
       def accumulableRow(acc: AccumulableInfo): Elem =
         <tr><td>{acc.name}</td><td>{acc.value}</td></tr>
@@ -434,13 +447,14 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val content =
         summary ++
         showAdditionalMetrics ++
+        dagViz ++
         <h4>Summary Metrics for {numCompleted} Completed Tasks</h4> ++
         <div>{summaryTable.getOrElse("No tasks have reported metrics yet.")}</div> ++
         <h4>Aggregated Metrics by Executor</h4> ++ executorTable.toNodeSeq ++
         maybeAccumulableTable ++
         <h4>Tasks</h4> ++ taskTable
 
-      UIUtils.headerSparkPage("Details for Stage %d".format(stageId), content, parent)
+      UIUtils.headerSparkPage(stageHeader, content, parent, showVisualization = true)
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala
index 1bd2d87e00796..55169956d8304 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala
@@ -26,19 +26,20 @@ import org.apache.spark.ui.{SparkUI, SparkUITab}
 private[ui] class StagesTab(parent: SparkUI) extends SparkUITab(parent, "stages") {
   val sc = parent.sc
   val killEnabled = parent.killEnabled
-  val listener = parent.jobProgressListener
+  val progressListener = parent.jobProgressListener
+  val operationGraphListener = parent.operationGraphListener
 
   attachPage(new AllStagesPage(this))
   attachPage(new StagePage(this))
   attachPage(new PoolPage(this))
 
-  def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR)
+  def isFairScheduler: Boolean = progressListener.schedulingMode.exists(_ == SchedulingMode.FAIR)
 
   def handleKillRequest(request: HttpServletRequest): Unit = {
     if (killEnabled && parent.securityManager.checkModifyPermissions(request.getRemoteUser)) {
       val killFlag = Option(request.getParameter("terminate")).getOrElse("false").toBoolean
       val stageId = Option(request.getParameter("id")).getOrElse("-1").toInt
-      if (stageId >= 0 && killFlag && listener.activeStages.contains(stageId)) {
+      if (stageId >= 0 && killFlag && progressListener.activeStages.contains(stageId)) {
         sc.get.cancelStage(stageId)
       }
       // Do a quick pause here to give Spark time to kill the stage so it shows up as
diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
new file mode 100644
index 0000000000000..a18c193540ce3
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -0,0 +1,205 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui.scope
+
+import scala.collection.mutable
+import scala.collection.mutable.ListBuffer
+
+import org.apache.spark.Logging
+import org.apache.spark.scheduler.StageInfo
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * A representation of a generic cluster graph used for storing information on RDD operations.
+ *
+ * Each graph is defined with a set of edges and a root cluster, which may contain children
+ * nodes and children clusters. Additionally, a graph may also have edges that enter or exit
+ * the graph from nodes that belong to adjacent graphs.
+ */
+private[ui] case class RDDOperationGraph(
+    edges: Seq[RDDOperationEdge],
+    outgoingEdges: Seq[RDDOperationEdge],
+    incomingEdges: Seq[RDDOperationEdge],
+    rootCluster: RDDOperationCluster)
+
+/** A node in an RDDOperationGraph. This represents an RDD. */
+private[ui] case class RDDOperationNode(id: Int, name: String, cached: Boolean)
+
+/**
+ * A directed edge connecting two nodes in an RDDOperationGraph.
+ * This represents an RDD dependency.
+ */
+private[ui] case class RDDOperationEdge(fromId: Int, toId: Int)
+
+/**
+ * A cluster that groups nodes together in an RDDOperationGraph.
+ *
+ * This represents any grouping of RDDs, including operation scopes (e.g. textFile, flatMap),
+ * stages, jobs, or any higher level construct. A cluster may be nested inside of other clusters.
+ */
+private[ui] class RDDOperationCluster(val id: String, val name: String) {
+  private val _childNodes = new ListBuffer[RDDOperationNode]
+  private val _childClusters = new ListBuffer[RDDOperationCluster]
+
+  def childNodes: Seq[RDDOperationNode] = _childNodes.iterator.toSeq
+  def childClusters: Seq[RDDOperationCluster] = _childClusters.iterator.toSeq
+  def attachChildNode(childNode: RDDOperationNode): Unit = { _childNodes += childNode }
+  def attachChildCluster(childCluster: RDDOperationCluster): Unit = {
+    _childClusters += childCluster
+  }
+
+  /** Return all the nodes container in this cluster, including ones nested in other clusters. */
+  def getAllNodes: Seq[RDDOperationNode] = {
+    _childNodes ++ _childClusters.flatMap(_.childNodes)
+  }
+}
+
+private[ui] object RDDOperationGraph extends Logging {
+
+  /**
+   * Construct a RDDOperationGraph for a given stage.
+   *
+   * The root cluster represents the stage, and all children clusters represent RDD operations.
+   * Each node represents an RDD, and each edge represents a dependency between two RDDs pointing
+   * from the parent to the child.
+   *
+   * This does not currently merge common operation scopes across stages. This may be worth
+   * supporting in the future if we decide to group certain stages within the same job under
+   * a common scope (e.g. part of a SQL query).
+   */
+  def makeOperationGraph(stage: StageInfo): RDDOperationGraph = {
+    val edges = new ListBuffer[RDDOperationEdge]
+    val nodes = new mutable.HashMap[Int, RDDOperationNode]
+    val clusters = new mutable.HashMap[String, RDDOperationCluster] // indexed by cluster ID
+
+    // Root cluster is the stage cluster
+    val stageClusterId = s"stage_${stage.stageId}"
+    val stageClusterName = s"Stage ${stage.stageId}" +
+      { if (stage.attemptId == 0) "" else s" (attempt ${stage.attemptId})" }
+    val rootCluster = new RDDOperationCluster(stageClusterId, stageClusterName)
+
+    // Find nodes, edges, and operation scopes that belong to this stage
+    stage.rddInfos.foreach { rdd =>
+      edges ++= rdd.parentIds.map { parentId => RDDOperationEdge(parentId, rdd.id) }
+
+      // TODO: differentiate between the intention to cache an RDD and whether it's actually cached
+      val node = nodes.getOrElseUpdate(
+        rdd.id, RDDOperationNode(rdd.id, rdd.name, rdd.storageLevel != StorageLevel.NONE))
+
+      if (rdd.scope == null) {
+        // This RDD has no encompassing scope, so we put it directly in the root cluster
+        // This should happen only if an RDD is instantiated outside of a public RDD API
+        rootCluster.attachChildNode(node)
+      } else {
+        // Otherwise, this RDD belongs to an inner cluster,
+        // which may be nested inside of other clusters
+        val rddScopes = rdd.scope.map { scope => scope.getAllScopes }.getOrElse(Seq.empty)
+        val rddClusters = rddScopes.map { scope =>
+          val clusterId = scope.name + "_" + scope.id
+          val clusterName = scope.name
+          clusters.getOrElseUpdate(clusterId, new RDDOperationCluster(clusterId, clusterName))
+        }
+        // Build the cluster hierarchy for this RDD
+        rddClusters.sliding(2).foreach { pc =>
+          if (pc.size == 2) {
+            val parentCluster = pc(0)
+            val childCluster = pc(1)
+            parentCluster.attachChildCluster(childCluster)
+          }
+        }
+        // Attach the outermost cluster to the root cluster, and the RDD to the innermost cluster
+        rddClusters.headOption.foreach { cluster => rootCluster.attachChildCluster(cluster) }
+        rddClusters.lastOption.foreach { cluster => cluster.attachChildNode(node) }
+      }
+    }
+
+    // Classify each edge as internal, outgoing or incoming
+    // This information is needed to reason about how stages relate to each other
+    val internalEdges = new ListBuffer[RDDOperationEdge]
+    val outgoingEdges = new ListBuffer[RDDOperationEdge]
+    val incomingEdges = new ListBuffer[RDDOperationEdge]
+    edges.foreach { case e: RDDOperationEdge =>
+      val fromThisGraph = nodes.contains(e.fromId)
+      val toThisGraph = nodes.contains(e.toId)
+      (fromThisGraph, toThisGraph) match {
+        case (true, true) => internalEdges += e
+        case (true, false) => outgoingEdges += e
+        case (false, true) => incomingEdges += e
+        // should never happen
+        case _ => logWarning(s"Found an orphan edge in stage ${stage.stageId}: $e")
+      }
+    }
+
+    RDDOperationGraph(internalEdges, outgoingEdges, incomingEdges, rootCluster)
+  }
+
+  /**
+   * Generate the content of a dot file that describes the specified graph.
+   *
+   * Note that this only uses a minimal subset of features available to the DOT specification.
+   * Part of the styling must be done here because the rendering library must take certain
+   * attributes into account when arranging the graph elements. More style is added in the
+   * visualization later through post-processing in JavaScript.
+   *
+   * For the complete DOT specification, see http://www.graphviz.org/Documentation/dotguide.pdf.
+   */
+  def makeDotFile(graph: RDDOperationGraph, forJob: Boolean): String = {
+    val dotFile = new StringBuilder
+    dotFile.append("digraph G {\n")
+    dotFile.append(makeDotSubgraph(graph.rootCluster, forJob, indent = "  "))
+    graph.edges.foreach { edge =>
+      dotFile.append(s"""  ${edge.fromId}->${edge.toId} [lineInterpolate="basis"];\n""")
+    }
+    dotFile.append("}")
+    val result = dotFile.toString()
+    logDebug(result)
+    result
+  }
+
+  /**
+   * Return the dot representation of a node in an RDDOperationGraph.
+   *
+   * On the job page, is displayed as a small circle without labels.
+   * On the stage page, it is displayed as a box with an embedded label.
+   */
+  private def makeDotNode(node: RDDOperationNode, forJob: Boolean): String = {
+    if (forJob) {
+      s"""${node.id} [label=" " shape="circle" padding="5" labelStyle="font-size: 0"]"""
+    } else {
+      s"""${node.id} [label="${node.name} (${node.id})"]"""
+    }
+  }
+
+  /** Return the dot representation of a subgraph in an RDDOperationGraph. */
+  private def makeDotSubgraph(
+      scope: RDDOperationCluster,
+      forJob: Boolean,
+      indent: String): String = {
+    val subgraph = new StringBuilder
+    subgraph.append(indent + s"subgraph cluster${scope.id} {\n")
+    subgraph.append(indent + s"""  label="${scope.name}";\n""")
+    scope.childNodes.foreach { node =>
+      subgraph.append(indent + s"  ${makeDotNode(node, forJob)};\n")
+    }
+    scope.childClusters.foreach { cscope =>
+      subgraph.append(makeDotSubgraph(cscope, forJob, indent + "  "))
+    }
+    subgraph.append(indent + "}\n")
+    subgraph.toString()
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala
new file mode 100644
index 0000000000000..2884a49f31122
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui.scope
+
+import scala.collection.mutable
+
+import org.apache.spark.SparkConf
+import org.apache.spark.scheduler._
+import org.apache.spark.ui.SparkUI
+
+/**
+ * A SparkListener that constructs a DAG of RDD operations.
+ */
+private[ui] class RDDOperationGraphListener(conf: SparkConf) extends SparkListener {
+  private val jobIdToStageIds = new mutable.HashMap[Int, Seq[Int]]
+  private val stageIdToGraph = new mutable.HashMap[Int, RDDOperationGraph]
+  private val stageIds = new mutable.ArrayBuffer[Int]
+
+  // How many jobs or stages to retain graph metadata for
+  private val retainedStages =
+    conf.getInt("spark.ui.retainedStages", SparkUI.DEFAULT_RETAINED_STAGES)
+
+  /** Return the graph metadata for the given stage, or None if no such information exists. */
+  def getOperationGraphForJob(jobId: Int): Seq[RDDOperationGraph] = {
+    jobIdToStageIds.get(jobId)
+      .map { sids => sids.flatMap { sid => stageIdToGraph.get(sid) } }
+      .getOrElse { Seq.empty }
+  }
+
+  /** Return the graph metadata for the given stage, or None if no such information exists. */
+  def getOperationGraphForStage(stageId: Int): Option[RDDOperationGraph] = {
+    stageIdToGraph.get(stageId)
+  }
+
+  /** On job start, construct a RDDOperationGraph for each stage in the job for display later. */
+  override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized {
+    val jobId = jobStart.jobId
+    val stageInfos = jobStart.stageInfos
+
+    stageInfos.foreach { stageInfo =>
+      stageIds += stageInfo.stageId
+      stageIdToGraph(stageInfo.stageId) = RDDOperationGraph.makeOperationGraph(stageInfo)
+    }
+    jobIdToStageIds(jobId) = stageInfos.map(_.stageId).sorted
+
+    // Remove graph metadata for old stages
+    if (stageIds.size >= retainedStages) {
+      val toRemove = math.max(retainedStages / 10, 1)
+      stageIds.take(toRemove).foreach { id => stageIdToGraph.remove(id) }
+      stageIds.trimStart(toRemove)
+    }
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
index 199f731b92bcc..05f94a7507f4f 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
@@ -21,8 +21,8 @@ import javax.servlet.http.HttpServletRequest
 
 import scala.xml.Node
 
-import org.apache.spark.storage.{BlockId, BlockStatus, StorageStatus, StorageUtils}
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.status.api.v1.{AllRDDResource, RDDDataDistribution, RDDPartitionInfo}
+import org.apache.spark.ui.{UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 /** Page showing storage details for a given RDD */
@@ -32,28 +32,19 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
-
     val rddId = parameterId.toInt
-    val storageStatusList = listener.storageStatusList
-    val rddInfo = listener.rddInfoList.find(_.id == rddId).getOrElse {
-      // Rather than crashing, render an "RDD Not Found" page
-      return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
-    }
+    val rddStorageInfo = AllRDDResource.getRDDStorageInfo(rddId, listener,includeDetails = true)
+      .getOrElse {
+        // Rather than crashing, render an "RDD Not Found" page
+        return UIUtils.headerSparkPage("RDD Not Found", Seq[Node](), parent)
+      }
 
     // Worker table
-    val workers = storageStatusList.map((rddId, _))
-    val workerTable = UIUtils.listingTable(workerHeader, workerRow, workers,
-      id = Some("rdd-storage-by-worker-table"))
+    val workerTable = UIUtils.listingTable(workerHeader, workerRow,
+      rddStorageInfo.dataDistribution.get, id = Some("rdd-storage-by-worker-table"))
 
     // Block table
-    val blockLocations = StorageUtils.getRddBlockLocations(rddId, storageStatusList)
-    val blocks = storageStatusList
-      .flatMap(_.rddBlocksById(rddId))
-      .sortWith(_._1.name < _._1.name)
-      .map { case (blockId, status) =>
-        (blockId, status, blockLocations.get(blockId).getOrElse(Seq[String]("Unknown")))
-      }
-    val blockTable = UIUtils.listingTable(blockHeader, blockRow, blocks,
+    val blockTable = UIUtils.listingTable(blockHeader, blockRow, rddStorageInfo.partitions.get,
       id = Some("rdd-storage-by-block-table"))
 
     val content =
@@ -62,23 +53,23 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
           <ul class="unstyled">
             <li>
               <strong>Storage Level:</strong>
-              {rddInfo.storageLevel.description}
+              {rddStorageInfo.storageLevel}
             </li>
             <li>
               <strong>Cached Partitions:</strong>
-              {rddInfo.numCachedPartitions}
+              {rddStorageInfo.numCachedPartitions}
             </li>
             <li>
               <strong>Total Partitions:</strong>
-              {rddInfo.numPartitions}
+              {rddStorageInfo.numPartitions}
             </li>
             <li>
               <strong>Memory Size:</strong>
-              {Utils.bytesToString(rddInfo.memSize)}
+              {Utils.bytesToString(rddStorageInfo.memoryUsed)}
             </li>
             <li>
               <strong>Disk Size:</strong>
-              {Utils.bytesToString(rddInfo.diskSize)}
+              {Utils.bytesToString(rddStorageInfo.diskUsed)}
             </li>
           </ul>
         </div>
@@ -86,19 +77,19 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
 
       <div class="row-fluid">
         <div class="span12">
-          <h4> Data Distribution on {workers.size} Executors </h4>
+          <h4> Data Distribution on {rddStorageInfo.dataDistribution.size} Executors </h4>
           {workerTable}
         </div>
       </div>
 
       <div class="row-fluid">
         <div class="span12">
-          <h4> {blocks.size} Partitions </h4>
+          <h4> {rddStorageInfo.partitions.size} Partitions </h4>
           {blockTable}
         </div>
       </div>;
 
-    UIUtils.headerSparkPage("RDD Storage Info for " + rddInfo.name, content, parent)
+    UIUtils.headerSparkPage("RDD Storage Info for " + rddStorageInfo.name, content, parent)
   }
 
   /** Header fields for the worker table */
@@ -116,34 +107,32 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
     "Executors")
 
   /** Render an HTML row representing a worker */
-  private def workerRow(worker: (Int, StorageStatus)): Seq[Node] = {
-    val (rddId, status) = worker
+  private def workerRow(worker: RDDDataDistribution): Seq[Node] = {
     <tr>
-      <td>{status.blockManagerId.host + ":" + status.blockManagerId.port}</td>
+      <td>{worker.address}</td>
       <td>
-        {Utils.bytesToString(status.memUsedByRdd(rddId))}
-        ({Utils.bytesToString(status.memRemaining)} Remaining)
+        {Utils.bytesToString(worker.memoryUsed)}
+        ({Utils.bytesToString(worker.memoryRemaining)} Remaining)
       </td>
-      <td>{Utils.bytesToString(status.diskUsedByRdd(rddId))}</td>
+      <td>{Utils.bytesToString(worker.diskUsed)}</td>
     </tr>
   }
 
   /** Render an HTML row representing a block */
-  private def blockRow(row: (BlockId, BlockStatus, Seq[String])): Seq[Node] = {
-    val (id, block, locations) = row
+  private def blockRow(row: RDDPartitionInfo): Seq[Node] = {
     <tr>
-      <td>{id}</td>
+      <td>{row.blockName}</td>
       <td>
-        {block.storageLevel.description}
+        {row.storageLevel}
       </td>
-      <td sorttable_customkey={block.memSize.toString}>
-        {Utils.bytesToString(block.memSize)}
+      <td sorttable_customkey={row.memoryUsed.toString}>
+        {Utils.bytesToString(row.memoryUsed)}
       </td>
-      <td sorttable_customkey={block.diskSize.toString}>
-        {Utils.bytesToString(block.diskSize)}
+      <td sorttable_customkey={row.diskUsed.toString}>
+        {Utils.bytesToString(row.diskUsed)}
       </td>
       <td>
-        {locations.map(l => <span>{l}<br/></span>)}
+        {row.executors.map(l => <span>{l}<br/></span>)}
       </td>
     </tr>
   }
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
index 59dc6b547c1d8..07db783c572cf 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala
@@ -22,7 +22,7 @@ import javax.servlet.http.HttpServletRequest
 import scala.xml.Node
 
 import org.apache.spark.storage.RDDInfo
-import org.apache.spark.ui.{WebUIPage, UIUtils}
+import org.apache.spark.ui.{UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 /** Page showing list of RDD's currently stored in the cluster */
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
index 045bd784990d1..0351749700962 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala
@@ -35,6 +35,8 @@ private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storag
 /**
  * :: DeveloperApi ::
  * A SparkListener that prepares information to be displayed on the BlockManagerUI.
+ *
+ * This class is thread-safe (unlike JobProgressListener)
  */
 @DeveloperApi
 class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener {
@@ -43,7 +45,9 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar
   def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList
 
   /** Filter RDD info to include only those with cached partitions */
-  def rddInfoList: Seq[RDDInfo] = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
+  def rddInfoList: Seq[RDDInfo] = synchronized {
+    _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq
+  }
 
   /** Update the storage info of the RDDs whose blocks are among the given updated blocks */
   private def updateRDDInfo(updatedBlocks: Seq[(BlockId, BlockStatus)]): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index b725df3b44596..de3316d083a22 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -183,7 +183,9 @@ private[spark] object AkkaUtils extends Logging {
           lastException = e
           logWarning(s"Error sending message [message = $message] in $attempts attempts", e)
       }
-      Thread.sleep(retryInterval)
+      if (attempts < maxAttempts) {
+        Thread.sleep(retryInterval)
+      }
     }
 
     throw new SparkException(
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 4ac0382d80815..6fe32e469c732 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -179,6 +179,11 @@ private[spark] object ClosureCleaner extends Logging {
       cleanTransitively: Boolean,
       accessedFields: Map[Class[_], Set[String]]): Unit = {
 
+    if (!isClosure(func.getClass)) {
+      logWarning("Expected a closure; got " + func.getClass.getName)
+      return
+    }
+
     // TODO: clean all inner closures first. This requires us to find the inner objects.
     // TODO: cache outerClasses / innerClasses / accessedFields
 
@@ -312,7 +317,9 @@ private[spark] object ClosureCleaner extends Logging {
 
   private def ensureSerializable(func: AnyRef) {
     try {
-      SparkEnv.get.closureSerializer.newInstance().serialize(func)
+      if (SparkEnv.get != null) {
+        SparkEnv.get.closureSerializer.newInstance().serialize(func)
+      }
     } catch {
       case ex: Exception => throw new SparkException("Task not serializable", ex)
     }
@@ -347,6 +354,9 @@ private[spark] object ClosureCleaner extends Logging {
   }
 }
 
+private[spark] class ReturnStatementInClosureException
+  extends SparkException("Return statements aren't allowed in Spark closures")
+
 private class ReturnStatementFinder extends ClassVisitor(ASM4) {
   override def visitMethod(access: Int, name: String, desc: String,
       sig: String, exceptions: Array[String]): MethodVisitor = {
@@ -354,7 +364,7 @@ private class ReturnStatementFinder extends ClassVisitor(ASM4) {
       new MethodVisitor(ASM4) {
         override def visitTypeInsn(op: Int, tp: String) {
           if (op == NEW && tp.contains("scala/runtime/NonLocalReturnControl")) {
-            throw new SparkException("Return statements aren't allowed in Spark closures")
+            throw new ReturnStatementInClosureException
           }
         }
       }
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index ee02fbd9ce069..3f162d1f6c3eb 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -28,10 +28,11 @@ import org.json4s.DefaultFormats
 import org.json4s.JsonDSL._
 import org.json4s.JsonAST._
 
+import org.apache.spark._
 import org.apache.spark.executor._
+import org.apache.spark.rdd.RDDOperationScope
 import org.apache.spark.scheduler._
 import org.apache.spark.storage._
-import org.apache.spark._
 
 /**
  * Serializes SparkListener events to/from JSON.  This protocol provides strong backwards-
@@ -228,6 +229,7 @@ private[spark] object JsonProtocol {
 
   def stageInfoToJson(stageInfo: StageInfo): JValue = {
     val rddInfo = JArray(stageInfo.rddInfos.map(rddInfoToJson).toList)
+    val parentIds = JArray(stageInfo.parentIds.map(JInt(_)).toList)
     val submissionTime = stageInfo.submissionTime.map(JInt(_)).getOrElse(JNothing)
     val completionTime = stageInfo.completionTime.map(JInt(_)).getOrElse(JNothing)
     val failureReason = stageInfo.failureReason.map(JString(_)).getOrElse(JNothing)
@@ -236,6 +238,7 @@ private[spark] object JsonProtocol {
     ("Stage Name" -> stageInfo.name) ~
     ("Number of Tasks" -> stageInfo.numTasks) ~
     ("RDD Info" -> rddInfo) ~
+    ("Parent IDs" -> parentIds) ~
     ("Details" -> stageInfo.details) ~
     ("Submission Time" -> submissionTime) ~
     ("Completion Time" -> completionTime) ~
@@ -368,8 +371,11 @@ private[spark] object JsonProtocol {
 
   def rddInfoToJson(rddInfo: RDDInfo): JValue = {
     val storageLevel = storageLevelToJson(rddInfo.storageLevel)
+    val parentIds = JArray(rddInfo.parentIds.map(JInt(_)).toList)
     ("RDD ID" -> rddInfo.id) ~
     ("Name" -> rddInfo.name) ~
+    ("Scope" -> rddInfo.scope.map(_.toJson)) ~
+    ("Parent IDs" -> parentIds) ~
     ("Storage Level" -> storageLevel) ~
     ("Number of Partitions" -> rddInfo.numPartitions) ~
     ("Number of Cached Partitions" -> rddInfo.numCachedPartitions) ~
@@ -519,7 +525,7 @@ private[spark] object JsonProtocol {
     // The "Stage Infos" field was added in Spark 1.2.0
     val stageInfos = Utils.jsonOption(json \ "Stage Infos")
       .map(_.extract[Seq[JValue]].map(stageInfoFromJson)).getOrElse {
-        stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, "unknown"))
+        stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, Seq.empty, "unknown"))
       }
     SparkListenerJobStart(jobId, submissionTime, stageInfos, properties)
   }
@@ -599,7 +605,10 @@ private[spark] object JsonProtocol {
     val attemptId = (json \ "Stage Attempt ID").extractOpt[Int].getOrElse(0)
     val stageName = (json \ "Stage Name").extract[String]
     val numTasks = (json \ "Number of Tasks").extract[Int]
-    val rddInfos = (json \ "RDD Info").extract[List[JValue]].map(rddInfoFromJson(_))
+    val rddInfos = (json \ "RDD Info").extract[List[JValue]].map(rddInfoFromJson)
+    val parentIds = Utils.jsonOption(json \ "Parent IDs")
+      .map { l => l.extract[List[JValue]].map(_.extract[Int]) }
+      .getOrElse(Seq.empty)
     val details = (json \ "Details").extractOpt[String].getOrElse("")
     val submissionTime = Utils.jsonOption(json \ "Submission Time").map(_.extract[Long])
     val completionTime = Utils.jsonOption(json \ "Completion Time").map(_.extract[Long])
@@ -609,7 +618,8 @@ private[spark] object JsonProtocol {
       case None => Seq[AccumulableInfo]()
     }
 
-    val stageInfo = new StageInfo(stageId, attemptId, stageName, numTasks, rddInfos, details)
+    val stageInfo = new StageInfo(
+      stageId, attemptId, stageName, numTasks, rddInfos, parentIds, details)
     stageInfo.submissionTime = submissionTime
     stageInfo.completionTime = completionTime
     stageInfo.failureReason = failureReason
@@ -785,16 +795,22 @@ private[spark] object JsonProtocol {
   def rddInfoFromJson(json: JValue): RDDInfo = {
     val rddId = (json \ "RDD ID").extract[Int]
     val name = (json \ "Name").extract[String]
+    val scope = Utils.jsonOption(json \ "Scope")
+      .map(_.extract[String])
+      .map(RDDOperationScope.fromJson)
+    val parentIds = Utils.jsonOption(json \ "Parent IDs")
+      .map { l => l.extract[List[JValue]].map(_.extract[Int]) }
+      .getOrElse(Seq.empty)
     val storageLevel = storageLevelFromJson(json \ "Storage Level")
     val numPartitions = (json \ "Number of Partitions").extract[Int]
     val numCachedPartitions = (json \ "Number of Cached Partitions").extract[Int]
     val memSize = (json \ "Memory Size").extract[Long]
-    // fallback to tachyon for backward compatability
+    // fallback to tachyon for backward compatibility
     val externalBlockStoreSize = (json \ "ExternalBlockStore Size").toSome
       .getOrElse(json \ "Tachyon Size").extract[Long]
     val diskSize = (json \ "Disk Size").extract[Long]
 
-    val rddInfo = new RDDInfo(rddId, name, numPartitions, storageLevel)
+    val rddInfo = new RDDInfo(rddId, name, numPartitions, storageLevel, parentIds, scope)
     rddInfo.numCachedPartitions = numCachedPartitions
     rddInfo.memSize = memSize
     rddInfo.externalBlockStoreSize = externalBlockStoreSize
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/json_expectation
new file mode 100644
index 0000000000000..6101177e4bb85
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/json_expectation
@@ -0,0 +1,53 @@
+[ {
+  "id" : "local-1427397477963",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-03-26T19:17:57.184GMT",
+    "endTime" : "2015-03-26T19:20:02.949GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1425081759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-28T00:02:38.277GMT",
+    "endTime" : "2015-02-28T00:02:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation
new file mode 100644
index 0000000000000..cb622e147249e
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/executors/json_expectation
@@ -0,0 +1,17 @@
+[ {
+  "id" : "<driver>",
+  "hostPort" : "localhost:57971",
+  "rddBlocks" : 8,
+  "memoryUsed" : 28000128,
+  "diskUsed" : 0,
+  "activeTasks" : 0,
+  "failedTasks" : 1,
+  "completedTasks" : 31,
+  "totalTasks" : 32,
+  "totalDuration" : 8820,
+  "totalInputBytes" : 28000288,
+  "totalShuffleRead" : 0,
+  "totalShuffleWrite" : 13180,
+  "maxMemory" : 278302556,
+  "executorLogs" : { }
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation
new file mode 100644
index 0000000000000..4a29072bdb6e4
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/0/json_expectation
@@ -0,0 +1,15 @@
+{
+  "jobId" : 0,
+  "name" : "count at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation
new file mode 100644
index 0000000000000..cab4750270dfa
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs/json_expectation
@@ -0,0 +1,43 @@
+[ {
+  "jobId" : 2,
+  "name" : "count at <console>:17",
+  "stageIds" : [ 3 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}, {
+  "jobId" : 1,
+  "name" : "count at <console>:20",
+  "stageIds" : [ 1, 2 ],
+  "status" : "FAILED",
+  "numTasks" : 16,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 15,
+  "numSkippedTasks" : 15,
+  "numFailedTasks" : 1,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 1
+}, {
+  "jobId" : 0,
+  "name" : "count at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation
new file mode 100644
index 0000000000000..cab4750270dfa
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded&status=failed/json_expectation
@@ -0,0 +1,43 @@
+[ {
+  "jobId" : 2,
+  "name" : "count at <console>:17",
+  "stageIds" : [ 3 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}, {
+  "jobId" : 1,
+  "name" : "count at <console>:20",
+  "stageIds" : [ 1, 2 ],
+  "status" : "FAILED",
+  "numTasks" : 16,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 15,
+  "numSkippedTasks" : 15,
+  "numFailedTasks" : 1,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 1
+}, {
+  "jobId" : 0,
+  "name" : "count at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation
new file mode 100644
index 0000000000000..6fd25befbf7e8
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/jobs?status=succeeded/json_expectation
@@ -0,0 +1,29 @@
+[ {
+  "jobId" : 2,
+  "name" : "count at <console>:17",
+  "stageIds" : [ 3 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+}, {
+  "jobId" : 0,
+  "name" : "count at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation
new file mode 100644
index 0000000000000..07489ad96414a
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/json_expectation
@@ -0,0 +1,10 @@
+{
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation
new file mode 100644
index 0000000000000..111cb8163eb3d
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/0/json_expectation
@@ -0,0 +1,270 @@
+{
+  "status" : "COMPLETE",
+  "stageId" : 1,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 8,
+  "numFailedTasks" : 0,
+  "executorRunTime" : 3476,
+  "inputBytes" : 28000128,
+  "inputRecords" : 0,
+  "outputBytes" : 0,
+  "outputRecords" : 0,
+  "shuffleReadBytes" : 0,
+  "shuffleReadRecords" : 0,
+  "shuffleWriteBytes" : 13180,
+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "map at <console>:14",
+  "details" : "org.apache.spark.rdd.RDD.map(RDD.scala:271)\n$line10.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:14)\n$line10.$read$$iwC$$iwC$$iwC.<init>(<console>:19)\n$line10.$read$$iwC$$iwC.<init>(<console>:21)\n$line10.$read$$iwC.<init>(<console>:23)\n$line10.$read.<init>(<console>:25)\n$line10.$read$.<init>(<console>:29)\n$line10.$read$.<clinit>(<console>)\n$line10.$eval$.<init>(<console>:7)\n$line10.$eval$.<clinit>(<console>)\n$line10.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ],
+  "tasks" : {
+    "8" : {
+      "taskId" : 8,
+      "index" : 0,
+      "attempt" : 0,
+      "launchTime" : "2015-02-03T16:43:05.829GMT",
+      "executorId" : "<driver>",
+      "host" : "localhost",
+      "taskLocality" : "PROCESS_LOCAL",
+      "speculative" : false,
+      "accumulatorUpdates" : [ ],
+      "taskMetrics" : {
+        "executorDeserializeTime" : 1,
+        "executorRunTime" : 435,
+        "resultSize" : 1902,
+        "jvmGcTime" : 19,
+        "resultSerializationTime" : 2,
+        "memoryBytesSpilled" : 0,
+        "diskBytesSpilled" : 0,
+        "inputMetrics" : {
+          "bytesRead" : 3500016,
+          "recordsRead" : 0
+        },
+        "shuffleWriteMetrics" : {
+          "bytesWritten" : 1648,
+          "writeTime" : 94000,
+          "recordsWritten" : 0
+        }
+      }
+    },
+    "11" : {
+      "taskId" : 11,
+      "index" : 3,
+      "attempt" : 0,
+      "launchTime" : "2015-02-03T16:43:05.830GMT",
+      "executorId" : "<driver>",
+      "host" : "localhost",
+      "taskLocality" : "PROCESS_LOCAL",
+      "speculative" : false,
+      "accumulatorUpdates" : [ ],
+      "taskMetrics" : {
+        "executorDeserializeTime" : 2,
+        "executorRunTime" : 434,
+        "resultSize" : 1902,
+        "jvmGcTime" : 19,
+        "resultSerializationTime" : 1,
+        "memoryBytesSpilled" : 0,
+        "diskBytesSpilled" : 0,
+        "inputMetrics" : {
+          "bytesRead" : 3500016,
+          "recordsRead" : 0
+        },
+        "shuffleWriteMetrics" : {
+          "bytesWritten" : 1647,
+          "writeTime" : 83000,
+          "recordsWritten" : 0
+        }
+      }
+    },
+    "14" : {
+      "taskId" : 14,
+      "index" : 6,
+      "attempt" : 0,
+      "launchTime" : "2015-02-03T16:43:05.832GMT",
+      "executorId" : "<driver>",
+      "host" : "localhost",
+      "taskLocality" : "PROCESS_LOCAL",
+      "speculative" : false,
+      "accumulatorUpdates" : [ ],
+      "taskMetrics" : {
+        "executorDeserializeTime" : 2,
+        "executorRunTime" : 434,
+        "resultSize" : 1902,
+        "jvmGcTime" : 19,
+        "resultSerializationTime" : 1,
+        "memoryBytesSpilled" : 0,
+        "diskBytesSpilled" : 0,
+        "inputMetrics" : {
+          "bytesRead" : 3500016,
+          "recordsRead" : 0
+        },
+        "shuffleWriteMetrics" : {
+          "bytesWritten" : 1648,
+          "writeTime" : 88000,
+          "recordsWritten" : 0
+        }
+      }
+    },
+    "13" : {
+      "taskId" : 13,
+      "index" : 5,
+      "attempt" : 0,
+      "launchTime" : "2015-02-03T16:43:05.831GMT",
+      "executorId" : "<driver>",
+      "host" : "localhost",
+      "taskLocality" : "PROCESS_LOCAL",
+      "speculative" : false,
+      "accumulatorUpdates" : [ ],
+      "taskMetrics" : {
+        "executorDeserializeTime" : 2,
+        "executorRunTime" : 434,
+        "resultSize" : 1902,
+        "jvmGcTime" : 19,
+        "resultSerializationTime" : 2,
+        "memoryBytesSpilled" : 0,
+        "diskBytesSpilled" : 0,
+        "inputMetrics" : {
+          "bytesRead" : 3500016,
+          "recordsRead" : 0
+        },
+        "shuffleWriteMetrics" : {
+          "bytesWritten" : 1648,
+          "writeTime" : 73000,
+          "recordsWritten" : 0
+        }
+      }
+    },
+    "10" : {
+      "taskId" : 10,
+      "index" : 2,
+      "attempt" : 0,
+      "launchTime" : "2015-02-03T16:43:05.830GMT",
+      "executorId" : "<driver>",
+      "host" : "localhost",
+      "taskLocality" : "PROCESS_LOCAL",
+      "speculative" : false,
+      "accumulatorUpdates" : [ ],
+      "taskMetrics" : {
+        "executorDeserializeTime" : 2,
+        "executorRunTime" : 434,
+        "resultSize" : 1902,
+        "jvmGcTime" : 19,
+        "resultSerializationTime" : 1,
+        "memoryBytesSpilled" : 0,
+        "diskBytesSpilled" : 0,
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new file mode 100644
index 0000000000000..ef339f89afa45
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/1/json_expectation
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diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/json_expectation
new file mode 100644
index 0000000000000..056fac7088594
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages/json_expectation
@@ -0,0 +1,89 @@
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=complete/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=complete/json_expectation
new file mode 100644
index 0000000000000..31ac9beea8788
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=complete/json_expectation
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+}, {
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+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation
new file mode 100644
index 0000000000000..bff6a4f69d077
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/stages?status=failed/json_expectation
@@ -0,0 +1,23 @@
+[ {
+  "status" : "FAILED",
+  "stageId" : 2,
+  "attemptId" : 0,
+  "numActiveTasks" : 0,
+  "numCompleteTasks" : 7,
+  "numFailedTasks" : 1,
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+  "shuffleWriteRecords" : 0,
+  "memoryBytesSpilled" : 0,
+  "diskBytesSpilled" : 0,
+  "name" : "count at <console>:20",
+  "details" : "org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line11.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line11.$read$$iwC$$iwC$$iwC.<init>(<console>:25)\n$line11.$read$$iwC$$iwC.<init>(<console>:27)\n$line11.$read$$iwC.<init>(<console>:29)\n$line11.$read.<init>(<console>:31)\n$line11.$read$.<init>(<console>:35)\n$line11.$read$.<clinit>(<console>)\n$line11.$eval$.<init>(<console>:7)\n$line11.$eval$.<clinit>(<console>)\n$line11.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)",
+  "schedulingPool" : "default",
+  "accumulatorUpdates" : [ ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation
new file mode 100644
index 0000000000000..38b5328ffbb03
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/0/json_expectation
@@ -0,0 +1,64 @@
+{
+  "id" : 0,
+  "name" : "0",
+  "numPartitions" : 8,
+  "numCachedPartitions" : 8,
+  "storageLevel" : "Memory Deserialized 1x Replicated",
+  "memoryUsed" : 28000128,
+  "diskUsed" : 0,
+  "dataDistribution" : [ {
+    "address" : "localhost:57971",
+    "memoryUsed" : 28000128,
+    "memoryRemaining" : 250302428,
+    "diskUsed" : 0
+  } ],
+  "partitions" : [ {
+    "blockName" : "rdd_0_0",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_1",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_2",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_3",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_4",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_5",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_6",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  }, {
+    "blockName" : "rdd_0_7",
+    "storageLevel" : "Memory Deserialized 1x Replicated",
+    "memoryUsed" : 3500016,
+    "diskUsed" : 0,
+    "executors" : [ "localhost:57971" ]
+  } ]
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation
new file mode 100644
index 0000000000000..f79a31022d214
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1422981780767/storage/rdd/json_expectation
@@ -0,0 +1,9 @@
+[ {
+  "id" : 0,
+  "name" : "0",
+  "numPartitions" : 8,
+  "numCachedPartitions" : 8,
+  "storageLevel" : "Memory Deserialized 1x Replicated",
+  "memoryUsed" : 28000128,
+  "diskUsed" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation
new file mode 100644
index 0000000000000..2e92e1fa0ec23
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/jobs/json_expectation
@@ -0,0 +1,15 @@
+[ {
+  "jobId" : 0,
+  "name" : "foreach at <console>:15",
+  "stageIds" : [ 0 ],
+  "status" : "SUCCEEDED",
+  "numTasks" : 8,
+  "numActiveTasks" : 0,
+  "numCompletedTasks" : 8,
+  "numSkippedTasks" : 8,
+  "numFailedTasks" : 0,
+  "numActiveStages" : 0,
+  "numCompletedStages" : 1,
+  "numSkippedStages" : 0,
+  "numFailedStages" : 0
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation
new file mode 100644
index 0000000000000..32d5731676ad5
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/json_expectation
@@ -0,0 +1,242 @@
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+  } ],
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+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation
new file mode 100644
index 0000000000000..c3febc5fc9447
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/0/0/taskList/json_expectation
@@ -0,0 +1,193 @@
+[ {
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+  "launchTime" : "2015-03-16T19:25:36.515GMT",
+  "executorId" : "<driver>",
+  "host" : "localhost",
+  "taskLocality" : "PROCESS_LOCAL",
+  "speculative" : false,
+  "accumulatorUpdates" : [ {
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+    "name" : "my counter",
+    "update" : "78",
+    "value" : "5050"
+  } ],
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation
new file mode 100644
index 0000000000000..79ccacd309693
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/1/stages/json_expectation
@@ -0,0 +1,27 @@
+[ {
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation
new file mode 100644
index 0000000000000..2e92e1fa0ec23
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/jobs/json_expectation
@@ -0,0 +1,15 @@
+[ {
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+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/stages/0/0/taskList/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/2/stages/0/0/taskList/json_expectation
new file mode 100644
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diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation
new file mode 100644
index 0000000000000..8f3d7160c723f
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1426533911241/json_expectation
@@ -0,0 +1,17 @@
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+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
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+  } ]
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskList/json_expectation
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new file mode 100644
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\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation
new file mode 100644
index 0000000000000..46f32c19abed5
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary/json_expectation
@@ -0,0 +1,15 @@
+{
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+  "shuffleWriteMetrics" : {
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+    "writeRecords" : [ 100.0, 100.0, 100.0, 100.0, 100.0 ],
+    "writeTime" : [ 278805.0, 308809.0, 327953.0, 367487.0, 944783.0 ]
+  }
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation
new file mode 100644
index 0000000000000..65b46388ced87
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99/json_expectation
@@ -0,0 +1,15 @@
+{
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+    "writeTime" : [ 260991.0, 327953.0, 8980526.0 ]
+  }
+}
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation
new file mode 100644
index 0000000000000..483632a3956ed
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-03T10:42:40.000CST/json_expectation
@@ -0,0 +1,10 @@
+[ {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation
new file mode 100644
index 0000000000000..4b85690fd9199
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications?maxDate=2015-02-10/json_expectation
@@ -0,0 +1,19 @@
+[ {
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation
new file mode 100644
index 0000000000000..e67f25252e934
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications?minDate=2015-02-10/json_expectation
@@ -0,0 +1,35 @@
+[ {
+  "id" : "local-1427397477963",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-03-26T19:17:57.184GMT",
+    "endTime" : "2015-03-26T19:20:02.949GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1425081759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-28T00:02:38.277GMT",
+    "endTime" : "2015-02-28T00:02:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation
new file mode 100644
index 0000000000000..6101177e4bb85
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications?status=completed/json_expectation
@@ -0,0 +1,53 @@
+[ {
+  "id" : "local-1427397477963",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-03-26T19:17:57.184GMT",
+    "endTime" : "2015-03-26T19:20:02.949GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1426533911241",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "attemptId" : "2",
+    "startTime" : "2015-03-17T23:11:50.242GMT",
+    "endTime" : "2015-03-17T23:12:25.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  }, {
+    "attemptId" : "1",
+    "startTime" : "2015-03-16T19:25:10.242GMT",
+    "endTime" : "2015-03-16T19:25:45.177GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1425081759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-28T00:02:38.277GMT",
+    "endTime" : "2015-02-28T00:02:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981780767",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:59.720GMT",
+    "endTime" : "2015-02-03T16:43:08.731GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+}, {
+  "id" : "local-1422981759269",
+  "name" : "Spark shell",
+  "attempts" : [ {
+    "startTime" : "2015-02-03T16:42:38.277GMT",
+    "endTime" : "2015-02-03T16:42:46.912GMT",
+    "sparkUser" : "irashid",
+    "completed" : true
+  } ]
+} ]
\ No newline at end of file
diff --git a/core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation b/core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation
new file mode 100644
index 0000000000000..8878e547a7984
--- /dev/null
+++ b/core/src/test/resources/HistoryServerExpectations/applications?status=running/json_expectation
@@ -0,0 +1 @@
+[ ]
\ No newline at end of file
diff --git a/core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1422981759269/APPLICATION_COMPLETE
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
new file mode 100755
index 0000000000000..4794e56d1107a
--- /dev/null
+++ b/core/src/test/resources/spark-events/local-1422981759269/EVENT_LOG_1
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diff --git a/core/src/test/resources/spark-events/local-1422981780767/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1422981780767/APPLICATION_COMPLETE
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diff --git a/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1422981780767/EVENT_LOG_1
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+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":18,"Index":2,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":34,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":16,"Index":0,"Attempt":0,"Launch Time":1422981786296,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786340,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":23,"Index":7,"Attempt":0,"Launch Time":1422981786299,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786341,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":1,"Executor Run Time":35,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":22,"Index":6,"Attempt":0,"Launch Time":1422981786298,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786342,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":2,"Executor Run Time":34,"Result Size":862,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":2,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"ExceptionFailure","Class Name":"java.lang.RuntimeException","Description":"got a 3, failing","Stack Trace":[{"Declaring Class":"$line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1","Method Name":"apply","File Name":"<console>","Line Number":18},{"Declaring Class":"$line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1","Method Name":"apply","File Name":"<console>","Line Number":17},{"Declaring Class":"scala.collection.Iterator$$anon$11","Method Name":"next","File Name":"Iterator.scala","Line Number":328},{"Declaring Class":"org.apache.spark.util.Utils$","Method Name":"getIteratorSize","File Name":"Utils.scala","Line Number":1311},{"Declaring Class":"org.apache.spark.rdd.RDD$$anonfun$count$1","Method Name":"apply","File Name":"RDD.scala","Line Number":910},{"Declaring Class":"org.apache.spark.rdd.RDD$$anonfun$count$1","Method Name":"apply","File Name":"RDD.scala","Line Number":910},{"Declaring Class":"org.apache.spark.SparkContext$$anonfun$runJob$4","Method Name":"apply","File Name":"SparkContext.scala","Line Number":1314},{"Declaring Class":"org.apache.spark.SparkContext$$anonfun$runJob$4","Method Name":"apply","File Name":"SparkContext.scala","Line Number":1314},{"Declaring Class":"org.apache.spark.scheduler.ResultTask","Method Name":"runTask","File Name":"ResultTask.scala","Line Number":61},{"Declaring Class":"org.apache.spark.scheduler.Task","Method Name":"run","File Name":"Task.scala","Line Number":56},{"Declaring Class":"org.apache.spark.executor.Executor$TaskRunner","Method Name":"run","File Name":"Executor.scala","Line Number":196},{"Declaring Class":"java.util.concurrent.ThreadPoolExecutor","Method Name":"runWorker","File Name":"ThreadPoolExecutor.java","Line Number":1145},{"Declaring Class":"java.util.concurrent.ThreadPoolExecutor$Worker","Method Name":"run","File Name":"ThreadPoolExecutor.java","Line Number":615},{"Declaring Class":"java.lang.Thread","Method Name":"run","File Name":"Thread.java","Line Number":745}],"Full Stack Trace":"java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n","Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":36,"Result Size":0,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}},"Task Info":{"Task ID":19,"Index":3,"Attempt":0,"Launch Time":1422981786297,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981786343,"Failed":true,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":36,"Result Size":0,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":8,"Fetch Wait Time":0,"Remote Bytes Read":0}}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":2,"Stage Attempt ID":0,"Stage Name":"count at <console>:20","Number of Tasks":8,"RDD Info":[{"RDD ID":3,"Name":"3","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line11.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:20)\n$line11.$read$$iwC$$iwC$$iwC.<init>(<console>:25)\n$line11.$read$$iwC$$iwC.<init>(<console>:27)\n$line11.$read$$iwC.<init>(<console>:29)\n$line11.$read.<init>(<console>:31)\n$line11.$read$.<init>(<console>:35)\n$line11.$read$.<clinit>(<console>)\n$line11.$eval$.<init>(<console>:7)\n$line11.$eval$.<clinit>(<console>)\n$line11.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Submission Time":1422981786296,"Completion Time":1422981786347,"Failure Reason":"Job aborted due to stage failure: Task 3 in stage 2.0 failed 1 times, most recent failure: Lost task 3.0 in stage 2.0 (TID 19, localhost): java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:","Accumulables":[]}}
+{"Event":"SparkListenerJobEnd","Job ID":1,"Job Result":{"Result":"JobFailed","Exception":{"Message":"Job aborted due to stage failure: Task 3 in stage 2.0 failed 1 times, most recent failure: Lost task 3.0 in stage 2.0 (TID 19, localhost): java.lang.RuntimeException: got a 3, failing\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:18)\n\tat $line11.$read$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:17)\n\tat scala.collection.Iterator$$anon$11.next(Iterator.scala:328)\n\tat org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1311)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1314)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:56)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)\n\tat java.lang.Thread.run(Thread.java:745)\n\nDriver stacktrace:","Stack Trace":[{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages","File Name":"DAGScheduler.scala","Line Number":1214},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":1203},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":1202},{"Declaring Class":"scala.collection.mutable.ResizableArray$class","Method Name":"foreach","File Name":"ResizableArray.scala","Line Number":59},{"Declaring Class":"scala.collection.mutable.ArrayBuffer","Method Name":"foreach","File Name":"ArrayBuffer.scala","Line Number":47},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"abortStage","File Name":"DAGScheduler.scala","Line Number":1202},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1","Method Name":"apply","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"scala.Option","Method Name":"foreach","File Name":"Option.scala","Line Number":236},{"Declaring Class":"org.apache.spark.scheduler.DAGScheduler","Method Name":"handleTaskSetFailed","File Name":"DAGScheduler.scala","Line Number":696},{"Declaring Class":"org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2","Method Name":"applyOrElse","File Name":"DAGScheduler.scala","Line Number":1420},{"Declaring Class":"akka.actor.Actor$class","Method Name":"aroundReceive","File Name":"Actor.scala","Line Number":465},{"Declaring Class":"org.apache.spark.scheduler.DAGSchedulerEventProcessActor","Method Name":"aroundReceive","File Name":"DAGScheduler.scala","Line Number":1375},{"Declaring Class":"akka.actor.ActorCell","Method Name":"receiveMessage","File Name":"ActorCell.scala","Line Number":516},{"Declaring Class":"akka.actor.ActorCell","Method Name":"invoke","File Name":"ActorCell.scala","Line Number":487},{"Declaring Class":"akka.dispatch.Mailbox","Method Name":"processMailbox","File Name":"Mailbox.scala","Line Number":238},{"Declaring Class":"akka.dispatch.Mailbox","Method Name":"run","File Name":"Mailbox.scala","Line Number":220},{"Declaring Class":"akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask","Method Name":"exec","File Name":"AbstractDispatcher.scala","Line Number":393},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinTask","Method Name":"doExec","File Name":"ForkJoinTask.java","Line Number":260},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinPool$WorkQueue","Method Name":"runTask","File Name":"ForkJoinPool.java","Line Number":1339},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinPool","Method Name":"runWorker","File Name":"ForkJoinPool.java","Line Number":1979},{"Declaring Class":"scala.concurrent.forkjoin.ForkJoinWorkerThread","Method Name":"run","File Name":"ForkJoinWorkerThread.java","Line Number":107}]}}}
+{"Event":"SparkListenerJobStart","Job ID":2,"Stage Infos":[{"Stage ID":3,"Stage Attempt ID":0,"Stage Name":"count at <console>:17","Number of Tasks":8,"RDD Info":[{"RDD ID":6,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":5,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Accumulables":[]}],"Stage IDs":[3]}
+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":3,"Stage Attempt ID":0,"Stage Name":"count at <console>:17","Number of Tasks":8,"RDD Info":[{"RDD ID":6,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0},{"RDD ID":5,"Name":"/Users/irashid/spark-examples/tmp_data/sums_2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":8,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"org.apache.spark.rdd.RDD.count(RDD.scala:910)\n$line19.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:17)\n$line19.$read$$iwC$$iwC$$iwC.<init>(<console>:22)\n$line19.$read$$iwC$$iwC.<init>(<console>:24)\n$line19.$read$$iwC.<init>(<console>:26)\n$line19.$read.<init>(<console>:28)\n$line19.$read$.<init>(<console>:32)\n$line19.$read$.<clinit>(<console>)\n$line19.$eval$.<init>(<console>:7)\n$line19.$eval$.<clinit>(<console>)\n$line19.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:606)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:852)\norg.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1125)\norg.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:674)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:705)\norg.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:669)","Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":24,"Index":0,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":25,"Index":1,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":26,"Index":2,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":27,"Index":3,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":28,"Index":4,"Attempt":0,"Launch Time":1422981787193,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":29,"Index":5,"Attempt":0,"Launch Time":1422981787193,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":30,"Index":6,"Attempt":0,"Launch Time":1422981787194,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":3,"Stage Attempt ID":0,"Task Info":{"Task ID":31,"Index":7,"Attempt":0,"Launch Time":1422981787194,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":25,"Index":1,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787222,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":5,"Executor Run Time":20,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":32}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":24,"Index":0,"Attempt":0,"Launch Time":1422981787191,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787223,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":5,"Executor Run Time":20,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":32}}}
+{"Event":"SparkListenerTaskEnd","Stage ID":3,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":26,"Index":2,"Attempt":0,"Launch Time":1422981787192,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1422981787223,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":4,"Executor Run Time":22,"Result Size":1812,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Input Metrics":{"Data Read Method":"Hadoop","Bytes Read":16}}}
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1425081759269/APPLICATION_COMPLETE
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1425081759269/EVENT_LOG_1
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index 0000000000000..9745b36b09e44
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diff --git a/core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1425081759269/SPARK_VERSION_1.2.0
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diff --git a/core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1426533911241/APPLICATION_COMPLETE
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diff --git a/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1426533911241/EVENT_LOG_1
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index 0000000000000..9ef5bd5d92de5
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index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE b/core/src/test/resources/spark-events/local-1426633911242/APPLICATION_COMPLETE
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index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1 b/core/src/test/resources/spark-events/local-1426633911242/EVENT_LOG_1
new file mode 100755
index 0000000000000..e7043282107d3
--- /dev/null
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diff --git a/core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0 b/core/src/test/resources/spark-events/local-1426633911242/SPARK_VERSION_1.2.0
new file mode 100755
index 0000000000000..e69de29bb2d1d
diff --git a/core/src/test/resources/spark-events/local-1427397477963 b/core/src/test/resources/spark-events/local-1427397477963
new file mode 100755
index 0000000000000..3bd251d48c87b
--- /dev/null
+++ b/core/src/test/resources/spark-events/local-1427397477963
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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":21,"Stage Attempt ID":0,"Stage Name":"next at <console>:26","Number of Tasks":1,"RDD Info":[{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":50,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"scala.collection.Iterator$$anon$13.next(Iterator.scala:372)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)\n$line40.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:37)\n$line40.$read$$iwC$$iwC$$iwC.<init>(<console>:39)\n$line40.$read$$iwC$$iwC.<init>(<console>:41)\n$line40.$read$$iwC.<init>(<console>:43)\n$line40.$read.<init>(<console>:45)\n$line40.$read$.<init>(<console>:49)\n$line40.$read$.<clinit>(<console>)\n$line40.$eval$.<init>(<console>:7)\n$line40.$eval$.<clinit>(<console>)\n$line40.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:483)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)","Accumulables":[]}}
+{"Event":"SparkListenerTaskStart","Stage ID":21,"Stage Attempt ID":0,"Task Info":{"Task ID":6010,"Index":0,"Attempt":0,"Launch Time":1427397601860,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskEnd","Stage ID":21,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":6010,"Index":0,"Attempt":0,"Launch Time":1427397601860,"Executor ID":"<driver>","Host":"localhost","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1427397601971,"Failed":false,"Accumulables":[]},"Task Metrics":{"Host Name":"localhost","Executor Deserialize Time":0,"Executor Run Time":109,"Result Size":1060,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":1000,"Fetch Wait Time":0,"Remote Bytes Read":0,"Local Bytes Read":191840,"Total Records Read":2000}}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":21,"Stage Attempt ID":0,"Stage Name":"next at <console>:26","Number of Tasks":1,"RDD Info":[{"RDD ID":2,"Name":"2","Storage Level":{"Use Disk":false,"Use Memory":false,"Use Tachyon":false,"Deserialized":false,"Replication":1},"Number of Partitions":50,"Number of Cached Partitions":0,"Memory Size":0,"Tachyon Size":0,"Disk Size":0}],"Details":"scala.collection.Iterator$$anon$13.next(Iterator.scala:372)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:26)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:31)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:33)\n$line40.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)\n$line40.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:37)\n$line40.$read$$iwC$$iwC$$iwC.<init>(<console>:39)\n$line40.$read$$iwC$$iwC.<init>(<console>:41)\n$line40.$read$$iwC.<init>(<console>:43)\n$line40.$read.<init>(<console>:45)\n$line40.$read$.<init>(<console>:49)\n$line40.$read$.<clinit>(<console>)\n$line40.$eval$.<init>(<console>:7)\n$line40.$eval$.<clinit>(<console>)\n$line40.$eval.$print(<console>)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:483)\norg.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)","Submission Time":1427397601860,"Completion Time":1427397601971,"Accumulables":[]}}
+{"Event":"SparkListenerJobEnd","Job ID":10,"Completion Time":1427397601971,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"SparkListenerApplicationEnd","Timestamp":1427397602949}
diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala
index 9bd5dfec8703a..c42dfbc82ada4 100644
--- a/core/src/test/scala/org/apache/spark/DriverSuite.scala
+++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala
@@ -28,7 +28,7 @@ import org.apache.spark.util.Utils
 
 class DriverSuite extends FunSuite with Timeouts {
 
-  test("driver should exit after finishing without cleanup (SPARK-530)") {
+  ignore("driver should exit after finishing without cleanup (SPARK-530)") {
     val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
     val masters = Table("master", "local", "local-cluster[2,1,512]")
     forAll(masters) { (master: String) =>
diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
index 49e6de4e0bafd..84f787ee3715d 100644
--- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
@@ -727,7 +727,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester {
   private val executorIdleTimeout = 3L
 
   private def createStageInfo(stageId: Int, numTasks: Int): StageInfo = {
-    new StageInfo(stageId, 0, "name", numTasks, Seq.empty, "no details")
+    new StageInfo(stageId, 0, "name", numTasks, Seq.empty, Seq.empty, "no details")
   }
 
   private def createTaskInfo(taskId: Int, taskIndex: Int, executorId: String): TaskInfo = {
diff --git a/core/src/test/scala/org/apache/spark/JsonTestUtils.scala b/core/src/test/scala/org/apache/spark/JsonTestUtils.scala
new file mode 100644
index 0000000000000..ba367cd476146
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/JsonTestUtils.scala
@@ -0,0 +1,34 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark
+
+import org.json4s._
+import org.json4s.jackson.JsonMethods
+
+trait JsonTestUtils {
+  def assertValidDataInJson(validateJson: JValue, expectedJson: JValue) {
+    val Diff(c, a, d) = validateJson.diff(expectedJson)
+    val validatePretty = JsonMethods.pretty(validateJson)
+    val expectedPretty = JsonMethods.pretty(expectedJson)
+    val errorMessage = s"Expected:\n$expectedPretty\nFound:\n$validatePretty"
+    import org.scalactic.TripleEquals._
+    assert(c === JNothing, s"$errorMessage\nChanged:\n${JsonMethods.pretty(c)}")
+    assert(a === JNothing, s"$errorMessage\nAdded:\n${JsonMethods.pretty(a)}")
+    assert(d === JNothing, s"$errorMessage\nDeleted:\n${JsonMethods.pretty(d)}")
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
index b58d62567afe1..e04a79284175c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala
@@ -28,9 +28,9 @@ import org.scalatest.FunSuite
 import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse}
 import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo}
 import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner}
-import org.apache.spark.{SecurityManager, SparkConf}
+import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf}
 
-class JsonProtocolSuite extends FunSuite {
+class JsonProtocolSuite extends FunSuite with JsonTestUtils {
 
   test("writeApplicationInfo") {
     val output = JsonProtocol.writeApplicationInfo(createAppInfo())
@@ -136,16 +136,6 @@ class JsonProtocolSuite extends FunSuite {
       case e: JsonParseException => fail("Invalid Json detected", e)
     }
   }
-
-  def assertValidDataInJson(validateJson: JValue, expectedJson: JValue) {
-    val Diff(c, a, d) = validateJson diff expectedJson
-    val validatePretty = JsonMethods.pretty(validateJson)
-    val expectedPretty = JsonMethods.pretty(expectedJson)
-    val errorMessage = s"Expected:\n$expectedPretty\nFound:\n$validatePretty"
-    assert(c === JNothing, s"$errorMessage\nChanged:\n${JsonMethods.pretty(c)}")
-    assert(a === JNothing, s"$errorMessage\nAdded:\n${JsonMethods.pretty(a)}")
-    assert(d === JNothing, s"$errorMessage\nDelected:\n${JsonMethods.pretty(d)}")
-  }
 }
 
 object JsonConstants {
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 029a1156fda6b..61c95419aedcf 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -320,7 +320,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties
     runSparkSubmit(args)
   }
 
-  test("includes jars passed in through --jars") {
+  ignore("includes jars passed in through --jars") {
     val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
     val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA"))
     val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB"))
diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
index 71ba9c18257b8..0744b68c695f9 100644
--- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala
@@ -14,22 +14,161 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-
 package org.apache.spark.deploy.history
 
-import javax.servlet.http.HttpServletRequest
-
-import scala.collection.mutable
+import java.io.{File, FileInputStream, FileWriter, IOException}
+import java.net.{HttpURLConnection, URL}
+import javax.servlet.http.{HttpServletRequest, HttpServletResponse}
 
-import org.apache.hadoop.fs.Path
-import org.mockito.Mockito.{when}
-import org.scalatest.FunSuite
-import org.scalatest.Matchers
+import org.apache.commons.io.{FileUtils, IOUtils}
+import org.mockito.Mockito.when
+import org.scalatest.{BeforeAndAfter, FunSuite, Matchers}
 import org.scalatest.mock.MockitoSugar
 
+import org.apache.spark.{JsonTestUtils, SecurityManager, SparkConf}
 import org.apache.spark.ui.SparkUI
 
-class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar {
+/**
+ * A collection of tests against the historyserver, including comparing responses from the json
+ * metrics api to a set of known "golden files".  If new endpoints / parameters are added,
+ * cases should be added to this test suite.  The expected outcomes can be genered by running
+ * the HistoryServerSuite.main.  Note that this will blindly generate new expectation files matching
+ * the current behavior -- the developer must verify that behavior is correct.
+ *
+ * Similarly, if the behavior is changed, HistoryServerSuite.main can be run to update the
+ * expectations.  However, in general this should be done with extreme caution, as the metrics
+ * are considered part of Spark's public api.
+ */
+class HistoryServerSuite extends FunSuite with BeforeAndAfter with Matchers with MockitoSugar
+  with JsonTestUtils {
+
+  private val logDir = new File("src/test/resources/spark-events")
+  private val expRoot = new File("src/test/resources/HistoryServerExpectations/")
+
+  private var provider: FsHistoryProvider = null
+  private var server: HistoryServer = null
+  private var port: Int = -1
+
+  def init(): Unit = {
+    val conf = new SparkConf()
+      .set("spark.history.fs.logDirectory", logDir.getAbsolutePath)
+      .set("spark.history.fs.updateInterval", "0")
+      .set("spark.testing", "true")
+    provider = new FsHistoryProvider(conf)
+    provider.checkForLogs()
+    val securityManager = new SecurityManager(conf)
+
+    server = new HistoryServer(conf, provider, securityManager, 18080)
+    server.initialize()
+    server.bind()
+    port = server.boundPort
+  }
+
+  def stop(): Unit = {
+    server.stop()
+  }
+
+  before {
+    init()
+  }
+
+  after{
+    stop()
+  }
+
+  val cases = Seq(
+    "application list json" -> "applications",
+    "completed app list json" -> "applications?status=completed",
+    "running app list json" -> "applications?status=running",
+    "minDate app list json" -> "applications?minDate=2015-02-10",
+    "maxDate app list json" -> "applications?maxDate=2015-02-10",
+    "maxDate2 app list json" -> "applications?maxDate=2015-02-03T10:42:40.000CST",
+    "one app json" -> "applications/local-1422981780767",
+    "one app multi-attempt json" -> "applications/local-1426533911241",
+    "job list json" -> "applications/local-1422981780767/jobs",
+    "job list from multi-attempt app json(1)" -> "applications/local-1426533911241/1/jobs",
+    "job list from multi-attempt app json(2)" -> "applications/local-1426533911241/2/jobs",
+    "one job json" -> "applications/local-1422981780767/jobs/0",
+    "succeeded job list json" -> "applications/local-1422981780767/jobs?status=succeeded",
+    "succeeded&failed job list json" ->
+      "applications/local-1422981780767/jobs?status=succeeded&status=failed",
+    "executor list json" -> "applications/local-1422981780767/executors",
+    "stage list json" -> "applications/local-1422981780767/stages",
+    "complete stage list json" -> "applications/local-1422981780767/stages?status=complete",
+    "failed stage list json" -> "applications/local-1422981780767/stages?status=failed",
+    "one stage json" -> "applications/local-1422981780767/stages/1",
+    "one stage attempt json" -> "applications/local-1422981780767/stages/1/0",
+
+    "stage task summary" -> "applications/local-1427397477963/stages/20/0/taskSummary",
+    "stage task summary w/ custom quantiles" ->
+      "applications/local-1427397477963/stages/20/0/taskSummary?quantiles=0.01,0.5,0.99",
+
+    "stage task list" -> "applications/local-1427397477963/stages/20/0/taskList",
+    "stage task list w/ offset & length" ->
+      "applications/local-1427397477963/stages/20/0/taskList?offset=10&length=50",
+    "stage task list w/ sortBy" ->
+      "applications/local-1427397477963/stages/20/0/taskList?sortBy=DECREASING_RUNTIME",
+    "stage task list w/ sortBy short names: -runtime" ->
+      "applications/local-1427397477963/stages/20/0/taskList?sortBy=-runtime",
+    "stage task list w/ sortBy short names: runtime" ->
+      "applications/local-1427397477963/stages/20/0/taskList?sortBy=runtime",
+
+    "stage list with accumulable json" -> "applications/local-1426533911241/1/stages",
+    "stage with accumulable json" -> "applications/local-1426533911241/1/stages/0/0",
+    "stage task list from multi-attempt app json(1)" ->
+      "applications/local-1426533911241/1/stages/0/0/taskList",
+    "stage task list from multi-attempt app json(2)" ->
+      "applications/local-1426533911241/2/stages/0/0/taskList",
+
+    "rdd list storage json" -> "applications/local-1422981780767/storage/rdd",
+    "one rdd storage json" -> "applications/local-1422981780767/storage/rdd/0"
+  )
+
+  // run a bunch of characterization tests -- just verify the behavior is the same as what is saved
+  // in the test resource folder
+  cases.foreach { case (name, path) =>
+    test(name) {
+      val (code, jsonOpt, errOpt) = getContentAndCode(path)
+      code should be (HttpServletResponse.SC_OK)
+      jsonOpt should be ('defined)
+      errOpt should be (None)
+      val json = jsonOpt.get
+      val exp = IOUtils.toString(new FileInputStream(
+        new File(expRoot, path + "/json_expectation")))
+      // compare the ASTs so formatting differences don't cause failures
+      import org.json4s._
+      import org.json4s.jackson.JsonMethods._
+      val jsonAst = parse(json)
+      val expAst = parse(exp)
+      assertValidDataInJson(jsonAst, expAst)
+    }
+  }
+
+  test("response codes on bad paths") {
+    val badAppId = getContentAndCode("applications/foobar")
+    badAppId._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    badAppId._3 should be (Some("unknown app: foobar"))
+
+    val badStageId = getContentAndCode("applications/local-1422981780767/stages/12345")
+    badStageId._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    badStageId._3 should be (Some("unknown stage: 12345"))
+
+    val badStageAttemptId = getContentAndCode("applications/local-1422981780767/stages/1/1")
+    badStageAttemptId._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    badStageAttemptId._3 should be (Some("unknown attempt for stage 1.  Found attempts: [0]"))
+
+    val badStageId2 = getContentAndCode("applications/local-1422981780767/stages/flimflam")
+    badStageId2._1 should be (HttpServletResponse.SC_NOT_FOUND)
+    // will take some mucking w/ jersey to get a better error msg in this case
+
+    val badQuantiles = getContentAndCode(
+      "applications/local-1427397477963/stages/20/0/taskSummary?quantiles=foo,0.1")
+    badQuantiles._1 should be (HttpServletResponse.SC_BAD_REQUEST)
+    badQuantiles._3 should be (Some("Bad value for parameter \"quantiles\".  Expected a double, " +
+      "got \"foo\""))
+
+    getContentAndCode("foobar")._1 should be (HttpServletResponse.SC_NOT_FOUND)
+  }
 
   test("generate history page with relative links") {
     val historyServer = mock[HistoryServer]
@@ -54,4 +193,70 @@ class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar {
     } yield (attrs.toString)
     justHrefs should contain(link)
   }
+
+  def getContentAndCode(path: String, port: Int = port): (Int, Option[String], Option[String]) = {
+    HistoryServerSuite.getContentAndCode(new URL(s"http://localhost:$port/json/v1/$path"))
+  }
+
+  def getUrl(path: String): String = {
+    HistoryServerSuite.getUrl(new URL(s"http://localhost:$port/json/v1/$path"))
+  }
+
+  def generateExpectation(path: String): Unit = {
+    val json = getUrl(path)
+    val dir = new File(expRoot, path)
+    dir.mkdirs()
+    val out = new FileWriter(new File(dir, "json_expectation"))
+    out.write(json)
+    out.close()
+  }
+}
+
+object HistoryServerSuite {
+  def main(args: Array[String]): Unit = {
+    // generate the "expected" results for the characterization tests.  Just blindly assume the
+    // current behavior is correct, and write out the returned json to the test/resource files
+
+    val suite = new HistoryServerSuite
+    FileUtils.deleteDirectory(suite.expRoot)
+    suite.expRoot.mkdirs()
+    try {
+      suite.init()
+      suite.cases.foreach { case (_, path) =>
+        suite.generateExpectation(path)
+      }
+    } finally {
+      suite.stop()
+    }
+  }
+
+  def getContentAndCode(url: URL): (Int, Option[String], Option[String]) = {
+    val connection = url.openConnection().asInstanceOf[HttpURLConnection]
+    connection.setRequestMethod("GET")
+    connection.connect()
+    val code = connection.getResponseCode()
+    val inString = try {
+      val in = Option(connection.getInputStream())
+      in.map{IOUtils.toString}
+    } catch {
+      case io: IOException => None
+    }
+    val errString = try {
+      val err = Option(connection.getErrorStream())
+      err.map{IOUtils.toString}
+    } catch {
+      case io: IOException => None
+    }
+    (code, inString, errString)
+  }
+
+  def getUrl(path: URL): String = {
+    val (code, resultOpt, error) = getContentAndCode(path)
+    if (code == 200) {
+      resultOpt.get
+    } else {
+      throw new RuntimeException(
+        "got code: " + code + " when getting " + path + " w/ error: " + error)
+    }
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
new file mode 100644
index 0000000000000..d75ecbf1f0b4d
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
@@ -0,0 +1,133 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import org.scalatest.{BeforeAndAfter, FunSuite}
+
+import org.apache.spark.{TaskContext, Partition, SparkContext}
+
+/**
+ *
+ */
+class RDDOperationScopeSuite extends FunSuite with BeforeAndAfter {
+  private var sc: SparkContext = null
+  private val scope1 = new RDDOperationScope("scope1")
+  private val scope2 = new RDDOperationScope("scope2", parent = Some(scope1))
+  private val scope3 = new RDDOperationScope("scope3", parent = Some(scope2))
+
+  before {
+    sc = new SparkContext("local", "test")
+  }
+
+  after {
+    sc.stop()
+  }
+
+  test("getAllScopes") {
+    assert(scope1.getAllScopes === Seq(scope1))
+    assert(scope2.getAllScopes === Seq(scope1, scope2))
+    assert(scope3.getAllScopes === Seq(scope1, scope2, scope3))
+  }
+
+  test("json de/serialization") {
+    val scope1Json = scope1.toJson
+    val scope2Json = scope2.toJson
+    val scope3Json = scope3.toJson
+    assert(scope1Json === s"""{"id":${scope1.id},"name":"scope1"}""")
+    assert(scope2Json === s"""{"id":${scope2.id},"name":"scope2","parent":$scope1Json}""")
+    assert(scope3Json === s"""{"id":${scope3.id},"name":"scope3","parent":$scope2Json}""")
+    assert(RDDOperationScope.fromJson(scope1Json) === scope1)
+    assert(RDDOperationScope.fromJson(scope2Json) === scope2)
+    assert(RDDOperationScope.fromJson(scope3Json) === scope3)
+  }
+
+  test("withScope") {
+    val rdd0: MyCoolRDD = new MyCoolRDD(sc)
+    var rdd1: MyCoolRDD = null
+    var rdd2: MyCoolRDD = null
+    var rdd3: MyCoolRDD = null
+    RDDOperationScope.withScope(sc, "scope1", allowNesting = false) {
+      rdd1 = new MyCoolRDD(sc)
+      RDDOperationScope.withScope(sc, "scope2", allowNesting = false) {
+        rdd2 = new MyCoolRDD(sc)
+        RDDOperationScope.withScope(sc, "scope3", allowNesting = false) {
+          rdd3 = new MyCoolRDD(sc)
+        }
+      }
+    }
+    assert(rdd0.scope.isEmpty)
+    assert(rdd1.scope.isDefined)
+    assert(rdd2.scope.isDefined)
+    assert(rdd3.scope.isDefined)
+    assert(rdd1.scope.get.getAllScopes.map(_.name) === Seq("scope1"))
+    assert(rdd2.scope.get.getAllScopes.map(_.name) === Seq("scope1"))
+    assert(rdd3.scope.get.getAllScopes.map(_.name) === Seq("scope1"))
+  }
+
+  test("withScope with partial nesting") {
+    val rdd0: MyCoolRDD = new MyCoolRDD(sc)
+    var rdd1: MyCoolRDD = null
+    var rdd2: MyCoolRDD = null
+    var rdd3: MyCoolRDD = null
+    RDDOperationScope.withScope(sc, "scope1", allowNesting = true) { // allow nesting here
+      rdd1 = new MyCoolRDD(sc)
+      RDDOperationScope.withScope(sc, "scope2", allowNesting = false) { // stop nesting here
+        rdd2 = new MyCoolRDD(sc)
+        RDDOperationScope.withScope(sc, "scope3", allowNesting = false) {
+          rdd3 = new MyCoolRDD(sc)
+        }
+      }
+    }
+    assert(rdd0.scope.isEmpty)
+    assert(rdd1.scope.isDefined)
+    assert(rdd2.scope.isDefined)
+    assert(rdd3.scope.isDefined)
+    assert(rdd1.scope.get.getAllScopes.map(_.name) === Seq("scope1"))
+    assert(rdd2.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2"))
+    assert(rdd3.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2"))
+  }
+
+  test("withScope with multiple layers of nesting") {
+    val rdd0: MyCoolRDD = new MyCoolRDD(sc)
+    var rdd1: MyCoolRDD = null
+    var rdd2: MyCoolRDD = null
+    var rdd3: MyCoolRDD = null
+    RDDOperationScope.withScope(sc, "scope1", allowNesting = true) {
+      rdd1 = new MyCoolRDD(sc)
+      RDDOperationScope.withScope(sc, "scope2", allowNesting = true) {
+        rdd2 = new MyCoolRDD(sc)
+        RDDOperationScope.withScope(sc, "scope3", allowNesting = true) {
+          rdd3 = new MyCoolRDD(sc)
+        }
+      }
+    }
+    assert(rdd0.scope.isEmpty)
+    assert(rdd1.scope.isDefined)
+    assert(rdd2.scope.isDefined)
+    assert(rdd3.scope.isDefined)
+    assert(rdd1.scope.get.getAllScopes.map(_.name) === Seq("scope1"))
+    assert(rdd2.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2"))
+    assert(rdd3.scope.get.getAllScopes.map(_.name) === Seq("scope1", "scope2", "scope3"))
+  }
+
+}
+
+private class MyCoolRDD(sc: SparkContext) extends RDD[Int](sc, Nil) {
+  override def getPartitions: Array[Partition] = Array.empty
+  override def compute(p: Partition, context: TaskContext): Iterator[Int] = { Nil.toIterator }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 2482603f420ee..6a8ae29aae675 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -174,6 +174,10 @@ class DAGSchedulerSuite
     dagEventProcessLoopTester = new DAGSchedulerEventProcessLoopTester(scheduler)
   }
 
+  after {
+    scheduler.stop()
+  }
+
   override def afterAll() {
     super.afterAll()
   }
@@ -261,8 +265,9 @@ class DAGSchedulerSuite
       override def taskSucceeded(partition: Int, value: Any) = numResults += 1
       override def jobFailed(exception: Exception) = throw exception
     }
-    submit(new MyRDD(sc, 0, Nil), Array(), listener = fakeListener)
+    val jobId = submit(new MyRDD(sc, 0, Nil), Array(), listener = fakeListener)
     assert(numResults === 0)
+    cancel(jobId)
   }
 
   test("run trivial job") {
diff --git a/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala b/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala
new file mode 100644
index 0000000000000..5274df904d395
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/status/api/v1/SimpleDateParamTest.scala
@@ -0,0 +1,29 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.status.api.v1
+
+import org.scalatest.{Matchers, FunSuite}
+
+class SimpleDateParamTest extends FunSuite with Matchers {
+
+  test("date parsing") {
+    new SimpleDateParam("2015-02-20T23:21:17.190GMT").timestamp should be (1424474477190L)
+    new SimpleDateParam("2015-02-20T17:21:17.190CST").timestamp should be (1424474477190L)
+    new SimpleDateParam("2015-02-20").timestamp should be (1424390400000L)  // GMT
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala
index ee1071cbcde69..17193ddbfd894 100644
--- a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala
@@ -287,8 +287,8 @@ class StorageSuite extends FunSuite {
 
   // For testing StorageUtils.updateRddInfo
   private def stockRDDInfos: Seq[RDDInfo] = {
-    val info0 = new RDDInfo(0, "0", 10, memAndDisk)
-    val info1 = new RDDInfo(1, "1", 3, memAndDisk)
+    val info0 = new RDDInfo(0, "0", 10, memAndDisk, Seq(3))
+    val info1 = new RDDInfo(1, "1", 3, memAndDisk, Seq(4))
     Seq(info0, info1)
   }
 
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index d53d7f3ba5ae7..117b2c3960820 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.ui
 
 import java.net.{HttpURLConnection, URL}
-import javax.servlet.http.HttpServletRequest
+import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
 import scala.collection.JavaConversions._
 import scala.xml.Node
 
+import org.json4s._
+import org.json4s.jackson.JsonMethods
 import org.openqa.selenium.htmlunit.HtmlUnitDriver
 import org.openqa.selenium.{By, WebDriver}
 import org.scalatest._
@@ -33,8 +35,9 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark.LocalSparkContext._
 import org.apache.spark._
 import org.apache.spark.api.java.StorageLevels
+import org.apache.spark.deploy.history.HistoryServerSuite
 import org.apache.spark.shuffle.FetchFailedException
-
+import org.apache.spark.status.api.v1.{JacksonMessageWriter, StageStatus}
 
 /**
  * Selenium tests for the Spark Web UI.
@@ -42,6 +45,8 @@ import org.apache.spark.shuffle.FetchFailedException
 class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll {
 
   implicit var webDriver: WebDriver = _
+  implicit val formats = DefaultFormats
+
 
   override def beforeAll(): Unit = {
     webDriver = new HtmlUnitDriver
@@ -76,28 +81,42 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       val rdd = sc.parallelize(Seq(1, 2, 3))
       rdd.persist(StorageLevels.DISK_ONLY).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage")
+        goToUi(ui, "/storage")
         val tableRowText = findAll(cssSelector("#storage-by-rdd-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.DISK_ONLY.description)
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage/rdd/?id=0")
+        goToUi(ui, "/storage/rdd/?id=0")
         val tableRowText = findAll(cssSelector("#rdd-storage-by-block-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.DISK_ONLY.description)
       }
 
+      val storageJson = getJson(ui, "storage/rdd")
+      storageJson.children.length should be (1)
+      (storageJson \ "storageLevel").extract[String] should be (StorageLevels.DISK_ONLY.description)
+      val rddJson = getJson(ui, "storage/rdd/0")
+      (rddJson  \ "storageLevel").extract[String] should be (StorageLevels.DISK_ONLY.description)
+
       rdd.unpersist()
       rdd.persist(StorageLevels.MEMORY_ONLY).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage")
+        goToUi(ui, "/storage")
         val tableRowText = findAll(cssSelector("#storage-by-rdd-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.MEMORY_ONLY.description)
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (ui.appUIAddress.stripSuffix("/") + "/storage/rdd/?id=0")
+        goToUi(ui, "/storage/rdd/?id=0")
         val tableRowText = findAll(cssSelector("#rdd-storage-by-block-table td")).map(_.text).toSeq
         tableRowText should contain (StorageLevels.MEMORY_ONLY.description)
       }
+
+      val updatedStorageJson = getJson(ui, "storage/rdd")
+      updatedStorageJson.children.length should be (1)
+      (updatedStorageJson \ "storageLevel").extract[String] should be (
+        StorageLevels.MEMORY_ONLY.description)
+      val updatedRddJson = getJson(ui, "storage/rdd/0")
+      (updatedRddJson  \ "storageLevel").extract[String] should be (
+        StorageLevels.MEMORY_ONLY.description)
     }
   }
 
@@ -108,10 +127,13 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         sc.parallelize(1 to 10).map { x => throw new Exception()}.collect()
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         find(id("active")) should be(None)  // Since we hide empty tables
         find(id("failed")).get.text should be("Failed Stages (1)")
       }
+      val stageJson = getJson(sc.ui.get, "stages")
+      stageJson.children.length should be (1)
+      (stageJson \ "status").extract[String] should be (StageStatus.FAILED.name())
 
       // Regression test for SPARK-2105
       class NotSerializable
@@ -120,12 +142,15 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         sc.parallelize(1 to 10).map { x => unserializableObject}.collect()
       }
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         find(id("active")) should be(None)  // Since we hide empty tables
         // The failure occurs before the stage becomes active, hence we should still show only one
         // failed stage, not two:
         find(id("failed")).get.text should be("Failed Stages (1)")
       }
+
+      val updatedStageJson = getJson(sc.ui.get, "stages")
+      updatedStageJson should be (stageJson)
     }
   }
 
@@ -138,7 +163,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
     withSpark(newSparkContext(killEnabled = true)) { sc =>
       runSlowJob(sc)
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         assert(hasKillLink)
       }
     }
@@ -146,7 +171,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
     withSpark(newSparkContext(killEnabled = false)) { sc =>
       runSlowJob(sc)
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages")
+        goToUi(sc, "/stages")
         assert(!hasKillLink)
       }
     }
@@ -157,7 +182,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       // If no job has been run in a job group, then "(Job Group)" should not appear in the header
       sc.parallelize(Seq(1, 2, 3)).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         val tableHeaders = findAll(cssSelector("th")).map(_.text).toSeq
         tableHeaders should not contain "Job Id (Job Group)"
       }
@@ -165,10 +190,22 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       sc.setJobGroup("my-job-group", "my-job-group-description")
       sc.parallelize(Seq(1, 2, 3)).count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         val tableHeaders = findAll(cssSelector("th")).map(_.text).toSeq
         tableHeaders should contain ("Job Id (Job Group)")
       }
+
+      val jobJson = getJson(sc.ui.get, "jobs")
+      for {
+        job @ JObject(_) <- jobJson
+        JInt(jobId) <- job \ "jobId"
+        jobGroup = job \ "jobGroup"
+      } {
+        jobId.toInt match {
+          case 0 => jobGroup should be (JNothing)
+          case 1 => jobGroup should be (JString("my-job-group"))
+        }
+      }
     }
   }
 
@@ -195,7 +232,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       mappedData.count()
       eventually(timeout(5 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         find(cssSelector(".stage-progress-cell")).get.text should be ("2/2 (1 failed)")
         // Ideally, the following test would pass, but currently we overcount completed tasks
         // if task recomputations occur:
@@ -204,6 +241,32 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
         // of completed tasks may be higher:
         find(cssSelector(".progress-cell .progress")).get.text should be ("3/2 (1 failed)")
       }
+      val jobJson = getJson(sc.ui.get, "jobs")
+      (jobJson \ "numTasks").extract[Int]should be (2)
+      (jobJson \ "numCompletedTasks").extract[Int] should be (3)
+      (jobJson \ "numFailedTasks").extract[Int] should be (1)
+      (jobJson \ "numCompletedStages").extract[Int] should be (2)
+      (jobJson \ "numFailedStages").extract[Int] should be (1)
+      val stageJson = getJson(sc.ui.get, "stages")
+
+      for {
+        stage @ JObject(_) <- stageJson
+        JString(status) <- stage \ "status"
+        JInt(stageId) <- stage \ "stageId"
+        JInt(attemptId) <- stage \ "attemptId"
+      } {
+        val exp = if (attemptId == 0 && stageId == 1) StageStatus.FAILED else StageStatus.COMPLETE
+        status should be (exp.name())
+      }
+
+      for {
+        stageId <- 0 to 1
+        attemptId <- 0 to 1
+      } {
+        val exp = if (attemptId == 0 && stageId == 1) StageStatus.FAILED else StageStatus.COMPLETE
+        val stageJson = getJson(sc.ui.get, s"stages/$stageId/$attemptId")
+        (stageJson \ "status").extract[String] should be (exp.name())
+      }
     }
   }
 
@@ -218,7 +281,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       // Start the job:
       rdd.countAsync()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs/job/?id=0")
+        goToUi(sc, "/jobs/job/?id=0")
         find(id("active")).get.text should be ("Active Stages (1)")
         find(id("pending")).get.text should be ("Pending Stages (2)")
         // Essentially, we want to check that none of the stage rows show
@@ -244,7 +307,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         // The completed jobs table should have two rows. The first row will be the most recent job:
         val firstRow = find(cssSelector("tbody tr")).get.underlying
         val firstRowColumns = firstRow.findElements(By.tagName("td"))
@@ -271,7 +334,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs/job/?id=1")
+        goToUi(sc, "/jobs/job/?id=1")
         find(id("pending")) should be (None)
         find(id("active")) should be (None)
         find(id("failed")) should be (None)
@@ -299,7 +362,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       rdd.count()
       rdd.count()
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/jobs")
+        goToUi(sc, "/jobs")
         findAll(cssSelector("tbody tr a")).foreach { link =>
           link.text.toLowerCase should include ("count")
           link.text.toLowerCase should not include "unknown"
@@ -321,7 +384,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       sparkUI.attachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/"))
+        goToUi(sc, "")
         find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="stages"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="storage"]""")) should not be(None)
@@ -330,12 +393,12 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check whether new page exists
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo")
+        goToUi(sc, "/foo")
         find(cssSelector("b")).get.text should include ("html magic")
       }
       sparkUI.detachTab(newTab)
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
-        go to (sc.ui.get.appUIAddress.stripSuffix("/"))
+        goToUi(sc, "")
         find(cssSelector("""ul li a[href*="jobs"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="stages"]""")) should not be(None)
         find(cssSelector("""ul li a[href*="storage"]""")) should not be(None)
@@ -344,7 +407,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check new page not exist
-        go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/foo")
+        goToUi(sc, "/foo")
         find(cssSelector("b")) should be(None)
       }
     }
@@ -371,4 +434,163 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before
       }
     }
   }
+
+  test("stage & job retention") {
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+      .set("spark.ui.enabled", "true")
+      .set("spark.ui.port", "0")
+      .set("spark.ui.retainedStages", "3")
+      .set("spark.ui.retainedJobs", "2")
+    val sc = new SparkContext(conf)
+    assert(sc.ui.isDefined)
+
+    withSpark(sc) { sc =>
+      // run a few jobs & stages ...
+      (0 until 5).foreach { idx =>
+        // NOTE: if we reverse the order, things don't really behave nicely
+        // we lose the stage for a job we keep, and then the job doesn't know
+        // about its last stage
+        sc.parallelize(idx to (idx + 3)).map(identity).groupBy(identity).map(identity)
+          .groupBy(identity).count()
+        sc.parallelize(idx to (idx + 3)).collect()
+      }
+
+      val expJobInfo = Seq(
+        ("9", "collect"),
+        ("8", "count")
+      )
+
+      eventually(timeout(1 second), interval(50 milliseconds)) {
+        goToUi(sc, "/jobs")
+        // The completed jobs table should have two rows. The first row will be the most recent job:
+        find("completed-summary").get.text should be ("Completed Jobs: 10, only showing 2")
+        find("completed").get.text should be ("Completed Jobs (10, only showing 2)")
+        val rows = findAll(cssSelector("tbody tr")).toIndexedSeq.map{_.underlying}
+        rows.size should be (expJobInfo.size)
+        for {
+          (row, idx) <- rows.zipWithIndex
+          columns = row.findElements(By.tagName("td"))
+          id = columns(0).getText()
+          description = columns(1).getText()
+        } {
+          id should be (expJobInfo(idx)._1)
+          description should include (expJobInfo(idx)._2)
+        }
+      }
+
+      val jobsJson = getJson(sc.ui.get, "jobs")
+      jobsJson.children.size should be (expJobInfo.size)
+      for {
+        (job @ JObject(_),idx) <- jobsJson.children.zipWithIndex
+        id = (job \ "jobId").extract[String]
+        name = (job \ "name").extract[String]
+      } {
+        withClue(s"idx = $idx; id = $id; name = ${name.substring(0,20)}") {
+          id should be (expJobInfo(idx)._1)
+          name should include (expJobInfo(idx)._2)
+        }
+      }
+
+      // what about when we query for a job that did exist, but has been cleared?
+      goToUi(sc, "/jobs/job/?id=7")
+      find("no-info").get.text should be ("No information to display for job 7")
+
+      val badJob = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get, "jobs/7"))
+      badJob._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badJob._2 should be (None)
+      badJob._3 should be (Some("unknown job: 7"))
+
+      val expStageInfo = Seq(
+        ("19", "collect"),
+        ("18", "count"),
+        ("17", "groupBy")
+      )
+
+      eventually(timeout(1 second), interval(50 milliseconds)) {
+        goToUi(sc, "/stages")
+        find("completed-summary").get.text should be ("Completed Stages: 20, only showing 3")
+        find("completed").get.text should be ("Completed Stages (20, only showing 3)")
+        val rows = findAll(cssSelector("tbody tr")).toIndexedSeq.map{_.underlying}
+        rows.size should be (3)
+        for {
+          (row, idx) <- rows.zipWithIndex
+          columns = row.findElements(By.tagName("td"))
+          id = columns(0).getText()
+          description = columns(1).getText()
+        } {
+          id should be (expStageInfo(idx)._1)
+          description should include (expStageInfo(idx)._2)
+        }
+      }
+
+      val stagesJson = getJson(sc.ui.get, "stages")
+      stagesJson.children.size should be (3)
+      for {
+        (stage @ JObject(_), idx) <- stagesJson.children.zipWithIndex
+        id = (stage \ "stageId").extract[String]
+        name = (stage \ "name").extract[String]
+      } {
+        id should be (expStageInfo(idx)._1)
+        name should include (expStageInfo(idx)._2)
+      }
+
+      // nonexistent stage
+
+      goToUi(sc, "/stages/stage/?id=12&attempt=0")
+      find("no-info").get.text should be ("No information to display for Stage 12 (Attempt 0)")
+      val badStage = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get,"stages/12/0"))
+      badStage._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badStage._2 should be (None)
+      badStage._3 should be (Some("unknown stage: 12"))
+
+      val badAttempt = HistoryServerSuite.getContentAndCode(jsonUrl(sc.ui.get,"stages/19/15"))
+      badAttempt._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badAttempt._2 should be (None)
+      badAttempt._3 should be (Some("unknown attempt for stage 19.  Found attempts: [0]"))
+
+      val badStageAttemptList = HistoryServerSuite.getContentAndCode(
+        jsonUrl(sc.ui.get, "stages/12"))
+      badStageAttemptList._1 should be (HttpServletResponse.SC_NOT_FOUND)
+      badStageAttemptList._2 should be (None)
+      badStageAttemptList._3 should be (Some("unknown stage: 12"))
+    }
+  }
+
+  test("live UI json application list") {
+    withSpark(newSparkContext()) { sc =>
+      val appListRawJson = HistoryServerSuite.getUrl(new URL(
+        sc.ui.get.appUIAddress + "/json/v1/applications"))
+      val appListJsonAst = JsonMethods.parse(appListRawJson)
+      appListJsonAst.children.length should be (1)
+      val attempts = (appListJsonAst \ "attempts").children
+      attempts.size should be (1)
+      (attempts(0) \ "completed").extract[Boolean] should be (false)
+      parseDate(attempts(0) \ "startTime") should be (sc.startTime)
+      parseDate(attempts(0) \ "endTime") should be (-1)
+      val oneAppJsonAst = getJson(sc.ui.get, "")
+      oneAppJsonAst should be (appListJsonAst.children(0))
+    }
+  }
+
+  def goToUi(sc: SparkContext, path: String): Unit = {
+    goToUi(sc.ui.get, path)
+  }
+
+  def goToUi(ui: SparkUI, path: String): Unit = {
+    go to (ui.appUIAddress.stripSuffix("/") + path)
+  }
+
+  def parseDate(json: JValue): Long = {
+    JacksonMessageWriter.makeISODateFormat.parse(json.extract[String]).getTime
+  }
+
+  def getJson(ui: SparkUI, path: String): JValue = {
+    JsonMethods.parse(HistoryServerSuite.getUrl(jsonUrl(ui, path)))
+  }
+
+  def jsonUrl(ui: SparkUI, path: String): URL = {
+    new URL(ui.appUIAddress + "/json/v1/applications/test/" + path)
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index 21d8267114133..967dd0821ebd0 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -34,12 +34,12 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
   val jobCompletionTime = 1421191296660L
 
   private def createStageStartEvent(stageId: Int) = {
-    val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, "")
+    val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, null, "")
     SparkListenerStageSubmitted(stageInfo)
   }
 
   private def createStageEndEvent(stageId: Int, failed: Boolean = false) = {
-    val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, "")
+    val stageInfo = new StageInfo(stageId, 0, stageId.toString, 0, null, null, "")
     if (failed) {
       stageInfo.failureReason = Some("Failed!")
     }
@@ -51,7 +51,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       stageIds: Seq[Int],
       jobGroup: Option[String] = None): SparkListenerJobStart = {
     val stageInfos = stageIds.map { stageId =>
-      new StageInfo(stageId, 0, stageId.toString, 0, null, "")
+      new StageInfo(stageId, 0, stageId.toString, 0, null, null, "")
     }
     val properties: Option[Properties] = jobGroup.map { groupId =>
       val props = new Properties()
diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
index 0ea9ea47b4288..7b38e6d9473e1 100644
--- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala
@@ -35,10 +35,10 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
   private val none = StorageLevel.NONE
   private val taskInfo = new TaskInfo(0, 0, 0, 0, "big", "dog", TaskLocality.ANY, false)
   private val taskInfo1 = new TaskInfo(1, 1, 1, 1, "big", "cat", TaskLocality.ANY, false)
-  private def rddInfo0 = new RDDInfo(0, "freedom", 100, memOnly)
-  private def rddInfo1 = new RDDInfo(1, "hostage", 200, memOnly)
-  private def rddInfo2 = new RDDInfo(2, "sanity", 300, memAndDisk)
-  private def rddInfo3 = new RDDInfo(3, "grace", 400, memAndDisk)
+  private def rddInfo0 = new RDDInfo(0, "freedom", 100, memOnly, Seq(10))
+  private def rddInfo1 = new RDDInfo(1, "hostage", 200, memOnly, Seq(10))
+  private def rddInfo2 = new RDDInfo(2, "sanity", 300, memAndDisk, Seq(10))
+  private def rddInfo3 = new RDDInfo(3, "grace", 400, memAndDisk, Seq(10))
   private val bm1 = BlockManagerId("big", "dog", 1)
 
   before {
@@ -54,7 +54,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     assert(storageListener.rddInfoList.isEmpty)
 
     // 2 RDDs are known, but none are cached
-    val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0, rddInfo1), "details")
+    val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0, rddInfo1), Seq.empty, "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 2)
     assert(storageListener.rddInfoList.isEmpty)
@@ -64,15 +64,16 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val rddInfo3Cached = rddInfo3
     rddInfo2Cached.numCachedPartitions = 1
     rddInfo3Cached.numCachedPartitions = 1
-    val stageInfo1 = new StageInfo(1, 0, "0", 100, Seq(rddInfo2Cached, rddInfo3Cached), "details")
+    val stageInfo1 = new StageInfo(
+      1, 0, "0", 100, Seq(rddInfo2Cached, rddInfo3Cached), Seq.empty, "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo1))
     assert(storageListener._rddInfoMap.size === 4)
     assert(storageListener.rddInfoList.size === 2)
 
     // Submitting RDDInfos with duplicate IDs does nothing
-    val rddInfo0Cached = new RDDInfo(0, "freedom", 100, StorageLevel.MEMORY_ONLY)
+    val rddInfo0Cached = new RDDInfo(0, "freedom", 100, StorageLevel.MEMORY_ONLY, Seq(10))
     rddInfo0Cached.numCachedPartitions = 1
-    val stageInfo0Cached = new StageInfo(0, 0, "0", 100, Seq(rddInfo0), "details")
+    val stageInfo0Cached = new StageInfo(0, 0, "0", 100, Seq(rddInfo0), Seq.empty, "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0Cached))
     assert(storageListener._rddInfoMap.size === 4)
     assert(storageListener.rddInfoList.size === 2)
@@ -88,7 +89,8 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val rddInfo1Cached = rddInfo1
     rddInfo0Cached.numCachedPartitions = 1
     rddInfo1Cached.numCachedPartitions = 1
-    val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(rddInfo0Cached, rddInfo1Cached), "details")
+    val stageInfo0 = new StageInfo(
+      0, 0, "0", 100, Seq(rddInfo0Cached, rddInfo1Cached), Seq.empty, "details")
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 2)
     assert(storageListener.rddInfoList.size === 2)
@@ -108,7 +110,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
     val myRddInfo1 = rddInfo1
     val myRddInfo2 = rddInfo2
     val stageInfo0 = new StageInfo(
-      0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details")
+      0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), Seq.empty, "details")
     bus.postToAll(SparkListenerBlockManagerAdded(1L, bm1, 1000L))
     bus.postToAll(SparkListenerStageSubmitted(stageInfo0))
     assert(storageListener._rddInfoMap.size === 3)
@@ -166,10 +168,10 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter {
 
   test("verify StorageTab contains all cached rdds") {
 
-    val rddInfo0 = new RDDInfo(0, "rdd0", 1, memOnly)
-    val rddInfo1 = new RDDInfo(1, "rdd1", 1 ,memOnly)
-    val stageInfo0 = new StageInfo(0, 0, "stage0", 1, Seq(rddInfo0), "details")
-    val stageInfo1 = new StageInfo(1, 0, "stage1", 1, Seq(rddInfo1), "details")
+    val rddInfo0 = new RDDInfo(0, "rdd0", 1, memOnly, Seq(4))
+    val rddInfo1 = new RDDInfo(1, "rdd1", 1 ,memOnly, Seq(4))
+    val stageInfo0 = new StageInfo(0, 0, "stage0", 1, Seq(rddInfo0), Seq.empty, "details")
+    val stageInfo1 = new StageInfo(1, 0, "stage1", 1, Seq(rddInfo1), Seq.empty, "details")
     val taskMetrics0 = new TaskMetrics
     val taskMetrics1 = new TaskMetrics
     val block0 = (RDDBlockId(0, 1), BlockStatus(memOnly, 100L, 0L, 0L))
diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
index ff1bfe0774a2f..446c3f24a74dd 100644
--- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala
@@ -17,10 +17,14 @@
 
 package org.apache.spark.util
 
+import java.io.NotSerializableException
+
 import org.scalatest.FunSuite
 
 import org.apache.spark.LocalSparkContext._
-import org.apache.spark.{SparkContext, SparkException}
+import org.apache.spark.{TaskContext, SparkContext, SparkException}
+import org.apache.spark.partial.CountEvaluator
+import org.apache.spark.rdd.RDD
 
 class ClosureCleanerSuite extends FunSuite {
   test("closures inside an object") {
@@ -52,17 +56,66 @@ class ClosureCleanerSuite extends FunSuite {
   }
 
   test("toplevel return statements in closures are identified at cleaning time") {
-    val ex = intercept[SparkException] {
+    intercept[ReturnStatementInClosureException] {
       TestObjectWithBogusReturns.run()
     }
-    
-    assert(ex.getMessage.contains("Return statements aren't allowed in Spark closures"))
   }
 
   test("return statements from named functions nested in closures don't raise exceptions") {
     val result = TestObjectWithNestedReturns.run()
     assert(result === 1)
   }
+
+  test("user provided closures are actually cleaned") {
+
+    // We use return statements as an indication that a closure is actually being cleaned
+    // We expect closure cleaner to find the return statements in the user provided closures
+    def expectCorrectException(body: => Unit): Unit = {
+      try {
+        body
+      } catch {
+        case rse: ReturnStatementInClosureException => // Success!
+        case e @ (_: NotSerializableException | _: SparkException) =>
+          fail(s"Expected ReturnStatementInClosureException, but got $e.\n" +
+            "This means the closure provided by user is not actually cleaned.")
+      }
+    }
+
+    withSpark(new SparkContext("local", "test")) { sc =>
+      val rdd = sc.parallelize(1 to 10)
+      val pairRdd = rdd.map { i => (i, i) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testMap(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testFlatMap(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testFilter(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testSortBy(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testGroupBy(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testKeyBy(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testMapPartitions(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testMapPartitionsWithIndex(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testZipPartitions2(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testZipPartitions3(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testZipPartitions4(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testForeach(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testForeachPartition(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testReduce(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testTreeReduce(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testFold(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testAggregate(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testTreeAggregate(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testCombineByKey(pairRdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testAggregateByKey(pairRdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testFoldByKey(pairRdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testReduceByKey(pairRdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testMapValues(pairRdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testFlatMapValues(pairRdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testForeachAsync(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testForeachPartitionAsync(rdd) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testRunJob1(sc) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testRunJob2(sc) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testRunApproximateJob(sc) }
+      expectCorrectException { TestUserClosuresActuallyCleaned.testSubmitJob(sc) }
+    }
+  }
 }
 
 // A non-serializable class we create in closures to make sure that we aren't
@@ -187,3 +240,90 @@ class TestClassWithNesting(val y: Int) extends Serializable {
     }
   }
 }
+
+/**
+ * Test whether closures passed in through public APIs are actually cleaned.
+ *
+ * We put a return statement in each of these closures as a mechanism to detect whether the
+ * ClosureCleaner actually cleaned our closure. If it did, then it would throw an appropriate
+ * exception explicitly complaining about the return statement. Otherwise, we know the
+ * ClosureCleaner did not actually clean our closure, in which case we should fail the test.
+ */
+private object TestUserClosuresActuallyCleaned {
+  def testMap(rdd: RDD[Int]): Unit = { rdd.map { _ => return; 0 }.count() }
+  def testFlatMap(rdd: RDD[Int]): Unit = { rdd.flatMap { _ => return; Seq() }.count() }
+  def testFilter(rdd: RDD[Int]): Unit = { rdd.filter { _ => return; true }.count() }
+  def testSortBy(rdd: RDD[Int]): Unit = { rdd.sortBy { _ => return; 1 }.count() }
+  def testKeyBy(rdd: RDD[Int]): Unit = { rdd.keyBy { _ => return; 1 }.count() }
+  def testGroupBy(rdd: RDD[Int]): Unit = { rdd.groupBy { _ => return; 1 }.count() }
+  def testMapPartitions(rdd: RDD[Int]): Unit = { rdd.mapPartitions { it => return; it }.count() }
+  def testMapPartitionsWithIndex(rdd: RDD[Int]): Unit = {
+    rdd.mapPartitionsWithIndex { (_, it) => return; it }.count()
+  }
+  def testZipPartitions2(rdd: RDD[Int]): Unit = {
+    rdd.zipPartitions(rdd) { case (it1, it2) => return; it1 }.count()
+  }
+  def testZipPartitions3(rdd: RDD[Int]): Unit = {
+    rdd.zipPartitions(rdd, rdd) { case (it1, it2, it3) => return; it1 }.count()
+  }
+  def testZipPartitions4(rdd: RDD[Int]): Unit = {
+    rdd.zipPartitions(rdd, rdd, rdd) { case (it1, it2, it3, it4) => return; it1 }.count()
+  }
+  def testForeach(rdd: RDD[Int]): Unit = { rdd.foreach { _ => return } }
+  def testForeachPartition(rdd: RDD[Int]): Unit = { rdd.foreachPartition { _ => return } }
+  def testReduce(rdd: RDD[Int]): Unit = { rdd.reduce { case (_, _) => return; 1 } }
+  def testTreeReduce(rdd: RDD[Int]): Unit = { rdd.treeReduce { case (_, _) => return; 1 } }
+  def testFold(rdd: RDD[Int]): Unit = { rdd.fold(0) { case (_, _) => return; 1 } }
+  def testAggregate(rdd: RDD[Int]): Unit = {
+    rdd.aggregate(0)({ case (_, _) => return; 1 }, { case (_, _) => return; 1 })
+  }
+  def testTreeAggregate(rdd: RDD[Int]): Unit = {
+    rdd.treeAggregate(0)({ case (_, _) => return; 1 }, { case (_, _) => return; 1 })
+  }
+
+  // Test pair RDD functions
+  def testCombineByKey(rdd: RDD[(Int, Int)]): Unit = {
+    rdd.combineByKey(
+      { _ => return; 1 }: Int => Int,
+      { case (_, _) => return; 1 }: (Int, Int) => Int,
+      { case (_, _) => return; 1 }: (Int, Int) => Int
+    ).count()
+  }
+  def testAggregateByKey(rdd: RDD[(Int, Int)]): Unit = {
+    rdd.aggregateByKey(0)({ case (_, _) => return; 1 }, { case (_, _) => return; 1 }).count()
+  }
+  def testFoldByKey(rdd: RDD[(Int, Int)]): Unit = { rdd.foldByKey(0) { case (_, _) => return; 1 } }
+  def testReduceByKey(rdd: RDD[(Int, Int)]): Unit = { rdd.reduceByKey { case (_, _) => return; 1 } }
+  def testMapValues(rdd: RDD[(Int, Int)]): Unit = { rdd.mapValues { _ => return; 1 } }
+  def testFlatMapValues(rdd: RDD[(Int, Int)]): Unit = { rdd.flatMapValues { _ => return; Seq() } }
+
+  // Test async RDD actions
+  def testForeachAsync(rdd: RDD[Int]): Unit = { rdd.foreachAsync { _ => return } }
+  def testForeachPartitionAsync(rdd: RDD[Int]): Unit = { rdd.foreachPartitionAsync { _ => return } }
+
+  // Test SparkContext runJob
+  def testRunJob1(sc: SparkContext): Unit = {
+    val rdd = sc.parallelize(1 to 10, 10)
+    sc.runJob(rdd, { (ctx: TaskContext, iter: Iterator[Int]) => return; 1 } )
+  }
+  def testRunJob2(sc: SparkContext): Unit = {
+    val rdd = sc.parallelize(1 to 10, 10)
+    sc.runJob(rdd, { iter: Iterator[Int] => return; 1 } )
+  }
+  def testRunApproximateJob(sc: SparkContext): Unit = {
+    val rdd = sc.parallelize(1 to 10, 10)
+    val evaluator = new CountEvaluator(1, 0.5)
+    sc.runApproximateJob(
+      rdd, { (ctx: TaskContext, iter: Iterator[Int]) => return; 1L }, evaluator, 1000)
+  }
+  def testSubmitJob(sc: SparkContext): Unit = {
+    val rdd = sc.parallelize(1 to 10, 10)
+    sc.submitJob(
+      rdd,
+      { _ => return; 1 }: Iterator[Int] => Int,
+      Seq.empty,
+      { case (_, _) => return }: (Int, Int) => Unit,
+      { return }
+    )
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 0c9cf5bc685c1..0c5221d10d79d 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -29,6 +29,7 @@ import org.scalatest.FunSuite
 
 import org.apache.spark._
 import org.apache.spark.executor._
+import org.apache.spark.rdd.RDDOperationScope
 import org.apache.spark.scheduler._
 import org.apache.spark.storage._
 
@@ -162,7 +163,7 @@ class JsonProtocolSuite extends FunSuite {
     assertEquals(exceptionFailure, JsonProtocol.taskEndReasonFromJson(oldEvent))
   }
 
-  test("StageInfo backward compatibility") {
+  test("StageInfo backward compatibility (details, accumulables)") {
     val info = makeStageInfo(1, 2, 3, 4L, 5L)
     val newJson = JsonProtocol.stageInfoToJson(info)
 
@@ -297,7 +298,7 @@ class JsonProtocolSuite extends FunSuite {
     val stageIds = Seq[Int](1, 2, 3, 4)
     val stageInfos = stageIds.map(x => makeStageInfo(x, x * 200, x * 300, x * 400, x * 500))
     val dummyStageInfos =
-      stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, "unknown"))
+      stageIds.map(id => new StageInfo(id, 0, "unknown", 0, Seq.empty, Seq.empty, "unknown"))
     val jobStart = SparkListenerJobStart(10, jobSubmissionTime, stageInfos, properties)
     val oldEvent = JsonProtocol.jobStartToJson(jobStart).removeField({_._1 == "Stage Infos"})
     val expectedJobStart =
@@ -323,6 +324,25 @@ class JsonProtocolSuite extends FunSuite {
     assertEquals(expectedJobEnd, JsonProtocol.jobEndFromJson(oldEndEvent))
   }
 
+  test("RDDInfo backward compatibility (scope, parent IDs)") {
+    // Prior to Spark 1.4.0, RDDInfo did not have the "Scope" and "Parent IDs" properties
+    val rddInfo = new RDDInfo(
+      1, "one", 100, StorageLevel.NONE, Seq(1, 6, 8), Some(new RDDOperationScope("fable")))
+    val oldRddInfoJson = JsonProtocol.rddInfoToJson(rddInfo)
+      .removeField({ _._1 == "Parent IDs"})
+      .removeField({ _._1 == "Scope"})
+    val expectedRddInfo = new RDDInfo(1, "one", 100, StorageLevel.NONE, Seq.empty, scope = None)
+    assertEquals(expectedRddInfo, JsonProtocol.rddInfoFromJson(oldRddInfoJson))
+  }
+
+  test("StageInfo backward compatibility (parent IDs)") {
+    // Prior to Spark 1.4.0, StageInfo did not have the "Parent IDs" property
+    val stageInfo = new StageInfo(1, 1, "me-stage", 1, Seq.empty, Seq(1, 2, 3), "details")
+    val oldStageInfo = JsonProtocol.stageInfoToJson(stageInfo).removeField({ _._1 == "Parent IDs"})
+    val expectedStageInfo = new StageInfo(1, 1, "me-stage", 1, Seq.empty, Seq.empty, "details")
+    assertEquals(expectedStageInfo, JsonProtocol.stageInfoFromJson(oldStageInfo))
+  }
+
   /** -------------------------- *
    | Helper test running methods |
    * --------------------------- */
@@ -645,7 +665,7 @@ class JsonProtocolSuite extends FunSuite {
   }
 
   private def makeRddInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = {
-    val r = new RDDInfo(a, "mayor", b, StorageLevel.MEMORY_AND_DISK)
+    val r = new RDDInfo(a, "mayor", b, StorageLevel.MEMORY_AND_DISK, Seq(1, 4, 7))
     r.numCachedPartitions = c
     r.memSize = d
     r.diskSize = e
@@ -654,7 +674,7 @@ class JsonProtocolSuite extends FunSuite {
 
   private def makeStageInfo(a: Int, b: Int, c: Int, d: Long, e: Long) = {
     val rddInfos = (0 until a % 5).map { i => makeRddInfo(a + i, b + i, c + i, d + i, e + i) }
-    val stageInfo = new StageInfo(a, 0, "greetings", b, rddInfos, "details")
+    val stageInfo = new StageInfo(a, 0, "greetings", b, rddInfos, Seq(100, 200, 300), "details")
     val (acc1, acc2) = (makeAccumulableInfo(1), makeAccumulableInfo(2))
     stageInfo.accumulables(acc1.id) = acc1
     stageInfo.accumulables(acc2.id) = acc2
@@ -747,6 +767,7 @@ class JsonProtocolSuite extends FunSuite {
       |    "Stage Name": "greetings",
       |    "Number of Tasks": 200,
       |    "RDD Info": [],
+      |    "ParentIDs" : [100, 200, 300],
       |    "Details": "details",
       |    "Accumulables": [
       |      {
@@ -785,6 +806,7 @@ class JsonProtocolSuite extends FunSuite {
       |      {
       |        "RDD ID": 101,
       |        "Name": "mayor",
+      |        "Parent IDs": [1, 4, 7],
       |        "Storage Level": {
       |          "Use Disk": true,
       |          "Use Memory": true,
@@ -799,6 +821,7 @@ class JsonProtocolSuite extends FunSuite {
       |        "Disk Size": 501
       |      }
       |    ],
+      |    "ParentIDs" : [100, 200, 300],
       |    "Details": "details",
       |    "Accumulables": [
       |      {
@@ -1168,6 +1191,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 1,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1182,6 +1206,7 @@ class JsonProtocolSuite extends FunSuite {
       |          "Disk Size": 500
       |        }
       |      ],
+      |      "Parent IDs" : [100, 200, 300],
       |      "Details": "details",
       |      "Accumulables": [
       |        {
@@ -1207,6 +1232,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 2,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1223,6 +1249,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 3,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1237,6 +1264,7 @@ class JsonProtocolSuite extends FunSuite {
       |          "Disk Size": 1001
       |        }
       |      ],
+      |      "ParentIDs" : [100, 200, 300],
       |      "Details": "details",
       |      "Accumulables": [
       |        {
@@ -1262,6 +1290,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 3,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1278,6 +1307,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 4,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1294,6 +1324,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 5,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1308,6 +1339,7 @@ class JsonProtocolSuite extends FunSuite {
       |          "Disk Size": 1502
       |        }
       |      ],
+      |      "ParentIDs" : [100, 200, 300],
       |      "Details": "details",
       |      "Accumulables": [
       |        {
@@ -1333,6 +1365,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 4,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1349,6 +1382,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 5,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1365,6 +1399,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 6,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1381,6 +1416,7 @@ class JsonProtocolSuite extends FunSuite {
       |        {
       |          "RDD ID": 7,
       |          "Name": "mayor",
+      |          "Parent IDs": [1, 4, 7],
       |          "Storage Level": {
       |            "Use Disk": true,
       |            "Use Memory": true,
@@ -1395,6 +1431,7 @@ class JsonProtocolSuite extends FunSuite {
       |          "Disk Size": 2003
       |        }
       |      ],
+      |      "ParentIDs" : [100, 200, 300],
       |      "Details": "details",
       |      "Accumulables": [
       |        {
diff --git a/dev/run-tests b/dev/run-tests
index 861d1671182c2..05c63bce4d40d 100755
--- a/dev/run-tests
+++ b/dev/run-tests
@@ -236,7 +236,7 @@ echo "========================================================================="
 CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS
 
 # add path for python 3 in jenkins
-export PATH="${PATH}:/home/anaonda/envs/py3k/bin"
+export PATH="${PATH}:/home/anaconda/envs/py3k/bin"
 ./python/run-tests
 
 echo ""
diff --git a/docs/building-spark.md b/docs/building-spark.md
index ea79c5bc276d3..287fcd3c4034f 100644
--- a/docs/building-spark.md
+++ b/docs/building-spark.md
@@ -66,7 +66,6 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro
     <tr><th>Hadoop version</th><th>Profile required</th></tr>
   </thead>
   <tbody>
-    <tr><td>0.23.x</td><td>hadoop-0.23</td></tr>
     <tr><td>1.x to 2.1.x</td><td>(none)</td></tr>
     <tr><td>2.2.x</td><td>hadoop-2.2</td></tr>
     <tr><td>2.3.x</td><td>hadoop-2.3</td></tr>
@@ -82,9 +81,6 @@ mvn -Dhadoop.version=1.2.1 -DskipTests clean package
 
 # Cloudera CDH 4.2.0 with MapReduce v1
 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package
-
-# Apache Hadoop 0.23.x
-mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package
 {% endhighlight %}
 
 You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later.
diff --git a/docs/configuration.md b/docs/configuration.md
index 64066bc0d70cd..0de824546c751 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1474,6 +1474,13 @@ Apart from these, the following properties are also available, and may be useful
     the new Kafka direct stream API.
   </td>
 </tr>
+<tr>
+  <td><code>spark.streaming.ui.retainedBatches</code></td>
+  <td>1000</td>
+  <td>
+    How many batches the Spark Streaming UI and status APIs remember before garbage collecting.
+  </td>
+</tr>
 </table>
 
 #### Cluster Managers
diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md
index 87dcc58feb494..96bd69ca3b33b 100644
--- a/docs/hadoop-third-party-distributions.md
+++ b/docs/hadoop-third-party-distributions.md
@@ -29,9 +29,6 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors.
         <tr><th>Release</th><th>Version code</th></tr>
         <tr><td>CDH 4.X.X (YARN mode)</td><td>2.0.0-cdh4.X.X</td></tr>
         <tr><td>CDH 4.X.X</td><td>2.0.0-mr1-cdh4.X.X</td></tr>
-        <tr><td>CDH 3u6</td><td>0.20.2-cdh3u6</td></tr>
-        <tr><td>CDH 3u5</td><td>0.20.2-cdh3u5</td></tr>
-        <tr><td>CDH 3u4</td><td>0.20.2-cdh3u4</td></tr>
       </table>
     </td>
     <td>
diff --git a/docs/monitoring.md b/docs/monitoring.md
index 8a85928d6d44d..1e0fc150862fb 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -174,6 +174,80 @@ making it easy to identify slow tasks, data skew, etc.
 
 Note that the history server only displays completed Spark jobs. One way to signal the completion of a Spark job is to stop the Spark Context explicitly (`sc.stop()`), or in Python using the `with SparkContext() as sc:` to handle the Spark Context setup and tear down, and still show the job history on the UI.
 
+## REST API
+
+In addition to viewing the metrics in the UI, they are also available as JSON.  This gives developers
+an easy way to create new visualizations and monitoring tools for Spark.  The JSON is available for
+both running applications, and in the history server.  The endpoints are mounted at `/json/v1`.  Eg.,
+for the history server, they would typically be accessible at `http://<server-url>:18080/json/v1`, and
+for a running application, at `http://localhost:4040/json/v1`.
+
+<table class="table">
+  <tr><th>Endpoint</th><th>Meaning</th></tr>
+  <tr>
+    <td><code>/applications</code></td>
+    <td>A list of all applications</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/jobs</code></td>
+    <td>A list of all jobs for a given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/jobs/[job-id]</code></td>
+    <td>Details for the given job</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages</code></td>
+    <td>A list of all stages for a given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]</code></td>
+    <td>A list of all attempts for the given stage</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]</code></td>
+    <td>Details for the given stage attempt</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskSummary</code></td>
+    <td>Summary metrics of all tasks in the given stage attempt</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/stages/[stage-id]/[stage-attempt-id]/taskList</code></td>
+    <td>A list of all tasks for the given stage attempt</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/executors</code></td>
+    <td>A list of all executors for the given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/storage/rdd</code></td>
+    <td>A list of stored RDDs for the given application</td>
+  </tr>
+  <tr>
+    <td><code>/applications/[app-id]/storage/rdd/[rdd-id]</code></td>
+    <td>Details for the storage status of a given RDD</td>
+  </tr>
+</table>
+
+When running on Yarn, each application has multiple attempts, so `[app-id]` is actually
+`[app-id]/[attempt-id]` in all cases.
+
+These endpoints have been strongly versioned to make it easier to develop applications on top.
+ In particular, Spark guarantees:
+
+* Endpoints will never be removed from one version
+* Individual fields will never be removed for any given endpoint
+* New endpoints may be added
+* New fields may be added to existing endpoints
+* New versions of the api may be added in the future at a separate endpoint (eg., `json/v2`).  New versions are *not* required to be backwards compatible.
+* Api versions may be dropped, but only after at least one minor release of co-existing with a new api version
+
+Note that even when examining the UI of a running applications, the `applications/[app-id]` portion is
+still required, though there is only one application available.  Eg. to see the list of jobs for the
+running app, you would go to `http://localhost:4040/json/v1/applications/[app-id]/jobs`.  This is to
+keep the paths consistent in both modes.
+
 # Metrics
 
 Spark has a configurable metrics system based on the 
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index b6701b64c2925..4fb4a90307ec8 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -133,6 +133,13 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
     Same as <code>spark.yarn.driver.memoryOverhead</code>, but for the Application Master in client mode.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.am.port</code></td>
+  <td>(random)</td>
+  <td>
+    Port for the YARN Application Master to listen on. In YARN client mode, this is used to communicate between the Spark driver running on a gateway and the Application Master running on YARN. In YARN cluster mode, this is used for the dynamic executor feature, where it handles the kill from the scheduler backend.
+  </td>
+</tr>
 <tr>
   <td><code>spark.yarn.queue</code></td>
   <td>default</td>
diff --git a/docs/tuning.md b/docs/tuning.md
index 1cb223e74f382..572c7270e4999 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -94,11 +94,13 @@ We will then cover tuning Spark's cache size and the Java garbage collector.
 
 ## Determining Memory Consumption
 
-The best way to size the amount of memory consumption your dataset will require is to create an RDD, put it into cache, and look at the SparkContext logs on your driver program. The logs will tell you how much memory each partition is consuming, which you can aggregate to get the total size of the RDD. You will see messages like this:
+The best way to size the amount of memory consumption a dataset will require is to create an RDD, put it
+into cache, and look at the "Storage" page in the web UI. The page will tell you how much memory the RDD
+is occupying.
 
-    INFO BlockManagerMasterActor: Added rdd_0_1 in memory on mbk.local:50311 (size: 717.5 KB, free: 332.3 MB)
-
-This means that partition 1 of RDD 0 consumed 717.5 KB.
+To estimate the memory consumption of a particular object, use `SizeEstimator`'s `estimate` method
+This is useful for experimenting with different data layouts to trim memory usage, as well as
+determining the amount of space a broadcast variable will occupy on each executor heap.
 
 ## Tuning Data Structures
 
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index 46377a99c4857..eac4f898a475d 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -28,7 +28,6 @@
 import org.apache.spark.ml.classification.ClassificationModel;
 import org.apache.spark.ml.param.IntParam;
 import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.ml.param.Params$;
 import org.apache.spark.mllib.linalg.BLAS;
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.Vectors;
@@ -129,16 +128,16 @@ MyJavaLogisticRegression setMaxIter(int value) {
 
   // This method is used by fit().
   // In Java, we have to make it public since Java does not understand Scala's protected modifier.
-  public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap) {
+  public MyJavaLogisticRegressionModel train(DataFrame dataset) {
     // Extract columns from data using helper method.
-    JavaRDD<LabeledPoint> oldDataset = extractLabeledPoints(dataset, paramMap).toJavaRDD();
+    JavaRDD<LabeledPoint> oldDataset = extractLabeledPoints(dataset).toJavaRDD();
 
     // Do learning to estimate the weight vector.
     int numFeatures = oldDataset.take(1).get(0).features().size();
     Vector weights = Vectors.zeros(numFeatures); // Learning would happen here.
 
     // Create a model, and return it.
-    return new MyJavaLogisticRegressionModel(this, paramMap, weights);
+    return new MyJavaLogisticRegressionModel(this, weights);
   }
 }
 
@@ -155,18 +154,11 @@ class MyJavaLogisticRegressionModel
   private MyJavaLogisticRegression parent_;
   public MyJavaLogisticRegression parent() { return parent_; }
 
-  private ParamMap fittingParamMap_;
-  public ParamMap fittingParamMap() { return fittingParamMap_; }
-
   private Vector weights_;
   public Vector weights() { return weights_; }
 
-  public MyJavaLogisticRegressionModel(
-      MyJavaLogisticRegression parent_,
-      ParamMap fittingParamMap_,
-      Vector weights_) {
+  public MyJavaLogisticRegressionModel(MyJavaLogisticRegression parent_, Vector weights_) {
     this.parent_ = parent_;
-    this.fittingParamMap_ = fittingParamMap_;
     this.weights_ = weights_;
   }
 
@@ -210,10 +202,8 @@ public Vector predictRaw(Vector features) {
    * In Java, we have to make this method public since Java does not understand Scala's protected
    * modifier.
    */
-  public MyJavaLogisticRegressionModel copy() {
-    MyJavaLogisticRegressionModel m =
-        new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_);
-    Params$.MODULE$.inheritValues(this.extractParamMap(), this, m);
-    return m;
+  @Override
+  public MyJavaLogisticRegressionModel copy(ParamMap extra) {
+    return copyValues(new MyJavaLogisticRegressionModel(parent_, weights_), extra);
   }
 }
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
index 4e02acce696e6..29158d5c85651 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
@@ -71,7 +71,7 @@ public static void main(String[] args) {
     // we can view the parameters it used during fit().
     // This prints the parameter (name: value) pairs, where names are unique IDs for this
     // LogisticRegression instance.
-    System.out.println("Model 1 was fit using parameters: " + model1.fittingParamMap());
+    System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
 
     // We may alternatively specify parameters using a ParamMap.
     ParamMap paramMap = new ParamMap();
@@ -87,7 +87,7 @@ public static void main(String[] args) {
     // Now learn a new model using the paramMapCombined parameters.
     // paramMapCombined overrides all parameters set earlier via lr.set* methods.
     LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
-    System.out.println("Model 2 was fit using parameters: " + model2.fittingParamMap());
+    System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
     // Prepare test documents.
     List<LabeledPoint> localTest = Lists.newArrayList(
diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py
index 19391506463f0..1456c87312841 100755
--- a/examples/src/main/python/kmeans.py
+++ b/examples/src/main/python/kmeans.py
@@ -68,14 +68,14 @@ def closestPoint(p, centers):
         closest = data.map(
             lambda p: (closestPoint(p, kPoints), (p, 1)))
         pointStats = closest.reduceByKey(
-            lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2))
+            lambda (p1, c1), (p2, c2): (p1 + p2, c1 + c2))
         newPoints = pointStats.map(
-            lambda xy: (xy[0], xy[1][0] / xy[1][1])).collect()
+            lambda st: (st[0], st[1][0] / st[1][1])).collect()
 
-        tempDist = sum(np.sum((kPoints[x] - y) ** 2) for (x, y) in newPoints)
+        tempDist = sum(np.sum((kPoints[iK] - p) ** 2) for (iK, p) in newPoints)
 
-        for (x, y) in newPoints:
-            kPoints[x] = y
+        for (iK, p) in newPoints:
+            kPoints[iK] = p
 
     print("Final centers: " + str(kPoints))
 
diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py
index f901a87fa63ac..002fc75799648 100755
--- a/examples/src/main/python/mllib/kmeans.py
+++ b/examples/src/main/python/mllib/kmeans.py
@@ -43,4 +43,5 @@ def parseVector(line):
     k = int(sys.argv[2])
     model = KMeans.train(data, k)
     print("Final centers: " + str(model.clusterCenters))
+    print("Total Cost: " + str(model.computeCost(data)))
     sc.stop()
diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
index f4684b42b5d41..849887d23c9cf 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
@@ -28,7 +28,19 @@ object HBaseTest {
   def main(args: Array[String]) {
     val sparkConf = new SparkConf().setAppName("HBaseTest")
     val sc = new SparkContext(sparkConf)
+
+    // please ensure HBASE_CONF_DIR is on classpath of spark driver
+    // e.g: set it through spark.driver.extraClassPath property
+    // in spark-defaults.conf or through --driver-class-path
+    // command line option of spark-submit
+
     val conf = HBaseConfiguration.create()
+
+    if (args.length < 1) {
+      System.err.println("Usage: HBaseTest <table_name>")
+      System.exit(1)
+    }
+
     // Other options for configuring scan behavior are available. More information available at
     // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html
     conf.set(TableInputFormat.INPUT_TABLE, args(0))
diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
index f73eac1e2b906..04fc0a033014a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala
@@ -99,7 +99,7 @@ object LocalKMeans {
 
       var pointStats = mappings.map { pair =>
         pair._2.reduceLeft [(Int, (Vector[Double], Int))] {
-          case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1 + y2))
+          case ((id1, (p1, c1)), (id2, (p2, c2))) => (id1, (p1 + p2, c1 + c2))
         }
       }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index 48e8d11cdf95b..b514d9123f5e7 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -79,7 +79,7 @@ object SparkKMeans {
     while(tempDist > convergeDist) {
       val closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
 
-      val pointStats = closest.reduceByKey{case ((x1, y1), (x2, y2)) => (x1 + x2, y1 + y2)}
+      val pointStats = closest.reduceByKey{case ((p1, c1), (p2, c2)) => (p1 + p2, c1 + c2)}
 
       val newPoints = pointStats.map {pair =>
         (pair._1, pair._2._1 * (1.0 / pair._2._2))}.collectAsMap()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
index 9002e99d82ad3..8340d91101ab3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala
@@ -276,16 +276,14 @@ object DecisionTreeExample {
     // Get the trained Decision Tree from the fitted PipelineModel
     algo match {
       case "classification" =>
-        val treeModel = pipelineModel.getModel[DecisionTreeClassificationModel](
-          dt.asInstanceOf[DecisionTreeClassifier])
+        val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeClassificationModel]
         if (treeModel.numNodes < 20) {
           println(treeModel.toDebugString) // Print full model.
         } else {
           println(treeModel) // Print model summary.
         }
       case "regression" =>
-        val treeModel = pipelineModel.getModel[DecisionTreeRegressionModel](
-          dt.asInstanceOf[DecisionTreeRegressor])
+        val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeRegressionModel]
         if (treeModel.numNodes < 20) {
           println(treeModel.toDebugString) // Print full model.
         } else {
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
index 2245fa429fda3..2a2d0677272a0 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
@@ -18,13 +18,12 @@
 package org.apache.spark.examples.ml
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.ml.classification.{Classifier, ClassifierParams, ClassificationModel}
-import org.apache.spark.ml.param.{Params, IntParam, ParamMap}
+import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams}
+import org.apache.spark.ml.param.{IntParam, ParamMap}
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 
-
 /**
  * A simple example demonstrating how to write your own learning algorithm using Estimator,
  * Transformer, and other abstractions.
@@ -99,7 +98,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams {
    * class since the maxIter parameter is only used during training (not in the Model).
    */
   val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations")
-  def getMaxIter: Int = getOrDefault(maxIter)
+  def getMaxIter: Int = $(maxIter)
 }
 
 /**
@@ -117,18 +116,16 @@ private class MyLogisticRegression
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   // This method is used by fit()
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): MyLogisticRegressionModel = {
+  override protected def train(dataset: DataFrame): MyLogisticRegressionModel = {
     // Extract columns from data using helper method.
-    val oldDataset = extractLabeledPoints(dataset, paramMap)
+    val oldDataset = extractLabeledPoints(dataset)
 
     // Do learning to estimate the weight vector.
     val numFeatures = oldDataset.take(1)(0).features.size
     val weights = Vectors.zeros(numFeatures) // Learning would happen here.
 
     // Create a model, and return it.
-    new MyLogisticRegressionModel(this, paramMap, weights)
+    new MyLogisticRegressionModel(this, weights)
   }
 }
 
@@ -139,7 +136,6 @@ private class MyLogisticRegression
  */
 private class MyLogisticRegressionModel(
     override val parent: MyLogisticRegression,
-    override val fittingParamMap: ParamMap,
     val weights: Vector)
   extends ClassificationModel[Vector, MyLogisticRegressionModel]
   with MyLogisticRegressionParams {
@@ -176,9 +172,7 @@ private class MyLogisticRegressionModel(
    *
    * This is used for the default implementation of [[transform()]].
    */
-  override protected def copy(): MyLogisticRegressionModel = {
-    val m = new MyLogisticRegressionModel(parent, fittingParamMap, weights)
-    Params.inheritValues(extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): MyLogisticRegressionModel = {
+    copyValues(new MyLogisticRegressionModel(parent, weights), extra)
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
index 5fccb142d4c3d..c5899b6683c79 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala
@@ -201,14 +201,14 @@ object GBTExample {
     // Get the trained GBT from the fitted PipelineModel
     algo match {
       case "classification" =>
-        val rfModel = pipelineModel.getModel[GBTClassificationModel](dt.asInstanceOf[GBTClassifier])
+        val rfModel = pipelineModel.stages.last.asInstanceOf[GBTClassificationModel]
         if (rfModel.totalNumNodes < 30) {
           println(rfModel.toDebugString) // Print full model.
         } else {
           println(rfModel) // Print model summary.
         }
       case "regression" =>
-        val rfModel = pipelineModel.getModel[GBTRegressionModel](dt.asInstanceOf[GBTRegressor])
+        val rfModel = pipelineModel.stages.last.asInstanceOf[GBTRegressionModel]
         if (rfModel.totalNumNodes < 30) {
           println(rfModel.toDebugString) // Print full model.
         } else {
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
index 9b909324ec82a..7f88d2681bcaa 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala
@@ -209,16 +209,14 @@ object RandomForestExample {
     // Get the trained Random Forest from the fitted PipelineModel
     algo match {
       case "classification" =>
-        val rfModel = pipelineModel.getModel[RandomForestClassificationModel](
-          dt.asInstanceOf[RandomForestClassifier])
+        val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestClassificationModel]
         if (rfModel.totalNumNodes < 30) {
           println(rfModel.toDebugString) // Print full model.
         } else {
           println(rfModel) // Print model summary.
         }
       case "regression" =>
-        val rfModel = pipelineModel.getModel[RandomForestRegressionModel](
-          dt.asInstanceOf[RandomForestRegressor])
+        val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestRegressionModel]
         if (rfModel.totalNumNodes < 30) {
           println(rfModel.toDebugString) // Print full model.
         } else {
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
index bf805149d0af6..e8a991f50e338 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
@@ -63,7 +63,7 @@ object SimpleParamsExample {
     // we can view the parameters it used during fit().
     // This prints the parameter (name: value) pairs, where names are unique IDs for this
     // LogisticRegression instance.
-    println("Model 1 was fit using parameters: " + model1.fittingParamMap)
+    println("Model 1 was fit using parameters: " + model1.parent.extractParamMap())
 
     // We may alternatively specify parameters using a ParamMap,
     // which supports several methods for specifying parameters.
@@ -78,7 +78,7 @@ object SimpleParamsExample {
     // Now learn a new model using the paramMapCombined parameters.
     // paramMapCombined overrides all parameters set earlier via lr.set* methods.
     val model2 = lr.fit(training.toDF(), paramMapCombined)
-    println("Model 2 was fit using parameters: " + model2.fittingParamMap)
+    println("Model 2 was fit using parameters: " + model2.parent.extractParamMap())
 
     // Prepare test data.
     val test = sc.parallelize(Seq(
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
index 2edea9b5b69ba..43c1b865b64a1 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
@@ -57,11 +57,11 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging
 
   before(beforeFunction())
 
-  test("flume polling test") {
+  ignore("flume polling test") {
     testMultipleTimes(testFlumePolling)
   }
 
-  test("flume polling test multiple hosts") {
+  ignore("flume polling test multiple hosts") {
     testMultipleTimes(testFlumePollingMultipleHost)
   }
 
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 1b1fc8051d052..6715aede7928a 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.streaming.kafka
 
-
 import scala.annotation.tailrec
 import scala.collection.mutable
 import scala.reflect.{classTag, ClassTag}
@@ -27,10 +26,10 @@ import kafka.message.MessageAndMetadata
 import kafka.serializer.Decoder
 
 import org.apache.spark.{Logging, SparkException}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
 import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
+import org.apache.spark.streaming.scheduler.InputInfo
 
 /**
  *  A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where
@@ -117,6 +116,11 @@ class DirectKafkaInputDStream[
     val rdd = KafkaRDD[K, V, U, T, R](
       context.sparkContext, kafkaParams, currentOffsets, untilOffsets, messageHandler)
 
+    // Report the record number of this batch interval to InputInfoTracker.
+    val numRecords = rdd.offsetRanges.map(r => r.untilOffset - r.fromOffset).sum
+    val inputInfo = InputInfo(id, numRecords)
+    ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
+
     currentOffsets = untilOffsets.map(kv => kv._1 -> kv._2.offset)
     Some(rdd)
   }
diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
index 415730f5559c5..b6d314dfc7783 100644
--- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
+++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.streaming.kafka
 
 import java.io.File
+import java.util.concurrent.atomic.AtomicLong
 
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
@@ -34,6 +35,7 @@ import org.apache.spark.{Logging, SparkConf, SparkContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Milliseconds, StreamingContext, Time}
 import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.scheduler._
 import org.apache.spark.util.Utils
 
 class DirectKafkaStreamSuite
@@ -290,7 +292,6 @@ class DirectKafkaStreamSuite
       },
       "Recovered ranges are not the same as the ones generated"
     )
-
     // Restart context, give more data and verify the total at the end
     // If the total is write that means each records has been received only once
     ssc.start()
@@ -301,6 +302,44 @@ class DirectKafkaStreamSuite
     ssc.stop()
   }
 
+  test("Direct Kafka stream report input information") {
+    val topic = "report-test"
+    val data = Map("a" -> 7, "b" -> 9)
+    kafkaTestUtils.createTopic(topic)
+    kafkaTestUtils.sendMessages(topic, data)
+
+    val totalSent = data.values.sum
+    val kafkaParams = Map(
+      "metadata.broker.list" -> kafkaTestUtils.brokerAddress,
+      "auto.offset.reset" -> "smallest"
+    )
+
+    import DirectKafkaStreamSuite._
+    ssc = new StreamingContext(sparkConf, Milliseconds(200))
+    val collector = new InputInfoCollector
+    ssc.addStreamingListener(collector)
+
+    val stream = withClue("Error creating direct stream") {
+      KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
+        ssc, kafkaParams, Set(topic))
+    }
+
+    val allReceived = new ArrayBuffer[(String, String)]
+
+    stream.foreachRDD { rdd => allReceived ++= rdd.collect() }
+    ssc.start()
+    eventually(timeout(20000.milliseconds), interval(200.milliseconds)) {
+      assert(allReceived.size === totalSent,
+        "didn't get expected number of messages, messages:\n" + allReceived.mkString("\n"))
+
+      // Calculate all the record number collected in the StreamingListener.
+      assert(collector.numRecordsSubmitted.get() === totalSent)
+      assert(collector.numRecordsStarted.get() === totalSent)
+      assert(collector.numRecordsCompleted.get() === totalSent)
+    }
+    ssc.stop()
+  }
+
   /** Get the generated offset ranges from the DirectKafkaStream */
   private def getOffsetRanges[K, V](
       kafkaStream: DStream[(K, V)]): Seq[(Time, Array[OffsetRange])] = {
@@ -313,4 +352,22 @@ class DirectKafkaStreamSuite
 object DirectKafkaStreamSuite {
   val collectedData = new mutable.ArrayBuffer[String]()
   var total = -1L
+
+  class InputInfoCollector extends StreamingListener {
+    val numRecordsSubmitted = new AtomicLong(0L)
+    val numRecordsStarted = new AtomicLong(0L)
+    val numRecordsCompleted = new AtomicLong(0L)
+
+    override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = {
+      numRecordsSubmitted.addAndGet(batchSubmitted.batchInfo.numRecords)
+    }
+
+    override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = {
+      numRecordsStarted.addAndGet(batchStarted.batchInfo.numRecords)
+    }
+
+    override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = {
+      numRecordsCompleted.addAndGet(batchCompleted.batchInfo.numRecords)
+    }
+  }
 }
diff --git a/make-distribution.sh b/make-distribution.sh
index 92177e19fe6be..1bfa9acb1fe6e 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -58,7 +58,7 @@ while (( "$#" )); do
     --hadoop)
       echo "Error: '--hadoop' is no longer supported:"
       echo "Error: use Maven profiles and options -Dhadoop.version and -Dyarn.version instead."
-      echo "Error: Related profiles include hadoop-0.23, hdaoop-2.2, hadoop-2.3 and hadoop-2.4."
+      echo "Error: Related profiles include hadoop-2.2, hadoop-2.3 and hadoop-2.4."
       exit_with_usage
       ;;
     --with-yarn)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
index d6b3503ebdd9a..7f3f3262a644f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala
@@ -34,13 +34,16 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
    * Fits a single model to the input data with optional parameters.
    *
    * @param dataset input dataset
-   * @param paramPairs Optional list of param pairs.
-   *                   These values override any specified in this Estimator's embedded ParamMap.
+   * @param firstParamPair the first param pair, overrides embedded params
+   * @param otherParamPairs other param pairs.  These values override any specified in this
+   *                        Estimator's embedded ParamMap.
    * @return fitted model
    */
   @varargs
-  def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = {
-    val map = ParamMap(paramPairs: _*)
+  def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M = {
+    val map = new ParamMap()
+      .put(firstParamPair)
+      .put(otherParamPairs: _*)
     fit(dataset, map)
   }
 
@@ -52,12 +55,19 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
    *                 These values override any specified in this Estimator's embedded ParamMap.
    * @return fitted model
    */
-  def fit(dataset: DataFrame, paramMap: ParamMap): M
+  def fit(dataset: DataFrame, paramMap: ParamMap): M = {
+    copy(paramMap).fit(dataset)
+  }
+
+  /**
+   * Fits a model to the input data.
+   */
+  def fit(dataset: DataFrame): M
 
   /**
    * Fits multiple models to the input data with multiple sets of parameters.
    * The default implementation uses a for loop on each parameter map.
-   * Subclasses could overwrite this to optimize multi-model training.
+   * Subclasses could override this to optimize multi-model training.
    *
    * @param dataset input dataset
    * @param paramMaps An array of parameter maps.
@@ -67,4 +77,8 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params {
   def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[M] = {
     paramMaps.map(fit(dataset, _))
   }
+
+  override def copy(extra: ParamMap): Estimator[M] = {
+    super.copy(extra).asInstanceOf[Estimator[M]]
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala
index 8b4b5fd8af986..5f2f8c94e9ff7 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.ml
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.sql.DataFrame
 
 /**
@@ -27,7 +26,7 @@ import org.apache.spark.sql.DataFrame
  * Abstract class for evaluators that compute metrics from predictions.
  */
 @AlphaComponent
-abstract class Evaluator extends Identifiable {
+abstract class Evaluator extends Params {
 
   /**
    * Evaluates the output.
@@ -36,5 +35,18 @@ abstract class Evaluator extends Identifiable {
    * @param paramMap parameter map that specifies the input columns and output metrics
    * @return metric
    */
-  def evaluate(dataset: DataFrame, paramMap: ParamMap): Double
+  def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = {
+    this.copy(paramMap).evaluate(dataset)
+  }
+
+  /**
+   * Evaluates the output.
+   * @param dataset a dataset that contains labels/observations and predictions.
+   * @return metric
+   */
+  def evaluate(dataset: DataFrame): Double
+
+  override def copy(extra: ParamMap): Evaluator = {
+    super.copy(extra).asInstanceOf[Evaluator]
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala
index a491bc7ee8295..9974efe7b1d25 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala
@@ -34,9 +34,8 @@ abstract class Model[M <: Model[M]] extends Transformer {
    */
   val parent: Estimator[M]
 
-  /**
-   * Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model.
-   * Note: For ensembles' component Models, this value can be null.
-   */
-  val fittingParamMap: ParamMap
+  override def copy(extra: ParamMap): M = {
+    // The default implementation of Params.copy doesn't work for models.
+    throw new NotImplementedError(s"${this.getClass} doesn't implement copy(extra: ParamMap)")
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index 6bfeecd764d75..33d430f5671ee 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.ListBuffer
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.param.{Params, Param, ParamMap}
+import org.apache.spark.ml.param.{Param, ParamMap, Params}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.types.StructType
 
@@ -30,40 +30,41 @@ import org.apache.spark.sql.types.StructType
  * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]].
  */
 @AlphaComponent
-abstract class PipelineStage extends Serializable with Logging {
+abstract class PipelineStage extends Params with Logging {
 
   /**
    * :: DeveloperApi ::
    *
-   * Derives the output schema from the input schema and parameters.
-   * The schema describes the columns and types of the data.
-   *
-   * @param schema  Input schema to this stage
-   * @param paramMap  Parameters passed to this stage
-   * @return  Output schema from this stage
+   * Derives the output schema from the input schema.
    */
   @DeveloperApi
-  def transformSchema(schema: StructType, paramMap: ParamMap): StructType
+  def transformSchema(schema: StructType): StructType
 
   /**
+   * :: DeveloperApi ::
+   *
    * Derives the output schema from the input schema and parameters, optionally with logging.
    *
    * This should be optimistic.  If it is unclear whether the schema will be valid, then it should
    * be assumed valid until proven otherwise.
    */
+  @DeveloperApi
   protected def transformSchema(
       schema: StructType,
-      paramMap: ParamMap,
       logging: Boolean): StructType = {
     if (logging) {
       logDebug(s"Input schema: ${schema.json}")
     }
-    val outputSchema = transformSchema(schema, paramMap)
+    val outputSchema = transformSchema(schema)
     if (logging) {
       logDebug(s"Expected output schema: ${outputSchema.json}")
     }
     outputSchema
   }
+
+  override def copy(extra: ParamMap): PipelineStage = {
+    super.copy(extra).asInstanceOf[PipelineStage]
+  }
 }
 
 /**
@@ -81,15 +82,22 @@ abstract class PipelineStage extends Serializable with Logging {
 @AlphaComponent
 class Pipeline extends Estimator[PipelineModel] {
 
-  /** param for pipeline stages */
+  /**
+   * param for pipeline stages
+   * @group param
+   */
   val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline")
+
+  /** @group setParam */
   def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this }
-  def getStages: Array[PipelineStage] = getOrDefault(stages)
 
-  override def validate(paramMap: ParamMap): Unit = {
+  /** @group getParam */
+  def getStages: Array[PipelineStage] = $(stages).clone()
+
+  override def validateParams(paramMap: ParamMap): Unit = {
     val map = extractParamMap(paramMap)
     getStages.foreach {
-      case pStage: Params => pStage.validate(map)
+      case pStage: Params => pStage.validateParams(map)
       case _ =>
     }
   }
@@ -104,13 +112,11 @@ class Pipeline extends Estimator[PipelineModel] {
    * pipeline stages. If there are no stages, the output model acts as an identity transformer.
    *
    * @param dataset input dataset
-   * @param paramMap parameter map
    * @return fitted pipeline
    */
-  override def fit(dataset: DataFrame, paramMap: ParamMap): PipelineModel = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val theStages = map(stages)
+  override def fit(dataset: DataFrame): PipelineModel = {
+    transformSchema(dataset.schema, logging = true)
+    val theStages = $(stages)
     // Search for the last estimator.
     var indexOfLastEstimator = -1
     theStages.view.zipWithIndex.foreach { case (stage, index) =>
@@ -126,7 +132,7 @@ class Pipeline extends Estimator[PipelineModel] {
       if (index <= indexOfLastEstimator) {
         val transformer = stage match {
           case estimator: Estimator[_] =>
-            estimator.fit(curDataset, paramMap)
+            estimator.fit(curDataset)
           case t: Transformer =>
             t
           case _ =>
@@ -134,7 +140,7 @@ class Pipeline extends Estimator[PipelineModel] {
               s"Do not support stage $stage of type ${stage.getClass}")
         }
         if (index < indexOfLastEstimator) {
-          curDataset = transformer.transform(curDataset, paramMap)
+          curDataset = transformer.transform(curDataset)
         }
         transformers += transformer
       } else {
@@ -142,15 +148,20 @@ class Pipeline extends Estimator[PipelineModel] {
       }
     }
 
-    new PipelineModel(this, map, transformers.toArray)
+    new PipelineModel(this, transformers.toArray)
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    val theStages = map(stages)
+  override def copy(extra: ParamMap): Pipeline = {
+    val map = extractParamMap(extra)
+    val newStages = map(stages).map(_.copy(extra))
+    new Pipeline().setStages(newStages)
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val theStages = $(stages)
     require(theStages.toSet.size == theStages.length,
       "Cannot have duplicate components in a pipeline.")
-    theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap))
+    theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur))
   }
 }
 
@@ -161,43 +172,24 @@ class Pipeline extends Estimator[PipelineModel] {
 @AlphaComponent
 class PipelineModel private[ml] (
     override val parent: Pipeline,
-    override val fittingParamMap: ParamMap,
-    private[ml] val stages: Array[Transformer])
+    val stages: Array[Transformer])
   extends Model[PipelineModel] with Logging {
 
-  override def validate(paramMap: ParamMap): Unit = {
-    val map = fittingParamMap ++ extractParamMap(paramMap)
-    stages.foreach(_.validate(map))
+  override def validateParams(): Unit = {
+    super.validateParams()
+    stages.foreach(_.validateParams())
   }
 
-  /**
-   * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input
-   * estimator does not exist in the pipeline.
-   */
-  def getModel[M <: Model[M]](stage: Estimator[M]): M = {
-    val matched = stages.filter {
-      case m: Model[_] => m.parent.eq(stage)
-      case _ => false
-    }
-    if (matched.isEmpty) {
-      throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.")
-    } else if (matched.length > 1) {
-      throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.")
-    } else {
-      matched.head.asInstanceOf[M]
-    }
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur))
   }
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap
-    val map = fittingParamMap ++ extractParamMap(paramMap)
-    transformSchema(dataset.schema, map, logging = true)
-    stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map))
+  override def transformSchema(schema: StructType): StructType = {
+    stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap
-    val map = fittingParamMap ++ extractParamMap(paramMap)
-    stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map))
+  override def copy(extra: ParamMap): PipelineModel = {
+    new PipelineModel(parent, stages)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
index 0acda71ec6045..d96b54e511e9c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
@@ -37,13 +37,18 @@ abstract class Transformer extends PipelineStage with Params {
   /**
    * Transforms the dataset with optional parameters
    * @param dataset input dataset
-   * @param paramPairs optional list of param pairs, overwrite embedded params
+   * @param firstParamPair the first param pair, overwrite embedded params
+   * @param otherParamPairs other param pairs, overwrite embedded params
    * @return transformed dataset
    */
   @varargs
-  def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame = {
+  def transform(
+      dataset: DataFrame,
+      firstParamPair: ParamPair[_],
+      otherParamPairs: ParamPair[_]*): DataFrame = {
     val map = new ParamMap()
-    paramPairs.foreach(map.put(_))
+      .put(firstParamPair)
+      .put(otherParamPairs: _*)
     transform(dataset, map)
   }
 
@@ -53,7 +58,18 @@ abstract class Transformer extends PipelineStage with Params {
    * @param paramMap additional parameters, overwrite embedded params
    * @return transformed dataset
    */
-  def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame
+  def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
+    this.copy(paramMap).transform(dataset)
+  }
+
+  /**
+   * Transforms the input dataset.
+   */
+  def transform(dataset: DataFrame): DataFrame
+
+  override def copy(extra: ParamMap): Transformer = {
+    super.copy(extra).asInstanceOf[Transformer]
+  }
 }
 
 /**
@@ -74,7 +90,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O
    * account of the embedded param map. So the param values should be determined solely by the input
    * param map.
    */
-  protected def createTransformFunc(paramMap: ParamMap): IN => OUT
+  protected def createTransformFunc: IN => OUT
 
   /**
    * Returns the data type of the output column.
@@ -86,22 +102,20 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O
    */
   protected def validateInputType(inputType: DataType): Unit = {}
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    val inputType = schema(map(inputCol)).dataType
+  override def transformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
     validateInputType(inputType)
-    if (schema.fieldNames.contains(map(outputCol))) {
-      throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.")
+    if (schema.fieldNames.contains($(outputCol))) {
+      throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.")
     }
     val outputFields = schema.fields :+
-      StructField(map(outputCol), outputDataType, nullable = false)
+      StructField($(outputCol), outputDataType, nullable = false)
     StructType(outputFields)
   }
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    dataset.withColumn(map(outputCol),
-      callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol))))
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    dataset.withColumn($(outputCol),
+      callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol))))
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 29339c98f51cf..d3361e24705c8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -19,7 +19,6 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams}
-import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
@@ -27,7 +26,6 @@ import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
 
-
 /**
  * :: DeveloperApi ::
  * Params for classification.
@@ -40,12 +38,10 @@ private[spark] trait ClassifierParams extends PredictorParams
 
   override protected def validateAndTransformSchema(
       schema: StructType,
-      paramMap: ParamMap,
       fitting: Boolean,
       featuresDataType: DataType): StructType = {
-    val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType)
-    val map = extractParamMap(paramMap)
-    SchemaUtils.appendColumn(parentSchema, map(rawPredictionCol), new VectorUDT)
+    val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType)
+    SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new VectorUDT)
   }
 }
 
@@ -102,27 +98,16 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
    *  - raw predictions (confidences) as [[rawPredictionCol]] of type [[Vector]].
    *
    * @param dataset input dataset
-   * @param paramMap additional parameters, overwrite embedded params
    * @return transformed dataset
    */
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
+  override def transform(dataset: DataFrame): DataFrame = {
     // This default implementation should be overridden as needed.
 
     // Check schema
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-
-    // Prepare model
-    val tmpModel = if (paramMap.size != 0) {
-      val tmpModel = this.copy()
-      Params.inheritValues(paramMap, parent, tmpModel)
-      tmpModel
-    } else {
-      this
-    }
+    transformSchema(dataset.schema, logging = true)
 
     val (numColsOutput, outputData) =
-      ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map)
+      ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this)
     if (numColsOutput == 0) {
       logWarning(s"$uid: ClassificationModel.transform() was called as NOOP" +
         " since no output columns were set.")
@@ -158,7 +143,6 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur
    */
   @DeveloperApi
   protected def predictRaw(features: FeaturesType): Vector
-
 }
 
 private[ml] object ClassificationModel {
@@ -167,38 +151,35 @@ private[ml] object ClassificationModel {
    * Added prediction column(s).  This is separated from [[ClassificationModel.transform()]]
    * since it is used by [[org.apache.spark.ml.classification.ProbabilisticClassificationModel]].
    * @param dataset  Input dataset
-   * @param map  Parameter map.  This will NOT be merged with the embedded paramMap; the merge
-   *             should already be done.
    * @return (number of columns added, transformed dataset)
    */
   def transformColumnsImpl[FeaturesType](
       dataset: DataFrame,
-      model: ClassificationModel[FeaturesType, _],
-      map: ParamMap): (Int, DataFrame) = {
+      model: ClassificationModel[FeaturesType, _]): (Int, DataFrame) = {
 
     // Output selected columns only.
     // This is a bit complicated since it tries to avoid repeated computation.
     var tmpData = dataset
     var numColsOutput = 0
-    if (map(model.rawPredictionCol) != "") {
+    if (model.getRawPredictionCol != "") {
       // output raw prediction
       val features2raw: FeaturesType => Vector = model.predictRaw
-      tmpData = tmpData.withColumn(map(model.rawPredictionCol),
-        callUDF(features2raw, new VectorUDT, col(map(model.featuresCol))))
+      tmpData = tmpData.withColumn(model.getRawPredictionCol,
+        callUDF(features2raw, new VectorUDT, col(model.getFeaturesCol)))
       numColsOutput += 1
-      if (map(model.predictionCol) != "") {
+      if (model.getPredictionCol != "") {
         val raw2pred: Vector => Double = (rawPred) => {
           rawPred.toArray.zipWithIndex.maxBy(_._1)._2
         }
-        tmpData = tmpData.withColumn(map(model.predictionCol),
-          callUDF(raw2pred, DoubleType, col(map(model.rawPredictionCol))))
+        tmpData = tmpData.withColumn(model.getPredictionCol,
+          callUDF(raw2pred, DoubleType, col(model.getRawPredictionCol)))
         numColsOutput += 1
       }
-    } else if (map(model.predictionCol) != "") {
+    } else if (model.getPredictionCol != "") {
       // output prediction
       val features2pred: FeaturesType => Double = model.predict
-      tmpData = tmpData.withColumn(map(model.predictionCol),
-        callUDF(features2pred, DoubleType, col(map(model.featuresCol))))
+      tmpData = tmpData.withColumn(model.getPredictionCol,
+        callUDF(features2pred, DoubleType, col(model.getFeaturesCol)))
       numColsOutput += 1
     }
     (numColsOutput, tmpData)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index ee2a8dc6db171..419e5ba05d38a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.impl.estimator.{Predictor, PredictionModel}
+import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
 import org.apache.spark.ml.impl.tree._
-import org.apache.spark.ml.param.{Params, ParamMap}
+import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, Node}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
@@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * :: AlphaComponent ::
  *
@@ -64,22 +63,20 @@ final class DecisionTreeClassifier
 
   override def setImpurity(value: String): this.type = super.setImpurity(value)
 
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): DecisionTreeClassificationModel = {
+  override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = {
     val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
-    val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match {
+      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
       case Some(n: Int) => n
       case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" +
-        s" with invalid label column ${paramMap(labelCol)}, without the number of classes" +
+        s" with invalid label column ${$(labelCol)}, without the number of classes" +
         " specified. See StringIndexer.")
         // TODO: Automatically index labels: SPARK-7126
     }
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap)
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy = getOldStrategy(categoricalFeatures, numClasses)
     val oldModel = OldDecisionTree.train(oldDataset, strategy)
-    DecisionTreeClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures)
+    DecisionTreeClassificationModel.fromOld(oldModel, this, categoricalFeatures)
   }
 
   /** (private[ml]) Create a Strategy instance to use with the old API. */
@@ -106,7 +103,6 @@ object DecisionTreeClassifier {
 @AlphaComponent
 final class DecisionTreeClassificationModel private[ml] (
     override val parent: DecisionTreeClassifier,
-    override val fittingParamMap: ParamMap,
     override val rootNode: Node)
   extends PredictionModel[Vector, DecisionTreeClassificationModel]
   with DecisionTreeModel with Serializable {
@@ -118,10 +114,8 @@ final class DecisionTreeClassificationModel private[ml] (
     rootNode.predict(features)
   }
 
-  override protected def copy(): DecisionTreeClassificationModel = {
-    val m = new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
+    copyValues(new DecisionTreeClassificationModel(parent, rootNode), extra)
   }
 
   override def toString: String = {
@@ -140,12 +134,11 @@ private[ml] object DecisionTreeClassificationModel {
   def fromOld(
       oldModel: OldDecisionTreeModel,
       parent: DecisionTreeClassifier,
-      fittingParamMap: ParamMap,
       categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = {
     require(oldModel.algo == OldAlgo.Classification,
       s"Cannot convert non-classification DecisionTreeModel (old API) to" +
         s" DecisionTreeClassificationModel (new API).  Algo is: ${oldModel.algo}")
     val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures)
-    new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode)
+    new DecisionTreeClassificationModel(parent, rootNode)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 3d849867d4c47..534ea95b1c538 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -23,7 +23,7 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
 import org.apache.spark.ml.impl.tree._
-import org.apache.spark.ml.param.{Param, Params, ParamMap}
+import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
@@ -31,12 +31,11 @@ import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.loss.{Loss => OldLoss, LogLoss => OldLogLoss}
+import org.apache.spark.mllib.tree.loss.{LogLoss => OldLogLoss, Loss => OldLoss}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * :: AlphaComponent ::
  *
@@ -112,7 +111,7 @@ final class GBTClassifier
   def setLossType(value: String): this.type = set(lossType, value)
 
   /** @group getParam */
-  def getLossType: String = getOrDefault(lossType).toLowerCase
+  def getLossType: String = $(lossType).toLowerCase
 
   /** (private[ml]) Convert new loss to old loss. */
   override private[ml] def getOldLossType: OldLoss = {
@@ -124,25 +123,23 @@ final class GBTClassifier
     }
   }
 
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): GBTClassificationModel = {
+  override protected def train(dataset: DataFrame): GBTClassificationModel = {
     val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
-    val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match {
+      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
       case Some(n: Int) => n
       case None => throw new IllegalArgumentException("GBTClassifier was given input" +
-        s" with invalid label column ${paramMap(labelCol)}, without the number of classes" +
+        s" with invalid label column ${$(labelCol)}, without the number of classes" +
         " specified. See StringIndexer.")
       // TODO: Automatically index labels: SPARK-7126
     }
     require(numClasses == 2,
       s"GBTClassifier only supports binary classification but was given numClasses = $numClasses")
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap)
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification)
     val oldGBT = new OldGBT(boostingStrategy)
     val oldModel = oldGBT.run(oldDataset)
-    GBTClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures)
+    GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures)
   }
 }
 
@@ -165,7 +162,6 @@ object GBTClassifier {
 @AlphaComponent
 final class GBTClassificationModel(
     override val parent: GBTClassifier,
-    override val fittingParamMap: ParamMap,
     private val _trees: Array[DecisionTreeRegressionModel],
     private val _treeWeights: Array[Double])
   extends PredictionModel[Vector, GBTClassificationModel]
@@ -188,10 +184,8 @@ final class GBTClassificationModel(
     if (prediction > 0.0) 1.0 else 0.0
   }
 
-  override protected def copy(): GBTClassificationModel = {
-    val m = new GBTClassificationModel(parent, fittingParamMap, _trees, _treeWeights)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): GBTClassificationModel = {
+    copyValues(new GBTClassificationModel(parent, _trees, _treeWeights), extra)
   }
 
   override def toString: String = {
@@ -210,14 +204,13 @@ private[ml] object GBTClassificationModel {
   def fromOld(
       oldModel: OldGBTModel,
       parent: GBTClassifier,
-      fittingParamMap: ParamMap,
       categoricalFeatures: Map[Int, Int]): GBTClassificationModel = {
     require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" +
       s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).")
     val newTrees = oldModel.trees.map { tree =>
       // parent, fittingParamMap for each tree is null since there are no good ways to set these.
-      DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures)
+      DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
     }
-    new GBTClassificationModel(parent, fittingParamMap, newTrees, oldModel.treeWeights)
+    new GBTClassificationModel(parent, newTrees, oldModel.treeWeights)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index cc8b0721cf2b6..b73be035e29b5 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -21,12 +21,11 @@ import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
-import org.apache.spark.mllib.linalg.{VectorUDT, BLAS, Vector, Vectors}
+import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT, Vectors}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.storage.StorageLevel
 
-
 /**
  * Params for logistic regression.
  */
@@ -59,9 +58,9 @@ class LogisticRegression
   /** @group setParam */
   def setThreshold(value: Double): this.type = set(threshold, value)
 
-  override protected def train(dataset: DataFrame, paramMap: ParamMap): LogisticRegressionModel = {
+  override protected def train(dataset: DataFrame): LogisticRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
-    val oldDataset = extractLabeledPoints(dataset, paramMap)
+    val oldDataset = extractLabeledPoints(dataset)
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) {
       oldDataset.persist(StorageLevel.MEMORY_AND_DISK)
@@ -69,17 +68,17 @@ class LogisticRegression
 
     // Train model
     val lr = new LogisticRegressionWithLBFGS()
-      .setIntercept(paramMap(fitIntercept))
+      .setIntercept($(fitIntercept))
     lr.optimizer
-      .setRegParam(paramMap(regParam))
-      .setNumIterations(paramMap(maxIter))
+      .setRegParam($(regParam))
+      .setNumIterations($(maxIter))
     val oldModel = lr.run(oldDataset)
-    val lrm = new LogisticRegressionModel(this, paramMap, oldModel.weights, oldModel.intercept)
+    val lrm = new LogisticRegressionModel(this, oldModel.weights, oldModel.intercept)
 
     if (handlePersistence) {
       oldDataset.unpersist()
     }
-    lrm
+    copyValues(lrm)
   }
 }
 
@@ -92,7 +91,6 @@ class LogisticRegression
 @AlphaComponent
 class LogisticRegressionModel private[ml] (
     override val parent: LogisticRegression,
-    override val fittingParamMap: ParamMap,
     val weights: Vector,
     val intercept: Double)
   extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
@@ -110,16 +108,14 @@ class LogisticRegressionModel private[ml] (
     1.0 / (1.0 + math.exp(-m))
   }
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
+  override def transform(dataset: DataFrame): DataFrame = {
     // This is overridden (a) to be more efficient (avoiding re-computing values when creating
     // multiple output columns) and (b) to handle threshold, which the abstractions do not use.
     // TODO: We should abstract away the steps defined by UDFs below so that the abstractions
     // can call whichever UDFs are needed to create the output columns.
 
     // Check schema
-    transformSchema(dataset.schema, paramMap, logging = true)
-
-    val map = extractParamMap(paramMap)
+    transformSchema(dataset.schema, logging = true)
 
     // Output selected columns only.
     // This is a bit complicated since it tries to avoid repeated computation.
@@ -128,41 +124,41 @@ class LogisticRegressionModel private[ml] (
     //   prediction (max margin)
     var tmpData = dataset
     var numColsOutput = 0
-    if (map(rawPredictionCol) != "") {
+    if ($(rawPredictionCol) != "") {
       val features2raw: Vector => Vector = (features) => predictRaw(features)
-      tmpData = tmpData.withColumn(map(rawPredictionCol),
-        callUDF(features2raw, new VectorUDT, col(map(featuresCol))))
+      tmpData = tmpData.withColumn($(rawPredictionCol),
+        callUDF(features2raw, new VectorUDT, col($(featuresCol))))
       numColsOutput += 1
     }
-    if (map(probabilityCol) != "") {
-      if (map(rawPredictionCol) != "") {
+    if ($(probabilityCol) != "") {
+      if ($(rawPredictionCol) != "") {
         val raw2prob = udf { (rawPreds: Vector) =>
           val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1)))
           Vectors.dense(1.0 - prob1, prob1): Vector
         }
-        tmpData = tmpData.withColumn(map(probabilityCol), raw2prob(col(map(rawPredictionCol))))
+        tmpData = tmpData.withColumn($(probabilityCol), raw2prob(col($(rawPredictionCol))))
       } else {
         val features2prob = udf { (features: Vector) => predictProbabilities(features) : Vector }
-        tmpData = tmpData.withColumn(map(probabilityCol), features2prob(col(map(featuresCol))))
+        tmpData = tmpData.withColumn($(probabilityCol), features2prob(col($(featuresCol))))
       }
       numColsOutput += 1
     }
-    if (map(predictionCol) != "") {
-      val t = map(threshold)
-      if (map(probabilityCol) != "") {
+    if ($(predictionCol) != "") {
+      val t = $(threshold)
+      if ($(probabilityCol) != "") {
         val predict = udf { probs: Vector =>
           if (probs(1) > t) 1.0 else 0.0
         }
-        tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(probabilityCol))))
-      } else if (map(rawPredictionCol) != "") {
+        tmpData = tmpData.withColumn($(predictionCol), predict(col($(probabilityCol))))
+      } else if ($(rawPredictionCol) != "") {
         val predict = udf { rawPreds: Vector =>
           val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1)))
           if (prob1 > t) 1.0 else 0.0
         }
-        tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(rawPredictionCol))))
+        tmpData = tmpData.withColumn($(predictionCol), predict(col($(rawPredictionCol))))
       } else {
         val predict = udf { features: Vector => this.predict(features) }
-        tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(featuresCol))))
+        tmpData = tmpData.withColumn($(predictionCol), predict(col($(featuresCol))))
       }
       numColsOutput += 1
     }
@@ -193,9 +189,7 @@ class LogisticRegressionModel private[ml] (
     Vectors.dense(0.0, m)
   }
 
-  override protected def copy(): LogisticRegressionModel = {
-    val m = new LogisticRegressionModel(parent, fittingParamMap, weights, intercept)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): LogisticRegressionModel = {
+    copyValues(new LogisticRegressionModel(parent, weights, intercept), extra)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index 10404548ccfde..8519841c5c26c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.param.{ParamMap, Params}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
@@ -34,12 +33,10 @@ private[classification] trait ProbabilisticClassifierParams
 
   override protected def validateAndTransformSchema(
       schema: StructType,
-      paramMap: ParamMap,
       fitting: Boolean,
       featuresDataType: DataType): StructType = {
-    val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType)
-    val map = extractParamMap(paramMap)
-    SchemaUtils.appendColumn(parentSchema, map(probabilityCol), new VectorUDT)
+    val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType)
+    SchemaUtils.appendColumn(parentSchema, $(probabilityCol), new VectorUDT)
   }
 }
 
@@ -95,36 +92,22 @@ private[spark] abstract class ProbabilisticClassificationModel[
    *  - probability of each class as [[probabilityCol]] of type [[Vector]].
    *
    * @param dataset input dataset
-   * @param paramMap additional parameters, overwrite embedded params
    * @return transformed dataset
    */
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
+  override def transform(dataset: DataFrame): DataFrame = {
     // This default implementation should be overridden as needed.
 
     // Check schema
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-
-    // Prepare model
-    val tmpModel = if (paramMap.size != 0) {
-      val tmpModel = this.copy()
-      Params.inheritValues(paramMap, parent, tmpModel)
-      tmpModel
-    } else {
-      this
-    }
+    transformSchema(dataset.schema, logging = true)
 
     val (numColsOutput, outputData) =
-      ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map)
+      ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this)
 
     // Output selected columns only.
-    if (map(probabilityCol) != "") {
+    if ($(probabilityCol) != "") {
       // output probabilities
-      val features2probs: FeaturesType => Vector = (features) => {
-        tmpModel.predictProbabilities(features)
-      }
-      outputData.withColumn(map(probabilityCol),
-        callUDF(features2probs, new VectorUDT, col(map(featuresCol))))
+      outputData.withColumn($(probabilityCol),
+        callUDF(predictProbabilities _, new VectorUDT, col($(featuresCol))))
     } else {
       if (numColsOutput == 0) {
         this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index cfd6508fce890..17f59bb42e129 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -22,18 +22,17 @@ import scala.collection.mutable
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
 import org.apache.spark.ml.impl.tree._
-import org.apache.spark.ml.param.{Params, ParamMap}
+import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * :: AlphaComponent ::
  *
@@ -81,24 +80,22 @@ final class RandomForestClassifier
   override def setFeatureSubsetStrategy(value: String): this.type =
     super.setFeatureSubsetStrategy(value)
 
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): RandomForestClassificationModel = {
+  override protected def train(dataset: DataFrame): RandomForestClassificationModel = {
     val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
-    val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match {
+      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match {
       case Some(n: Int) => n
       case None => throw new IllegalArgumentException("RandomForestClassifier was given input" +
-        s" with invalid label column ${paramMap(labelCol)}, without the number of classes" +
+        s" with invalid label column ${$(labelCol)}, without the number of classes" +
         " specified. See StringIndexer.")
       // TODO: Automatically index labels: SPARK-7126
     }
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap)
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
     val oldModel = OldRandomForest.trainClassifier(
       oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt)
-    RandomForestClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures)
+    RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures)
   }
 }
 
@@ -123,7 +120,6 @@ object RandomForestClassifier {
 @AlphaComponent
 final class RandomForestClassificationModel private[ml] (
     override val parent: RandomForestClassifier,
-    override val fittingParamMap: ParamMap,
     private val _trees: Array[DecisionTreeClassificationModel])
   extends PredictionModel[Vector, RandomForestClassificationModel]
   with TreeEnsembleModel with Serializable {
@@ -150,10 +146,8 @@ final class RandomForestClassificationModel private[ml] (
     votes.maxBy(_._2)._1
   }
 
-  override protected def copy(): RandomForestClassificationModel = {
-    val m = new RandomForestClassificationModel(parent, fittingParamMap, _trees)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): RandomForestClassificationModel = {
+    copyValues(new RandomForestClassificationModel(parent, _trees), extra)
   }
 
   override def toString: String = {
@@ -172,14 +166,13 @@ private[ml] object RandomForestClassificationModel {
   def fromOld(
       oldModel: OldRandomForestModel,
       parent: RandomForestClassifier,
-      fittingParamMap: ParamMap,
       categoricalFeatures: Map[Int, Int]): RandomForestClassificationModel = {
     require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" +
       s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).")
     val newTrees = oldModel.trees.map { tree =>
       // parent, fittingParamMap for each tree is null since there are no good ways to set these.
-      DecisionTreeClassificationModel.fromOld(tree, null, null, categoricalFeatures)
+      DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures)
     }
-    new RandomForestClassificationModel(parent, fittingParamMap, newTrees)
+    new RandomForestClassificationModel(parent, newTrees)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index c865eb9fe092d..e5a73c6087a11 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -33,8 +33,7 @@ import org.apache.spark.sql.types.DoubleType
  * Evaluator for binary classification, which expects two input columns: score and label.
  */
 @AlphaComponent
-class BinaryClassificationEvaluator extends Evaluator with Params
-  with HasRawPredictionCol with HasLabelCol {
+class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol {
 
   /**
    * param for metric name in evaluation
@@ -44,7 +43,7 @@ class BinaryClassificationEvaluator extends Evaluator with Params
     "metric name in evaluation (areaUnderROC|areaUnderPR)")
 
   /** @group getParam */
-  def getMetricName: String = getOrDefault(metricName)
+  def getMetricName: String = $(metricName)
 
   /** @group setParam */
   def setMetricName(value: String): this.type = set(metricName, value)
@@ -57,20 +56,18 @@ class BinaryClassificationEvaluator extends Evaluator with Params
 
   setDefault(metricName -> "areaUnderROC")
 
-  override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = {
-    val map = extractParamMap(paramMap)
-
+  override def evaluate(dataset: DataFrame): Double = {
     val schema = dataset.schema
-    SchemaUtils.checkColumnType(schema, map(rawPredictionCol), new VectorUDT)
-    SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType)
+    SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT)
+    SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
 
     // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2.
-    val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol))
+    val scoreAndLabels = dataset.select($(rawPredictionCol), $(labelCol))
       .map { case Row(rawPrediction: Vector, label: Double) =>
         (rawPrediction(1), label)
       }
     val metrics = new BinaryClassificationMetrics(scoreAndLabels)
-    val metric = map(metricName) match {
+    val metric = $(metricName) match {
       case "areaUnderROC" =>
         metrics.areaUnderROC()
       case "areaUnderPR" =>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
index f3ce6dfca2c1c..6eb1db6971111 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
@@ -44,7 +44,7 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol {
     new DoubleParam(this, "threshold", "threshold used to binarize continuous features")
 
   /** @group getParam */
-  def getThreshold: Double = getOrDefault(threshold)
+  def getThreshold: Double = $(threshold)
 
   /** @group setParam */
   def setThreshold(value: Double): this.type = set(threshold, value)
@@ -57,23 +57,21 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol {
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val td = map(threshold)
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    val td = $(threshold)
     val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 }
-    val outputColName = map(outputCol)
+    val outputColName = $(outputCol)
     val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata()
     dataset.select(col("*"),
-      binarizer(col(map(inputCol))).as(outputColName, metadata))
+      binarizer(col($(inputCol))).as(outputColName, metadata))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    SchemaUtils.checkColumnType(schema, map(inputCol), DoubleType)
+  override def transformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
 
     val inputFields = schema.fields
-    val outputColName = map(outputCol)
+    val outputColName = $(outputCol)
 
     require(inputFields.forall(_.name != outputColName),
       s"Output column $outputColName already exists.")
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
index 0b3128f9ee8cd..c305a819a8966 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap}
+import org.apache.spark.ml.param.{IntParam, ParamValidators}
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{VectorUDT, Vector}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -42,13 +42,13 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] {
   setDefault(numFeatures -> (1 << 18))
 
   /** @group getParam */
-  def getNumFeatures: Int = getOrDefault(numFeatures)
+  def getNumFeatures: Int = $(numFeatures)
 
   /** @group setParam */
   def setNumFeatures(value: Int): this.type = set(numFeatures, value)
 
-  override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = {
-    val hashingTF = new feature.HashingTF(paramMap(numFeatures))
+  override protected def createTransformFunc: Iterable[_] => Vector = {
+    val hashingTF = new feature.HashingTF($(numFeatures))
     hashingTF.transform
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index e6a62d998bb97..d901a20aed002 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -43,7 +43,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
   setDefault(minDocFreq -> 0)
 
   /** @group getParam */
-  def getMinDocFreq: Int = getOrDefault(minDocFreq)
+  def getMinDocFreq: Int = $(minDocFreq)
 
   /** @group setParam */
   def setMinDocFreq(value: Int): this.type = set(minDocFreq, value)
@@ -51,10 +51,9 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
   /**
    * Validate and transform the input schema.
    */
-  protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    SchemaUtils.checkColumnType(schema, map(inputCol), new VectorUDT)
-    SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT)
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
+    SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
   }
 }
 
@@ -71,18 +70,15 @@ final class IDF extends Estimator[IDFModel] with IDFBase {
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): IDFModel = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v }
-    val idf = new feature.IDF(map(minDocFreq)).fit(input)
-    val model = new IDFModel(this, map, idf)
-    Params.inheritValues(map, this, model)
-    model
+  override def fit(dataset: DataFrame): IDFModel = {
+    transformSchema(dataset.schema, logging = true)
+    val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v }
+    val idf = new feature.IDF($(minDocFreq)).fit(input)
+    copyValues(new IDFModel(this, idf))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
 
@@ -93,7 +89,6 @@ final class IDF extends Estimator[IDFModel] with IDFBase {
 @AlphaComponent
 class IDFModel private[ml] (
     override val parent: IDF,
-    override val fittingParamMap: ParamMap,
     idfModel: feature.IDFModel)
   extends Model[IDFModel] with IDFBase {
 
@@ -103,14 +98,13 @@ class IDFModel private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
     val idf = udf { vec: Vector => idfModel.transform(vec) }
-    dataset.withColumn(map(outputCol), idf(col(map(inputCol))))
+    dataset.withColumn($(outputCol), idf(col($(inputCol))))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
index bd2b5f6067e2d..755b46a64c7f1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
@@ -19,9 +19,9 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{ParamValidators, DoubleParam, ParamMap}
+import org.apache.spark.ml.param.{DoubleParam, ParamValidators}
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{VectorUDT, Vector}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -41,13 +41,13 @@ class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] {
   setDefault(p -> 2.0)
 
   /** @group getParam */
-  def getP: Double = getOrDefault(p)
+  def getP: Double = $(p)
 
   /** @group setParam */
   def setP(value: Double): this.type = set(p, value)
 
-  override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = {
-    val normalizer = new feature.Normalizer(paramMap(p))
+  override protected def createTransformFunc: Vector => Vector = {
+    val normalizer = new feature.Normalizer($(p))
     normalizer.transform
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
new file mode 100644
index 0000000000000..46514ae5f0e84
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/OneHotEncoder.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.attribute.{Attribute, BinaryAttribute, NominalAttribute}
+import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
+
+/**
+ * A one-hot encoder that maps a column of label indices to a column of binary vectors, with
+ * at most a single one-value. By default, the binary vector has an element for each category, so
+ * with 5 categories, an input value of 2.0 would map to an output vector of
+ * (0.0, 0.0, 1.0, 0.0, 0.0). If includeFirst is set to false, the first category is omitted, so the
+ * output vector for the previous example would be (0.0, 1.0, 0.0, 0.0) and an input value
+ * of 0.0 would map to a vector of all zeros. Including the first category makes the vector columns
+ * linearly dependent because they sum up to one.
+ */
+@AlphaComponent
+class OneHotEncoder extends UnaryTransformer[Double, Vector, OneHotEncoder]
+  with HasInputCol with HasOutputCol {
+
+  /**
+   * Whether to include a component in the encoded vectors for the first category, defaults to true.
+   * @group param
+   */
+  final val includeFirst: BooleanParam =
+    new BooleanParam(this, "includeFirst", "include first category")
+  setDefault(includeFirst -> true)
+
+  private var categories: Array[String] = _
+
+  /** @group setParam */
+  def setIncludeFirst(value: Boolean): this.type = set(includeFirst, value)
+
+  /** @group setParam */
+  override def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  override def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  override def transformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType)
+    val inputFields = schema.fields
+    val outputColName = $(outputCol)
+    require(inputFields.forall(_.name != $(outputCol)),
+      s"Output column ${$(outputCol)} already exists.")
+
+    val inputColAttr = Attribute.fromStructField(schema($(inputCol)))
+    categories = inputColAttr match {
+      case nominal: NominalAttribute =>
+        nominal.values.getOrElse((0 until nominal.numValues.get).map(_.toString).toArray)
+      case binary: BinaryAttribute => binary.values.getOrElse(Array("0", "1"))
+      case _ =>
+        throw new SparkException(s"OneHotEncoder input column ${$(inputCol)} is not nominal")
+    }
+
+    val attrValues = (if ($(includeFirst)) categories else categories.drop(1)).toArray
+    val attr = NominalAttribute.defaultAttr.withName(outputColName).withValues(attrValues)
+    val outputFields = inputFields :+ attr.toStructField()
+    StructType(outputFields)
+  }
+
+  protected override def createTransformFunc(): (Double) => Vector = {
+    val first = $(includeFirst)
+    val vecLen = if (first) categories.length else categories.length - 1
+    val oneValue = Array(1.0)
+    val emptyValues = Array[Double]()
+    val emptyIndices = Array[Int]()
+    label: Double => {
+      val values = if (first || label != 0.0) oneValue else emptyValues
+      val indices = if (first) {
+        Array(label.toInt)
+      } else if (label != 0.0) {
+        Array(label.toInt - 1)
+      } else {
+        emptyIndices
+      }
+      Vectors.sparse(vecLen, indices, values)
+    }
+  }
+
+  /**
+   * Returns the data type of the output column.
+   */
+  protected def outputDataType: DataType = new VectorUDT
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
index 1b7c939c2dffe..63e190c8aae53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable
 
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap}
+import org.apache.spark.ml.param.{IntParam, ParamValidators}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.sql.types.DataType
 
@@ -47,14 +47,13 @@ class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExp
   setDefault(degree -> 2)
 
   /** @group getParam */
-  def getDegree: Int = getOrDefault(degree)
+  def getDegree: Int = $(degree)
 
   /** @group setParam */
   def setDegree(value: Int): this.type = set(degree, value)
 
-  override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { v =>
-    val d = paramMap(degree)
-    PolynomialExpansion.expand(v, d)
+  override protected def createTransformFunc: Vector => Vector = { v =>
+    PolynomialExpansion.expand(v, $(degree))
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index a0e9ed32e0e4c..7cad59ff3fa37 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -71,25 +71,21 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP
   /** @group setParam */
   def setWithStd(value: Boolean): this.type = set(withStd, value)
   
-  override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v }
-    val scaler = new feature.StandardScaler(withMean = map(withMean), withStd = map(withStd))
+  override def fit(dataset: DataFrame): StandardScalerModel = {
+    transformSchema(dataset.schema, logging = true)
+    val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v }
+    val scaler = new feature.StandardScaler(withMean = $(withMean), withStd = $(withStd))
     val scalerModel = scaler.fit(input)
-    val model = new StandardScalerModel(this, map, scalerModel)
-    Params.inheritValues(map, this, model)
-    model
+    copyValues(new StandardScalerModel(this, scalerModel))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    val inputType = schema(map(inputCol)).dataType
+  override def transformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
     require(inputType.isInstanceOf[VectorUDT],
-      s"Input column ${map(inputCol)} must be a vector column")
-    require(!schema.fieldNames.contains(map(outputCol)),
-      s"Output column ${map(outputCol)} already exists.")
-    val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false)
+      s"Input column ${$(inputCol)} must be a vector column")
+    require(!schema.fieldNames.contains($(outputCol)),
+      s"Output column ${$(outputCol)} already exists.")
+    val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
     StructType(outputFields)
   }
 }
@@ -101,7 +97,6 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP
 @AlphaComponent
 class StandardScalerModel private[ml] (
     override val parent: StandardScaler,
-    override val fittingParamMap: ParamMap,
     scaler: feature.StandardScalerModel)
   extends Model[StandardScalerModel] with StandardScalerParams {
 
@@ -111,21 +106,19 @@ class StandardScalerModel private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val scale = udf((v: Vector) => { scaler.transform(v) } : Vector)
-    dataset.withColumn(map(outputCol), scale(col(map(inputCol))))
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    val scale = udf { scaler.transform _ }
+    dataset.withColumn($(outputCol), scale(col($(inputCol))))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    val inputType = schema(map(inputCol)).dataType
+  override def transformSchema(schema: StructType): StructType = {
+    val inputType = schema($(inputCol)).dataType
     require(inputType.isInstanceOf[VectorUDT],
-      s"Input column ${map(inputCol)} must be a vector column")
-    require(!schema.fieldNames.contains(map(outputCol)),
-      s"Output column ${map(outputCol)} already exists.")
-    val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false)
+      s"Input column ${$(inputCol)} must be a vector column")
+    require(!schema.fieldNames.contains($(outputCol)),
+      s"Output column ${$(outputCol)} already exists.")
+    val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
     StructType(outputFields)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 9db3b29e10d69..3d78537ad84cb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -34,18 +34,17 @@ import org.apache.spark.util.collection.OpenHashMap
 private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol {
 
   /** Validates and transforms the input schema. */
-  protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    val inputColName = map(inputCol)
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    val inputColName = $(inputCol)
     val inputDataType = schema(inputColName).dataType
     require(inputDataType == StringType || inputDataType.isInstanceOf[NumericType],
       s"The input column $inputColName must be either string type or numeric type, " +
         s"but got $inputDataType.")
     val inputFields = schema.fields
-    val outputColName = map(outputCol)
+    val outputColName = $(outputCol)
     require(inputFields.forall(_.name != outputColName),
       s"Output column $outputColName already exists.")
-    val attr = NominalAttribute.defaultAttr.withName(map(outputCol))
+    val attr = NominalAttribute.defaultAttr.withName($(outputCol))
     val outputFields = inputFields :+ attr.toStructField()
     StructType(outputFields)
   }
@@ -69,19 +68,16 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase
 
   // TODO: handle unseen labels
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = {
-    val map = extractParamMap(paramMap)
-    val counts = dataset.select(col(map(inputCol)).cast(StringType))
+  override def fit(dataset: DataFrame): StringIndexerModel = {
+    val counts = dataset.select(col($(inputCol)).cast(StringType))
       .map(_.getString(0))
       .countByValue()
     val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray
-    val model = new StringIndexerModel(this, map, labels)
-    Params.inheritValues(map, this, model)
-    model
+    copyValues(new StringIndexerModel(this, labels))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
 
@@ -92,7 +88,6 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase
 @AlphaComponent
 class StringIndexerModel private[ml] (
     override val parent: StringIndexer,
-    override val fittingParamMap: ParamMap,
     labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
 
   private val labelToIndex: OpenHashMap[String, Double] = {
@@ -112,8 +107,7 @@ class StringIndexerModel private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    val map = extractParamMap(paramMap)
+  override def transform(dataset: DataFrame): DataFrame = {
     val indexer = udf { label: String =>
       if (labelToIndex.contains(label)) {
         labelToIndex(label)
@@ -122,14 +116,14 @@ class StringIndexerModel private[ml] (
         throw new SparkException(s"Unseen label: $label.")
       }
     }
-    val outputColName = map(outputCol)
+    val outputColName = $(outputCol)
     val metadata = NominalAttribute.defaultAttr
       .withName(outputColName).withValues(labels).toMetadata()
     dataset.select(col("*"),
-      indexer(dataset(map(inputCol)).cast(StringType)).as(outputColName, metadata))
+      indexer(dataset($(inputCol)).cast(StringType)).as(outputColName, metadata))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
index 01752ba482d0c..2863b7621526e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.feature
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param._
-import org.apache.spark.sql.types.{DataType, StringType, ArrayType}
+import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
 
 /**
  * :: AlphaComponent ::
@@ -29,7 +29,7 @@ import org.apache.spark.sql.types.{DataType, StringType, ArrayType}
 @AlphaComponent
 class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] {
 
-  override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = {
+  override protected def createTransformFunc: String => Seq[String] = {
     _.toLowerCase.split("\\s")
   }
 
@@ -62,7 +62,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize
   def setMinTokenLength(value: Int): this.type = set(minTokenLength, value)
 
   /** @group getParam */
-  def getMinTokenLength: Int = getOrDefault(minTokenLength)
+  def getMinTokenLength: Int = $(minTokenLength)
 
   /**
    * Indicates whether regex splits on gaps (true) or matching tokens (false).
@@ -75,7 +75,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize
   def setGaps(value: Boolean): this.type = set(gaps, value)
 
   /** @group getParam */
-  def getGaps: Boolean = getOrDefault(gaps)
+  def getGaps: Boolean = $(gaps)
 
   /**
    * Regex pattern used by tokenizer.
@@ -88,14 +88,14 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize
   def setPattern(value: String): this.type = set(pattern, value)
 
   /** @group getParam */
-  def getPattern: String = getOrDefault(pattern)
+  def getPattern: String = $(pattern)
 
   setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+")
 
-  override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str =>
-    val re = paramMap(pattern).r
-    val tokens = if (paramMap(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq
-    val minLength = paramMap(minTokenLength)
+  override protected def createTransformFunc: String => Seq[String] = { str =>
+    val re = $(pattern).r
+    val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq
+    val minLength = $(minTokenLength)
     tokens.filter(_.length >= minLength)
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
index 5e781a326d98c..8f2e62a8e2081 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala
@@ -22,7 +22,6 @@ import scala.collection.mutable.ArrayBuilder
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.Transformer
-import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
 import org.apache.spark.sql.{DataFrame, Row}
@@ -42,13 +41,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol {
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    val map = extractParamMap(paramMap)
+  override def transform(dataset: DataFrame): DataFrame = {
     val assembleFunc = udf { r: Row =>
       VectorAssembler.assemble(r.toSeq: _*)
     }
     val schema = dataset.schema
-    val inputColNames = map(inputCols)
+    val inputColNames = $(inputCols)
     val args = inputColNames.map { c =>
       schema(c).dataType match {
         case DoubleType => dataset(c)
@@ -56,13 +54,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol {
         case _: NumericType | BooleanType => dataset(c).cast(DoubleType).as(s"${c}_double_$uid")
       }
     }
-    dataset.select(col("*"), assembleFunc(struct(args : _*)).as(map(outputCol)))
+    dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol)))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    val inputColNames = map(inputCols)
-    val outputColName = map(outputCol)
+  override def transformSchema(schema: StructType): StructType = {
+    val inputColNames = $(inputCols)
+    val outputColName = $(outputCol)
     val inputDataTypes = inputColNames.map(name => schema(name).dataType)
     inputDataTypes.foreach {
       case _: NumericType | BooleanType =>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index ed833c63c7ef1..07ea579d69893 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -18,19 +18,17 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.ml.{Estimator, Model}
-import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute,
-  Attribute, AttributeGroup}
-import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap, Params}
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, NumericAttribute}
+import org.apache.spark.ml.param.{IntParam, ParamValidators, Params}
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT}
-import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT}
+import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.functions.callUDF
 import org.apache.spark.sql.types.{StructField, StructType}
 import org.apache.spark.util.collection.OpenHashSet
 
-
 /** Private trait for params for VectorIndexer and VectorIndexerModel */
 private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOutputCol {
 
@@ -49,7 +47,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
   setDefault(maxCategories -> 20)
 
   /** @group getParam */
-  def getMaxCategories: Int = getOrDefault(maxCategories)
+  def getMaxCategories: Int = $(maxCategories)
 }
 
 /**
@@ -100,33 +98,29 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val firstRow = dataset.select(map(inputCol)).take(1)
+  override def fit(dataset: DataFrame): VectorIndexerModel = {
+    transformSchema(dataset.schema, logging = true)
+    val firstRow = dataset.select($(inputCol)).take(1)
     require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.")
     val numFeatures = firstRow(0).getAs[Vector](0).size
-    val vectorDataset = dataset.select(map(inputCol)).map { case Row(v: Vector) => v }
-    val maxCats = map(maxCategories)
+    val vectorDataset = dataset.select($(inputCol)).map { case Row(v: Vector) => v }
+    val maxCats = $(maxCategories)
     val categoryStats: VectorIndexer.CategoryStats = vectorDataset.mapPartitions { iter =>
       val localCatStats = new VectorIndexer.CategoryStats(numFeatures, maxCats)
       iter.foreach(localCatStats.addVector)
       Iterator(localCatStats)
     }.reduce((stats1, stats2) => stats1.merge(stats2))
-    val model = new VectorIndexerModel(this, map, numFeatures, categoryStats.getCategoryMaps)
-    Params.inheritValues(map, this, model)
-    model
+    copyValues(new VectorIndexerModel(this, numFeatures, categoryStats.getCategoryMaps))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
+  override def transformSchema(schema: StructType): StructType = {
     // We do not transfer feature metadata since we do not know what types of features we will
     // produce in transform().
-    val map = extractParamMap(paramMap)
     val dataType = new VectorUDT
-    require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol")
-    require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol")
-    SchemaUtils.checkColumnType(schema, map(inputCol), dataType)
-    SchemaUtils.appendColumn(schema, map(outputCol), dataType)
+    require(isDefined(inputCol), s"VectorIndexer requires input column parameter: $inputCol")
+    require(isDefined(outputCol), s"VectorIndexer requires output column parameter: $outputCol")
+    SchemaUtils.checkColumnType(schema, $(inputCol), dataType)
+    SchemaUtils.appendColumn(schema, $(outputCol), dataType)
   }
 }
 
@@ -243,7 +237,6 @@ private object VectorIndexer {
 @AlphaComponent
 class VectorIndexerModel private[ml] (
     override val parent: VectorIndexer,
-    override val fittingParamMap: ParamMap,
     val numFeatures: Int,
     val categoryMaps: Map[Int, Map[Double, Int]])
   extends Model[VectorIndexerModel] with VectorIndexerParams {
@@ -326,35 +319,33 @@ class VectorIndexerModel private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val newField = prepOutputField(dataset.schema, map)
-    val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol)))
-    dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata))
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    val newField = prepOutputField(dataset.schema)
+    val newCol = callUDF(transformFunc, new VectorUDT, dataset($(inputCol)))
+    dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
+  override def transformSchema(schema: StructType): StructType = {
     val dataType = new VectorUDT
-    require(map.contains(inputCol),
+    require(isDefined(inputCol),
       s"VectorIndexerModel requires input column parameter: $inputCol")
-    require(map.contains(outputCol),
+    require(isDefined(outputCol),
       s"VectorIndexerModel requires output column parameter: $outputCol")
-    SchemaUtils.checkColumnType(schema, map(inputCol), dataType)
+    SchemaUtils.checkColumnType(schema, $(inputCol), dataType)
 
     // If the input metadata specifies numFeatures, compare with expected numFeatures.
-    val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol)))
+    val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol)))
     val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) {
       Some(origAttrGroup.attributes.get.length)
     } else {
       origAttrGroup.numAttributes
     }
     require(origNumFeatures.forall(_ == numFeatures), "VectorIndexerModel expected" +
-      s" $numFeatures features, but input column ${map(inputCol)} had metadata specifying" +
+      s" $numFeatures features, but input column ${$(inputCol)} had metadata specifying" +
       s" ${origAttrGroup.numAttributes.get} features.")
 
-    val newField = prepOutputField(schema, map)
+    val newField = prepOutputField(schema)
     val outputFields = schema.fields :+ newField
     StructType(outputFields)
   }
@@ -362,11 +353,10 @@ class VectorIndexerModel private[ml] (
   /**
    * Prepare the output column field, including per-feature metadata.
    * @param schema  Input schema
-   * @param map  Parameter map (with this class' embedded parameter map folded in)
    * @return  Output column field.  This field does not contain non-ML metadata.
    */
-  private def prepOutputField(schema: StructType, map: ParamMap): StructField = {
-    val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol)))
+  private def prepOutputField(schema: StructType): StructField = {
+    val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol)))
     val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) {
       // Convert original attributes to modified attributes
       val origAttrs: Array[Attribute] = origAttrGroup.attributes.get
@@ -389,7 +379,7 @@ class VectorIndexerModel private[ml] (
     } else {
       partialFeatureAttributes
     }
-    val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes)
+    val newAttributeGroup = new AttributeGroup($(outputCol), featureAttributes)
     newAttributeGroup.toStructField()
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 0163fa8bd8a5b..34ff92970129f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -18,16 +18,16 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.mllib.linalg.BLAS._
+import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{DataFrame, Row}
 
 /**
  * Params for [[Word2Vec]] and [[Word2VecModel]].
@@ -43,7 +43,7 @@ private[feature] trait Word2VecBase extends Params
   setDefault(vectorSize -> 100)
 
   /** @group getParam */
-  def getVectorSize: Int = getOrDefault(vectorSize)
+  def getVectorSize: Int = $(vectorSize)
 
   /**
    * Number of partitions for sentences of words.
@@ -53,7 +53,7 @@ private[feature] trait Word2VecBase extends Params
   setDefault(numPartitions -> 1)
 
   /** @group getParam */
-  def getNumPartitions: Int = getOrDefault(numPartitions)
+  def getNumPartitions: Int = $(numPartitions)
 
   /**
    * The minimum number of times a token must appear to be included in the word2vec model's
@@ -64,7 +64,7 @@ private[feature] trait Word2VecBase extends Params
   setDefault(minCount -> 5)
 
   /** @group getParam */
-  def getMinCount: Int = getOrDefault(minCount)
+  def getMinCount: Int = $(minCount)
 
   setDefault(stepSize -> 0.025)
   setDefault(maxIter -> 1)
@@ -73,10 +73,9 @@ private[feature] trait Word2VecBase extends Params
   /**
    * Validate and transform the input schema.
    */
-  protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    SchemaUtils.checkColumnType(schema, map(inputCol), new ArrayType(StringType, true))
-    SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT)
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true))
+    SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
   }
 }
 
@@ -112,25 +111,22 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase {
   /** @group setParam */
   def setMinCount(value: Int): this.type = set(minCount, value)
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): Word2VecModel = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val input = dataset.select(map(inputCol)).map { case Row(v: Seq[String]) => v }
+  override def fit(dataset: DataFrame): Word2VecModel = {
+    transformSchema(dataset.schema, logging = true)
+    val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0))
     val wordVectors = new feature.Word2Vec()
-      .setLearningRate(map(stepSize))
-      .setMinCount(map(minCount))
-      .setNumIterations(map(maxIter))
-      .setNumPartitions(map(numPartitions))
-      .setSeed(map(seed))
-      .setVectorSize(map(vectorSize))
+      .setLearningRate($(stepSize))
+      .setMinCount($(minCount))
+      .setNumIterations($(maxIter))
+      .setNumPartitions($(numPartitions))
+      .setSeed($(seed))
+      .setVectorSize($(vectorSize))
       .fit(input)
-    val model = new Word2VecModel(this, map, wordVectors)
-    Params.inheritValues(map, this, model)
-    model
+    copyValues(new Word2VecModel(this, wordVectors))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
 
@@ -141,7 +137,6 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase {
 @AlphaComponent
 class Word2VecModel private[ml] (
     override val parent: Word2Vec,
-    override val fittingParamMap: ParamMap,
     wordVectors: feature.Word2VecModel)
   extends Model[Word2VecModel] with Word2VecBase {
 
@@ -155,15 +150,14 @@ class Word2VecModel private[ml] (
    * Transform a sentence column to a vector column to represent the whole sentence. The transform
    * is performed by averaging all word vectors it contains.
    */
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
+  override def transform(dataset: DataFrame): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
     val bWordVectors = dataset.sqlContext.sparkContext.broadcast(wordVectors)
     val word2Vec = udf { sentence: Seq[String] =>
       if (sentence.size == 0) {
-        Vectors.sparse(map(vectorSize), Array.empty[Int], Array.empty[Double])
+        Vectors.sparse($(vectorSize), Array.empty[Int], Array.empty[Double])
       } else {
-        val cum = Vectors.zeros(map(vectorSize))
+        val cum = Vectors.zeros($(vectorSize))
         val model = bWordVectors.value.getVectors
         for (word <- sentence) {
           if (model.contains(word)) {
@@ -176,10 +170,10 @@ class Word2VecModel private[ml] (
         cum
       }
     }
-    dataset.withColumn(map(outputCol), word2Vec(col(map(inputCol))))
+    dataset.withColumn($(outputCol), word2Vec(col($(inputCol))))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala
index 195333a5cc47f..e8b3628140e99 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala
@@ -18,18 +18,17 @@
 package org.apache.spark.ml.impl.estimator
 
 import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
-import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
-import org.apache.spark.mllib.linalg.{VectorUDT, Vector}
+import org.apache.spark.ml.util.SchemaUtils
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
 
-
 /**
  * :: DeveloperApi ::
  *
@@ -44,7 +43,6 @@ private[spark] trait PredictorParams extends Params
   /**
    * Validates and transforms the input schema with the provided param map.
    * @param schema input schema
-   * @param paramMap additional parameters
    * @param fitting whether this is in fitting
    * @param featuresDataType  SQL DataType for FeaturesType.
    *                          E.g., [[org.apache.spark.mllib.linalg.VectorUDT]] for vector features.
@@ -52,17 +50,15 @@ private[spark] trait PredictorParams extends Params
    */
   protected def validateAndTransformSchema(
       schema: StructType,
-      paramMap: ParamMap,
       fitting: Boolean,
       featuresDataType: DataType): StructType = {
-    val map = extractParamMap(paramMap)
     // TODO: Support casting Array[Double] and Array[Float] to Vector when FeaturesType = Vector
-    SchemaUtils.checkColumnType(schema, map(featuresCol), featuresDataType)
+    SchemaUtils.checkColumnType(schema, $(featuresCol), featuresDataType)
     if (fitting) {
       // TODO: Allow other numeric types
-      SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType)
+      SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
     }
-    SchemaUtils.appendColumn(schema, map(predictionCol), DoubleType)
+    SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType)
   }
 }
 
@@ -96,14 +92,15 @@ private[spark] abstract class Predictor[
   /** @group setParam */
   def setPredictionCol(value: String): Learner = set(predictionCol, value).asInstanceOf[Learner]
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): M = {
+  override def fit(dataset: DataFrame): M = {
     // This handles a few items such as schema validation.
     // Developers only need to implement train().
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-    val model = train(dataset, map)
-    Params.inheritValues(map, this, model) // copy params to model
-    model
+    transformSchema(dataset.schema, logging = true)
+    copyValues(train(dataset))
+  }
+
+  override def copy(extra: ParamMap): Learner = {
+    super.copy(extra).asInstanceOf[Learner]
   }
 
   /**
@@ -114,12 +111,10 @@ private[spark] abstract class Predictor[
    * and copying parameters into the model.
    *
    * @param dataset  Training dataset
-   * @param paramMap  Parameter map.  Unlike [[fit()]]'s paramMap, this paramMap has already
-   *                  been combined with the embedded ParamMap.
    * @return  Fitted model
    */
   @DeveloperApi
-  protected def train(dataset: DataFrame, paramMap: ParamMap): M
+  protected def train(dataset: DataFrame): M
 
   /**
    * :: DeveloperApi ::
@@ -134,17 +129,16 @@ private[spark] abstract class Predictor[
   @DeveloperApi
   protected def featuresDataType: DataType = new VectorUDT
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap, fitting = true, featuresDataType)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema, fitting = true, featuresDataType)
   }
 
   /**
    * Extract [[labelCol]] and [[featuresCol]] from the given dataset,
    * and put it in an RDD with strong types.
    */
-  protected def extractLabeledPoints(dataset: DataFrame, paramMap: ParamMap): RDD[LabeledPoint] = {
-    val map = extractParamMap(paramMap)
-    dataset.select(map(labelCol), map(featuresCol))
+  protected def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint] = {
+    dataset.select($(labelCol), $(featuresCol))
       .map { case Row(label: Double, features: Vector) =>
       LabeledPoint(label, features)
     }
@@ -186,8 +180,8 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
   @DeveloperApi
   protected def featuresDataType: DataType = new VectorUDT
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap, fitting = false, featuresDataType)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema, fitting = false, featuresDataType)
   }
 
   /**
@@ -195,30 +189,16 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
    * the predictions as a new column [[predictionCol]].
    *
    * @param dataset input dataset
-   * @param paramMap additional parameters, overwrite embedded params
    * @return transformed dataset with [[predictionCol]] of type [[Double]]
    */
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
+  override def transform(dataset: DataFrame): DataFrame = {
     // This default implementation should be overridden as needed.
 
     // Check schema
-    transformSchema(dataset.schema, paramMap, logging = true)
-    val map = extractParamMap(paramMap)
-
-    // Prepare model
-    val tmpModel = if (paramMap.size != 0) {
-      val tmpModel = this.copy()
-      Params.inheritValues(paramMap, parent, tmpModel)
-      tmpModel
-    } else {
-      this
-    }
+    transformSchema(dataset.schema, logging = true)
 
-    if (map(predictionCol) != "") {
-      val pred: FeaturesType => Double = (features) => {
-        tmpModel.predict(features)
-      }
-      dataset.withColumn(map(predictionCol), callUDF(pred, DoubleType, col(map(featuresCol))))
+    if ($(predictionCol) != "") {
+      dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType, col($(featuresCol))))
     } else {
       this.logWarning(s"$uid: Predictor.transform() was called as NOOP" +
         " since no output columns were set.")
@@ -234,10 +214,4 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel
    */
   @DeveloperApi
   protected def predict(features: FeaturesType): Double
-
-  /**
-   * Create a copy of the model.
-   * The copy is shallow, except for the embedded paramMap, which gets a deep copy.
-   */
-  protected def copy(): M
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala
index fb770622e71f0..0e225627d4ee3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala
@@ -20,14 +20,11 @@ package org.apache.spark.ml.impl.tree
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.impl.estimator.PredictorParams
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasSeed, HasMaxIter}
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo,
-  BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
-import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy,
-  Impurity => OldImpurity, Variance => OldVariance}
+import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed}
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
 import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
 
-
 /**
  * :: DeveloperApi ::
  * Parameters for Decision Tree-based algorithms.
@@ -123,43 +120,43 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
   def setMaxDepth(value: Int): this.type = set(maxDepth, value)
 
   /** @group getParam */
-  final def getMaxDepth: Int = getOrDefault(maxDepth)
+  final def getMaxDepth: Int = $(maxDepth)
 
   /** @group setParam */
   def setMaxBins(value: Int): this.type = set(maxBins, value)
 
   /** @group getParam */
-  final def getMaxBins: Int = getOrDefault(maxBins)
+  final def getMaxBins: Int = $(maxBins)
 
   /** @group setParam */
   def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value)
 
   /** @group getParam */
-  final def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode)
+  final def getMinInstancesPerNode: Int = $(minInstancesPerNode)
 
   /** @group setParam */
   def setMinInfoGain(value: Double): this.type = set(minInfoGain, value)
 
   /** @group getParam */
-  final def getMinInfoGain: Double = getOrDefault(minInfoGain)
+  final def getMinInfoGain: Double = $(minInfoGain)
 
   /** @group expertSetParam */
   def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value)
 
   /** @group expertGetParam */
-  final def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB)
+  final def getMaxMemoryInMB: Int = $(maxMemoryInMB)
 
   /** @group expertSetParam */
   def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value)
 
   /** @group expertGetParam */
-  final def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds)
+  final def getCacheNodeIds: Boolean = $(cacheNodeIds)
 
   /** @group expertSetParam */
   def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
 
   /** @group expertGetParam */
-  final def getCheckpointInterval: Int = getOrDefault(checkpointInterval)
+  final def getCheckpointInterval: Int = $(checkpointInterval)
 
   /** (private[ml]) Create a Strategy instance to use with the old API. */
   private[ml] def getOldStrategy(
@@ -206,7 +203,7 @@ private[ml] trait TreeClassifierParams extends Params {
   def setImpurity(value: String): this.type = set(impurity, value)
 
   /** @group getParam */
-  final def getImpurity: String = getOrDefault(impurity).toLowerCase
+  final def getImpurity: String = $(impurity).toLowerCase
 
   /** Convert new impurity to old impurity. */
   private[ml] def getOldImpurity: OldImpurity = {
@@ -248,7 +245,7 @@ private[ml] trait TreeRegressorParams extends Params {
   def setImpurity(value: String): this.type = set(impurity, value)
 
   /** @group getParam */
-  final def getImpurity: String = getOrDefault(impurity).toLowerCase
+  final def getImpurity: String = $(impurity).toLowerCase
 
   /** Convert new impurity to old impurity. */
   private[ml] def getOldImpurity: OldImpurity = {
@@ -291,7 +288,7 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed {
   def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value)
 
   /** @group getParam */
-  final def getSubsamplingRate: Double = getOrDefault(subsamplingRate)
+  final def getSubsamplingRate: Double = $(subsamplingRate)
 
   /** @group setParam */
   def setSeed(value: Long): this.type = set(seed, value)
@@ -364,13 +361,13 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams {
   def setNumTrees(value: Int): this.type = set(numTrees, value)
 
   /** @group getParam */
-  final def getNumTrees: Int = getOrDefault(numTrees)
+  final def getNumTrees: Int = $(numTrees)
 
   /** @group setParam */
   def setFeatureSubsetStrategy(value: String): this.type = set(featureSubsetStrategy, value)
 
   /** @group getParam */
-  final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy).toLowerCase
+  final def getFeatureSubsetStrategy: String = $(featureSubsetStrategy).toLowerCase
 }
 
 private[ml] object RandomForestParams {
@@ -418,7 +415,7 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter {
   def setStepSize(value: Double): this.type = set(stepSize, value)
 
   /** @group getParam */
-  final def getStepSize: Double = getOrDefault(stepSize)
+  final def getStepSize: Double = $(stepSize)
 
   /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */
   private[ml] def getOldBoostingStrategy(
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index df6360dce6013..51ce19d29cd29 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -23,7 +23,7 @@ import java.util.NoSuchElementException
 import scala.annotation.varargs
 import scala.collection.mutable
 
-import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.util.Identifiable
 
 /**
@@ -49,7 +49,7 @@ class Param[T] (val parent: Params, val name: String, val doc: String, val isVal
    * Assert that the given value is valid for this parameter.
    *
    * Note: Parameter checks involving interactions between multiple parameters should be
-   *       implemented in [[Params.validate()]].  Checks for input/output columns should be
+   *       implemented in [[Params.validateParams()]].  Checks for input/output columns should be
    *       implemented in [[org.apache.spark.ml.PipelineStage.transformSchema()]].
    *
    * DEVELOPERS: This method is only called by [[ParamPair]], which means that all parameters
@@ -258,7 +258,9 @@ trait Params extends Identifiable with Serializable {
    * [[Param.validate()]].  This method does not handle input/output column parameters;
    * those are checked during schema validation.
    */
-  def validate(paramMap: ParamMap): Unit = { }
+  def validateParams(paramMap: ParamMap): Unit = {
+    copy(paramMap).validateParams()
+  }
 
   /**
    * Validates parameter values stored internally.
@@ -269,7 +271,11 @@ trait Params extends Identifiable with Serializable {
    * [[Param.validate()]].  This method does not handle input/output column parameters;
    * those are checked during schema validation.
    */
-  def validate(): Unit = validate(ParamMap.empty)
+  def validateParams(): Unit = {
+    params.filter(isDefined _).foreach { param =>
+      param.asInstanceOf[Param[Any]].validate($(param))
+    }
+  }
 
   /**
    * Returns the documentation of all params.
@@ -288,6 +294,11 @@ trait Params extends Identifiable with Serializable {
     defaultParamMap.contains(param) || paramMap.contains(param)
   }
 
+  /** Tests whether this instance contains a param with a given name. */
+  def hasParam(paramName: String): Boolean = {
+    params.exists(_.name == paramName)
+  }
+
   /** Gets a param by its name. */
   def getParam(paramName: String): Param[Any] = {
     params.find(_.name == paramName).getOrElse {
@@ -337,6 +348,9 @@ trait Params extends Identifiable with Serializable {
     get(param).orElse(getDefault(param)).get
   }
 
+  /** An alias for [[getOrDefault()]]. */
+  protected final def $[T](param: Param[T]): T = getOrDefault(param)
+
   /**
    * Sets a default value for a param.
    * @param param  param to set the default value. Make sure that this param is initialized before
@@ -382,19 +396,31 @@ trait Params extends Identifiable with Serializable {
     defaultParamMap.contains(param)
   }
 
+  /**
+   * Creates a copy of this instance with a randomly generated uid and some extra params.
+   * The default implementation calls the default constructor to create a new instance, then
+   * copies the embedded and extra parameters over and returns the new instance.
+   * Subclasses should override this method if the default approach is not sufficient.
+   */
+  def copy(extra: ParamMap): Params = {
+    val that = this.getClass.newInstance()
+    copyValues(that, extra)
+    that
+  }
+
   /**
    * Extracts the embedded default param values and user-supplied values, and then merges them with
    * extra values from input into a flat param map, where the latter value is used if there exist
    * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap.
    */
-  protected final def extractParamMap(extraParamMap: ParamMap): ParamMap = {
+  final def extractParamMap(extraParamMap: ParamMap): ParamMap = {
     defaultParamMap ++ paramMap ++ extraParamMap
   }
 
   /**
    * [[extractParamMap]] with no extra values.
    */
-  protected final def extractParamMap(): ParamMap = {
+  final def extractParamMap(): ParamMap = {
     extractParamMap(ParamMap.empty)
   }
 
@@ -408,34 +434,21 @@ trait Params extends Identifiable with Serializable {
   private def shouldOwn(param: Param[_]): Unit = {
     require(param.parent.eq(this), s"Param $param does not belong to $this.")
   }
-}
 
-/**
- * :: DeveloperApi ::
- *
- * Helper functionality for developers.
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
- */
-@DeveloperApi
-private[spark] object Params {
-
-  /**
-   * Copies parameter values from the parent estimator to the child model it produced.
-   * @param paramMap the param map that holds parameters of the parent
-   * @param parent the parent estimator
-   * @param child the child model
-   */
-  def inheritValues[E <: Params, M <: E](
-      paramMap: ParamMap,
-      parent: E,
-      child: M): Unit = {
-    val childParams = child.params.map(_.name).toSet
-    parent.params.foreach { param =>
-      if (paramMap.contains(param) && childParams.contains(param.name)) {
-        child.set(child.getParam(param.name), paramMap(param))
+  /**
+   * Copies param values from this instance to another instance for params shared by them.
+   * @param to the target instance
+   * @param extra extra params to be copied
+   * @return the target instance with param values copied
+   */
+  protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = {
+    val map = extractParamMap(extra)
+    params.foreach { param =>
+      if (map.contains(param) && to.hasParam(param.name)) {
+        to.set(param.name, map(param))
       }
     }
+    to
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 7da4bb4b4bf25..d379172e0bf53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -21,8 +21,6 @@ import java.io.PrintWriter
 
 import scala.reflect.ClassTag
 
-import org.apache.spark.ml.param.ParamValidators
-
 /**
  * Code generator for shared params (sharedParams.scala). Run under the Spark folder with
  * {{{
@@ -142,7 +140,7 @@ private[shared] object SharedParamsCodeGen {
       |  final val $name: $Param = new $Param(this, "$name", "$doc"$isValid)
       |$setDefault
       |  /** @group getParam */
-      |  final def get$Name: $T = getOrDefault($name)
+      |  final def get$Name: $T = $$($name)
       |}
       |""".stripMargin
   }
@@ -169,7 +167,6 @@ private[shared] object SharedParamsCodeGen {
         |
         |package org.apache.spark.ml.param.shared
         |
-        |import org.apache.spark.annotation.DeveloperApi
         |import org.apache.spark.ml.param._
         |import org.apache.spark.util.Utils
         |
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index e1549f46a68d4..fb1874ccfc8dc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.ml.param.shared
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param._
 import org.apache.spark.util.Utils
 
@@ -37,7 +36,7 @@ private[ml] trait HasRegParam extends Params {
   final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter (>= 0)", ParamValidators.gtEq(0))
 
   /** @group getParam */
-  final def getRegParam: Double = getOrDefault(regParam)
+  final def getRegParam: Double = $(regParam)
 }
 
 /**
@@ -52,7 +51,7 @@ private[ml] trait HasMaxIter extends Params {
   final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations (>= 0)", ParamValidators.gtEq(0))
 
   /** @group getParam */
-  final def getMaxIter: Int = getOrDefault(maxIter)
+  final def getMaxIter: Int = $(maxIter)
 }
 
 /**
@@ -69,7 +68,7 @@ private[ml] trait HasFeaturesCol extends Params {
   setDefault(featuresCol, "features")
 
   /** @group getParam */
-  final def getFeaturesCol: String = getOrDefault(featuresCol)
+  final def getFeaturesCol: String = $(featuresCol)
 }
 
 /**
@@ -86,7 +85,7 @@ private[ml] trait HasLabelCol extends Params {
   setDefault(labelCol, "label")
 
   /** @group getParam */
-  final def getLabelCol: String = getOrDefault(labelCol)
+  final def getLabelCol: String = $(labelCol)
 }
 
 /**
@@ -103,7 +102,7 @@ private[ml] trait HasPredictionCol extends Params {
   setDefault(predictionCol, "prediction")
 
   /** @group getParam */
-  final def getPredictionCol: String = getOrDefault(predictionCol)
+  final def getPredictionCol: String = $(predictionCol)
 }
 
 /**
@@ -120,7 +119,7 @@ private[ml] trait HasRawPredictionCol extends Params {
   setDefault(rawPredictionCol, "rawPrediction")
 
   /** @group getParam */
-  final def getRawPredictionCol: String = getOrDefault(rawPredictionCol)
+  final def getRawPredictionCol: String = $(rawPredictionCol)
 }
 
 /**
@@ -137,7 +136,7 @@ private[ml] trait HasProbabilityCol extends Params {
   setDefault(probabilityCol, "probability")
 
   /** @group getParam */
-  final def getProbabilityCol: String = getOrDefault(probabilityCol)
+  final def getProbabilityCol: String = $(probabilityCol)
 }
 
 /**
@@ -152,7 +151,7 @@ private[ml] trait HasThreshold extends Params {
   final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1))
 
   /** @group getParam */
-  final def getThreshold: Double = getOrDefault(threshold)
+  final def getThreshold: Double = $(threshold)
 }
 
 /**
@@ -167,7 +166,7 @@ private[ml] trait HasInputCol extends Params {
   final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name")
 
   /** @group getParam */
-  final def getInputCol: String = getOrDefault(inputCol)
+  final def getInputCol: String = $(inputCol)
 }
 
 /**
@@ -182,7 +181,7 @@ private[ml] trait HasInputCols extends Params {
   final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names")
 
   /** @group getParam */
-  final def getInputCols: Array[String] = getOrDefault(inputCols)
+  final def getInputCols: Array[String] = $(inputCols)
 }
 
 /**
@@ -197,7 +196,7 @@ private[ml] trait HasOutputCol extends Params {
   final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name")
 
   /** @group getParam */
-  final def getOutputCol: String = getOrDefault(outputCol)
+  final def getOutputCol: String = $(outputCol)
 }
 
 /**
@@ -212,7 +211,7 @@ private[ml] trait HasCheckpointInterval extends Params {
   final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1))
 
   /** @group getParam */
-  final def getCheckpointInterval: Int = getOrDefault(checkpointInterval)
+  final def getCheckpointInterval: Int = $(checkpointInterval)
 }
 
 /**
@@ -229,7 +228,7 @@ private[ml] trait HasFitIntercept extends Params {
   setDefault(fitIntercept, true)
 
   /** @group getParam */
-  final def getFitIntercept: Boolean = getOrDefault(fitIntercept)
+  final def getFitIntercept: Boolean = $(fitIntercept)
 }
 
 /**
@@ -246,7 +245,7 @@ private[ml] trait HasSeed extends Params {
   setDefault(seed, Utils.random.nextLong())
 
   /** @group getParam */
-  final def getSeed: Long = getOrDefault(seed)
+  final def getSeed: Long = $(seed)
 }
 
 /**
@@ -261,7 +260,7 @@ private[ml] trait HasElasticNetParam extends Params {
   final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", ParamValidators.inRange(0, 1))
 
   /** @group getParam */
-  final def getElasticNetParam: Double = getOrDefault(elasticNetParam)
+  final def getElasticNetParam: Double = $(elasticNetParam)
 }
 
 /**
@@ -276,7 +275,7 @@ private[ml] trait HasTol extends Params {
   final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms")
 
   /** @group getParam */
-  final def getTol: Double = getOrDefault(tol)
+  final def getTol: Double = $(tol)
 }
 
 /**
@@ -291,6 +290,6 @@ private[ml] trait HasStepSize extends Params {
   final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size to be used for each iteration of optimization.")
 
   /** @group getParam */
-  final def getStepSize: Double = getOrDefault(stepSize)
+  final def getStepSize: Double = $(stepSize)
 }
 // scalastyle:on
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index f9f2b2764ddb1..6cf4b40075281 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -59,7 +59,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
   val rank = new IntParam(this, "rank", "rank of the factorization", ParamValidators.gtEq(1))
 
   /** @group getParam */
-  def getRank: Int = getOrDefault(rank)
+  def getRank: Int = $(rank)
 
   /**
    * Param for number of user blocks (>= 1).
@@ -70,7 +70,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
     ParamValidators.gtEq(1))
 
   /** @group getParam */
-  def getNumUserBlocks: Int = getOrDefault(numUserBlocks)
+  def getNumUserBlocks: Int = $(numUserBlocks)
 
   /**
    * Param for number of item blocks (>= 1).
@@ -81,7 +81,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
       ParamValidators.gtEq(1))
 
   /** @group getParam */
-  def getNumItemBlocks: Int = getOrDefault(numItemBlocks)
+  def getNumItemBlocks: Int = $(numItemBlocks)
 
   /**
    * Param to decide whether to use implicit preference.
@@ -91,7 +91,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
   val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference")
 
   /** @group getParam */
-  def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs)
+  def getImplicitPrefs: Boolean = $(implicitPrefs)
 
   /**
    * Param for the alpha parameter in the implicit preference formulation (>= 0).
@@ -102,7 +102,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
     ParamValidators.gtEq(0))
 
   /** @group getParam */
-  def getAlpha: Double = getOrDefault(alpha)
+  def getAlpha: Double = $(alpha)
 
   /**
    * Param for the column name for user ids.
@@ -112,7 +112,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
   val userCol = new Param[String](this, "userCol", "column name for user ids")
 
   /** @group getParam */
-  def getUserCol: String = getOrDefault(userCol)
+  def getUserCol: String = $(userCol)
 
   /**
    * Param for the column name for item ids.
@@ -122,7 +122,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
   val itemCol = new Param[String](this, "itemCol", "column name for item ids")
 
   /** @group getParam */
-  def getItemCol: String = getOrDefault(itemCol)
+  def getItemCol: String = $(itemCol)
 
   /**
    * Param for the column name for ratings.
@@ -132,7 +132,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
   val ratingCol = new Param[String](this, "ratingCol", "column name for ratings")
 
   /** @group getParam */
-  def getRatingCol: String = getOrDefault(ratingCol)
+  def getRatingCol: String = $(ratingCol)
 
   /**
    * Param for whether to apply nonnegativity constraints.
@@ -143,7 +143,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
     this, "nonnegative", "whether to use nonnegative constraint for least squares")
 
   /** @group getParam */
-  def getNonnegative: Boolean = getOrDefault(nonnegative)
+  def getNonnegative: Boolean = $(nonnegative)
 
   setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10,
     implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item",
@@ -152,19 +152,17 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
   /**
    * Validates and transforms the input schema.
    * @param schema input schema
-   * @param paramMap extra params
    * @return output schema
    */
-  protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    assert(schema(map(userCol)).dataType == IntegerType)
-    assert(schema(map(itemCol)).dataType== IntegerType)
-    val ratingType = schema(map(ratingCol)).dataType
-    assert(ratingType == FloatType || ratingType == DoubleType)
-    val predictionColName = map(predictionCol)
-    assert(!schema.fieldNames.contains(predictionColName),
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    require(schema($(userCol)).dataType == IntegerType)
+    require(schema($(itemCol)).dataType== IntegerType)
+    val ratingType = schema($(ratingCol)).dataType
+    require(ratingType == FloatType || ratingType == DoubleType)
+    val predictionColName = $(predictionCol)
+    require(!schema.fieldNames.contains(predictionColName),
       s"Prediction column $predictionColName already exists.")
-    val newFields = schema.fields :+ StructField(map(predictionCol), FloatType, nullable = false)
+    val newFields = schema.fields :+ StructField($(predictionCol), FloatType, nullable = false)
     StructType(newFields)
   }
 }
@@ -174,7 +172,6 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR
  */
 class ALSModel private[ml] (
     override val parent: ALS,
-    override val fittingParamMap: ParamMap,
     k: Int,
     userFactors: RDD[(Int, Array[Float])],
     itemFactors: RDD[(Int, Array[Float])])
@@ -183,9 +180,8 @@ class ALSModel private[ml] (
   /** @group setParam */
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
+  override def transform(dataset: DataFrame): DataFrame = {
     import dataset.sqlContext.implicits._
-    val map = extractParamMap(paramMap)
     val users = userFactors.toDF("id", "features")
     val items = itemFactors.toDF("id", "features")
 
@@ -199,13 +195,13 @@ class ALSModel private[ml] (
       }
     }
     dataset
-      .join(users, dataset(map(userCol)) === users("id"), "left")
-      .join(items, dataset(map(itemCol)) === items("id"), "left")
-      .select(dataset("*"), predict(users("features"), items("features")).as(map(predictionCol)))
+      .join(users, dataset($(userCol)) === users("id"), "left")
+      .join(items, dataset($(itemCol)) === items("id"), "left")
+      .select(dataset("*"), predict(users("features"), items("features")).as($(predictionCol)))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
 
@@ -292,25 +288,22 @@ class ALS extends Estimator[ALSModel] with ALSParams {
     this
   }
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = {
-    val map = extractParamMap(paramMap)
+  override def fit(dataset: DataFrame): ALSModel = {
     val ratings = dataset
-      .select(col(map(userCol)), col(map(itemCol)), col(map(ratingCol)).cast(FloatType))
+      .select(col($(userCol)), col($(itemCol)), col($(ratingCol)).cast(FloatType))
       .map { row =>
         Rating(row.getInt(0), row.getInt(1), row.getFloat(2))
       }
-    val (userFactors, itemFactors) = ALS.train(ratings, rank = map(rank),
-      numUserBlocks = map(numUserBlocks), numItemBlocks = map(numItemBlocks),
-      maxIter = map(maxIter), regParam = map(regParam), implicitPrefs = map(implicitPrefs),
-      alpha = map(alpha), nonnegative = map(nonnegative),
-      checkpointInterval = map(checkpointInterval))
-    val model = new ALSModel(this, map, map(rank), userFactors, itemFactors)
-    Params.inheritValues(map, this, model)
-    model
+    val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank),
+      numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks),
+      maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs),
+      alpha = $(alpha), nonnegative = $(nonnegative),
+      checkpointInterval = $(checkpointInterval))
+    copyValues(new ALSModel(this, $(rank), userFactors, itemFactors))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    validateAndTransformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 756725a64b0f3..b07c26fe79b36 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.regression
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
 import org.apache.spark.ml.impl.tree._
-import org.apache.spark.ml.param.{Params, ParamMap}
+import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, Node}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
@@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * :: AlphaComponent ::
  *
@@ -63,15 +62,13 @@ final class DecisionTreeRegressor
 
   override def setImpurity(value: String): this.type = super.setImpurity(value)
 
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): DecisionTreeRegressionModel = {
+  override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = {
     val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap)
+      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy = getOldStrategy(categoricalFeatures)
     val oldModel = OldDecisionTree.train(oldDataset, strategy)
-    DecisionTreeRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures)
+    DecisionTreeRegressionModel.fromOld(oldModel, this, categoricalFeatures)
   }
 
   /** (private[ml]) Create a Strategy instance to use with the old API. */
@@ -96,7 +93,6 @@ object DecisionTreeRegressor {
 @AlphaComponent
 final class DecisionTreeRegressionModel private[ml] (
     override val parent: DecisionTreeRegressor,
-    override val fittingParamMap: ParamMap,
     override val rootNode: Node)
   extends PredictionModel[Vector, DecisionTreeRegressionModel]
   with DecisionTreeModel with Serializable {
@@ -108,10 +104,8 @@ final class DecisionTreeRegressionModel private[ml] (
     rootNode.predict(features)
   }
 
-  override protected def copy(): DecisionTreeRegressionModel = {
-    val m = new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): DecisionTreeRegressionModel = {
+    copyValues(new DecisionTreeRegressionModel(parent, rootNode), extra)
   }
 
   override def toString: String = {
@@ -130,12 +124,11 @@ private[ml] object DecisionTreeRegressionModel {
   def fromOld(
       oldModel: OldDecisionTreeModel,
       parent: DecisionTreeRegressor,
-      fittingParamMap: ParamMap,
       categoricalFeatures: Map[Int, Int]): DecisionTreeRegressionModel = {
     require(oldModel.algo == OldAlgo.Regression,
       s"Cannot convert non-regression DecisionTreeModel (old API) to" +
         s" DecisionTreeRegressionModel (new API).  Algo is: ${oldModel.algo}")
     val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures)
-    new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode)
+    new DecisionTreeRegressionModel(parent, rootNode)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 76c98376930c5..bc796958e4545 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -23,20 +23,18 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
 import org.apache.spark.ml.impl.tree._
-import org.apache.spark.ml.param.{Params, ParamMap, Param}
+import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
-import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss,
-  SquaredError => OldSquaredError}
+import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, SquaredError => OldSquaredError}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * :: AlphaComponent ::
  *
@@ -111,7 +109,7 @@ final class GBTRegressor
   def setLossType(value: String): this.type = set(lossType, value)
 
   /** @group getParam */
-  def getLossType: String = getOrDefault(lossType).toLowerCase
+  def getLossType: String = $(lossType).toLowerCase
 
   /** (private[ml]) Convert new loss to old loss. */
   override private[ml] def getOldLossType: OldLoss = {
@@ -124,16 +122,14 @@ final class GBTRegressor
     }
   }
 
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): GBTRegressionModel = {
+  override protected def train(dataset: DataFrame): GBTRegressionModel = {
     val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap)
+      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression)
     val oldGBT = new OldGBT(boostingStrategy)
     val oldModel = oldGBT.run(oldDataset)
-    GBTRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures)
+    GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures)
   }
 }
 
@@ -155,7 +151,6 @@ object GBTRegressor {
 @AlphaComponent
 final class GBTRegressionModel(
     override val parent: GBTRegressor,
-    override val fittingParamMap: ParamMap,
     private val _trees: Array[DecisionTreeRegressionModel],
     private val _treeWeights: Array[Double])
   extends PredictionModel[Vector, GBTRegressionModel]
@@ -178,10 +173,8 @@ final class GBTRegressionModel(
     if (prediction > 0.0) 1.0 else 0.0
   }
 
-  override protected def copy(): GBTRegressionModel = {
-    val m = new GBTRegressionModel(parent, fittingParamMap, _trees, _treeWeights)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): GBTRegressionModel = {
+    copyValues(new GBTRegressionModel(parent, _trees, _treeWeights), extra)
   }
 
   override def toString: String = {
@@ -200,14 +193,13 @@ private[ml] object GBTRegressionModel {
   def fromOld(
       oldModel: OldGBTModel,
       parent: GBTRegressor,
-      fittingParamMap: ParamMap,
       categoricalFeatures: Map[Int, Int]): GBTRegressionModel = {
     require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" +
       s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).")
     val newTrees = oldModel.trees.map { tree =>
       // parent, fittingParamMap for each tree is null since there are no good ways to set these.
-      DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures)
+      DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
     }
-    new GBTRegressionModel(parent, fittingParamMap, newTrees, oldModel.treeWeights)
+    new GBTRegressionModel(parent, newTrees, oldModel.treeWeights)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 0b81c48466be9..66c475f2d9840 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -19,22 +19,22 @@ package org.apache.spark.ml.regression
 
 import scala.collection.mutable
 
-import breeze.linalg.{norm => brzNorm, DenseVector => BDV}
-import breeze.optimize.{LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN}
-import breeze.optimize.{CachedDiffFunction, DiffFunction}
+import breeze.linalg.{DenseVector => BDV, norm => brzNorm}
+import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS,
+  OWLQN => BreezeOWLQN}
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.param.{Params, ParamMap}
-import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, HasRegParam}
-import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
+import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.StatCounter
-import org.apache.spark.Logging
 
 /**
  * Params for linear regression.
@@ -96,9 +96,9 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
   def setTol(value: Double): this.type = set(tol, value)
   setDefault(tol -> 1E-6)
 
-  override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = {
+  override protected def train(dataset: DataFrame): LinearRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist instances.
-    val instances = extractLabeledPoints(dataset, paramMap).map {
+    val instances = extractLabeledPoints(dataset).map {
       case LabeledPoint(label: Double, features: Vector) => (label, features)
     }
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
@@ -125,7 +125,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
       logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " +
         s"and the intercept will be the mean of the label; as a result, training is not needed.")
       if (handlePersistence) instances.unpersist()
-      return new LinearRegressionModel(this, paramMap, Vectors.sparse(numFeatures, Seq()), yMean)
+      return new LinearRegressionModel(this, Vectors.sparse(numFeatures, Seq()), yMean)
     }
 
     val featuresMean = summarizer.mean.toArray
@@ -133,17 +133,17 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
 
     // Since we implicitly do the feature scaling when we compute the cost function
     // to improve the convergence, the effective regParam will be changed.
-    val effectiveRegParam = paramMap(regParam) / yStd
-    val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam
-    val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) * effectiveRegParam
+    val effectiveRegParam = $(regParam) / yStd
+    val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam
+    val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam
 
     val costFun = new LeastSquaresCostFun(instances, yStd, yMean,
       featuresStd, featuresMean, effectiveL2RegParam)
 
-    val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
-      new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol))
+    val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
+      new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
     } else {
-      new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol))
+      new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegParam, $(tol))
     }
 
     val initialWeights = Vectors.zeros(numFeatures)
@@ -178,7 +178,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
     if (handlePersistence) instances.unpersist()
 
     // TODO: Converts to sparse format based on the storage, but may base on the scoring speed.
-    new LinearRegressionModel(this, paramMap, weights.compressed, intercept)
+    new LinearRegressionModel(this, weights.compressed, intercept)
   }
 }
 
@@ -190,7 +190,6 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress
 @AlphaComponent
 class LinearRegressionModel private[ml] (
     override val parent: LinearRegression,
-    override val fittingParamMap: ParamMap,
     val weights: Vector,
     val intercept: Double)
   extends RegressionModel[Vector, LinearRegressionModel]
@@ -200,10 +199,8 @@ class LinearRegressionModel private[ml] (
     dot(features, weights) + intercept
   }
 
-  override protected def copy(): LinearRegressionModel = {
-    val m = new LinearRegressionModel(parent, fittingParamMap, weights, intercept)
-    Params.inheritValues(extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): LinearRegressionModel = {
+    copyValues(new LinearRegressionModel(parent, weights, intercept), extra)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 2171ef3d32c26..0468a1be1ba74 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -20,18 +20,17 @@ package org.apache.spark.ml.regression
 import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor}
 import org.apache.spark.ml.impl.tree.{RandomForestParams, TreeRegressorParams}
-import org.apache.spark.ml.param.{Params, ParamMap}
+import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel}
 import org.apache.spark.ml.util.MetadataUtils
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest}
-import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
+import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * :: AlphaComponent ::
  *
@@ -77,17 +76,15 @@ final class RandomForestRegressor
   override def setFeatureSubsetStrategy(value: String): this.type =
     super.setFeatureSubsetStrategy(value)
 
-  override protected def train(
-      dataset: DataFrame,
-      paramMap: ParamMap): RandomForestRegressionModel = {
+  override protected def train(dataset: DataFrame): RandomForestRegressionModel = {
     val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol)))
-    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap)
+      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset)
     val strategy =
       super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity)
     val oldModel = OldRandomForest.trainRegressor(
       oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt)
-    RandomForestRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures)
+    RandomForestRegressionModel.fromOld(oldModel, this, categoricalFeatures)
   }
 }
 
@@ -110,7 +107,6 @@ object RandomForestRegressor {
 @AlphaComponent
 final class RandomForestRegressionModel private[ml] (
     override val parent: RandomForestRegressor,
-    override val fittingParamMap: ParamMap,
     private val _trees: Array[DecisionTreeRegressionModel])
   extends PredictionModel[Vector, RandomForestRegressionModel]
   with TreeEnsembleModel with Serializable {
@@ -132,10 +128,8 @@ final class RandomForestRegressionModel private[ml] (
     _trees.map(_.rootNode.predict(features)).sum / numTrees
   }
 
-  override protected def copy(): RandomForestRegressionModel = {
-    val m = new RandomForestRegressionModel(parent, fittingParamMap, _trees)
-    Params.inheritValues(this.extractParamMap(), this, m)
-    m
+  override def copy(extra: ParamMap): RandomForestRegressionModel = {
+    copyValues(new RandomForestRegressionModel(parent, _trees), extra)
   }
 
   override def toString: String = {
@@ -154,14 +148,13 @@ private[ml] object RandomForestRegressionModel {
   def fromOld(
       oldModel: OldRandomForestModel,
       parent: RandomForestRegressor,
-      fittingParamMap: ParamMap,
       categoricalFeatures: Map[Int, Int]): RandomForestRegressionModel = {
     require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" +
       s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).")
     val newTrees = oldModel.trees.map { tree =>
       // parent, fittingParamMap for each tree is null since there are no good ways to set these.
-      DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures)
+      DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
     }
-    new RandomForestRegressionModel(parent, fittingParamMap, newTrees)
+    new RandomForestRegressionModel(parent, newTrees)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
index d679085eeafe1..c6b3327db6ad3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.regression
 
-import org.apache.spark.annotation.{DeveloperApi, AlphaComponent}
+import org.apache.spark.annotation.{AlphaComponent, DeveloperApi}
 import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams}
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index d1ad0893cd044..cee2aa6e85523 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -39,7 +39,7 @@ private[ml] trait CrossValidatorParams extends Params {
   val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection")
 
   /** @group getParam */
-  def getEstimator: Estimator[_] = getOrDefault(estimator)
+  def getEstimator: Estimator[_] = $(estimator)
 
   /**
    * param for estimator param maps
@@ -49,7 +49,7 @@ private[ml] trait CrossValidatorParams extends Params {
     new Param(this, "estimatorParamMaps", "param maps for the estimator")
 
   /** @group getParam */
-  def getEstimatorParamMaps: Array[ParamMap] = getOrDefault(estimatorParamMaps)
+  def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps)
 
   /**
    * param for the evaluator for selection
@@ -58,7 +58,7 @@ private[ml] trait CrossValidatorParams extends Params {
   val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection")
 
   /** @group getParam */
-  def getEvaluator: Evaluator = getOrDefault(evaluator)
+  def getEvaluator: Evaluator = $(evaluator)
 
   /**
    * Param for number of folds for cross validation.  Must be >= 2.
@@ -69,7 +69,7 @@ private[ml] trait CrossValidatorParams extends Params {
     "number of folds for cross validation (>= 2)", ParamValidators.gtEq(2))
 
   /** @group getParam */
-  def getNumFolds: Int = getOrDefault(numFolds)
+  def getNumFolds: Int = $(numFolds)
 
   setDefault(numFolds -> 3)
 }
@@ -95,23 +95,22 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
   /** @group setParam */
   def setNumFolds(value: Int): this.type = set(numFolds, value)
 
-  override def validate(paramMap: ParamMap): Unit = {
+  override def validateParams(paramMap: ParamMap): Unit = {
     getEstimatorParamMaps.foreach { eMap =>
-      getEstimator.validate(eMap ++ paramMap)
+      getEstimator.validateParams(eMap ++ paramMap)
     }
   }
 
-  override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = {
-    val map = extractParamMap(paramMap)
+  override def fit(dataset: DataFrame): CrossValidatorModel = {
     val schema = dataset.schema
-    transformSchema(dataset.schema, paramMap, logging = true)
+    transformSchema(dataset.schema, logging = true)
     val sqlCtx = dataset.sqlContext
-    val est = map(estimator)
-    val eval = map(evaluator)
-    val epm = map(estimatorParamMaps)
+    val est = $(estimator)
+    val eval = $(evaluator)
+    val epm = $(estimatorParamMaps)
     val numModels = epm.length
     val metrics = new Array[Double](epm.length)
-    val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0)
+    val splits = MLUtils.kFold(dataset.rdd, $(numFolds), 0)
     splits.zipWithIndex.foreach { case ((training, validation), splitIndex) =>
       val trainingDataset = sqlCtx.createDataFrame(training, schema).cache()
       val validationDataset = sqlCtx.createDataFrame(validation, schema).cache()
@@ -121,27 +120,24 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
       trainingDataset.unpersist()
       var i = 0
       while (i < numModels) {
-        val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map)
+        val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
         logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
         metrics(i) += metric
         i += 1
       }
       validationDataset.unpersist()
     }
-    f2jBLAS.dscal(numModels, 1.0 / map(numFolds), metrics, 1)
+    f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1)
     logInfo(s"Average cross-validation metrics: ${metrics.toSeq}")
     val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
     logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
     logInfo(s"Best cross-validation metric: $bestMetric.")
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
-    val cvModel = new CrossValidatorModel(this, map, bestModel)
-    Params.inheritValues(map, this, cvModel)
-    cvModel
+    copyValues(new CrossValidatorModel(this, bestModel))
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    val map = extractParamMap(paramMap)
-    map(estimator).transformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    $(estimator).transformSchema(schema)
   }
 }
 
@@ -152,19 +148,18 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP
 @AlphaComponent
 class CrossValidatorModel private[ml] (
     override val parent: CrossValidator,
-    override val fittingParamMap: ParamMap,
     val bestModel: Model[_])
   extends Model[CrossValidatorModel] with CrossValidatorParams {
 
-  override def validate(paramMap: ParamMap): Unit = {
-    bestModel.validate(paramMap)
+  override def validateParams(paramMap: ParamMap): Unit = {
+    bestModel.validateParams(paramMap)
   }
 
-  override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
-    bestModel.transform(dataset, paramMap)
+  override def transform(dataset: DataFrame): DataFrame = {
+    bestModel.transform(dataset)
   }
 
-  override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
-    bestModel.transformSchema(schema, paramMap)
+  override def transformSchema(schema: StructType): StructType = {
+    bestModel.transformSchema(schema)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 6237b64c8f984..b086cec083381 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -291,12 +291,16 @@ private[python] class PythonMLLibAPI extends Serializable {
       maxIterations: Int,
       runs: Int,
       initializationMode: String,
-      seed: java.lang.Long): KMeansModel = {
+      seed: java.lang.Long,
+      initializationSteps: Int,
+      epsilon: Double): KMeansModel = {
     val kMeansAlg = new KMeans()
       .setK(k)
       .setMaxIterations(maxIterations)
       .setRuns(runs)
       .setInitializationMode(initializationMode)
+      .setInitializationSteps(initializationSteps)
+      .setEpsilon(epsilon)
 
     if (seed != null) kMeansAlg.setSeed(seed)
 
@@ -307,6 +311,15 @@ private[python] class PythonMLLibAPI extends Serializable {
     }
   }
 
+  /**
+   * Java stub for Python mllib KMeansModel.computeCost()
+   */
+  def computeCostKmeansModel(
+      data: JavaRDD[Vector],
+      centers: java.util.ArrayList[Vector]): Double = {
+    new KMeansModel(centers).computeCost(data)
+  }
+
   /**
    * Java stub for Python mllib GaussianMixture.run()
    * Returns a list containing weights, mean and covariance of each mixture component.
@@ -1015,6 +1028,61 @@ private[spark] object SerDe extends Serializable {
     }
   }
 
+  // Pickler for SparseMatrix
+  private[python] class SparseMatrixPickler extends BasePickler[SparseMatrix] {
+
+    def saveState(obj: Object, out: OutputStream, pickler: Pickler): Unit = {
+      val s = obj.asInstanceOf[SparseMatrix]
+      val order = ByteOrder.nativeOrder()
+
+      val colPtrsBytes = new Array[Byte](4 * s.colPtrs.length)
+      val indicesBytes = new Array[Byte](4 * s.rowIndices.length)
+      val valuesBytes = new Array[Byte](8 * s.values.length)
+      val isTransposed = if (s.isTransposed) 1 else 0
+      ByteBuffer.wrap(colPtrsBytes).order(order).asIntBuffer().put(s.colPtrs)
+      ByteBuffer.wrap(indicesBytes).order(order).asIntBuffer().put(s.rowIndices)
+      ByteBuffer.wrap(valuesBytes).order(order).asDoubleBuffer().put(s.values)
+
+      out.write(Opcodes.MARK)
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(s.numRows))
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(s.numCols))
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(colPtrsBytes.length))
+      out.write(colPtrsBytes)
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(indicesBytes.length))
+      out.write(indicesBytes)
+      out.write(Opcodes.BINSTRING)
+      out.write(PickleUtils.integer_to_bytes(valuesBytes.length))
+      out.write(valuesBytes)
+      out.write(Opcodes.BININT)
+      out.write(PickleUtils.integer_to_bytes(isTransposed))
+      out.write(Opcodes.TUPLE)
+    }
+
+    def construct(args: Array[Object]): Object = {
+      if (args.length != 6) {
+        throw new PickleException("should be 6")
+      }
+      val order = ByteOrder.nativeOrder()
+      val colPtrsBytes = getBytes(args(2))
+      val indicesBytes = getBytes(args(3))
+      val valuesBytes = getBytes(args(4))
+      val colPtrs = new Array[Int](colPtrsBytes.length / 4)
+      val rowIndices = new Array[Int](indicesBytes.length / 4)
+      val values = new Array[Double](valuesBytes.length / 8)
+      ByteBuffer.wrap(colPtrsBytes).order(order).asIntBuffer().get(colPtrs)
+      ByteBuffer.wrap(indicesBytes).order(order).asIntBuffer().get(rowIndices)
+      ByteBuffer.wrap(valuesBytes).order(order).asDoubleBuffer().get(values)
+      val isTransposed = args(5).asInstanceOf[Int] == 1
+      new SparseMatrix(
+        args(0).asInstanceOf[Int], args(1).asInstanceOf[Int], colPtrs, rowIndices, values,
+        isTransposed)
+    }
+  }
+
   // Pickler for SparseVector
   private[python] class SparseVectorPickler extends BasePickler[SparseVector] {
 
@@ -1099,6 +1167,7 @@ private[spark] object SerDe extends Serializable {
       if (!initialized) {
         new DenseVectorPickler().register()
         new DenseMatrixPickler().register()
+        new SparseMatrixPickler().register()
         new SparseVectorPickler().register()
         new LabeledPointPickler().register()
         new RatingPickler().register()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 37bf88b73b911..c8daa2388e868 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -78,35 +78,29 @@ class LDA private (
    *
    * This is the parameter to a symmetric Dirichlet distribution.
    */
-  def getDocConcentration: Double = {
-    if (this.docConcentration == -1) {
-      (50.0 / k) + 1.0
-    } else {
-      this.docConcentration
-    }
-  }
+  def getDocConcentration: Double = this.docConcentration
 
   /**
    * Concentration parameter (commonly named "alpha") for the prior placed on documents'
    * distributions over topics ("theta").
    *
-   * This is the parameter to a symmetric Dirichlet distribution.
+   * This is the parameter to a symmetric Dirichlet distribution, where larger values
+   * mean more smoothing (more regularization).
    *
-   * This value should be > 1.0, where larger values mean more smoothing (more regularization).
    * If set to -1, then docConcentration is set automatically.
    *  (default = -1 = automatic)
    *
-   * Automatic setting of parameter:
-   *  - For EM: default = (50 / k) + 1.
-   *     - The 50/k is common in LDA libraries.
-   *     - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM.
-   *
-   * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions),
-   *       but values in (0,1) are not yet supported.
+   * Optimizer-specific parameter settings:
+   *  - EM
+   *     - Value should be > 1.0
+   *     - default = (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows
+   *       Asuncion et al. (2009), who recommend a +1 adjustment for EM.
+   *  - Online
+   *     - Value should be >= 0
+   *     - default = (1.0 / k), following the implementation from
+   *       [[https://github.com/Blei-Lab/onlineldavb]].
    */
   def setDocConcentration(docConcentration: Double): this.type = {
-    require(docConcentration > 1.0 || docConcentration == -1.0,
-      s"LDA docConcentration must be > 1.0 (or -1 for auto), but was set to $docConcentration")
     this.docConcentration = docConcentration
     this
   }
@@ -126,13 +120,7 @@ class LDA private (
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
    */
-  def getTopicConcentration: Double = {
-    if (this.topicConcentration == -1) {
-      1.1
-    } else {
-      this.topicConcentration
-    }
-  }
+  def getTopicConcentration: Double = this.topicConcentration
 
   /**
    * Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics'
@@ -143,21 +131,20 @@ class LDA private (
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
    *
-   * This value should be > 0.0.
    * If set to -1, then topicConcentration is set automatically.
    *  (default = -1 = automatic)
    *
-   * Automatic setting of parameter:
-   *  - For EM: default = 0.1 + 1.
-   *     - The 0.1 gives a small amount of smoothing.
-   *     - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM.
-   *
-   * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions),
-   *       but values in (0,1) are not yet supported.
+   * Optimizer-specific parameter settings:
+   *  - EM
+   *     - Value should be > 1.0
+   *     - default = 0.1 + 1, where 0.1 gives a small amount of smoothing and +1 follows
+   *       Asuncion et al. (2009), who recommend a +1 adjustment for EM.
+   *  - Online
+   *     - Value should be >= 0
+   *     - default = (1.0 / k), following the implementation from
+   *       [[https://github.com/Blei-Lab/onlineldavb]].
    */
   def setTopicConcentration(topicConcentration: Double): this.type = {
-    require(topicConcentration > 1.0 || topicConcentration == -1.0,
-      s"LDA topicConcentration must be > 1.0 (or -1 for auto), but was set to $topicConcentration")
     this.topicConcentration = topicConcentration
     this
   }
@@ -223,14 +210,15 @@ class LDA private (
 
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
-   * Currently "em" is supported.
+   * Currently "em", "online" is supported.
    */
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =
       optimizerName.toLowerCase match {
         case "em" => new EMLDAOptimizer
+        case "online" => new OnlineLDAOptimizer
         case other =>
-          throw new IllegalArgumentException(s"Only em is supported but got $other.")
+          throw new IllegalArgumentException(s"Only em, online are supported but got $other.")
       }
     this
   }
@@ -245,8 +233,7 @@ class LDA private (
    * @return  Inferred LDA model
    */
   def run(documents: RDD[(Long, Vector)]): LDAModel = {
-    val state = ldaOptimizer.initialState(documents, k, getDocConcentration, getTopicConcentration,
-      seed, checkpointInterval)
+    val state = ldaOptimizer.initialize(documents, this)
     var iter = 0
     val iterationTimes = Array.fill[Double](maxIterations)(0)
     while (iter < maxIterations) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index ffd72a294c6c6..093aa0f315ab2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -19,13 +19,15 @@ package org.apache.spark.mllib.clustering
 
 import java.util.Random
 
-import breeze.linalg.{DenseVector => BDV, normalize}
+import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kron}
+import breeze.numerics.{digamma, exp, abs}
+import breeze.stats.distributions.{Gamma, RandBasis}
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
-import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vector}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -35,7 +37,7 @@ import org.apache.spark.rdd.RDD
  * hold optimizer-specific parameters for users to set.
  */
 @Experimental
-trait LDAOptimizer{
+trait LDAOptimizer {
 
   /*
     DEVELOPERS NOTE:
@@ -49,13 +51,7 @@ trait LDAOptimizer{
    * Initializer for the optimizer. LDA passes the common parameters to the optimizer and
    * the internal structure can be initialized properly.
    */
-  private[clustering] def initialState(
-      docs: RDD[(Long, Vector)],
-      k: Int,
-      docConcentration: Double,
-      topicConcentration: Double,
-      randomSeed: Long,
-      checkpointInterval: Int): LDAOptimizer
+  private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer
 
   private[clustering] def next(): LDAOptimizer
 
@@ -80,12 +76,12 @@ trait LDAOptimizer{
  *
  */
 @Experimental
-class EMLDAOptimizer extends LDAOptimizer{
+class EMLDAOptimizer extends LDAOptimizer {
 
   import LDA._
 
   /**
-   * Following fields will only be initialized through initialState method
+   * The following fields will only be initialized through the initialize() method
    */
   private[clustering] var graph: Graph[TopicCounts, TokenCount] = null
   private[clustering] var k: Int = 0
@@ -98,13 +94,23 @@ class EMLDAOptimizer extends LDAOptimizer{
   /**
    * Compute bipartite term/doc graph.
    */
-  private[clustering] override def initialState(
-      docs: RDD[(Long, Vector)],
-      k: Int,
-      docConcentration: Double,
-      topicConcentration: Double,
-      randomSeed: Long,
-      checkpointInterval: Int): LDAOptimizer = {
+  override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = {
+
+    val docConcentration = lda.getDocConcentration
+    val topicConcentration = lda.getTopicConcentration
+    val k = lda.getK
+
+    // Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions),
+    // but values in (0,1) are not yet supported.
+    require(docConcentration > 1.0 || docConcentration == -1.0, s"LDA docConcentration must be" +
+      s" > 1.0 (or -1 for auto) for EM Optimizer, but was set to $docConcentration")
+    require(topicConcentration > 1.0 || topicConcentration == -1.0, s"LDA topicConcentration " +
+      s"must be > 1.0 (or -1 for auto) for EM Optimizer, but was set to $topicConcentration")
+
+    this.docConcentration = if (docConcentration == -1) (50.0 / k) + 1.0 else docConcentration
+    this.topicConcentration = if (topicConcentration == -1) 1.1 else topicConcentration
+    val randomSeed = lda.getSeed
+
     // For each document, create an edge (Document -> Term) for each unique term in the document.
     val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) =>
       // Add edges for terms with non-zero counts.
@@ -113,11 +119,9 @@ class EMLDAOptimizer extends LDAOptimizer{
       }
     }
 
-    val vocabSize = docs.take(1).head._2.size
-
     // Create vertices.
     // Initially, we use random soft assignments of tokens to topics (random gamma).
-    def createVertices(): RDD[(VertexId, TopicCounts)] = {
+    val docTermVertices: RDD[(VertexId, TopicCounts)] = {
       val verticesTMP: RDD[(VertexId, TopicCounts)] =
         edges.mapPartitionsWithIndex { case (partIndex, partEdges) =>
           val random = new Random(partIndex + randomSeed)
@@ -130,22 +134,18 @@ class EMLDAOptimizer extends LDAOptimizer{
       verticesTMP.reduceByKey(_ + _)
     }
 
-    val docTermVertices = createVertices()
-
     // Partition such that edges are grouped by document
     this.graph = Graph(docTermVertices, edges).partitionBy(PartitionStrategy.EdgePartition1D)
     this.k = k
-    this.vocabSize = vocabSize
-    this.docConcentration = docConcentration
-    this.topicConcentration = topicConcentration
-    this.checkpointInterval = checkpointInterval
+    this.vocabSize = docs.take(1).head._2.size
+    this.checkpointInterval = lda.getCheckpointInterval
     this.graphCheckpointer = new
       PeriodicGraphCheckpointer[TopicCounts, TokenCount](graph, checkpointInterval)
     this.globalTopicTotals = computeGlobalTopicTotals()
     this
   }
 
-  private[clustering] override def next(): EMLDAOptimizer = {
+  override private[clustering] def next(): EMLDAOptimizer = {
     require(graph != null, "graph is null, EMLDAOptimizer not initialized.")
 
     val eta = topicConcentration
@@ -202,9 +202,269 @@ class EMLDAOptimizer extends LDAOptimizer{
     graph.vertices.filter(isTermVertex).values.fold(BDV.zeros[Double](numTopics))(_ += _)
   }
 
-  private[clustering] override def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
+  override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
     require(graph != null, "graph is null, EMLDAOptimizer not initialized.")
     this.graphCheckpointer.deleteAllCheckpoints()
     new DistributedLDAModel(this, iterationTimes)
   }
 }
+
+
+/**
+ * :: Experimental ::
+ *
+ * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA
+ * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic
+ * distribution adaptively.
+ *
+ * Original Online LDA paper:
+ *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
+ */
+@Experimental
+class OnlineLDAOptimizer extends LDAOptimizer {
+
+  // LDA common parameters
+  private var k: Int = 0
+  private var corpusSize: Long = 0
+  private var vocabSize: Int = 0
+
+  /** alias for docConcentration */
+  private var alpha: Double = 0
+
+  /** (private[clustering] for debugging)  Get docConcentration */
+  private[clustering] def getAlpha: Double = alpha
+
+  /** alias for topicConcentration */
+  private var eta: Double = 0
+
+  /** (private[clustering] for debugging)  Get topicConcentration */
+  private[clustering] def getEta: Double = eta
+
+  private var randomGenerator: java.util.Random = null
+
+  // Online LDA specific parameters
+  // Learning rate is: (tau_0 + t)^{-kappa}
+  private var tau_0: Double = 1024
+  private var kappa: Double = 0.51
+  private var miniBatchFraction: Double = 0.05
+
+  // internal data structure
+  private var docs: RDD[(Long, Vector)] = null
+
+  /** Dirichlet parameter for the posterior over topics */
+  private var lambda: BDM[Double] = null
+
+  /** (private[clustering] for debugging) Get parameter for topics */
+  private[clustering] def getLambda: BDM[Double] = lambda
+
+  /** Current iteration (count of invocations of [[next()]]) */
+  private var iteration: Int = 0
+  private var gammaShape: Double = 100
+
+  /**
+   * A (positive) learning parameter that downweights early iterations. Larger values make early
+   * iterations count less.
+   */
+  def getTau_0: Double = this.tau_0
+
+  /**
+   * A (positive) learning parameter that downweights early iterations. Larger values make early
+   * iterations count less.
+   * Default: 1024, following the original Online LDA paper.
+   */
+  def setTau_0(tau_0: Double): this.type = {
+    require(tau_0 > 0,  s"LDA tau_0 must be positive, but was set to $tau_0")
+    this.tau_0 = tau_0
+    this
+  }
+
+  /**
+   * Learning rate: exponential decay rate
+   */
+  def getKappa: Double = this.kappa
+
+  /**
+   * Learning rate: exponential decay rate---should be between
+   * (0.5, 1.0] to guarantee asymptotic convergence.
+   * Default: 0.51, based on the original Online LDA paper.
+   */
+  def setKappa(kappa: Double): this.type = {
+    require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa")
+    this.kappa = kappa
+    this
+  }
+
+  /**
+   * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
+   */
+  def getMiniBatchFraction: Double = this.miniBatchFraction
+
+  /**
+   * Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in
+   * each iteration.
+   *
+   * Note that this should be adjusted in synch with [[LDA.setMaxIterations()]]
+   * so the entire corpus is used.  Specifically, set both so that
+   * maxIterations * miniBatchFraction >= 1.
+   *
+   * Default: 0.05, i.e., 5% of total documents.
+   */
+  def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
+    require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0,
+      s"Online LDA miniBatchFraction must be in range (0,1], but was set to $miniBatchFraction")
+    this.miniBatchFraction = miniBatchFraction
+    this
+  }
+
+  /**
+   * (private[clustering])
+   * Set the Dirichlet parameter for the posterior over topics.
+   * This is only used for testing now. In the future, it can help support training stop/resume.
+   */
+  private[clustering] def setLambda(lambda: BDM[Double]): this.type = {
+    this.lambda = lambda
+    this
+  }
+
+  /**
+   * (private[clustering])
+   * Used for random initialization of the variational parameters.
+   * Larger value produces values closer to 1.0.
+   * This is only used for testing currently.
+   */
+  private[clustering] def setGammaShape(shape: Double): this.type = {
+    this.gammaShape = shape
+    this
+  }
+
+  override private[clustering] def initialize(
+      docs: RDD[(Long, Vector)],
+      lda: LDA):  OnlineLDAOptimizer = {
+    this.k = lda.getK
+    this.corpusSize = docs.count()
+    this.vocabSize = docs.first()._2.size
+    this.alpha = if (lda.getDocConcentration == -1) 1.0 / k else lda.getDocConcentration
+    this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration
+    this.randomGenerator = new Random(lda.getSeed)
+
+    this.docs = docs
+
+    // Initialize the variational distribution q(beta|lambda)
+    this.lambda = getGammaMatrix(k, vocabSize)
+    this.iteration = 0
+    this
+  }
+
+  override private[clustering] def next(): OnlineLDAOptimizer = {
+    val batch = docs.sample(withReplacement = true, miniBatchFraction, randomGenerator.nextLong())
+    if (batch.isEmpty()) return this
+    submitMiniBatch(batch)
+  }
+
+  /**
+   * Submit a subset (like 1%, decide by the miniBatchFraction) of the corpus to the Online LDA
+   * model, and it will update the topic distribution adaptively for the terms appearing in the
+   * subset.
+   */
+  private[clustering] def submitMiniBatch(batch: RDD[(Long, Vector)]): OnlineLDAOptimizer = {
+    iteration += 1
+    val k = this.k
+    val vocabSize = this.vocabSize
+    val Elogbeta = dirichletExpectation(lambda)
+    val expElogbeta = exp(Elogbeta)
+    val alpha = this.alpha
+    val gammaShape = this.gammaShape
+
+    val stats: RDD[BDM[Double]] = batch.mapPartitions { docs =>
+      val stat = BDM.zeros[Double](k, vocabSize)
+      docs.foreach { doc =>
+        val termCounts = doc._2
+        val (ids: List[Int], cts: Array[Double]) = termCounts match {
+          case v: DenseVector => ((0 until v.size).toList, v.values)
+          case v: SparseVector => (v.indices.toList, v.values)
+          case v => throw new IllegalArgumentException("Online LDA does not support vector type "
+            + v.getClass)
+        }
+
+        // Initialize the variational distribution q(theta|gamma) for the mini-batch
+        var gammad = new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k).t // 1 * K
+        var Elogthetad = digamma(gammad) - digamma(sum(gammad))     // 1 * K
+        var expElogthetad = exp(Elogthetad)                         // 1 * K
+        val expElogbetad = expElogbeta(::, ids).toDenseMatrix       // K * ids
+
+        var phinorm = expElogthetad * expElogbetad + 1e-100         // 1 * ids
+        var meanchange = 1D
+        val ctsVector = new BDV[Double](cts).t                      // 1 * ids
+
+        // Iterate between gamma and phi until convergence
+        while (meanchange > 1e-3) {
+          val lastgamma = gammad
+          //        1*K                  1 * ids               ids * k
+          gammad = (expElogthetad :* ((ctsVector / phinorm) * expElogbetad.t)) + alpha
+          Elogthetad = digamma(gammad) - digamma(sum(gammad))
+          expElogthetad = exp(Elogthetad)
+          phinorm = expElogthetad * expElogbetad + 1e-100
+          meanchange = sum(abs(gammad - lastgamma)) / k
+        }
+
+        val m1 = expElogthetad.t
+        val m2 = (ctsVector / phinorm).t.toDenseVector
+        var i = 0
+        while (i < ids.size) {
+          stat(::, ids(i)) := stat(::, ids(i)) + m1 * m2(i)
+          i += 1
+        }
+      }
+      Iterator(stat)
+    }
+
+    val statsSum: BDM[Double] = stats.reduce(_ += _)
+    val batchResult = statsSum :* expElogbeta
+
+    // Note that this is an optimization to avoid batch.count
+    update(batchResult, iteration, (miniBatchFraction * corpusSize).ceil.toInt)
+    this
+  }
+
+  override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
+    new LocalLDAModel(Matrices.fromBreeze(lambda).transpose)
+  }
+
+  /**
+   * Update lambda based on the batch submitted. batchSize can be different for each iteration.
+   */
+  private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = {
+    val tau_0 = this.getTau_0
+    val kappa = this.getKappa
+
+    // weight of the mini-batch.
+    val weight = math.pow(tau_0 + iter, -kappa)
+
+    // Update lambda based on documents.
+    lambda = lambda * (1 - weight) +
+      (stat * (corpusSize.toDouble / batchSize.toDouble) + eta) * weight
+  }
+
+  /**
+   * Get a random matrix to initialize lambda
+   */
+  private def getGammaMatrix(row: Int, col: Int): BDM[Double] = {
+    val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister(
+      randomGenerator.nextLong()))
+    val gammaRandomGenerator = new Gamma(gammaShape, 1.0 / gammaShape)(randBasis)
+    val temp = gammaRandomGenerator.sample(row * col).toArray
+    new BDM[Double](col, row, temp).t
+  }
+
+  /**
+   * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently the implementation
+   * uses digamma which is accurate but expensive.
+   */
+  private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = {
+    val rowSum =  sum(alpha(breeze.linalg.*, ::))
+    val digAlpha = digamma(alpha)
+    val digRowSum = digamma(rowSum)
+    val result = digAlpha(::, breeze.linalg.*) - digRowSum
+    result
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index f1a6ed230186e..f73896e37c05e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -107,7 +107,10 @@ private[tree] object DecisionTreeMetadata extends Logging {
       numTrees: Int,
       featureSubsetStrategy: String): DecisionTreeMetadata = {
 
-    val numFeatures = input.take(1)(0).features.size
+    val numFeatures = input.map(_.features.size).take(1).headOption.getOrElse {
+      throw new IllegalArgumentException(s"DecisionTree requires size of input RDD > 0, " +
+        s"but was given by empty one.")
+    }
     val numExamples = input.count()
     val numClasses = strategy.algo match {
       case Classification => strategy.numClasses
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 3f8e59de0f05c..7e7189a2b1d53 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -84,9 +84,10 @@ public void logisticRegressionWithSetters() {
       .setThreshold(0.6)
       .setProbabilityCol("myProbability");
     LogisticRegressionModel model = lr.fit(dataset);
-    assert(model.fittingParamMap().apply(lr.maxIter()).equals(10));
-    assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0));
-    assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6));
+    LogisticRegression parent = model.parent();
+    assert(parent.getMaxIter() == 10);
+    assert(parent.getRegParam() == 1.0);
+    assert(parent.getThreshold() == 0.6);
     assert(model.getThreshold() == 0.6);
 
     // Modify model params, and check that the params worked.
@@ -109,9 +110,10 @@ public void logisticRegressionWithSetters() {
     // Call fit() with new params, and check as many params as we can.
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
         lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
-    assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5));
-    assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1));
-    assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4));
+    LogisticRegression parent2 = model2.parent();
+    assert(parent2.getMaxIter() == 5);
+    assert(parent2.getRegParam() == 0.1);
+    assert(parent2.getThreshold() == 0.4);
     assert(model2.getThreshold() == 0.4);
     assert(model2.getProbabilityCol().equals("theProb"));
   }
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
index 0cc36c8d56d70..a82b86d560b6e 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
@@ -23,14 +23,15 @@
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
+import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import static org.apache.spark.mllib.classification.LogisticRegressionSuite
-    .generateLogisticInputAsList;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
+import static org.apache.spark.mllib.classification.LogisticRegressionSuite
+  .generateLogisticInputAsList;
 
 
 public class JavaLinearRegressionSuite implements Serializable {
@@ -65,8 +66,8 @@ public void linearRegressionDefaultParams() {
     DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction");
     predictions.collect();
     // Check defaults
-    assert(model.getFeaturesCol().equals("features"));
-    assert(model.getPredictionCol().equals("prediction"));
+    assertEquals("features", model.getFeaturesCol());
+    assertEquals("prediction", model.getPredictionCol());
   }
 
   @Test
@@ -76,14 +77,16 @@ public void linearRegressionWithSetters() {
         .setMaxIter(10)
         .setRegParam(1.0);
     LinearRegressionModel model = lr.fit(dataset);
-    assert(model.fittingParamMap().apply(lr.maxIter()).equals(10));
-    assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0));
+    LinearRegression parent = model.parent();
+    assertEquals(10, parent.getMaxIter());
+    assertEquals(1.0, parent.getRegParam(), 0.0);
 
     // Call fit() with new params, and check as many params as we can.
     LinearRegressionModel model2 =
         lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred"));
-    assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5));
-    assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1));
-    assert(model2.getPredictionCol().equals("thePred"));
+    LinearRegression parent2 = model2.parent();
+    assertEquals(5, parent2.getMaxIter());
+    assertEquals(0.1, parent2.getRegParam(), 0.0);
+    assertEquals("thePred", model2.getPredictionCol());
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java
index 0bb6b489f2757..08eeca53f0721 100644
--- a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java
@@ -68,8 +68,8 @@ public void crossValidationWithLogisticRegression() {
       .setEvaluator(eval)
       .setNumFolds(3);
     CrossValidatorModel cvModel = cv.fit(dataset);
-    ParamMap bestParamMap = cvModel.bestModel().fittingParamMap();
-    Assert.assertEquals(0.001, bestParamMap.apply(lr.regParam()));
-    Assert.assertEquals(10, bestParamMap.apply(lr.maxIter()));
+    LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent();
+    Assert.assertEquals(0.001, parent.getRegParam(), 0.0);
+    Assert.assertEquals(10, parent.getMaxIter());
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index fbe171b4b1ab1..f394d903966de 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -20,7 +20,6 @@
 import java.io.Serializable;
 import java.util.ArrayList;
 
-import org.apache.spark.api.java.JavaRDD;
 import scala.Tuple2;
 
 import org.junit.After;
@@ -30,6 +29,7 @@
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.linalg.Matrix;
 import org.apache.spark.mllib.linalg.Vector;
@@ -109,11 +109,45 @@ public void distributedLDAModel() {
     assert(model.logPrior() < 0.0);
   }
 
+  @Test
+  public void OnlineOptimizerCompatibility() {
+    int k = 3;
+    double topicSmoothing = 1.2;
+    double termSmoothing = 1.2;
+
+    // Train a model
+    OnlineLDAOptimizer op = new OnlineLDAOptimizer()
+      .setTau_0(1024)
+      .setKappa(0.51)
+      .setGammaShape(1e40)
+      .setMiniBatchFraction(0.5);
+
+    LDA lda = new LDA();
+    lda.setK(k)
+      .setDocConcentration(topicSmoothing)
+      .setTopicConcentration(termSmoothing)
+      .setMaxIterations(5)
+      .setSeed(12345)
+      .setOptimizer(op);
+
+    LDAModel model = lda.run(corpus);
+
+    // Check: basic parameters
+    assertEquals(model.k(), k);
+    assertEquals(model.vocabSize(), tinyVocabSize);
+
+    // Check: topic summaries
+    Tuple2<int[], double[]>[] roundedTopicSummary = model.describeTopics();
+    assertEquals(roundedTopicSummary.length, k);
+    Tuple2<int[], double[]>[] roundedLocalTopicSummary = model.describeTopics();
+    assertEquals(roundedLocalTopicSummary.length, k);
+  }
+
   private static int tinyK = LDASuite$.MODULE$.tinyK();
   private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
   private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
   private static Tuple2<int[], double[]>[] tinyTopicDescription =
       LDASuite$.MODULE$.tinyTopicDescription();
-  JavaPairRDD<Long, Vector> corpus;
+  private JavaPairRDD<Long, Vector> corpus;
 
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
index 2f175fb117941..2b04a3034782e 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
@@ -42,30 +42,32 @@ class PipelineSuite extends FunSuite {
     val dataset3 = mock[DataFrame]
     val dataset4 = mock[DataFrame]
 
-    when(estimator0.fit(meq(dataset0), any[ParamMap]())).thenReturn(model0)
-    when(model0.transform(meq(dataset0), any[ParamMap]())).thenReturn(dataset1)
+    when(estimator0.copy(any[ParamMap])).thenReturn(estimator0)
+    when(model0.copy(any[ParamMap])).thenReturn(model0)
+    when(transformer1.copy(any[ParamMap])).thenReturn(transformer1)
+    when(estimator2.copy(any[ParamMap])).thenReturn(estimator2)
+    when(model2.copy(any[ParamMap])).thenReturn(model2)
+    when(transformer3.copy(any[ParamMap])).thenReturn(transformer3)
+
+    when(estimator0.fit(meq(dataset0))).thenReturn(model0)
+    when(model0.transform(meq(dataset0))).thenReturn(dataset1)
     when(model0.parent).thenReturn(estimator0)
-    when(transformer1.transform(meq(dataset1), any[ParamMap])).thenReturn(dataset2)
-    when(estimator2.fit(meq(dataset2), any[ParamMap]())).thenReturn(model2)
-    when(model2.transform(meq(dataset2), any[ParamMap]())).thenReturn(dataset3)
+    when(transformer1.transform(meq(dataset1))).thenReturn(dataset2)
+    when(estimator2.fit(meq(dataset2))).thenReturn(model2)
+    when(model2.transform(meq(dataset2))).thenReturn(dataset3)
     when(model2.parent).thenReturn(estimator2)
-    when(transformer3.transform(meq(dataset3), any[ParamMap]())).thenReturn(dataset4)
+    when(transformer3.transform(meq(dataset3))).thenReturn(dataset4)
 
     val pipeline = new Pipeline()
       .setStages(Array(estimator0, transformer1, estimator2, transformer3))
     val pipelineModel = pipeline.fit(dataset0)
 
-    assert(pipelineModel.stages.size === 4)
+    assert(pipelineModel.stages.length === 4)
     assert(pipelineModel.stages(0).eq(model0))
     assert(pipelineModel.stages(1).eq(transformer1))
     assert(pipelineModel.stages(2).eq(model2))
     assert(pipelineModel.stages(3).eq(transformer3))
 
-    assert(pipelineModel.getModel(estimator0).eq(model0))
-    assert(pipelineModel.getModel(estimator2).eq(model2))
-    intercept[NoSuchElementException] {
-      pipelineModel.getModel(mock[Estimator[MyModel]])
-    }
     val output = pipelineModel.transform(dataset0)
     assert(output.eq(dataset4))
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index 9b31adecdcb1c..03af4ecd7a7e0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -267,8 +267,8 @@ private[ml] object DecisionTreeClassifierSuite extends FunSuite {
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
     val newTree = dt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
-    val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(oldTree, newTree.parent,
-      newTree.fittingParamMap, categoricalFeatures)
+    val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(
+      oldTree, newTree.parent, categoricalFeatures)
     TreeTests.checkEqual(oldTreeAsNew, newTree)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index e6ccc2c93cba8..16c758b82c7cd 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -129,8 +129,8 @@ private object GBTClassifierSuite {
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2)
     val newModel = gbt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
-    val oldModelAsNew = GBTClassificationModel.fromOld(oldModel, newModel.parent,
-      newModel.fittingParamMap, categoricalFeatures)
+    val oldModelAsNew = GBTClassificationModel.fromOld(
+      oldModel, newModel.parent, categoricalFeatures)
     TreeTests.checkEqual(oldModelAsNew, newModel)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 35d8c2e16c6cd..6dd1fdf05514e 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -74,9 +74,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
       .setThreshold(0.6)
       .setProbabilityCol("myProbability")
     val model = lr.fit(dataset)
-    assert(model.fittingParamMap.get(lr.maxIter) === Some(10))
-    assert(model.fittingParamMap.get(lr.regParam) === Some(1.0))
-    assert(model.fittingParamMap.get(lr.threshold) === Some(0.6))
+    val parent = model.parent
+    assert(parent.getMaxIter === 10)
+    assert(parent.getRegParam === 1.0)
+    assert(parent.getThreshold === 0.6)
     assert(model.getThreshold === 0.6)
 
     // Modify model params, and check that the params worked.
@@ -99,9 +100,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     // Call fit() with new params, and check as many params as we can.
     val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4,
       lr.probabilityCol -> "theProb")
-    assert(model2.fittingParamMap.get(lr.maxIter).get === 5)
-    assert(model2.fittingParamMap.get(lr.regParam).get === 0.1)
-    assert(model2.fittingParamMap.get(lr.threshold).get === 0.4)
+    val parent2 = model2.parent
+    assert(parent2.getMaxIter === 5)
+    assert(parent2.getRegParam === 0.1)
+    assert(parent2.getThreshold === 0.4)
     assert(model2.getThreshold === 0.4)
     assert(model2.getProbabilityCol == "theProb")
   }
@@ -117,7 +119,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     val results = model.transform(dataset)
 
     // Compare rawPrediction with probability
-    results.select("rawPrediction", "probability").collect().map {
+    results.select("rawPrediction", "probability").collect().foreach {
       case Row(raw: Vector, prob: Vector) =>
         assert(raw.size === 2)
         assert(prob.size === 2)
@@ -127,7 +129,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     }
 
     // Compare prediction with probability
-    results.select("prediction", "probability").collect().map {
+    results.select("prediction", "probability").collect().foreach {
       case Row(pred: Double, prob: Vector) =>
         val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2
         assert(pred == predFromProb)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index ed41a9664f94f..c41def9330504 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -159,8 +159,8 @@ private object RandomForestClassifierSuite {
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
     val newModel = rf.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
-    val oldModelAsNew = RandomForestClassificationModel.fromOld(oldModel, newModel.parent,
-      newModel.fittingParamMap, categoricalFeatures)
+    val oldModelAsNew = RandomForestClassificationModel.fromOld(
+      oldModel, newModel.parent, categoricalFeatures)
     TreeTests.checkEqual(oldModelAsNew, newModel)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
new file mode 100644
index 0000000000000..92ec407b98d69
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/OneHotEncoderSuite.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.{DataFrame, SQLContext}
+
+
+class OneHotEncoderSuite extends FunSuite with MLlibTestSparkContext {
+  private var sqlContext: SQLContext = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sqlContext = new SQLContext(sc)
+  }
+
+  def stringIndexed(): DataFrame = {
+    val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2)
+    val df = sqlContext.createDataFrame(data).toDF("id", "label")
+    val indexer = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .fit(df)
+    indexer.transform(df)
+  }
+
+  test("OneHotEncoder includeFirst = true") {
+    val transformed = stringIndexed()
+    val encoder = new OneHotEncoder()
+      .setInputCol("labelIndex")
+      .setOutputCol("labelVec")
+    val encoded = encoder.transform(transformed)
+
+    val output = encoded.select("id", "labelVec").map { r =>
+      val vec = r.get(1).asInstanceOf[Vector]
+      (r.getInt(0), vec(0), vec(1), vec(2))
+    }.collect().toSet
+    // a -> 0, b -> 2, c -> 1
+    val expected = Set((0, 1.0, 0.0, 0.0), (1, 0.0, 0.0, 1.0), (2, 0.0, 1.0, 0.0),
+      (3, 1.0, 0.0, 0.0), (4, 1.0, 0.0, 0.0), (5, 0.0, 1.0, 0.0))
+    assert(output === expected)
+  }
+
+  test("OneHotEncoder includeFirst = false") {
+    val transformed = stringIndexed()
+    val encoder = new OneHotEncoder()
+      .setIncludeFirst(false)
+      .setInputCol("labelIndex")
+      .setOutputCol("labelVec")
+    val encoded = encoder.transform(transformed)
+
+    val output = encoded.select("id", "labelVec").map { r =>
+      val vec = r.get(1).asInstanceOf[Vector]
+      (r.getInt(0), vec(0), vec(1))
+    }.collect().toSet
+    // a -> 0, b -> 2, c -> 1
+    val expected = Set((0, 0.0, 0.0), (1, 0.0, 1.0), (2, 1.0, 0.0),
+      (3, 0.0, 0.0), (4, 0.0, 0.0), (5, 1.0, 0.0))
+    assert(output === expected)
+  }
+
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index f8852606abbf2..6056e7d3f6ff8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -122,19 +122,21 @@ class ParamsSuite extends FunSuite {
 
     assert(solver.getParam("inputCol").eq(inputCol))
     assert(solver.getParam("maxIter").eq(maxIter))
+    assert(solver.hasParam("inputCol"))
+    assert(!solver.hasParam("abc"))
     intercept[NoSuchElementException] {
       solver.getParam("abc")
     }
 
     intercept[IllegalArgumentException] {
-      solver.validate()
+      solver.validateParams()
     }
-    solver.validate(ParamMap(inputCol -> "input"))
+    solver.validateParams(ParamMap(inputCol -> "input"))
     solver.setInputCol("input")
     assert(solver.isSet(inputCol))
     assert(solver.isDefined(inputCol))
     assert(solver.getInputCol === "input")
-    solver.validate()
+    solver.validateParams()
     intercept[IllegalArgumentException] {
       ParamMap(maxIter -> -10)
     }
@@ -144,6 +146,11 @@ class ParamsSuite extends FunSuite {
 
     solver.clearMaxIter()
     assert(!solver.isSet(maxIter))
+
+    val copied = solver.copy(ParamMap(solver.maxIter -> 50))
+    assert(copied.uid !== solver.uid)
+    assert(copied.getInputCol === solver.getInputCol)
+    assert(copied.getMaxIter === 50)
   }
 
   test("ParamValidate") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
index 6f9c9cb5166ae..dc16073640407 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
@@ -23,15 +23,19 @@ import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter}
 class TestParams extends Params with HasMaxIter with HasInputCol {
 
   def setMaxIter(value: Int): this.type = { set(maxIter, value); this }
+
   def setInputCol(value: String): this.type = { set(inputCol, value); this }
 
   setDefault(maxIter -> 10)
 
-  override def validate(paramMap: ParamMap): Unit = {
-    val m = extractParamMap(paramMap)
-    // Note: maxIter is validated when it is set.
-    require(m.contains(inputCol))
+  def clearMaxIter(): this.type = clear(maxIter)
+
+  override def validateParams(): Unit = {
+    super.validateParams()
+    require(isDefined(inputCol))
   }
 
-  def clearMaxIter(): this.type = clear(maxIter)
+  override def copy(extra: ParamMap): TestParams = {
+    super.copy(extra).asInstanceOf[TestParams]
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
index c87a171b4b229..5aa81b44ddaf9 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
@@ -84,8 +84,8 @@ private[ml] object DecisionTreeRegressorSuite extends FunSuite {
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
     val newTree = dt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
-    val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(oldTree, newTree.parent,
-      newTree.fittingParamMap, categoricalFeatures)
+    val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(
+      oldTree, newTree.parent, categoricalFeatures)
     TreeTests.checkEqual(oldTreeAsNew, newTree)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index 4aec36948ac92..25b36ab08b67c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -130,8 +130,7 @@ private object GBTRegressorSuite {
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
     val newModel = gbt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
-    val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent,
-      newModel.fittingParamMap, categoricalFeatures)
+    val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, categoricalFeatures)
     TreeTests.checkEqual(oldModelAsNew, newModel)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
index c6dc1cc29b6ff..45f09f4fdab81 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
@@ -115,8 +115,8 @@ private object RandomForestRegressorSuite extends FunSuite {
     val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0)
     val newModel = rf.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
-    val oldModelAsNew = RandomForestRegressionModel.fromOld(oldModel, newModel.parent,
-      newModel.fittingParamMap, categoricalFeatures)
+    val oldModelAsNew = RandomForestRegressionModel.fromOld(
+      oldModel, newModel.parent, categoricalFeatures)
     TreeTests.checkEqual(oldModelAsNew, newModel)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
index 761ea821ef7c6..05313d440fbf6 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -49,8 +49,8 @@ class CrossValidatorSuite extends FunSuite with MLlibTestSparkContext {
       .setEvaluator(eval)
       .setNumFolds(3)
     val cvModel = cv.fit(dataset)
-    val bestParamMap = cvModel.bestModel.fittingParamMap
-    assert(bestParamMap(lr.regParam) === 0.001)
-    assert(bestParamMap(lr.maxIter) === 10)
+    val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression]
+    assert(parent.getRegParam === 0.001)
+    assert(parent.getMaxIter === 10)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
index db8ed62fa46ce..a629dba8a426f 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/api/python/PythonMLLibAPISuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.api.python
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors}
+import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors, SparseMatrix}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.recommendation.Rating
 
@@ -77,6 +77,16 @@ class PythonMLLibAPISuite extends FunSuite {
     val emptyMatrix = Matrices.dense(0, 0, empty)
     val ne = SerDe.loads(SerDe.dumps(emptyMatrix)).asInstanceOf[DenseMatrix]
     assert(emptyMatrix == ne)
+
+    val sm = new SparseMatrix(3, 2, Array(0, 1, 3), Array(1, 0, 2), Array(0.9, 1.2, 3.4))
+    val nsm = SerDe.loads(SerDe.dumps(sm)).asInstanceOf[SparseMatrix]
+    assert(sm.toArray === nsm.toArray)
+
+    val smt = new SparseMatrix(
+      3, 3, Array(0, 2, 3, 5), Array(0, 2, 1, 0, 2), Array(0.9, 1.2, 3.4, 5.7, 8.9),
+      isTransposed=true)
+    val nsmt = SerDe.loads(SerDe.dumps(smt)).asInstanceOf[SparseMatrix]
+    assert(smt.toArray === nsmt.toArray)
   }
 
   test("pickle rating") {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 41ec794146c69..2dcc881f5abd2 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.mllib.clustering
 
+import breeze.linalg.{DenseMatrix => BDM}
+
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors}
@@ -37,7 +39,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
 
     // Check: describeTopics() with all terms
     val fullTopicSummary = model.describeTopics()
-    assert(fullTopicSummary.size === tinyK)
+    assert(fullTopicSummary.length === tinyK)
     fullTopicSummary.zip(tinyTopicDescription).foreach {
       case ((algTerms, algTermWeights), (terms, termWeights)) =>
         assert(algTerms === terms)
@@ -54,7 +56,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     }
   }
 
-  test("running and DistributedLDAModel") {
+  test("running and DistributedLDAModel with default Optimizer (EM)") {
     val k = 3
     val topicSmoothing = 1.2
     val termSmoothing = 1.2
@@ -99,7 +101,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     // Check: per-doc topic distributions
     val topicDistributions = model.topicDistributions.collect()
     //  Ensure all documents are covered.
-    assert(topicDistributions.size === tinyCorpus.size)
+    assert(topicDistributions.length === tinyCorpus.length)
     assert(tinyCorpus.map(_._1).toSet === topicDistributions.map(_._1).toSet)
     //  Ensure we have proper distributions
     topicDistributions.foreach { case (docId, topicDistribution) =>
@@ -131,6 +133,87 @@ class LDASuite extends FunSuite with MLlibTestSparkContext {
     assert(lda.getBeta === 3.0)
     assert(lda.getTopicConcentration === 3.0)
   }
+
+  test("OnlineLDAOptimizer initialization") {
+    val lda = new LDA().setK(2)
+    val corpus = sc.parallelize(tinyCorpus, 2)
+    val op = new OnlineLDAOptimizer().initialize(corpus, lda)
+    op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau_0(567)
+    assert(op.getAlpha == 0.5) // default 1.0 / k
+    assert(op.getEta == 0.5)   // default 1.0 / k
+    assert(op.getKappa == 0.9876)
+    assert(op.getMiniBatchFraction == 0.123)
+    assert(op.getTau_0 == 567)
+  }
+
+  test("OnlineLDAOptimizer one iteration") {
+    // run OnlineLDAOptimizer for 1 iteration to verify it's consistency with Blei-lab,
+    // [[https://github.com/Blei-Lab/onlineldavb]]
+    val k = 2
+    val vocabSize = 6
+
+    def docs: Array[(Long, Vector)] = Array(
+      Vectors.sparse(vocabSize, Array(0, 1, 2), Array(1, 1, 1)), // apple, orange, banana
+      Vectors.sparse(vocabSize, Array(3, 4, 5), Array(1, 1, 1)) // tiger, cat, dog
+    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
+    val corpus = sc.parallelize(docs, 2)
+
+    // Set GammaShape large to avoid the stochastic impact.
+    val op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51).setGammaShape(1e40)
+      .setMiniBatchFraction(1)
+    val lda = new LDA().setK(k).setMaxIterations(1).setOptimizer(op).setSeed(12345)
+
+    val state = op.initialize(corpus, lda)
+    // override lambda to simulate an intermediate state
+    //    [[ 1.1  1.2  1.3  0.9  0.8  0.7]
+    //     [ 0.9  0.8  0.7  1.1  1.2  1.3]]
+    op.setLambda(new BDM[Double](k, vocabSize,
+      Array(1.1, 0.9, 1.2, 0.8, 1.3, 0.7, 0.9, 1.1, 0.8, 1.2, 0.7, 1.3)))
+
+    // run for one iteration
+    state.submitMiniBatch(corpus)
+
+    // verify the result, Note this generate the identical result as
+    // [[https://github.com/Blei-Lab/onlineldavb]]
+    val topic1 = op.getLambda(0, ::).inner.toArray.map("%.4f".format(_)).mkString(", ")
+    val topic2 = op.getLambda(1, ::).inner.toArray.map("%.4f".format(_)).mkString(", ")
+    assert("1.1101, 1.2076, 1.3050, 0.8899, 0.7924, 0.6950" == topic1)
+    assert("0.8899, 0.7924, 0.6950, 1.1101, 1.2076, 1.3050" == topic2)
+  }
+
+  test("OnlineLDAOptimizer with toy data") {
+    def toydata: Array[(Long, Vector)] = Array(
+      Vectors.sparse(6, Array(0, 1), Array(1, 1)),
+      Vectors.sparse(6, Array(1, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(0, 2), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 4), Array(1, 1)),
+      Vectors.sparse(6, Array(3, 5), Array(1, 1)),
+      Vectors.sparse(6, Array(4, 5), Array(1, 1))
+    ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
+
+    val docs = sc.parallelize(toydata)
+    val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau_0(1024).setKappa(0.51)
+      .setGammaShape(1e10)
+    val lda = new LDA().setK(2)
+      .setDocConcentration(0.01)
+      .setTopicConcentration(0.01)
+      .setMaxIterations(100)
+      .setOptimizer(op)
+      .setSeed(12345)
+
+    val ldaModel = lda.run(docs)
+    val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 10)
+    val topics = topicIndices.map { case (terms, termWeights) =>
+      terms.zip(termWeights)
+    }
+
+    // check distribution for each topic, typical distribution is (0.3, 0.3, 0.3, 0.02, 0.02, 0.02)
+    topics.foreach { topic =>
+      val smalls = topic.filter(t => t._2 < 0.1).map(_._2)
+      assert(smalls.length == 3 && smalls.sum < 0.2)
+    }
+  }
+
 }
 
 private[clustering] object LDASuite {
diff --git a/pom.xml b/pom.xml
index 4313f940036c8..94cf28332cf47 100644
--- a/pom.xml
+++ b/pom.xml
@@ -416,6 +416,11 @@
         <artifactId>commons-math3</artifactId>
         <version>${commons.math3.version}</version>
       </dependency>
+      <dependency>
+        <groupId>org.apache.ivy</groupId>
+        <artifactId>ivy</artifactId>
+        <version>${ivy.version}</version>
+      </dependency>
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
         <artifactId>jsr305</artifactId>
@@ -618,6 +623,18 @@
           </exclusion>
         </exclusions>
       </dependency>
+      <dependency>
+        <groupId>com.sun.jersey</groupId>
+        <artifactId>jersey-server</artifactId>
+        <version>1.9</version>
+        <scope>${hadoop.deps.scope}</scope>
+      </dependency>
+      <dependency>
+        <groupId>com.sun.jersey</groupId>
+        <artifactId>jersey-core</artifactId>
+        <version>1.9</version>
+        <scope>${hadoop.deps.scope}</scope>
+      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>
@@ -1614,20 +1631,6 @@
     http://hadoop.apache.org/docs/ra.b.c/hadoop-project-dist/hadoop-common/dependency-analysis.html
     -->
 
-    <profile>
-      <id>hadoop-0.23</id>
-      <!-- SPARK-1121: Adds an explicit dependency on Avro to work around a Hadoop 0.23.X issue -->
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.avro</groupId>
-          <artifactId>avro</artifactId>
-        </dependency>
-      </dependencies>
-      <properties>
-        <hadoop.version>0.23.10</hadoop.version>
-      </properties>
-    </profile>
-
     <profile>
       <id>hadoop-2.2</id>
       <properties>
diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst
index 4da6d4a74a299..220ea595df903 100644
--- a/python/docs/pyspark.ml.rst
+++ b/python/docs/pyspark.ml.rst
@@ -24,3 +24,19 @@ pyspark.ml.classification module
     :members:
     :undoc-members:
     :inherited-members:
+
+pyspark.ml.tuning module
+--------------------------------
+
+.. automodule:: pyspark.ml.tuning
+    :members:
+    :undoc-members:
+    :inherited-members:
+
+pyspark.ml.evaluation module
+--------------------------------
+
+.. automodule:: pyspark.ml.evaluation
+    :members:
+    :undoc-members:
+    :inherited-members:
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
new file mode 100644
index 0000000000000..02020ebff94c2
--- /dev/null
+++ b/python/pyspark/ml/evaluation.py
@@ -0,0 +1,107 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from pyspark.ml.wrapper import JavaEvaluator
+from pyspark.ml.param import Param, Params
+from pyspark.ml.param.shared import HasLabelCol, HasRawPredictionCol
+from pyspark.ml.util import keyword_only
+from pyspark.mllib.common import inherit_doc
+
+__all__ = ['BinaryClassificationEvaluator']
+
+
+@inherit_doc
+class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
+    """
+    Evaluator for binary classification, which expects two input
+    columns: rawPrediction and label.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> scoreAndLabels = map(lambda x: (Vectors.dense([1.0 - x[0], x[0]]), x[1]),
+    ...    [(0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)])
+    >>> dataset = sqlContext.createDataFrame(scoreAndLabels, ["raw", "label"])
+    ...
+    >>> evaluator = BinaryClassificationEvaluator(rawPredictionCol="raw")
+    >>> evaluator.evaluate(dataset)
+    0.70...
+    >>> evaluator.evaluate(dataset, {evaluator.metricName: "areaUnderPR"})
+    0.83...
+    """
+
+    _java_class = "org.apache.spark.ml.evaluation.BinaryClassificationEvaluator"
+
+    # a placeholder to make it appear in the generated doc
+    metricName = Param(Params._dummy(), "metricName",
+                       "metric name in evaluation (areaUnderROC|areaUnderPR)")
+
+    @keyword_only
+    def __init__(self, rawPredictionCol="rawPrediction", labelCol="label",
+                 metricName="areaUnderROC"):
+        """
+        __init__(self, rawPredictionCol="rawPrediction", labelCol="label", \
+                 metricName="areaUnderROC")
+        """
+        super(BinaryClassificationEvaluator, self).__init__()
+        #: param for metric name in evaluation (areaUnderROC|areaUnderPR)
+        self.metricName = Param(self, "metricName",
+                                "metric name in evaluation (areaUnderROC|areaUnderPR)")
+        self._setDefault(rawPredictionCol="rawPrediction", labelCol="label",
+                         metricName="areaUnderROC")
+        kwargs = self.__init__._input_kwargs
+        self._set(**kwargs)
+
+    def setMetricName(self, value):
+        """
+        Sets the value of :py:attr:`metricName`.
+        """
+        self.paramMap[self.metricName] = value
+        return self
+
+    def getMetricName(self):
+        """
+        Gets the value of metricName or its default value.
+        """
+        return self.getOrDefault(self.metricName)
+
+    @keyword_only
+    def setParams(self, rawPredictionCol="rawPrediction", labelCol="label",
+                  metricName="areaUnderROC"):
+        """
+        setParams(self, rawPredictionCol="rawPrediction", labelCol="label", \
+                  metricName="areaUnderROC")
+        Sets params for binary classification evaluator.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+
+if __name__ == "__main__":
+    import doctest
+    from pyspark.context import SparkContext
+    from pyspark.sql import SQLContext
+    globs = globals().copy()
+    # The small batch size here ensures that we see multiple batches,
+    # even in these small test examples:
+    sc = SparkContext("local[2]", "ml.evaluation tests")
+    sqlContext = SQLContext(sc)
+    globs['sc'] = sc
+    globs['sqlContext'] = sqlContext
+    (failure_count, test_count) = doctest.testmod(
+        globs=globs, optionflags=doctest.ELLIPSIS)
+    sc.stop()
+    if failure_count:
+        exit(-1)
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 6a3192465d66d..c71c823db2c81 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -93,6 +93,7 @@ def get$Name(self):
         ("featuresCol", "features column name", "'features'"),
         ("labelCol", "label column name", "'label'"),
         ("predictionCol", "prediction column name", "'prediction'"),
+        ("rawPredictionCol", "raw prediction column name", "'rawPrediction'"),
         ("inputCol", "input column name", None),
         ("outputCol", "output column name", None),
         ("numFeatures", "number of features", None)]
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 13b6749998ad0..4f243844f8caa 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -165,6 +165,35 @@ def getPredictionCol(self):
         return self.getOrDefault(self.predictionCol)
 
 
+class HasRawPredictionCol(Params):
+    """
+    Mixin for param rawPredictionCol: raw prediction column name.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction column name")
+
+    def __init__(self):
+        super(HasRawPredictionCol, self).__init__()
+        #: param for raw prediction column name
+        self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction column name")
+        if 'rawPrediction' is not None:
+            self._setDefault(rawPredictionCol='rawPrediction')
+
+    def setRawPredictionCol(self, value):
+        """
+        Sets the value of :py:attr:`rawPredictionCol`.
+        """
+        self.paramMap[self.rawPredictionCol] = value
+        return self
+
+    def getRawPredictionCol(self):
+        """
+        Gets the value of rawPredictionCol or its default value.
+        """
+        return self.getOrDefault(self.rawPredictionCol)
+
+
 class HasInputCol(Params):
     """
     Mixin for param inputCol: input column name.
diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index 7c1ec3026da6f..7b875e4b71254 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -22,7 +22,7 @@
 from pyspark.mllib.common import inherit_doc
 
 
-__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel']
+__all__ = ['Estimator', 'Transformer', 'Pipeline', 'PipelineModel', 'Evaluator']
 
 
 @inherit_doc
@@ -168,3 +168,24 @@ def transform(self, dataset, params={}):
         for t in self.transformers:
             dataset = t.transform(dataset, paramMap)
         return dataset
+
+
+class Evaluator(object):
+    """
+    Base class for evaluators that compute metrics from predictions.
+    """
+
+    __metaclass__ = ABCMeta
+
+    @abstractmethod
+    def evaluate(self, dataset, params={}):
+        """
+        Evaluates the output.
+
+        :param dataset: a dataset that contains labels/observations and
+                        predictions
+        :param params: an optional param map that overrides embedded
+                       params
+        :return: metric
+        """
+        raise NotImplementedError()
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
new file mode 100644
index 0000000000000..1773ab5bdcdb1
--- /dev/null
+++ b/python/pyspark/ml/tuning.py
@@ -0,0 +1,84 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import itertools
+
+__all__ = ['ParamGridBuilder']
+
+
+class ParamGridBuilder(object):
+    """
+    Builder for a param grid used in grid search-based model selection.
+
+    >>> from classification import LogisticRegression
+    >>> lr = LogisticRegression()
+    >>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \
+            .baseOn([lr.predictionCol, 'p']) \
+            .addGrid(lr.regParam, [1.0, 2.0, 3.0]) \
+            .addGrid(lr.maxIter, [1, 5]) \
+            .addGrid(lr.featuresCol, ['f']) \
+            .build()
+    >>> expected = [ \
+{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
+{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
+{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
+{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
+{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \
+{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
+    >>> len(output) == len(expected)
+    True
+    >>> all([m in expected for m in output])
+    True
+    """
+
+    def __init__(self):
+        self._param_grid = {}
+
+    def addGrid(self, param, values):
+        """
+        Sets the given parameters in this grid to fixed values.
+        """
+        self._param_grid[param] = values
+
+        return self
+
+    def baseOn(self, *args):
+        """
+        Sets the given parameters in this grid to fixed values.
+        Accepts either a parameter dictionary or a list of (parameter, value) pairs.
+        """
+        if isinstance(args[0], dict):
+            self.baseOn(*args[0].items())
+        else:
+            for (param, value) in args:
+                self.addGrid(param, [value])
+
+        return self
+
+    def build(self):
+        """
+        Builds and returns all combinations of parameters specified
+        by the param grid.
+        """
+        keys = self._param_grid.keys()
+        grid_values = self._param_grid.values()
+        return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)]
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 394f23c5e9b12..73741c4b40dfb 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -20,7 +20,7 @@
 from pyspark import SparkContext
 from pyspark.sql import DataFrame
 from pyspark.ml.param import Params
-from pyspark.ml.pipeline import Estimator, Transformer
+from pyspark.ml.pipeline import Estimator, Transformer, Evaluator
 from pyspark.mllib.common import inherit_doc
 
 
@@ -147,3 +147,18 @@ def __init__(self, java_model):
 
     def _java_obj(self):
         return self._java_model
+
+
+@inherit_doc
+class JavaEvaluator(Evaluator, JavaWrapper):
+    """
+    Base class for :py:class:`Evaluator`s that wrap Java/Scala
+    implementations.
+    """
+
+    __metaclass__ = ABCMeta
+
+    def evaluate(self, dataset, params={}):
+        java_obj = self._java_obj()
+        self._transfer_params_to_java(params, java_obj)
+        return java_obj.evaluate(dataset._jdf, self._empty_java_param_map())
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py
index abbb7cf60eece..04e67158514f5 100644
--- a/python/pyspark/mllib/clustering.py
+++ b/python/pyspark/mllib/clustering.py
@@ -40,11 +40,16 @@ class KMeansModel(Saveable, Loader):
 
     >>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
     >>> model = KMeans.train(
-    ...     sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random")
+    ...     sc.parallelize(data), 2, maxIterations=10, runs=30, initializationMode="random",
+    ...                    seed=50, initializationSteps=5, epsilon=1e-4)
     >>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0, 1.0]))
     True
     >>> model.predict(array([8.0, 9.0])) == model.predict(array([9.0, 8.0]))
     True
+    >>> model.k
+    2
+    >>> model.computeCost(sc.parallelize(data))
+    2.0000000000000004
     >>> model = KMeans.train(sc.parallelize(data), 2)
     >>> sparse_data = [
     ...     SparseVector(3, {1: 1.0}),
@@ -52,7 +57,8 @@ class KMeansModel(Saveable, Loader):
     ...     SparseVector(3, {2: 1.0}),
     ...     SparseVector(3, {2: 1.1})
     ... ]
-    >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||")
+    >>> model = KMeans.train(sc.parallelize(sparse_data), 2, initializationMode="k-means||",
+    ...                                     seed=50, initializationSteps=5, epsilon=1e-4)
     >>> model.predict(array([0., 1., 0.])) == model.predict(array([0, 1.1, 0.]))
     True
     >>> model.predict(array([0., 0., 1.])) == model.predict(array([0, 0, 1.1]))
@@ -83,6 +89,11 @@ def clusterCenters(self):
         """Get the cluster centers, represented as a list of NumPy arrays."""
         return self.centers
 
+    @property
+    def k(self):
+        """Total number of clusters."""
+        return len(self.centers)
+
     def predict(self, x):
         """Find the cluster to which x belongs in this model."""
         best = 0
@@ -95,6 +106,15 @@ def predict(self, x):
                 best_distance = distance
         return best
 
+    def computeCost(self, rdd):
+        """
+        Return the K-means cost (sum of squared distances of points to
+        their nearest center) for this model on the given data.
+        """
+        cost = callMLlibFunc("computeCostKmeansModel", rdd.map(_convert_to_vector),
+                             [_convert_to_vector(c) for c in self.centers])
+        return cost
+
     def save(self, sc, path):
         java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers])
         java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers)
@@ -109,10 +129,11 @@ def load(cls, sc, path):
 class KMeans(object):
 
     @classmethod
-    def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||", seed=None):
+    def train(cls, rdd, k, maxIterations=100, runs=1, initializationMode="k-means||",
+              seed=None, initializationSteps=5, epsilon=1e-4):
         """Train a k-means clustering model."""
         model = callMLlibFunc("trainKMeansModel", rdd.map(_convert_to_vector), k, maxIterations,
-                              runs, initializationMode, seed)
+                              runs, initializationMode, seed, initializationSteps, epsilon)
         centers = callJavaFunc(rdd.context, model.clusterCenters)
         return KMeansModel([c.toArray() for c in centers])
 
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py
index a57c0b3ae0d00..9f3b0baf9f19f 100644
--- a/python/pyspark/mllib/linalg.py
+++ b/python/pyspark/mllib/linalg.py
@@ -755,7 +755,7 @@ def __reduce__(self):
         return SparseMatrix, (
             self.numRows, self.numCols, self.colPtrs.tostring(),
             self.rowIndices.tostring(), self.values.tostring(),
-            self.isTransposed)
+            int(self.isTransposed))
 
     def __getitem__(self, indices):
         i, j = indices
@@ -801,7 +801,7 @@ def toDense(self):
 
     # TODO: More efficient implementation:
     def __eq__(self, other):
-        return np.all(self.toArray == other.toArray)
+        return np.all(self.toArray() == other.toArray())
 
 
 class Matrices(object):
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 1b008b93bc137..d05cfe2af04b2 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -92,6 +92,9 @@ def test_serialize(self):
         self._test_serialize(SparseVector(4, {1: 1, 3: 2}))
         self._test_serialize(SparseVector(3, {}))
         self._test_serialize(DenseMatrix(2, 3, range(6)))
+        sm1 = SparseMatrix(
+            3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0])
+        self._test_serialize(sm1)
 
     def test_dot(self):
         sv = SparseVector(4, {1: 1, 3: 2})
@@ -233,7 +236,8 @@ def test_kmeans(self):
             [1.1, 0],
             [1.2, 0],
         ]
-        clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||")
+        clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||",
+                                initializationSteps=7, epsilon=1e-4)
         self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1]))
         self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3]))
 
@@ -243,9 +247,11 @@ def test_kmeans_deterministic(self):
         Y = range(0, 100, 10)
         data = [[x, y] for x, y in zip(X, Y)]
         clusters1 = KMeans.train(self.sc.parallelize(data),
-                                 3, initializationMode="k-means||", seed=42)
+                                 3, initializationMode="k-means||",
+                                 seed=42, initializationSteps=7, epsilon=1e-4)
         clusters2 = KMeans.train(self.sc.parallelize(data),
-                                 3, initializationMode="k-means||", seed=42)
+                                 3, initializationMode="k-means||",
+                                 seed=42, initializationSteps=7, epsilon=1e-4)
         centers1 = clusters1.centers
         centers2 = clusters2.centers
         for c1, c2 in zip(centers1, centers2):
diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py
index 95fb91ad43457..fd98e116d2cf1 100644
--- a/python/pyspark/sql/_types.py
+++ b/python/pyspark/sql/_types.py
@@ -652,7 +652,7 @@ def _python_to_sql_converter(dataType):
 
     if isinstance(dataType, StructType):
         names, types = zip(*[(f.name, f.dataType) for f in dataType.fields])
-        converters = map(_python_to_sql_converter, types)
+        converters = [_python_to_sql_converter(t) for t in types]
 
         def converter(obj):
             if isinstance(obj, dict):
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 8ddcff8fcdf98..24f370543def4 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -275,9 +275,12 @@ def show(self, n=20):
         >>> df
         DataFrame[age: int, name: string]
         >>> df.show()
-        age name
-        2   Alice
-        5   Bob
+        +---+-----+
+        |age| name|
+        +---+-----+
+        |  2|Alice|
+        |  5|  Bob|
+        +---+-----+
         """
         print(self._jdf.showString(n))
 
@@ -591,12 +594,15 @@ def describe(self, *cols):
         given, this function computes statistics for all numerical columns.
 
         >>> df.describe().show()
-        summary age
-        count   2
-        mean    3.5
-        stddev  1.5
-        min     2
-        max     5
+        +-------+---+
+        |summary|age|
+        +-------+---+
+        |  count|  2|
+        |   mean|3.5|
+        | stddev|1.5|
+        |    min|  2|
+        |    max|  5|
+        +-------+---+
         """
         jdf = self._jdf.describe(self._jseq(cols))
         return DataFrame(jdf, self.sql_ctx)
@@ -801,12 +807,18 @@ def dropna(self, how='any', thresh=None, subset=None):
         :param subset: optional list of column names to consider.
 
         >>> df4.dropna().show()
-        age height name
-        10  80     Alice
+        +---+------+-----+
+        |age|height| name|
+        +---+------+-----+
+        | 10|    80|Alice|
+        +---+------+-----+
 
         >>> df4.na.drop().show()
-        age height name
-        10  80     Alice
+        +---+------+-----+
+        |age|height| name|
+        +---+------+-----+
+        | 10|    80|Alice|
+        +---+------+-----+
         """
         if how is not None and how not in ['any', 'all']:
             raise ValueError("how ('" + how + "') should be 'any' or 'all'")
@@ -837,25 +849,34 @@ def fillna(self, value, subset=None):
             then the non-string column is simply ignored.
 
         >>> df4.fillna(50).show()
-        age height name
-        10  80     Alice
-        5   50     Bob
-        50  50     Tom
-        50  50     null
+        +---+------+-----+
+        |age|height| name|
+        +---+------+-----+
+        | 10|    80|Alice|
+        |  5|    50|  Bob|
+        | 50|    50|  Tom|
+        | 50|    50| null|
+        +---+------+-----+
 
         >>> df4.fillna({'age': 50, 'name': 'unknown'}).show()
-        age height name
-        10  80     Alice
-        5   null   Bob
-        50  null   Tom
-        50  null   unknown
+        +---+------+-------+
+        |age|height|   name|
+        +---+------+-------+
+        | 10|    80|  Alice|
+        |  5|  null|    Bob|
+        | 50|  null|    Tom|
+        | 50|  null|unknown|
+        +---+------+-------+
 
         >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show()
-        age height name
-        10  80     Alice
-        5   null   Bob
-        50  null   Tom
-        50  null   unknown
+        +---+------+-------+
+        |age|height|   name|
+        +---+------+-------+
+        | 10|    80|  Alice|
+        |  5|  null|    Bob|
+        | 50|  null|    Tom|
+        | 50|  null|unknown|
+        +---+------+-------+
         """
         if not isinstance(value, (float, int, long, basestring, dict)):
             raise ValueError("value should be a float, int, long, string, or dict")
@@ -875,6 +896,27 @@ def fillna(self, value, subset=None):
 
             return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx)
 
+    def corr(self, col1, col2, method=None):
+        """
+        Calculates the correlation of two columns of a DataFrame as a double value. Currently only
+        supports the Pearson Correlation Coefficient.
+        :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases.
+
+        :param col1: The name of the first column
+        :param col2: The name of the second column
+        :param method: The correlation method. Currently only supports "pearson"
+        """
+        if not isinstance(col1, str):
+            raise ValueError("col1 should be a string.")
+        if not isinstance(col2, str):
+            raise ValueError("col2 should be a string.")
+        if not method:
+            method = "pearson"
+        if not method == "pearson":
+            raise ValueError("Currently only the calculation of the Pearson Correlation " +
+                             "coefficient is supported.")
+        return self._jdf.stat().corr(col1, col2, method)
+
     def cov(self, col1, col2):
         """
         Calculate the sample covariance for the given columns, specified by their names, as a
@@ -889,6 +931,27 @@ def cov(self, col1, col2):
             raise ValueError("col2 should be a string.")
         return self._jdf.stat().cov(col1, col2)
 
+    def crosstab(self, col1, col2):
+        """
+        Computes a pair-wise frequency table of the given columns. Also known as a contingency
+        table. The number of distinct values for each column should be less than 1e4. At most 1e6
+        non-zero pair frequencies will be returned.
+        The first column of each row will be the distinct values of `col1` and the column names
+        will be the distinct values of `col2`. The name of the first column will be `$col1_$col2`.
+        Pairs that have no occurrences will have `null` as their counts.
+        :func:`DataFrame.crosstab` and :func:`DataFrameStatFunctions.crosstab` are aliases.
+
+        :param col1: The name of the first column. Distinct items will make the first item of
+            each row.
+        :param col2: The name of the second column. Distinct items will make the column names
+            of the DataFrame.
+        """
+        if not isinstance(col1, str):
+            raise ValueError("col1 should be a string.")
+        if not isinstance(col2, str):
+            raise ValueError("col2 should be a string.")
+        return DataFrame(self._jdf.stat().crosstab(col1, col2), self.sql_ctx)
+
     def freqItems(self, cols, support=None):
         """
         Finding frequent items for columns, possibly with false positives. Using the
@@ -1220,11 +1283,17 @@ def getItem(self, key):
 
         >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"])
         >>> df.select(df.l.getItem(0), df.d.getItem("key")).show()
-        l[0] d[key]
-        1    value
+        +----+------+
+        |l[0]|d[key]|
+        +----+------+
+        |   1| value|
+        +----+------+
         >>> df.select(df.l[0], df.d["key"]).show()
-        l[0] d[key]
-        1    value
+        +----+------+
+        |l[0]|d[key]|
+        +----+------+
+        |   1| value|
+        +----+------+
         """
         return self[key]
 
@@ -1234,11 +1303,17 @@ def getField(self, name):
         >>> from pyspark.sql import Row
         >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF()
         >>> df.select(df.r.getField("b")).show()
-        r.b
-        b
+        +---+
+        |r.b|
+        +---+
+        |  b|
+        +---+
         >>> df.select(df.r.a).show()
-        r.a
-        1
+        +---+
+        |r.a|
+        +---+
+        |  1|
+        +---+
         """
         return Column(self._jc.getField(name))
 
@@ -1330,6 +1405,13 @@ def cast(self, dataType):
             raise TypeError("unexpected type: %s" % type(dataType))
         return Column(jc)
 
+    @ignore_unicode_prefix
+    def between(self, lowerBound, upperBound):
+        """ A boolean expression that is evaluated to true if the value of this
+        expression is between the given columns.
+        """
+        return (self >= lowerBound) & (self <= upperBound)
+
     def __repr__(self):
         return 'Column<%s>' % self._jc.toString().encode('utf8')
 
@@ -1359,11 +1441,21 @@ class DataFrameStatFunctions(object):
     def __init__(self, df):
         self.df = df
 
+    def corr(self, col1, col2, method=None):
+        return self.df.corr(col1, col2, method)
+
+    corr.__doc__ = DataFrame.corr.__doc__
+
     def cov(self, col1, col2):
         return self.df.cov(col1, col2)
 
     cov.__doc__ = DataFrame.cov.__doc__
 
+    def crosstab(self, col1, col2):
+        return self.df.crosstab(col1, col2)
+
+    crosstab.__doc__ = DataFrame.crosstab.__doc__
+
     def freqItems(self, cols, support=None):
         return self.df.freqItems(cols, support)
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 613efc0ac029d..46c4c88e98eb1 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -394,11 +394,26 @@ def test_aggregator(self):
         self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0])
         self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0])
 
+    def test_corr(self):
+        import math
+        df = self.sc.parallelize([Row(a=i, b=math.sqrt(i)) for i in range(10)]).toDF()
+        corr = df.stat.corr("a", "b")
+        self.assertTrue(abs(corr - 0.95734012) < 1e-6)
+
     def test_cov(self):
         df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF()
         cov = df.stat.cov("a", "b")
         self.assertTrue(abs(cov - 55.0 / 3) < 1e-6)
 
+    def test_crosstab(self):
+        df = self.sc.parallelize([Row(a=i % 3, b=i % 2) for i in range(1, 7)]).toDF()
+        ct = df.stat.crosstab("a", "b").collect()
+        ct = sorted(ct, key=lambda x: x[0])
+        for i, row in enumerate(ct):
+            self.assertEqual(row[0], str(i))
+            self.assertTrue(row[1], 1)
+            self.assertTrue(row[2], 1)
+
     def test_math_functions(self):
         df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF()
         from pyspark.sql import mathfunctions as functions
@@ -438,6 +453,14 @@ def test_rand_functions(self):
         for row in rndn:
             assert row[1] >= -4.0 and row[1] <= 4.0, "got: %s" % row[1]
 
+    def test_between_function(self):
+        df = self.sc.parallelize([
+            Row(a=1, b=2, c=3),
+            Row(a=2, b=1, c=3),
+            Row(a=4, b=1, c=4)]).toDF()
+        self.assertEqual([Row(a=2, b=1, c=3), Row(a=4, b=1, c=4)],
+                         df.filter(df.a.between(df.b, df.c)).collect())
+
     def test_save_and_load(self):
         df = self.df
         tmpPath = tempfile.mkdtemp()
diff --git a/python/run-tests b/python/run-tests
index 88b63b84fdc27..f9ca26467f17e 100755
--- a/python/run-tests
+++ b/python/run-tests
@@ -98,7 +98,9 @@ function run_ml_tests() {
     echo "Run ml tests ..."
     run_test "pyspark/ml/feature.py"
     run_test "pyspark/ml/classification.py"
+    run_test "pyspark/ml/tuning.py"
     run_test "pyspark/ml/tests.py"
+    run_test "pyspark/ml/evaluation.py"
 }
 
 function run_streaming_tests() {
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 0a358ed408aa1..bb546b3086b33 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -145,7 +145,7 @@ public void pointTo(
 
   private void assertIndexIsValid(int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
-    assert index < numFields : "index (" + index + ") should <= " + numFields;
+    assert index < numFields : "index (" + index + ") should < " + numFields;
   }
 
   @Override
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index b2f8157a1a61f..18c24b651921a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -27,6 +27,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery}
  */
 class NoSuchTableException extends Exception
 
+class NoSuchDatabaseException extends Exception
+
 /**
  * An interface for looking up relations by name.  Used by an [[Analyzer]].
  */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 73c9a1c7afdad..831fb4fe95fe7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -239,37 +239,43 @@ trait HiveTypeCoercion {
         a.makeCopy(Array(a.left, Cast(a.right, DoubleType)))
 
       // we should cast all timestamp/date/string compare into string compare
-      case p: BinaryPredicate if p.left.dataType == StringType
-        && p.right.dataType == DateType =>
+      case p: BinaryComparison if p.left.dataType == StringType &&
+                                  p.right.dataType == DateType =>
         p.makeCopy(Array(p.left, Cast(p.right, StringType)))
-      case p: BinaryPredicate if p.left.dataType == DateType
-        && p.right.dataType == StringType =>
+      case p: BinaryComparison if p.left.dataType == DateType &&
+                                  p.right.dataType == StringType =>
         p.makeCopy(Array(Cast(p.left, StringType), p.right))
-      case p: BinaryPredicate if p.left.dataType == StringType
-        && p.right.dataType == TimestampType =>
+      case p: BinaryComparison if p.left.dataType == StringType &&
+                                  p.right.dataType == TimestampType =>
         p.makeCopy(Array(p.left, Cast(p.right, StringType)))
-      case p: BinaryPredicate if p.left.dataType == TimestampType
-        && p.right.dataType == StringType =>
+      case p: BinaryComparison if p.left.dataType == TimestampType &&
+                                  p.right.dataType == StringType =>
         p.makeCopy(Array(Cast(p.left, StringType), p.right))
-      case p: BinaryPredicate if p.left.dataType == TimestampType
-        && p.right.dataType == DateType =>
+      case p: BinaryComparison if p.left.dataType == TimestampType &&
+                                  p.right.dataType == DateType =>
         p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType)))
-      case p: BinaryPredicate if p.left.dataType == DateType
-        && p.right.dataType == TimestampType =>
+      case p: BinaryComparison if p.left.dataType == DateType &&
+                                  p.right.dataType == TimestampType =>
         p.makeCopy(Array(Cast(p.left, StringType), Cast(p.right, StringType)))
 
-      case p: BinaryPredicate if p.left.dataType == StringType && p.right.dataType != StringType =>
+      case p: BinaryComparison if p.left.dataType == StringType &&
+                                  p.right.dataType != StringType =>
         p.makeCopy(Array(Cast(p.left, DoubleType), p.right))
-      case p: BinaryPredicate if p.left.dataType != StringType && p.right.dataType == StringType =>
+      case p: BinaryComparison if p.left.dataType != StringType &&
+                                  p.right.dataType == StringType =>
         p.makeCopy(Array(p.left, Cast(p.right, DoubleType)))
 
-      case i @ In(a, b) if a.dataType == DateType && b.forall(_.dataType == StringType) =>
+      case i @ In(a, b) if a.dataType == DateType &&
+                           b.forall(_.dataType == StringType) =>
         i.makeCopy(Array(Cast(a, StringType), b))
-      case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == StringType) =>
+      case i @ In(a, b) if a.dataType == TimestampType &&
+                           b.forall(_.dataType == StringType) =>
         i.makeCopy(Array(Cast(a, StringType), b))
-      case i @ In(a, b) if a.dataType == DateType && b.forall(_.dataType == TimestampType) =>
+      case i @ In(a, b) if a.dataType == DateType &&
+                           b.forall(_.dataType == TimestampType) =>
         i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
-      case i @ In(a, b) if a.dataType == TimestampType && b.forall(_.dataType == DateType) =>
+      case i @ In(a, b) if a.dataType == TimestampType &&
+                           b.forall(_.dataType == DateType) =>
         i.makeCopy(Array(Cast(a, StringType), b.map(Cast(_, StringType))))
 
       case Sum(e) if e.dataType == StringType =>
@@ -420,19 +426,19 @@ trait HiveTypeCoercion {
           )
 
         case LessThan(e1 @ DecimalType.Expression(p1, s1),
-        e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
+                      e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
           LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited))
 
         case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1),
-        e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
+                             e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
           LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited))
 
         case GreaterThan(e1 @ DecimalType.Expression(p1, s1),
-        e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
+                         e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
           GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited))
 
         case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1),
-        e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
+                                e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
           GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited))
 
         // Promote integers inside a binary expression with fixed-precision decimals to decimals,
@@ -481,8 +487,8 @@ trait HiveTypeCoercion {
       // No need to change the EqualNullSafe operators, too
       case e: EqualNullSafe => e
       // Otherwise turn them to Byte types so that there exists and ordering.
-      case p: BinaryComparison
-          if p.left.dataType == BooleanType && p.right.dataType == BooleanType =>
+      case p: BinaryComparison if p.left.dataType == BooleanType &&
+                                  p.right.dataType == BooleanType =>
         p.makeCopy(Array(Cast(p.left, ByteType), Cast(p.right, ByteType)))
     }
   }
@@ -564,10 +570,6 @@ trait HiveTypeCoercion {
       case Sum(e @ TimestampType()) => Sum(Cast(e, DoubleType))
       case Average(e @ TimestampType()) => Average(Cast(e, DoubleType))
 
-      // Compatible with Hive
-      case Substring(e, start, len) if e.dataType != StringType =>
-        Substring(Cast(e, StringType), start, len)
-
       // Coalesce should return the first non-null value, which could be any column
       // from the list. So we need to make sure the return type is deterministic and
       // compatible with every child column.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index 1d71c1b4b0c7c..4fd1bc4dd642d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.errors.TreeNodeException
 import org.apache.spark.sql.catalyst.trees
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types._
@@ -86,6 +85,8 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express
 
   override def foldable: Boolean = left.foldable && right.foldable
 
+  override def nullable: Boolean = left.nullable || right.nullable
+
   override def toString: String = s"($left $symbol $right)"
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 140ccd8d3796f..c7a37ad966df6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -74,14 +74,12 @@ abstract class BinaryArithmetic extends BinaryExpression {
 
   type EvaluatedType = Any
 
-  def nullable: Boolean = left.nullable || right.nullable
-
   override lazy val resolved =
     left.resolved && right.resolved &&
     left.dataType == right.dataType &&
     !DecimalType.isFixed(left.dataType)
 
-  def dataType: DataType = {
+  override def dataType: DataType = {
     if (!resolved) {
       throw new UnresolvedException(this,
         s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}")
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 9cb00cb2732ff..26c38c56c04f5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -70,16 +70,14 @@ trait PredicateHelper {
     expr.references.subsetOf(plan.outputSet)
 }
 
-abstract class BinaryPredicate extends BinaryExpression with Predicate {
-  self: Product =>
-  override def nullable: Boolean = left.nullable || right.nullable
-}
 
-case class Not(child: Expression) extends UnaryExpression with Predicate {
+case class Not(child: Expression) extends UnaryExpression with Predicate with ExpectsInputTypes {
   override def foldable: Boolean = child.foldable
   override def nullable: Boolean = child.nullable
   override def toString: String = s"NOT $child"
 
+  override def expectedChildTypes: Seq[DataType] = Seq(BooleanType)
+
   override def eval(input: Row): Any = {
     child.eval(input) match {
       case null => null
@@ -120,7 +118,11 @@ case class InSet(value: Expression, hset: Set[Any])
   }
 }
 
-case class And(left: Expression, right: Expression) extends BinaryPredicate {
+case class And(left: Expression, right: Expression)
+  extends BinaryExpression with Predicate with ExpectsInputTypes {
+
+  override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+
   override def symbol: String = "&&"
 
   override def eval(input: Row): Any = {
@@ -142,7 +144,11 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate {
   }
 }
 
-case class Or(left: Expression, right: Expression) extends BinaryPredicate {
+case class Or(left: Expression, right: Expression)
+  extends BinaryExpression with Predicate with ExpectsInputTypes {
+
+  override def expectedChildTypes: Seq[DataType] = Seq(BooleanType, BooleanType)
+
   override def symbol: String = "||"
 
   override def eval(input: Row): Any = {
@@ -164,7 +170,7 @@ case class Or(left: Expression, right: Expression) extends BinaryPredicate {
   }
 }
 
-abstract class BinaryComparison extends BinaryPredicate {
+abstract class BinaryComparison extends BinaryExpression with Predicate {
   self: Product =>
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index d597bf7ce756a..d6f23df30ffb4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -22,7 +22,7 @@ import java.util.regex.Pattern
 import org.apache.spark.sql.catalyst.analysis.UnresolvedException
 import org.apache.spark.sql.types._
 
-trait StringRegexExpression {
+trait StringRegexExpression extends ExpectsInputTypes {
   self: BinaryExpression =>
 
   type EvaluatedType = Any
@@ -32,6 +32,7 @@ trait StringRegexExpression {
 
   override def nullable: Boolean = left.nullable || right.nullable
   override def dataType: DataType = BooleanType
+  override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType)
 
   // try cache the pattern for Literal
   private lazy val cache: Pattern = right match {
@@ -57,11 +58,11 @@ trait StringRegexExpression {
       if(r == null) {
         null
       } else {
-        val regex = pattern(r.asInstanceOf[UTF8String].toString)
+        val regex = pattern(r.asInstanceOf[UTF8String].toString())
         if(regex == null) {
           null
         } else {
-          matches(regex, l.asInstanceOf[UTF8String].toString)
+          matches(regex, l.asInstanceOf[UTF8String].toString())
         }
       }
     }
@@ -110,7 +111,7 @@ case class RLike(left: Expression, right: Expression)
   override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
 }
 
-trait CaseConversionExpression {
+trait CaseConversionExpression extends ExpectsInputTypes {
   self: UnaryExpression =>
 
   type EvaluatedType = Any
@@ -118,8 +119,9 @@ trait CaseConversionExpression {
   def convert(v: UTF8String): UTF8String
 
   override def foldable: Boolean = child.foldable
-  def nullable: Boolean = child.nullable
-  def dataType: DataType = StringType
+  override def nullable: Boolean = child.nullable
+  override def dataType: DataType = StringType
+  override def expectedChildTypes: Seq[DataType] = Seq(StringType)
 
   override def eval(input: Row): Any = {
     val evaluated = child.eval(input)
@@ -136,7 +138,7 @@ trait CaseConversionExpression {
  */
 case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
   
-  override def convert(v: UTF8String): UTF8String = v.toUpperCase
+  override def convert(v: UTF8String): UTF8String = v.toUpperCase()
 
   override def toString: String = s"Upper($child)"
 }
@@ -146,21 +148,21 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
  */
 case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
   
-  override def convert(v: UTF8String): UTF8String = v.toLowerCase
+  override def convert(v: UTF8String): UTF8String = v.toLowerCase()
 
   override def toString: String = s"Lower($child)"
 }
 
 /** A base trait for functions that compare two strings, returning a boolean. */
 trait StringComparison {
-  self: BinaryPredicate =>
+  self: BinaryExpression =>
+
+  def compare(l: UTF8String, r: UTF8String): Boolean
 
   override type EvaluatedType = Any
 
   override def nullable: Boolean = left.nullable || right.nullable
 
-  def compare(l: UTF8String, r: UTF8String): Boolean
-
   override def eval(input: Row): Any = {
     val leftEval = left.eval(input)
     if(leftEval == null) {
@@ -181,31 +183,35 @@ trait StringComparison {
  * A function that returns true if the string `left` contains the string `right`.
  */
 case class Contains(left: Expression, right: Expression)
-    extends BinaryPredicate with StringComparison {
+    extends BinaryExpression with Predicate with StringComparison with ExpectsInputTypes {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r)
+  override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType)
 }
 
 /**
  * A function that returns true if the string `left` starts with the string `right`.
  */
 case class StartsWith(left: Expression, right: Expression)
-    extends BinaryPredicate with StringComparison {
+    extends BinaryExpression with Predicate with StringComparison with ExpectsInputTypes {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r)
+  override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType)
 }
 
 /**
  * A function that returns true if the string `left` ends with the string `right`.
  */
 case class EndsWith(left: Expression, right: Expression)
-    extends BinaryPredicate with StringComparison {
+    extends BinaryExpression with Predicate with StringComparison with ExpectsInputTypes {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r)
+  override def expectedChildTypes: Seq[DataType] = Seq(StringType, StringType)
 }
 
 /**
  * A function that takes a substring of its first argument starting at a given position.
  * Defined for String and Binary types.
  */
-case class Substring(str: Expression, pos: Expression, len: Expression) extends Expression {
+case class Substring(str: Expression, pos: Expression, len: Expression)
+  extends Expression with ExpectsInputTypes {
   
   type EvaluatedType = Any
 
@@ -219,6 +225,8 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends
     if (str.dataType == BinaryType) str.dataType else StringType
   }
 
+  override def expectedChildTypes: Seq[DataType] = Seq(StringType, IntegerType, IntegerType)
+
   override def children: Seq[Expression] = str :: pos :: len :: Nil
 
   @inline
@@ -258,7 +266,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends
           val (st, end) = slicePos(start, length, () => ba.length)
           ba.slice(st, end)
         case s: UTF8String =>
-          val (st, end) = slicePos(start, length, () => s.length)
+          val (st, end) = slicePos(start, length, () => s.length())
           s.slice(st, end)
       }
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
index c86214a2aa944..9d613a940ee86 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala
@@ -17,12 +17,31 @@
 
 package org.apache.spark.sql.catalyst
 
-import java.io.{PrintWriter, ByteArrayOutputStream, FileInputStream, File}
+import java.io._
 
 import org.apache.spark.util.Utils
 
 package object util {
 
+  /** Silences output to stderr or stdout for the duration of f */
+  def quietly[A](f: => A): A = {
+    val origErr = System.err
+    val origOut = System.out
+    try {
+      System.setErr(new PrintStream(new OutputStream {
+        def write(b: Int) = {}
+      }))
+      System.setOut(new PrintStream(new OutputStream {
+        def write(b: Int) = {}
+      }))
+
+      f
+    } finally {
+      System.setErr(origErr)
+      System.setOut(origOut)
+    }
+  }
+
   def fileToString(file: File, encoding: String = "UTF-8"): String = {
     val inStream = new FileInputStream(file)
     val outStream = new ByteArrayOutputStream
@@ -42,10 +61,9 @@ package object util {
     new String(outStream.toByteArray, encoding)
   }
 
-  def resourceToString(
-      resource:String,
-      encoding: String = "UTF-8",
-      classLoader: ClassLoader = Utils.getSparkClassLoader): String = {
+  def resourceToBytes(
+      resource: String,
+      classLoader: ClassLoader = Utils.getSparkClassLoader): Array[Byte] = {
     val inStream = classLoader.getResourceAsStream(resource)
     val outStream = new ByteArrayOutputStream
     try {
@@ -61,7 +79,14 @@ package object util {
     finally {
       inStream.close()
     }
-    new String(outStream.toByteArray, encoding)
+    outStream.toByteArray
+  }
+
+  def resourceToString(
+      resource:String,
+      encoding: String = "UTF-8",
+      classLoader: ClassLoader = Utils.getSparkClassLoader): String = {
+    new String(resourceToBytes(resource, classLoader), encoding)
   }
 
   def stringToFile(file: File, str: String): File = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 33f9d0b37d006..c0503bf047052 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -295,6 +295,15 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    */
   def eqNullSafe(other: Any): Column = this <=> other
 
+  /**
+   * True if the current column is between the lower bound and upper bound, inclusive.
+   *
+   * @group java_expr_ops
+   */
+  def between(lowerBound: Any, upperBound: Any): Column = {
+    (this >= lowerBound) && (this <= upperBound)
+  }
+
   /**
    * True if the current expression is null.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index c421006c8fd2d..cf344710ff8b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.io.CharArrayWriter
 import java.sql.DriverManager
 
+
 import scala.collection.JavaConversions._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
@@ -28,6 +29,7 @@ import scala.util.control.NonFatal
 
 import com.fasterxml.jackson.core.JsonFactory
 
+import org.apache.commons.lang3.StringUtils
 import org.apache.spark.annotation.{DeveloperApi, Experimental}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.python.SerDeUtil
@@ -175,6 +177,7 @@ class DataFrame private[sql](
    * @param numRows Number of rows to show
    */
   private[sql] def showString(numRows: Int): String = {
+    val sb = new StringBuilder
     val data = take(numRows)
     val numCols = schema.fieldNames.length
 
@@ -194,12 +197,25 @@ class DataFrame private[sql](
       }
     }
 
-    // Pad the cells
-    rows.map { row =>
-      row.zipWithIndex.map { case (cell, i) =>
-        String.format(s"%-${colWidths(i)}s", cell)
-      }.mkString(" ")
-    }.mkString("\n")
+    // Create SeparateLine
+    val sep: String = colWidths.map("-" * _).addString(sb, "+", "+", "+\n").toString()
+
+    // column names
+    rows.head.zipWithIndex.map { case (cell, i) =>
+      StringUtils.leftPad(cell.toString, colWidths(i))
+    }.addString(sb, "|", "|", "|\n")
+
+    sb.append(sep)
+
+    // data
+    rows.tail.map {
+      _.zipWithIndex.map { case (cell, i) =>
+        StringUtils.leftPad(cell.toString, colWidths(i))
+      }.addString(sb, "|", "|", "|\n")
+    }
+
+    sb.append(sep)
+    sb.toString()
   }
 
   override def toString: String = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index e8fa82947759b..cb88deab35968 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -27,6 +27,60 @@ import org.apache.spark.sql.execution.stat._
 @Experimental
 final class DataFrameStatFunctions private[sql](df: DataFrame) {
 
+  /**
+   * Calculate the sample covariance of two numerical columns of a DataFrame.
+   * @param col1 the name of the first column
+   * @param col2 the name of the second column
+   * @return the covariance of the two columns.
+   */
+  def cov(col1: String, col2: String): Double = {
+    StatFunctions.calculateCov(df, Seq(col1, col2))
+  }
+
+  /*
+   * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson
+   * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in 
+   * MLlib's Statistics.
+   *
+   * @param col1 the name of the column
+   * @param col2 the name of the column to calculate the correlation against
+   * @return The Pearson Correlation Coefficient as a Double.
+   */
+  def corr(col1: String, col2: String, method: String): Double = {
+    require(method == "pearson", "Currently only the calculation of the Pearson Correlation " +
+      "coefficient is supported.")
+    StatFunctions.pearsonCorrelation(df, Seq(col1, col2))
+  }
+
+  /**
+   * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame.
+   *
+   * @param col1 the name of the column
+   * @param col2 the name of the column to calculate the correlation against
+   * @return The Pearson Correlation Coefficient as a Double.
+   */
+  def corr(col1: String, col2: String): Double = {
+    corr(col1, col2, "pearson")
+  }
+
+  /**
+   * Computes a pair-wise frequency table of the given columns. Also known as a contingency table.
+   * The number of distinct values for each column should be less than 1e4. At most 1e6 non-zero
+   * pair frequencies will be returned.
+   * The first column of each row will be the distinct values of `col1` and the column names will
+   * be the distinct values of `col2`. The name of the first column will be `$col1_$col2`. Counts
+   * will be returned as `Long`s. Pairs that have no occurrences will have `null` as their counts.
+   *
+   * @param col1 The name of the first column. Distinct items will make the first item of
+   *             each row.
+   * @param col2 The name of the second column. Distinct items will make the column names
+   *             of the DataFrame.
+   * @return A Local DataFrame containing the table
+   */
+  def crosstab(col1: String, col2: String): DataFrame = {
+    StatFunctions.crossTabulate(df, col1, col2)
+  }
+
   /**
    * Finding frequent items for columns, possibly with false positives. Using the
    * frequent element count algorithm described in
@@ -68,14 +122,4 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
   def freqItems(cols: Seq[String]): DataFrame = {
     FrequentItems.singlePassFreqItems(df, cols, 0.01)
   }
-
-  /**
-   * Calculate the sample covariance of two numerical columns of a DataFrame.
-   * @param col1 the name of the first column
-   * @param col2 the name of the second column
-   * @return the covariance of the two columns.
-   */
-  def cov(col1: String, col2: String): Double = {
-    StatFunctions.calculateCov(df, Seq(col1, col2))
-  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 2fa602a6082dc..99db959a8741c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -52,6 +52,8 @@ private[spark] object SQLConf {
 
   // This is only used for the thriftserver
   val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool"
+  val THRIFTSERVER_UI_STATEMENT_LIMIT = "spark.sql.thriftserver.ui.retainedStatements"
+  val THRIFTSERVER_UI_SESSION_LIMIT = "spark.sql.thriftserver.ui.retainedSessions"
 
   // This is used to set the default data source
   val DEFAULT_DATA_SOURCE_NAME = "spark.sql.sources.default"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
index d4a94c24d9866..386ac969f1e7d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala
@@ -17,35 +17,52 @@
 
 package org.apache.spark.sql.execution.stat
 
-import org.apache.spark.sql.catalyst.expressions.Cast
+import org.apache.spark.Logging
 import org.apache.spark.sql.{Column, DataFrame}
-import org.apache.spark.sql.types.{DoubleType, NumericType}
+import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, Cast}
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
 
-private[sql] object StatFunctions {
+private[sql] object StatFunctions extends Logging {
   
+  /** Calculate the Pearson Correlation Coefficient for the given columns */
+  private[sql] def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double = {
+    val counts = collectStatisticalData(df, cols)
+    counts.Ck / math.sqrt(counts.MkX * counts.MkY)
+  }
+
   /** Helper class to simplify tracking and merging counts. */
   private class CovarianceCounter extends Serializable {
-    var xAvg = 0.0
-    var yAvg = 0.0
-    var Ck = 0.0
-    var count = 0L
+    var xAvg = 0.0 // the mean of all examples seen so far in col1
+    var yAvg = 0.0 // the mean of all examples seen so far in col2
+    var Ck = 0.0 // the co-moment after k examples
+    var MkX = 0.0 // sum of squares of differences from the (current) mean for col1
+    var MkY = 0.0 // sum of squares of differences from the (current) mean for col1
+    var count = 0L // count of observed examples
     // add an example to the calculation
     def add(x: Double, y: Double): this.type = {
-      val oldX = xAvg
+      val deltaX = x - xAvg
+      val deltaY = y - yAvg
       count += 1
-      xAvg += (x - xAvg) / count
-      yAvg += (y - yAvg) / count
-      Ck += (y - yAvg) * (x - oldX)
+      xAvg += deltaX / count
+      yAvg += deltaY / count
+      Ck += deltaX * (y - yAvg)
+      MkX += deltaX * (x - xAvg)
+      MkY += deltaY * (y - yAvg)
       this
     }
     // merge counters from other partitions. Formula can be found at:
-    // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance
+    // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
     def merge(other: CovarianceCounter): this.type = {
       val totalCount = count + other.count
-      Ck += other.Ck + 
-        (xAvg - other.xAvg) * (yAvg - other.yAvg) * count / totalCount * other.count
+      val deltaX = xAvg - other.xAvg
+      val deltaY = yAvg - other.yAvg
+      Ck += other.Ck + deltaX * deltaY * count / totalCount * other.count
       xAvg = (xAvg * count + other.xAvg * other.count) / totalCount
       yAvg = (yAvg * count + other.yAvg * other.count) / totalCount
+      MkX += other.MkX + deltaX * deltaX * count / totalCount * other.count
+      MkY += other.MkY + deltaY * deltaY * count / totalCount * other.count
       count = totalCount
       this
     }
@@ -53,13 +70,7 @@ private[sql] object StatFunctions {
     def cov: Double = Ck / (count - 1)
   }
 
-  /**
-   * Calculate the covariance of two numerical columns of a DataFrame.
-   * @param df The DataFrame
-   * @param cols the column names
-   * @return the covariance of the two columns.
-   */
-  private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = {
+  private def collectStatisticalData(df: DataFrame, cols: Seq[String]): CovarianceCounter = {
     require(cols.length == 2, "Currently cov supports calculating the covariance " +
       "between two columns.")
     cols.map(name => (name, df.schema.fields.find(_.name == name))).foreach { case (name, data) =>
@@ -68,13 +79,51 @@ private[sql] object StatFunctions {
         s"with dataType ${data.get.dataType} not supported.")
     }
     val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType)))
-    val counts = df.select(columns:_*).rdd.aggregate(new CovarianceCounter)(
+    df.select(columns: _*).rdd.aggregate(new CovarianceCounter)(
       seqOp = (counter, row) => {
         counter.add(row.getDouble(0), row.getDouble(1))
       },
       combOp = (baseCounter, other) => {
         baseCounter.merge(other)
-      })
+    })
+  }
+
+  /**
+   * Calculate the covariance of two numerical columns of a DataFrame.
+   * @param df The DataFrame
+   * @param cols the column names
+   * @return the covariance of the two columns.
+   */
+  private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = {
+    val counts = collectStatisticalData(df, cols)
     counts.cov
   }
+
+  /** Generate a table of frequencies for the elements of two columns. */
+  private[sql] def crossTabulate(df: DataFrame, col1: String, col2: String): DataFrame = {
+    val tableName = s"${col1}_$col2"
+    val counts = df.groupBy(col1, col2).agg(col(col1), col(col2), count("*")).take(1e6.toInt)
+    if (counts.length == 1e6.toInt) {
+      logWarning("The maximum limit of 1e6 pairs have been collected, which may not be all of " +
+        "the pairs. Please try reducing the amount of distinct items in your columns.")
+    }
+    // get the distinct values of column 2, so that we can make them the column names
+    val distinctCol2 = counts.map(_.get(1)).distinct.zipWithIndex.toMap
+    val columnSize = distinctCol2.size
+    require(columnSize < 1e4, s"The number of distinct values for $col2, can't " +
+      s"exceed 1e4. Currently $columnSize")
+    val table = counts.groupBy(_.get(0)).map { case (col1Item, rows) =>
+      val countsRow = new GenericMutableRow(columnSize + 1)
+      rows.foreach { row =>
+        countsRow.setLong(distinctCol2.get(row.get(1)).get + 1, row.getLong(2))
+      }
+      // the value of col1 is the first value, the rest are the counts
+      countsRow.setString(0, col1Item.toString)
+      countsRow
+    }.toSeq
+    val headerNames = distinctCol2.map(r => StructField(r._1.toString, LongType)).toSeq
+    val schema = StructType(StructField(tableName, StringType) +: headerNames)
+
+    new DataFrame(df.sqlContext, LocalRelation(schema.toAttributes, table))
+  }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 96fe66d0b84a6..58cc8e5be6075 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -34,6 +34,7 @@
 
 import java.io.Serializable;
 import java.util.Arrays;
+import java.util.Comparator;
 import java.util.List;
 import java.util.Map;
 
@@ -178,6 +179,33 @@ public void testCreateDataFrameFromJavaBeans() {
       Assert.assertEquals(bean.getD().get(i), d.apply(i));
     }
   }
+
+  private static Comparator<Row> CrosstabRowComparator = new Comparator<Row>() {
+    public int compare(Row row1, Row row2) {
+      String item1 = row1.getString(0);
+      String item2 = row2.getString(0);
+      return item1.compareTo(item2);
+    }
+  };
+
+  @Test
+  public void testCrosstab() {
+    DataFrame df = context.table("testData2");
+    DataFrame crosstab = df.stat().crosstab("a", "b");
+    String[] columnNames = crosstab.schema().fieldNames();
+    Assert.assertEquals(columnNames[0], "a_b");
+    Assert.assertEquals(columnNames[1], "1");
+    Assert.assertEquals(columnNames[2], "2");
+    Row[] rows = crosstab.collect();
+    Arrays.sort(rows, CrosstabRowComparator);
+    Integer count = 1;
+    for (Row row : rows) {
+      Assert.assertEquals(row.get(0).toString(), count.toString());
+      Assert.assertEquals(row.getLong(1), 1L);
+      Assert.assertEquals(row.getLong(2), 1L);
+      count++;
+    }
+  }
   
   @Test
   public void testFrequentItems() {
@@ -187,6 +215,13 @@ public void testFrequentItems() {
     Assert.assertTrue(results.collect()[0].getSeq(0).contains(1));
   }
 
+  @Test
+  public void testCorrelation() {
+    DataFrame df = context.table("testData2");
+    Double pearsonCorr = df.stat().corr("a", "b", "pearson");
+    Assert.assertTrue(Math.abs(pearsonCorr) < 1e-6);
+  }
+
   @Test
   public void testCovariance() {
     DataFrame df = context.table("testData2");
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 6322faf4d9907..3c1ad656fc855 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -208,6 +208,20 @@ class ColumnExpressionSuite extends QueryTest {
       testData2.collect().toSeq.filter(r => r.getInt(0) <= r.getInt(1)))
   }
 
+  test("between") {
+    val testData = TestSQLContext.sparkContext.parallelize(
+      (0, 1, 2) ::
+      (1, 2, 3) ::
+      (2, 1, 0) ::
+      (2, 2, 4) ::
+      (3, 1, 6) ::
+      (3, 2, 0) :: Nil).toDF("a", "b", "c")
+    val expectAnswer = testData.collect().toSeq.
+      filter(r => r.getInt(0) >= r.getInt(1) && r.getInt(0) <= r.getInt(2))
+
+    checkAnswer(testData.filter($"a".between($"b", $"c")), expectAnswer)
+  }
+
   val booleanData = TestSQLContext.createDataFrame(TestSQLContext.sparkContext.parallelize(
     Row(false, false) ::
       Row(false, true) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 4f5a2ff696789..46b1845a9180c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -24,16 +24,70 @@ import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.test.TestSQLContext.implicits._
 
 class DataFrameStatSuite extends FunSuite  {
-
-  import TestData._
+  
   val sqlCtx = TestSQLContext
   def toLetter(i: Int): String = (i + 97).toChar.toString
-  
+
+  test("pearson correlation") {
+    val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c")
+    val corr1 = df.stat.corr("a", "b", "pearson")
+    assert(math.abs(corr1 - 1.0) < 1e-12)
+    val corr2 = df.stat.corr("a", "c", "pearson")
+    assert(math.abs(corr2 + 1.0) < 1e-12)
+    // non-trivial example. To reproduce in python, use:
+    // >>> from scipy.stats import pearsonr
+    // >>> import numpy as np
+    // >>> a = np.array(range(20))
+    // >>> b = np.array([x * x - 2 * x + 3.5 for x in range(20)])
+    // >>> pearsonr(a, b)
+    // (0.95723391394758572, 3.8902121417802199e-11)
+    // In R, use:
+    // > a <- 0:19
+    // > b <- mapply(function(x) x * x - 2 * x + 3.5, a)
+    // > cor(a, b)
+    // [1] 0.957233913947585835
+    val df2 = Seq.tabulate(20)(x => (x, x * x - 2 * x + 3.5)).toDF("a", "b")
+    val corr3 = df2.stat.corr("a", "b", "pearson")
+    assert(math.abs(corr3 - 0.95723391394758572) < 1e-12)
+  }
+
+  test("covariance") {
+    val df = Seq.tabulate(10)(i => (i, 2.0 * i, toLetter(i))).toDF("singles", "doubles", "letters")
+
+    val results = df.stat.cov("singles", "doubles")
+    assert(math.abs(results - 55.0 / 3) < 1e-12)
+    intercept[IllegalArgumentException] {
+      df.stat.cov("singles", "letters") // doesn't accept non-numerical dataTypes
+    }
+    val decimalData = Seq.tabulate(6)(i => (BigDecimal(i % 3), BigDecimal(i % 2))).toDF("a", "b")
+    val decimalRes = decimalData.stat.cov("a", "b")
+    assert(math.abs(decimalRes) < 1e-12)
+  }
+
+  test("crosstab") {
+    val df = Seq((0, 0), (2, 1), (1, 0), (2, 0), (0, 0), (2, 0)).toDF("a", "b")
+    val crosstab = df.stat.crosstab("a", "b")
+    val columnNames = crosstab.schema.fieldNames
+    assert(columnNames(0) === "a_b")
+    assert(columnNames(1) === "0")
+    assert(columnNames(2) === "1")
+    val rows: Array[Row] = crosstab.collect().sortBy(_.getString(0))
+    assert(rows(0).get(0).toString === "0")
+    assert(rows(0).getLong(1) === 2L)
+    assert(rows(0).get(2) === null)
+    assert(rows(1).get(0).toString === "1")
+    assert(rows(1).getLong(1) === 1L)
+    assert(rows(1).get(2) === null)
+    assert(rows(2).get(0).toString === "2")
+    assert(rows(2).getLong(1) === 2L)
+    assert(rows(2).getLong(2) === 1L)
+  }
+
   test("Frequent Items") {
-    val rows = Array.tabulate(1000) { i =>
+    val rows = Seq.tabulate(1000) { i =>
       if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0)
     }
-    val df = sqlCtx.sparkContext.parallelize(rows).toDF("numbers", "letters", "negDoubles")
+    val df = rows.toDF("numbers", "letters", "negDoubles")
 
     val results = df.stat.freqItems(Array("numbers", "letters"), 0.1)
     val items = results.collect().head
@@ -43,19 +97,5 @@ class DataFrameStatSuite extends FunSuite  {
     val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1)
     val items2 = singleColResults.collect().head
     items2.getSeq[Double](0) should contain (-1.0)
-
-  }
-
-  test("covariance") {
-    val rows = Array.tabulate(10)(i => (i, 2.0 * i, toLetter(i)))
-    val df = sqlCtx.sparkContext.parallelize(rows).toDF("singles", "doubles", "letters")
-
-    val results = df.stat.cov("singles", "doubles")
-    assert(math.abs(results - 55.0 / 3) < 1e-6)
-    intercept[IllegalArgumentException] {
-      df.stat.cov("singles", "letters") // doesn't accept non-numerical dataTypes
-    }
-    val decimalRes = decimalData.stat.cov("a", "b")
-    assert(math.abs(decimalRes) < 1e-6)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index e286fef23caa4..ff31e15e2d472 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -598,6 +598,25 @@ class DataFrameSuite extends QueryTest {
     testData.select($"*").show(1000)
   }
 
+  test("SPARK-7319 showString") {
+    val expectedAnswer = """+---+-----+
+                           ||key|value|
+                           |+---+-----+
+                           ||  1|    1|
+                           |+---+-----+
+                           |""".stripMargin
+    assert(testData.select($"*").showString(1) === expectedAnswer)
+  }
+
+  test("SPARK-7327 show with empty dataFrame") {
+    val expectedAnswer = """+---+-----+
+                           ||key|value|
+                           |+---+-----+
+                           |+---+-----+
+                           |""".stripMargin
+    assert(testData.select($"*").filter($"key" < 0).showString(1) === expectedAnswer)
+  }
+
   test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") {
     val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
     val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false)))
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index f38c796241df1..437f697d25bf3 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -57,6 +57,18 @@
       <groupId>${hive.group}</groupId>
       <artifactId>hive-beeline</artifactId>
     </dependency>
+    <!-- Added for selenium: -->
+    <dependency>
+      <groupId>org.seleniumhq.selenium</groupId>
+      <artifactId>selenium-java</artifactId>
+      <scope>test</scope>
+      <exclusions>
+        <exclusion>
+          <groupId>io.netty</groupId>
+          <artifactId>netty</artifactId>
+        </exclusion>
+      </exclusions>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index 832596fc8bee5..0be5a92c2546c 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -22,20 +22,27 @@ import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService}
 import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
+import org.apache.spark.sql.SQLConf
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkContext, SparkConf, Logging}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
-import org.apache.spark.scheduler.{SparkListenerApplicationEnd, SparkListener}
+import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListenerApplicationEnd, SparkListener}
+import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab
 import org.apache.spark.util.Utils
 
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
 /**
  * The main entry point for the Spark SQL port of HiveServer2.  Starts up a `SparkSQLContext` and a
  * `HiveThriftServer2` thrift server.
  */
 object HiveThriftServer2 extends Logging {
   var LOG = LogFactory.getLog(classOf[HiveServer2])
+  var uiTab: Option[ThriftServerTab] = _
+  var listener: HiveThriftServer2Listener = _
 
   /**
    * :: DeveloperApi ::
@@ -46,7 +53,13 @@ object HiveThriftServer2 extends Logging {
     val server = new HiveThriftServer2(sqlContext)
     server.init(sqlContext.hiveconf)
     server.start()
-    sqlContext.sparkContext.addSparkListener(new HiveThriftServer2Listener(server))
+    listener = new HiveThriftServer2Listener(server, sqlContext.conf)
+    sqlContext.sparkContext.addSparkListener(listener)
+    uiTab = if (sqlContext.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) {
+      Some(new ThriftServerTab(sqlContext.sparkContext))
+    } else {
+      None
+    }
   }
 
   def main(args: Array[String]) {
@@ -58,14 +71,23 @@ object HiveThriftServer2 extends Logging {
     logInfo("Starting SparkContext")
     SparkSQLEnv.init()
 
-    Utils.addShutdownHook { () => SparkSQLEnv.stop() }
+    Utils.addShutdownHook { () =>
+      SparkSQLEnv.stop()
+      uiTab.foreach(_.detach())
+    }
 
     try {
       val server = new HiveThriftServer2(SparkSQLEnv.hiveContext)
       server.init(SparkSQLEnv.hiveContext.hiveconf)
       server.start()
       logInfo("HiveThriftServer2 started")
-      SparkSQLEnv.sparkContext.addSparkListener(new HiveThriftServer2Listener(server))
+      listener = new HiveThriftServer2Listener(server, SparkSQLEnv.hiveContext.conf)
+      SparkSQLEnv.sparkContext.addSparkListener(listener)
+      uiTab = if (SparkSQLEnv.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) {
+        Some(new ThriftServerTab(SparkSQLEnv.sparkContext))
+      } else {
+        None
+      }
     } catch {
       case e: Exception =>
         logError("Error starting HiveThriftServer2", e)
@@ -73,15 +95,140 @@ object HiveThriftServer2 extends Logging {
     }
   }
 
+  private[thriftserver] class SessionInfo(
+      val sessionId: String,
+      val startTimestamp: Long,
+      val ip: String,
+      val userName: String) {
+    var finishTimestamp: Long = 0L
+    var totalExecution: Int = 0
+    def totalTime: Long = {
+      if (finishTimestamp == 0L) {
+        System.currentTimeMillis - startTimestamp
+      } else {
+        finishTimestamp - startTimestamp
+      }
+    }
+  }
+
+  private[thriftserver] object ExecutionState extends Enumeration {
+    val STARTED, COMPILED, FAILED, FINISHED = Value
+    type ExecutionState = Value
+  }
+
+  private[thriftserver] class ExecutionInfo(
+      val statement: String,
+      val sessionId: String,
+      val startTimestamp: Long,
+      val userName: String) {
+    var finishTimestamp: Long = 0L
+    var executePlan: String = ""
+    var detail: String = ""
+    var state: ExecutionState.Value = ExecutionState.STARTED
+    val jobId: ArrayBuffer[String] = ArrayBuffer[String]()
+    var groupId: String = ""
+    def totalTime: Long = {
+      if (finishTimestamp == 0L) {
+        System.currentTimeMillis - startTimestamp
+      } else {
+        finishTimestamp - startTimestamp
+      }
+    }
+  }
+
+
   /**
    * A inner sparkListener called in sc.stop to clean up the HiveThriftServer2
    */
-  class HiveThriftServer2Listener(val server: HiveServer2) extends SparkListener {
+  private[thriftserver] class HiveThriftServer2Listener(
+      val server: HiveServer2,
+      val conf: SQLConf) extends SparkListener {
+
     override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = {
       server.stop()
     }
-  }
 
+    val sessionList = new mutable.LinkedHashMap[String, SessionInfo]
+    val executionList = new mutable.LinkedHashMap[String, ExecutionInfo]
+    val retainedStatements =
+      conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT, "200").toInt
+    val retainedSessions =
+      conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT, "200").toInt
+    var totalRunning = 0
+
+    override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
+      for {
+        props <- Option(jobStart.properties)
+        groupId <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID))
+        (_, info) <- executionList if info.groupId == groupId
+      } {
+        info.jobId += jobStart.jobId.toString
+        info.groupId = groupId
+      }
+    }
+
+    def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = {
+      val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName)
+      sessionList.put(sessionId, info)
+      trimSessionIfNecessary()
+    }
+
+    def onSessionClosed(sessionId: String): Unit = {
+      sessionList(sessionId).finishTimestamp = System.currentTimeMillis
+    }
+
+    def onStatementStart(
+        id: String,
+        sessionId: String,
+        statement: String,
+        groupId: String,
+        userName: String = "UNKNOWN"): Unit = {
+      val info = new ExecutionInfo(statement, sessionId, System.currentTimeMillis, userName)
+      info.state = ExecutionState.STARTED
+      executionList.put(id, info)
+      trimExecutionIfNecessary()
+      sessionList(sessionId).totalExecution += 1
+      executionList(id).groupId = groupId
+      totalRunning += 1
+    }
+
+    def onStatementParsed(id: String, executionPlan: String): Unit = {
+      executionList(id).executePlan = executionPlan
+      executionList(id).state = ExecutionState.COMPILED
+    }
+
+    def onStatementError(id: String, errorMessage: String, errorTrace: String): Unit = {
+      executionList(id).finishTimestamp = System.currentTimeMillis
+      executionList(id).detail = errorMessage
+      executionList(id).state = ExecutionState.FAILED
+      totalRunning -= 1
+    }
+
+    def onStatementFinish(id: String): Unit = {
+      executionList(id).finishTimestamp = System.currentTimeMillis
+      executionList(id).state = ExecutionState.FINISHED
+      totalRunning -= 1
+    }
+
+    private def trimExecutionIfNecessary() = synchronized {
+      if (executionList.size > retainedStatements) {
+        val toRemove = math.max(retainedStatements / 10, 1)
+        executionList.take(toRemove).foreach { s =>
+          executionList.remove(s._1)
+        }
+      }
+    }
+
+    private def trimSessionIfNecessary() = synchronized {
+      if (sessionList.size > retainedSessions) {
+        val toRemove = math.max(retainedSessions / 10, 1)
+        sessionList.take(toRemove).foreach { s =>
+          sessionList.remove(s._1)
+        }
+      }
+
+    }
+  }
 }
 
 private[hive] class HiveThriftServer2(hiveContext: HiveContext)
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
new file mode 100644
index 0000000000000..71b16b6bebffb
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
@@ -0,0 +1,190 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver.ui
+
+import java.util.Calendar
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import org.apache.commons.lang3.StringEscapeUtils
+import org.apache.spark.Logging
+import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{SessionInfo, ExecutionState, ExecutionInfo}
+import org.apache.spark.ui.UIUtils._
+import org.apache.spark.ui._
+
+
+/** Page for Spark Web UI that shows statistics of a streaming job */
+private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("") with Logging {
+
+  private val listener = parent.listener
+  private val startTime = Calendar.getInstance().getTime()
+  private val emptyCell = "-"
+
+  /** Render the page */
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val content =
+      generateBasicStats() ++
+      <br/> ++
+      <h4>
+        {listener.sessionList.size} session(s) are online,
+        running {listener.totalRunning} SQL statement(s)
+      </h4> ++
+      generateSessionStatsTable() ++
+      generateSQLStatsTable()
+    UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000))
+  }
+
+  /** Generate basic stats of the streaming program */
+  private def generateBasicStats(): Seq[Node] = {
+    val timeSinceStart = System.currentTimeMillis() - startTime.getTime
+    <ul class ="unstyled">
+      <li>
+        <strong>Started at: </strong> {startTime.toString}
+      </li>
+      <li>
+        <strong>Time since start: </strong>{formatDurationVerbose(timeSinceStart)}
+      </li>
+    </ul>
+  }
+
+  /** Generate stats of batch statements of the thrift server program */
+  private def generateSQLStatsTable(): Seq[Node] = {
+    val numStatement = listener.executionList.size
+    val table = if (numStatement > 0) {
+      val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration",
+        "Statement", "State", "Detail")
+      val dataRows = listener.executionList.values
+
+      def generateDataRow(info: ExecutionInfo): Seq[Node] = {
+        val jobLink = info.jobId.map { id: String =>
+          <a href={"%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), id)}>
+            [{id}]
+          </a>
+        }
+        val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan
+        <tr>
+          <td>{info.userName}</td>
+          <td>
+            {jobLink}
+          </td>
+          <td>{info.groupId}</td>
+          <td>{formatDate(info.startTimestamp)}</td>
+          <td>{if(info.finishTimestamp > 0) formatDate(info.finishTimestamp)}</td>
+          <td>{formatDurationOption(Some(info.totalTime))}</td>
+          <td>{info.statement}</td>
+          <td>{info.state}</td>
+          {errorMessageCell(detail)}
+        </tr>
+      }
+
+      Some(UIUtils.listingTable(headerRow, generateDataRow,
+        dataRows, false, None, Seq(null), false))
+    } else {
+      None
+    }
+
+    val content =
+      <h5 id="sqlstat">SQL Statistics</h5> ++
+        <div>
+          <ul class="unstyled">
+            {table.getOrElse("No statistics have been generated yet.")}
+          </ul>
+        </div>
+
+    content
+  }
+
+  private def errorMessageCell(errorMessage: String): Seq[Node] = {
+    val isMultiline = errorMessage.indexOf('\n') >= 0
+    val errorSummary = StringEscapeUtils.escapeHtml4(
+      if (isMultiline) {
+        errorMessage.substring(0, errorMessage.indexOf('\n'))
+      } else {
+        errorMessage
+      })
+    val details = if (isMultiline) {
+      // scalastyle:off
+      <span onclick="this.parentNode.querySelector('.stacktrace-details').classList.toggle('collapsed')"
+            class="expand-details">
+        + details
+      </span> ++
+      <div class="stacktrace-details collapsed">
+        <pre>{errorMessage}</pre>
+      </div>
+      // scalastyle:on
+    } else {
+      ""
+    }
+    <td>{errorSummary}{details}</td>
+  }
+
+  /** Generate stats of batch sessions of the thrift server program */
+  private def generateSessionStatsTable(): Seq[Node] = {
+    val numBatches = listener.sessionList.size
+    val table = if (numBatches > 0) {
+      val dataRows =
+        listener.sessionList.values
+      val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration",
+        "Total Execute")
+      def generateDataRow(session: SessionInfo): Seq[Node] = {
+        val sessionLink = "%s/ThriftServer/session?id=%s"
+          .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId)
+        <tr>
+          <td> {session.userName} </td>
+          <td> {session.ip} </td>
+          <td> <a href={sessionLink}> {session.sessionId} </a> </td>,
+          <td> {formatDate(session.startTimestamp)} </td>
+          <td> {if(session.finishTimestamp > 0) formatDate(session.finishTimestamp)} </td>
+          <td> {formatDurationOption(Some(session.totalTime))} </td>
+          <td> {session.totalExecution.toString} </td>
+        </tr>
+      }
+      Some(UIUtils.listingTable(headerRow, generateDataRow, dataRows, true, None, Seq(null), false))
+    } else {
+      None
+    }
+
+    val content =
+      <h5 id="sessionstat">Session Statistics</h5> ++
+      <div>
+        <ul class="unstyled">
+          {table.getOrElse("No statistics have been generated yet.")}
+        </ul>
+      </div>
+
+    content
+  }
+
+
+  /**
+   * Returns a human-readable string representing a duration such as "5 second 35 ms"
+   */
+  private def formatDurationOption(msOption: Option[Long]): String = {
+    msOption.map(formatDurationVerbose).getOrElse(emptyCell)
+  }
+
+  /** Generate HTML table from string data */
+  private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = {
+    def generateDataRow(data: Seq[String]): Seq[Node] = {
+      <tr> {data.map(d => <td>{d}</td>)} </tr>
+    }
+    UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true)
+  }
+}
+
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
new file mode 100644
index 0000000000000..33ba038ecce73
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
@@ -0,0 +1,197 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver.ui
+
+import java.util.Calendar
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import org.apache.commons.lang3.StringEscapeUtils
+import org.apache.spark.Logging
+import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{ExecutionInfo, ExecutionState}
+import org.apache.spark.ui.UIUtils._
+import org.apache.spark.ui._
+
+/** Page for Spark Web UI that shows statistics of a streaming job */
+private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
+  extends WebUIPage("session") with Logging {
+
+  private val listener = parent.listener
+  private val startTime = Calendar.getInstance().getTime()
+  private val emptyCell = "-"
+
+  /** Render the page */
+  def render(request: HttpServletRequest): Seq[Node] = {
+    val parameterId = request.getParameter("id")
+    require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
+    val sessionStat = listener.sessionList.find(stat => {
+      stat._1 == parameterId
+    }).getOrElse(null)
+    require(sessionStat != null, "Invalid sessionID[" + parameterId + "]")
+
+    val content =
+      generateBasicStats() ++
+      <br/> ++
+      <h4>
+        User {sessionStat._2.userName},
+        IP {sessionStat._2.ip},
+        Session created at {formatDate(sessionStat._2.startTimestamp)},
+        Total run {sessionStat._2.totalExecution} SQL
+      </h4> ++
+      generateSQLStatsTable(sessionStat._2.sessionId)
+    UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000))
+  }
+
+  /** Generate basic stats of the streaming program */
+  private def generateBasicStats(): Seq[Node] = {
+    val timeSinceStart = System.currentTimeMillis() - startTime.getTime
+    <ul class ="unstyled">
+      <li>
+        <strong>Started at: </strong> {startTime.toString}
+      </li>
+      <li>
+        <strong>Time since start: </strong>{formatDurationVerbose(timeSinceStart)}
+      </li>
+    </ul>
+  }
+
+  /** Generate stats of batch statements of the thrift server program */
+  private def generateSQLStatsTable(sessionID: String): Seq[Node] = {
+    val executionList = listener.executionList
+      .filter(_._2.sessionId == sessionID)
+    val numStatement = executionList.size
+    val table = if (numStatement > 0) {
+      val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration",
+        "Statement", "State", "Detail")
+      val dataRows = executionList.values.toSeq.sortBy(_.startTimestamp).reverse
+
+      def generateDataRow(info: ExecutionInfo): Seq[Node] = {
+        val jobLink = info.jobId.map { id: String =>
+          <a href={"%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), id)}>
+            [{id}]
+          </a>
+        }
+        val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan
+        <tr>
+          <td>{info.userName}</td>
+          <td>
+            {jobLink}
+          </td>
+          <td>{info.groupId}</td>
+          <td>{formatDate(info.startTimestamp)}</td>
+          <td>{formatDate(info.finishTimestamp)}</td>
+          <td>{formatDurationOption(Some(info.totalTime))}</td>
+          <td>{info.statement}</td>
+          <td>{info.state}</td>
+          {errorMessageCell(detail)}
+        </tr>
+      }
+
+      Some(UIUtils.listingTable(headerRow, generateDataRow,
+        dataRows, false, None, Seq(null), false))
+    } else {
+      None
+    }
+
+    val content =
+      <h5>SQL Statistics</h5> ++
+        <div>
+          <ul class="unstyled">
+            {table.getOrElse("No statistics have been generated yet.")}
+          </ul>
+        </div>
+
+    content
+  }
+
+  private def errorMessageCell(errorMessage: String): Seq[Node] = {
+    val isMultiline = errorMessage.indexOf('\n') >= 0
+    val errorSummary = StringEscapeUtils.escapeHtml4(
+      if (isMultiline) {
+        errorMessage.substring(0, errorMessage.indexOf('\n'))
+      } else {
+        errorMessage
+      })
+    val details = if (isMultiline) {
+      // scalastyle:off
+      <span onclick="this.parentNode.querySelector('.stacktrace-details').classList.toggle('collapsed')"
+            class="expand-details">
+        + details
+      </span> ++
+      <div class="stacktrace-details collapsed">
+        <pre>{errorMessage}</pre>
+      </div>
+      // scalastyle:on
+    } else {
+      ""
+    }
+    <td>{errorSummary}{details}</td>
+  }
+
+  /** Generate stats of batch sessions of the thrift server program */
+  private def generateSessionStatsTable(): Seq[Node] = {
+    val numBatches = listener.sessionList.size
+    val table = if (numBatches > 0) {
+      val dataRows =
+        listener.sessionList.values.toSeq.sortBy(_.startTimestamp).reverse.map ( session =>
+        Seq(
+          session.userName,
+          session.ip,
+          session.sessionId,
+          formatDate(session.startTimestamp),
+          formatDate(session.finishTimestamp),
+          formatDurationOption(Some(session.totalTime)),
+          session.totalExecution.toString
+        )
+      ).toSeq
+      val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration",
+        "Total Execute")
+      Some(listingTable(headerRow, dataRows))
+    } else {
+      None
+    }
+
+    val content =
+      <h5>Session Statistics</h5> ++
+      <div>
+        <ul class="unstyled">
+          {table.getOrElse("No statistics have been generated yet.")}
+        </ul>
+      </div>
+
+    content
+  }
+
+
+  /**
+   * Returns a human-readable string representing a duration such as "5 second 35 ms"
+   */
+  private def formatDurationOption(msOption: Option[Long]): String = {
+    msOption.map(formatDurationVerbose).getOrElse(emptyCell)
+  }
+
+  /** Generate HTML table from string data */
+  private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = {
+    def generateDataRow(data: Seq[String]): Seq[Node] = {
+      <tr> {data.map(d => <td>{d}</td>)} </tr>
+    }
+    UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true)
+  }
+}
+
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
new file mode 100644
index 0000000000000..343031f10c75c
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
@@ -0,0 +1,50 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver.ui
+
+import org.apache.spark.sql.hive.thriftserver.{HiveThriftServer2, SparkSQLEnv}
+import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab._
+import org.apache.spark.ui.{SparkUI, SparkUITab}
+import org.apache.spark.{SparkContext, Logging, SparkException}
+
+/**
+ * Spark Web UI tab that shows statistics of a streaming job.
+ * This assumes the given SparkContext has enabled its SparkUI.
+ */
+private[thriftserver] class ThriftServerTab(sparkContext: SparkContext)
+  extends SparkUITab(getSparkUI(sparkContext), "ThriftServer") with Logging {
+
+  val parent = getSparkUI(sparkContext)
+  val listener = HiveThriftServer2.listener
+
+  attachPage(new ThriftServerPage(this))
+  attachPage(new ThriftServerSessionPage(this))
+  parent.attachTab(this)
+
+  def detach() {
+    getSparkUI(sparkContext).detachTab(this)
+  }
+}
+
+private[thriftserver] object ThriftServerTab {
+  def getSparkUI(sparkContext: SparkContext): SparkUI = {
+    sparkContext.ui.getOrElse {
+      throw new SparkException("Parent SparkUI to attach this tab to not found!")
+    }
+  }
+}
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 4cf95e7bdfb2b..1fadea97fd07f 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -409,24 +409,24 @@ abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll wit
   private val CLASS_NAME = HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$")
   private val LOG_FILE_MARK = s"starting $CLASS_NAME, logging to "
 
-  private val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator)
-  private val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator)
+  protected val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator)
+  protected val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator)
 
   private var listeningPort: Int = _
   protected def serverPort: Int = listeningPort
 
   protected def user = System.getProperty("user.name")
 
-  private var warehousePath: File = _
-  private var metastorePath: File = _
-  private def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true"
+  protected var warehousePath: File = _
+  protected var metastorePath: File = _
+  protected def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true"
 
   private val pidDir: File = Utils.createTempDir("thriftserver-pid")
   private var logPath: File = _
   private var logTailingProcess: Process = _
   private var diagnosisBuffer: ArrayBuffer[String] = ArrayBuffer.empty[String]
 
-  private def serverStartCommand(port: Int) = {
+  protected def serverStartCommand(port: Int) = {
     val portConf = if (mode == ServerMode.binary) {
       ConfVars.HIVE_SERVER2_THRIFT_PORT
     } else {
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
new file mode 100644
index 0000000000000..47541015a3611
--- /dev/null
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.thriftserver
+
+
+
+import scala.util.Random
+
+import org.openqa.selenium.WebDriver
+import org.openqa.selenium.htmlunit.HtmlUnitDriver
+import org.scalatest.{Matchers, BeforeAndAfterAll}
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.selenium.WebBrowser
+import org.scalatest.time.SpanSugar._
+
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
+import org.apache.spark.sql.hive.HiveContext
+
+
+class UISeleniumSuite
+  extends HiveThriftJdbcTest
+  with WebBrowser with Matchers with BeforeAndAfterAll {
+
+  implicit var webDriver: WebDriver = _
+  var server: HiveThriftServer2 = _
+  var hc: HiveContext = _
+  val uiPort = 20000 + Random.nextInt(10000)
+  override def mode: ServerMode.Value = ServerMode.binary
+
+  override def beforeAll(): Unit = {
+    webDriver = new HtmlUnitDriver
+    super.beforeAll()
+  }
+
+  override def afterAll(): Unit = {
+    if (webDriver != null) {
+      webDriver.quit()
+    }
+    super.afterAll()
+  }
+
+  override protected def serverStartCommand(port: Int) = {
+    val portConf = if (mode == ServerMode.binary) {
+      ConfVars.HIVE_SERVER2_THRIFT_PORT
+    } else {
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT
+    }
+
+    s"""$startScript
+        |  --master local
+        |  --hiveconf hive.root.logger=INFO,console
+        |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri
+        |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath
+        |  --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost
+        |  --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=$mode
+        |  --hiveconf $portConf=$port
+        |  --driver-class-path ${sys.props("java.class.path")}
+        |  --conf spark.ui.enabled=true
+        |  --conf spark.ui.port=$uiPort
+     """.stripMargin.split("\\s+").toSeq
+  }
+
+  test("thrift server ui test") {
+    withJdbcStatement(statement =>{
+      val baseURL = s"http://localhost:${uiPort}"
+
+      val queries = Seq(
+        "CREATE TABLE test_map(key INT, value STRING)",
+        s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map")
+
+      queries.foreach(statement.execute)
+
+      eventually(timeout(10 seconds), interval(50 milliseconds)) {
+        go to (baseURL)
+        find(cssSelector("""ul li a[href*="ThriftServer"]""")) should not be(None)
+      }
+
+      eventually(timeout(10 seconds), interval(50 milliseconds)) {
+        go to (baseURL + "/ThriftServer")
+        find(id("sessionstat")) should not be(None)
+        find(id("sqlstat")) should not be(None)
+
+        // check whether statements exists
+        queries.foreach { line =>
+          findAll(cssSelector("""ul table tbody tr td""")).map(_.text).toList should contain (line)
+        }
+      }
+    })
+  }
+}
diff --git a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala
index 95a6e86d0546d..b3a79ba1c7d6b 100644
--- a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala
+++ b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver
 
 import java.sql.{Date, Timestamp}
 import java.util.concurrent.Executors
-import java.util.{ArrayList => JArrayList, Map => JMap}
+import java.util.{ArrayList => JArrayList, Map => JMap, UUID}
 
 import org.apache.commons.logging.Log
 import org.apache.hadoop.hive.conf.HiveConf
@@ -190,9 +190,12 @@ private[hive] class SparkExecuteStatementOperation(
   }
 
   def run(): Unit = {
+    val statementId = UUID.randomUUID().toString
     logInfo(s"Running query '$statement'")
     setState(OperationState.RUNNING)
-    hiveContext.sparkContext.setJobDescription(statement)
+    HiveThriftServer2.listener.onStatementStart(
+      statementId, parentSession.getSessionHandle.getSessionId.toString, statement, statementId)
+    hiveContext.sparkContext.setJobGroup(statementId, statement)
     sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool =>
       hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool)
     }
@@ -205,6 +208,7 @@ private[hive] class SparkExecuteStatementOperation(
           logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.")
         case _ =>
       }
+      HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString())
       iter = {
         val useIncrementalCollect =
           hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean
@@ -221,10 +225,13 @@ private[hive] class SparkExecuteStatementOperation(
       // HiveServer will silently swallow them.
       case e: Throwable =>
         setState(OperationState.ERROR)
+        HiveThriftServer2.listener.onStatementError(
+          statementId, e.getMessage, e.getStackTraceString)
         logError("Error executing query:",e)
         throw new HiveSQLException(e.toString)
     }
     setState(OperationState.FINISHED)
+    HiveThriftServer2.listener.onStatementFinish(statementId)
   }
 }
 
@@ -255,11 +262,14 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
       withImpersonation: Boolean,
       delegationToken: String): SessionHandle = {
     hiveContext.openSession()
-
-    super.openSession(username, passwd, sessionConf, withImpersonation, delegationToken)
+    val sessionHandle = super.openSession(
+      username, passwd, sessionConf, withImpersonation, delegationToken)
+    HiveThriftServer2.listener.onSessionCreated("UNKNOWN", sessionHandle.getSessionId.toString)
+    sessionHandle
   }
 
   override def closeSession(sessionHandle: SessionHandle) {
+    HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString)
     super.closeSession(sessionHandle)
     sparkSqlOperationManager.sessionToActivePool -= sessionHandle
 
diff --git a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala
index 178eb1af7cdcd..b9d4f1c58c982 100644
--- a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala
+++ b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver
 
 import java.sql.{Date, Timestamp}
 import java.util.concurrent.Executors
-import java.util.{ArrayList => JArrayList, List => JList, Map => JMap}
+import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, UUID}
 
 import org.apache.commons.logging.Log
 import org.apache.hadoop.hive.conf.HiveConf
@@ -36,7 +36,7 @@ import org.apache.hive.service.cli._
 import org.apache.hive.service.cli.operation.ExecuteStatementOperation
 import org.apache.hive.service.cli.session.{SessionManager, HiveSession}
 
-import org.apache.spark.Logging
+import org.apache.spark.{SparkContext, Logging}
 import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf}
 import org.apache.spark.sql.execution.SetCommand
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
@@ -161,9 +161,16 @@ private[hive] class SparkExecuteStatementOperation(
   }
 
   def run(): Unit = {
+    val statementId = UUID.randomUUID().toString
     logInfo(s"Running query '$statement'")
     setState(OperationState.RUNNING)
-    hiveContext.sparkContext.setJobDescription(statement)
+    HiveThriftServer2.listener.onStatementStart(
+      statementId,
+      parentSession.getSessionHandle.getSessionId.toString,
+      statement,
+      statementId,
+      parentSession.getUsername)
+    hiveContext.sparkContext.setJobGroup(statementId, statement)
     sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool =>
       hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool)
     }
@@ -176,6 +183,7 @@ private[hive] class SparkExecuteStatementOperation(
           logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.")
         case _ =>
       }
+      HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString())
       iter = {
         val useIncrementalCollect =
           hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean
@@ -192,10 +200,13 @@ private[hive] class SparkExecuteStatementOperation(
       // HiveServer will silently swallow them.
       case e: Throwable =>
         setState(OperationState.ERROR)
+        HiveThriftServer2.listener.onStatementError(
+          statementId, e.getMessage, e.getStackTraceString)
         logError("Error executing query:", e)
         throw new HiveSQLException(e.toString)
     }
     setState(OperationState.FINISHED)
+    HiveThriftServer2.listener.onStatementFinish(statementId)
   }
 }
 
@@ -227,11 +238,16 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
       withImpersonation: Boolean,
       delegationToken: String): SessionHandle = {
     hiveContext.openSession()
-
-    super.openSession(protocol, username, passwd, sessionConf, withImpersonation, delegationToken)
+    val sessionHandle = super.openSession(
+      protocol, username, passwd, sessionConf, withImpersonation, delegationToken)
+    val session = super.getSession(sessionHandle)
+    HiveThriftServer2.listener.onSessionCreated(
+      session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername)
+    sessionHandle
   }
 
   override def closeSession(sessionHandle: SessionHandle) {
+    HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString)
     super.closeSession(sessionHandle)
     sparkSqlOperationManager.sessionToActivePool -= sessionHandle
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
new file mode 100644
index 0000000000000..a863aa77cb7e0
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -0,0 +1,149 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException}
+
+case class HiveDatabase(
+    name: String,
+    location: String)
+
+abstract class TableType { val name: String }
+case object ExternalTable extends TableType { override val name = "EXTERNAL_TABLE" }
+case object IndexTable extends TableType { override val name = "INDEX_TABLE" }
+case object ManagedTable extends TableType { override val name = "MANAGED_TABLE" }
+case object VirtualView extends TableType { override val name = "VIRTUAL_VIEW" }
+
+case class HiveStorageDescriptor(
+    location: String,
+    inputFormat: String,
+    outputFormat: String,
+    serde: String)
+
+case class HivePartition(
+    values: Seq[String],
+    storage: HiveStorageDescriptor)
+
+case class HiveColumn(name: String, hiveType: String, comment: String)
+case class HiveTable(
+    specifiedDatabase: Option[String],
+    name: String,
+    schema: Seq[HiveColumn],
+    partitionColumns: Seq[HiveColumn],
+    properties: Map[String, String],
+    serdeProperties: Map[String, String],
+    tableType: TableType,
+    location: Option[String] = None,
+    inputFormat: Option[String] = None,
+    outputFormat: Option[String] = None,
+    serde: Option[String] = None) {
+
+  @transient
+  private[client] var client: ClientInterface = _
+
+  private[client] def withClient(ci: ClientInterface): this.type = {
+    client = ci
+    this
+  }
+
+  def database: String = specifiedDatabase.getOrElse(sys.error("database not resolved"))
+
+  def isPartitioned: Boolean = partitionColumns.nonEmpty
+
+  def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this)
+
+  // Hive does not support backticks when passing names to the client.
+  def qualifiedName: String = s"$database.$name"
+}
+
+/**
+ * An externally visible interface to the Hive client.  This interface is shared across both the
+ * internal and external classloaders for a given version of Hive and thus must expose only
+ * shared classes.
+ */
+trait ClientInterface {
+  /**
+   * Runs a HiveQL command using Hive, returning the results as a list of strings.  Each row will
+   * result in one string.
+   */
+  def runSqlHive(sql: String): Seq[String]
+
+  /** Returns the names of all tables in the given database. */
+  def listTables(dbName: String): Seq[String]
+
+  /** Returns the name of the active database. */
+  def currentDatabase: String
+
+  /** Returns the metadata for specified database, throwing an exception if it doesn't exist */
+  def getDatabase(name: String): HiveDatabase = {
+    getDatabaseOption(name).getOrElse(throw new NoSuchDatabaseException)
+  }
+
+  /** Returns the metadata for a given database, or None if it doesn't exist. */
+  def getDatabaseOption(name: String): Option[HiveDatabase]
+
+  /** Returns the specified table, or throws [[NoSuchTableException]]. */
+  def getTable(dbName: String, tableName: String): HiveTable = {
+    getTableOption(dbName, tableName).getOrElse(throw new NoSuchTableException)
+  }
+
+  /** Returns the metadata for the specified table or None if it doens't exist. */
+  def getTableOption(dbName: String, tableName: String): Option[HiveTable]
+
+  /** Creates a table with the given metadata. */
+  def createTable(table: HiveTable): Unit
+
+  /** Updates the given table with new metadata. */
+  def alterTable(table: HiveTable): Unit
+
+  /** Creates a new database with the given name. */
+  def createDatabase(database: HiveDatabase): Unit
+
+  /** Returns all partitions for the given table. */
+  def getAllPartitions(hTable: HiveTable): Seq[HivePartition]
+
+  /** Loads a static partition into an existing table. */
+  def loadPartition(
+      loadPath: String,
+      tableName: String,
+      partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering
+      replace: Boolean,
+      holdDDLTime: Boolean,
+      inheritTableSpecs: Boolean,
+      isSkewedStoreAsSubdir: Boolean): Unit
+
+  /** Loads data into an existing table. */
+  def loadTable(
+      loadPath: String, // TODO URI
+      tableName: String,
+      replace: Boolean,
+      holdDDLTime: Boolean): Unit
+
+  /** Loads new dynamic partitions into an existing table. */
+  def loadDynamicPartitions(
+      loadPath: String,
+      tableName: String,
+      partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering
+      replace: Boolean,
+      numDP: Int,
+      holdDDLTime: Boolean,
+      listBucketingEnabled: Boolean): Unit
+
+  /** Used for testing only.  Removes all metadata from this instance of Hive. */
+  def reset(): Unit
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
new file mode 100644
index 0000000000000..ea52fea037f1f
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -0,0 +1,395 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import java.io.{BufferedReader, InputStreamReader, File, PrintStream}
+import java.net.URI
+import java.util.{ArrayList => JArrayList}
+
+import scala.collection.JavaConversions._
+import scala.language.reflectiveCalls
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.hive.metastore.api.Database
+import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.metastore.api
+import org.apache.hadoop.hive.metastore.api.FieldSchema
+import org.apache.hadoop.hive.ql.metadata
+import org.apache.hadoop.hive.ql.metadata.Hive
+import org.apache.hadoop.hive.ql.session.SessionState
+import org.apache.hadoop.hive.ql.processors._
+import org.apache.hadoop.hive.ql.Driver
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.execution.QueryExecutionException
+
+
+/**
+ * A class that wraps the HiveClient and converts its responses to externally visible classes.
+ * Note that this class is typically loaded with an internal classloader for each instantiation,
+ * allowing it to interact directly with a specific isolated version of Hive.  Loading this class
+ * with the isolated classloader however will result in it only being visible as a ClientInterface,
+ * not a ClientWrapper.
+ *
+ * This class needs to interact with multiple versions of Hive, but will always be compiled with
+ * the 'native', execution version of Hive.  Therefore, any places where hive breaks compatibility
+ * must use reflection after matching on `version`.
+ *
+ * @param version the version of hive used when pick function calls that are not compatible.
+ * @param config  a collection of configuration options that will be added to the hive conf before
+ *                opening the hive client.
+ */
+class ClientWrapper(
+    version: HiveVersion,
+    config: Map[String, String])
+  extends ClientInterface
+  with Logging
+  with ReflectionMagic {
+
+  private val conf = new HiveConf(classOf[SessionState])
+  config.foreach { case (k, v) =>
+    logDebug(s"Hive Config: $k=$v")
+    conf.set(k, v)
+  }
+
+  // Circular buffer to hold what hive prints to STDOUT and ERR.  Only printed when failures occur.
+  private val outputBuffer = new java.io.OutputStream {
+    var pos: Int = 0
+    var buffer = new Array[Int](10240)
+    def write(i: Int): Unit = {
+      buffer(pos) = i
+      pos = (pos + 1) % buffer.size
+    }
+
+    override def toString: String = {
+      val (end, start) = buffer.splitAt(pos)
+      val input = new java.io.InputStream {
+        val iterator = (start ++ end).iterator
+
+        def read(): Int = if (iterator.hasNext) iterator.next() else -1
+      }
+      val reader = new BufferedReader(new InputStreamReader(input))
+      val stringBuilder = new StringBuilder
+      var line = reader.readLine()
+      while(line != null) {
+        stringBuilder.append(line)
+        stringBuilder.append("\n")
+        line = reader.readLine()
+      }
+      stringBuilder.toString()
+    }
+  }
+
+  val state = {
+    val original = Thread.currentThread().getContextClassLoader
+    Thread.currentThread().setContextClassLoader(getClass.getClassLoader)
+    val ret = try {
+      val newState = new SessionState(conf)
+      SessionState.start(newState)
+      newState.out = new PrintStream(outputBuffer, true, "UTF-8")
+      newState.err = new PrintStream(outputBuffer, true, "UTF-8")
+      newState
+    } finally {
+      Thread.currentThread().setContextClassLoader(original)
+    }
+    ret
+  }
+
+  private val client = Hive.get(conf)
+
+  /**
+   * Runs `f` with ThreadLocal session state and classloaders configured for this version of hive.
+   */
+  private def withHiveState[A](f: => A): A = synchronized {
+    val original = Thread.currentThread().getContextClassLoader
+    Thread.currentThread().setContextClassLoader(getClass.getClassLoader)
+    Hive.set(client)
+    version match {
+      case hive.v12 =>
+        classOf[SessionState]
+          .callStatic[SessionState, SessionState]("start", state)
+      case hive.v13 =>
+        classOf[SessionState]
+          .callStatic[SessionState, SessionState]("setCurrentSessionState", state)
+    }
+    val ret = try f finally {
+      Thread.currentThread().setContextClassLoader(original)
+    }
+    ret
+  }
+
+  override def currentDatabase: String = withHiveState {
+    state.getCurrentDatabase
+  }
+
+  override def createDatabase(database: HiveDatabase): Unit = withHiveState {
+    client.createDatabase(
+      new Database(
+        database.name,
+        "",
+        new File(database.location).toURI.toString,
+        new java.util.HashMap),
+        true)
+  }
+
+  override def getDatabaseOption(name: String): Option[HiveDatabase] = withHiveState {
+    Option(client.getDatabase(name)).map { d =>
+      HiveDatabase(
+        name = d.getName,
+        location = d.getLocationUri)
+    }
+  }
+
+  override def getTableOption(
+      dbName: String,
+      tableName: String): Option[HiveTable] = withHiveState {
+
+    logDebug(s"Looking up $dbName.$tableName")
+
+    val hiveTable = Option(client.getTable(dbName, tableName, false))
+    val converted = hiveTable.map { h =>
+
+      HiveTable(
+        name = h.getTableName,
+        specifiedDatabase = Option(h.getDbName),
+        schema = h.getCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
+        partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
+        properties = h.getParameters.toMap,
+        serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap,
+        tableType = ManagedTable, // TODO
+        location = version match {
+          case hive.v12 => Option(h.call[URI]("getDataLocation")).map(_.toString)
+          case hive.v13 => Option(h.call[Path]("getDataLocation")).map(_.toString)
+        },
+        inputFormat = Option(h.getInputFormatClass).map(_.getName),
+        outputFormat = Option(h.getOutputFormatClass).map(_.getName),
+        serde = Option(h.getSerializationLib)).withClient(this)
+    }
+    converted
+  }
+
+  private def toInputFormat(name: String) =
+    Class.forName(name).asInstanceOf[Class[_ <: org.apache.hadoop.mapred.InputFormat[_, _]]]
+
+  private def toOutputFormat(name: String) =
+    Class.forName(name)
+      .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]]
+
+  private def toQlTable(table: HiveTable): metadata.Table = {
+    val qlTable = new metadata.Table(table.database, table.name)
+
+    qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    qlTable.setPartCols(
+      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) }
+    table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) }
+    version match {
+      case hive.v12 =>
+        table.location.map(new URI(_)).foreach(u => qlTable.call[URI, Unit]("setDataLocation", u))
+      case hive.v13 =>
+        table.location
+          .map(new org.apache.hadoop.fs.Path(_))
+          .foreach(qlTable.call[Path, Unit]("setDataLocation", _))
+    }
+    table.inputFormat.map(toInputFormat).foreach(qlTable.setInputFormatClass)
+    table.outputFormat.map(toOutputFormat).foreach(qlTable.setOutputFormatClass)
+    table.serde.foreach(qlTable.setSerializationLib)
+
+    qlTable
+  }
+
+  override def createTable(table: HiveTable): Unit = withHiveState {
+    val qlTable = toQlTable(table)
+    client.createTable(qlTable)
+  }
+
+  override def alterTable(table: HiveTable): Unit = withHiveState {
+    val qlTable = toQlTable(table)
+    client.alterTable(table.qualifiedName, qlTable)
+  }
+
+  override def getAllPartitions(hTable: HiveTable): Seq[HivePartition] = withHiveState {
+    val qlTable = toQlTable(hTable)
+    val qlPartitions = version match {
+      case hive.v12 =>
+        client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsForPruner", qlTable)
+      case hive.v13 =>
+        client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsOf", qlTable)
+    }
+    qlPartitions.map(_.getTPartition).map { p =>
+      HivePartition(
+        values = Option(p.getValues).map(_.toSeq).getOrElse(Seq.empty),
+        storage = HiveStorageDescriptor(
+          location = p.getSd.getLocation,
+          inputFormat = p.getSd.getInputFormat,
+          outputFormat = p.getSd.getOutputFormat,
+          serde = p.getSd.getSerdeInfo.getSerializationLib))
+    }.toSeq
+  }
+
+  override def listTables(dbName: String): Seq[String] = withHiveState {
+    client.getAllTables
+  }
+
+  /**
+   * Runs the specified SQL query using Hive.
+   */
+  override def runSqlHive(sql: String): Seq[String] = {
+    val maxResults = 100000
+    val results = runHive(sql, maxResults)
+    // It is very confusing when you only get back some of the results...
+    if (results.size == maxResults) sys.error("RESULTS POSSIBLY TRUNCATED")
+    results
+  }
+
+  /**
+   * Execute the command using Hive and return the results as a sequence. Each element
+   * in the sequence is one row.
+   */
+  protected def runHive(cmd: String, maxRows: Int = 1000): Seq[String] = withHiveState {
+    logDebug(s"Running hiveql '$cmd'")
+    if (cmd.toLowerCase.startsWith("set")) { logDebug(s"Changing config: $cmd") }
+    try {
+      val cmd_trimmed: String = cmd.trim()
+      val tokens: Array[String] = cmd_trimmed.split("\\s+")
+      val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
+      val proc: CommandProcessor = version match {
+        case hive.v12 =>
+          classOf[CommandProcessorFactory]
+            .callStatic[String, HiveConf, CommandProcessor]("get", cmd_1, conf)
+        case hive.v13 =>
+          classOf[CommandProcessorFactory]
+            .callStatic[Array[String], HiveConf, CommandProcessor]("get", Array(tokens(0)), conf)
+      }
+
+      proc match {
+        case driver: Driver =>
+          val response: CommandProcessorResponse = driver.run(cmd)
+          // Throw an exception if there is an error in query processing.
+          if (response.getResponseCode != 0) {
+            driver.close()
+            throw new QueryExecutionException(response.getErrorMessage)
+          }
+          driver.setMaxRows(maxRows)
+
+          val results = version match {
+            case hive.v12 =>
+              val res = new JArrayList[String]
+              driver.call[JArrayList[String], Boolean]("getResults", res)
+              res.toSeq
+            case hive.v13 =>
+              val res = new JArrayList[Object]
+              driver.call[JArrayList[Object], Boolean]("getResults", res)
+              res.map { r =>
+                r match {
+                  case s: String => s
+                  case a: Array[Object] => a(0).asInstanceOf[String]
+                }
+              }
+          }
+          driver.close()
+          results
+
+        case _ =>
+          if (state.out != null) {
+            state.out.println(tokens(0) + " " + cmd_1)
+          }
+          Seq(proc.run(cmd_1).getResponseCode.toString)
+      }
+    } catch {
+      case e: Exception =>
+        logError(
+          s"""
+            |======================
+            |HIVE FAILURE OUTPUT
+            |======================
+            |${outputBuffer.toString}
+            |======================
+            |END HIVE FAILURE OUTPUT
+            |======================
+          """.stripMargin)
+        throw e
+    }
+  }
+
+  def loadPartition(
+      loadPath: String,
+      tableName: String,
+      partSpec: java.util.LinkedHashMap[String, String],
+      replace: Boolean,
+      holdDDLTime: Boolean,
+      inheritTableSpecs: Boolean,
+      isSkewedStoreAsSubdir: Boolean): Unit = withHiveState {
+
+    client.loadPartition(
+      new Path(loadPath), // TODO: Use URI
+      tableName,
+      partSpec,
+      replace,
+      holdDDLTime,
+      inheritTableSpecs,
+      isSkewedStoreAsSubdir)
+  }
+
+  def loadTable(
+      loadPath: String, // TODO URI
+      tableName: String,
+      replace: Boolean,
+      holdDDLTime: Boolean): Unit = withHiveState {
+    client.loadTable(
+      new Path(loadPath),
+      tableName,
+      replace,
+      holdDDLTime)
+  }
+
+  def loadDynamicPartitions(
+      loadPath: String,
+      tableName: String,
+      partSpec: java.util.LinkedHashMap[String, String],
+      replace: Boolean,
+      numDP: Int,
+      holdDDLTime: Boolean,
+      listBucketingEnabled: Boolean): Unit = withHiveState {
+    client.loadDynamicPartitions(
+      new Path(loadPath),
+      tableName,
+      partSpec,
+      replace,
+      numDP,
+      holdDDLTime,
+      listBucketingEnabled)
+  }
+
+  def reset(): Unit = withHiveState {
+    client.getAllTables("default").foreach { t =>
+        logDebug(s"Deleting table $t")
+        val table = client.getTable("default", t)
+        client.getIndexes("default", t, 255).foreach { index =>
+          client.dropIndex("default", t, index.getIndexName, true)
+        }
+        if (!table.isIndexTable) {
+          client.dropTable("default", t)
+        }
+      }
+      client.getAllDatabases.filterNot(_ == "default").foreach { db =>
+        logDebug(s"Dropping Database: $db")
+        client.dropDatabase(db, true, false, true)
+      }
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
new file mode 100644
index 0000000000000..710dbca6e3c66
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -0,0 +1,172 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import java.io.File
+import java.net.URLClassLoader
+import java.util
+
+import scala.language.reflectiveCalls
+import scala.util.Try
+
+import org.apache.commons.io.{FileUtils, IOUtils}
+
+import org.apache.spark.Logging
+import org.apache.spark.deploy.SparkSubmitUtils
+
+import org.apache.spark.sql.catalyst.util.quietly
+
+/** Factory for `IsolatedClientLoader` with specific versions of hive. */
+object IsolatedClientLoader {
+  /**
+   * Creates isolated Hive client loaders by downloading the requested version from maven.
+   */
+  def forVersion(
+      version: String,
+      config: Map[String, String] = Map.empty): IsolatedClientLoader = synchronized {
+    val resolvedVersion = hiveVersion(version)
+    val files = resolvedVersions.getOrElseUpdate(resolvedVersion, downloadVersion(resolvedVersion))
+    new IsolatedClientLoader(hiveVersion(version), files, config)
+  }
+
+  def hiveVersion(version: String): HiveVersion = version match {
+    case "12" | "0.12" | "0.12.0" => hive.v12
+    case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13
+  }
+
+  private def downloadVersion(version: HiveVersion): Seq[File] = {
+    val hiveArtifacts =
+      (Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") ++
+        (if (version.hasBuiltinsJar) "hive-builtins" :: Nil else Nil))
+        .map(a => s"org.apache.hive:$a:${version.fullVersion}") :+
+        "com.google.guava:guava:14.0.1" :+
+        "org.apache.hadoop:hadoop-client:2.4.0" :+
+        "mysql:mysql-connector-java:5.1.12"
+
+    val classpath = quietly {
+      SparkSubmitUtils.resolveMavenCoordinates(
+        hiveArtifacts.mkString(","),
+        Some("http://www.datanucleus.org/downloads/maven2"),
+        None)
+    }
+    val allFiles = classpath.split(",").map(new File(_)).toSet
+
+    // TODO: Remove copy logic.
+    val tempDir = File.createTempFile("hive", "v" + version.toString)
+    tempDir.delete()
+    tempDir.mkdir()
+
+    allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir))
+    tempDir.listFiles()
+  }
+
+  private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[File]]
+}
+
+/**
+ * Creates a Hive `ClientInterface` using a classloader that works according to the following rules:
+ *  - Shared classes: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader`
+ *    allowing the results of calls to the `ClientInterface` to be visible externally.
+ *  - Hive classes: new instances are loaded from `execJars`.  These classes are not
+ *    accessible externally due to their custom loading.
+ *  - ClientWrapper: a new copy is created for each instance of `IsolatedClassLoader`.
+ *    This new instance is able to see a specific version of hive without using reflection. Since
+ *    this is a unique instance, it is not visible externally other than as a generic
+ *    `ClientInterface`, unless `isolationOn` is set to `false`.
+ *
+ * @param version The version of hive on the classpath.  used to pick specific function signatures
+ *                that are not compatibile accross versions.
+ * @param execJars A collection of jar files that must include hive and hadoop.
+ * @param config   A set of options that will be added to the HiveConf of the constructed client.
+ * @param isolationOn When true, custom versions of barrier classes will be constructed.  Must be
+ *                    true unless loading the version of hive that is on Sparks classloader.
+ * @param rootClassLoader The system root classloader.  Must not know about hive classes.
+ * @param baseClassLoader The spark classloader that is used to load shared classes.
+ *
+ */
+class IsolatedClientLoader(
+    val version: HiveVersion,
+    val execJars: Seq[File] = Seq.empty,
+    val config: Map[String, String] = Map.empty,
+    val isolationOn: Boolean = true,
+    val rootClassLoader: ClassLoader = ClassLoader.getSystemClassLoader.getParent.getParent,
+    val baseClassLoader: ClassLoader = Thread.currentThread().getContextClassLoader)
+  extends Logging {
+
+  // Check to make sure that the root classloader does not know about Hive.
+  assert(Try(baseClassLoader.loadClass("org.apache.hive.HiveConf")).isFailure)
+
+  /** All jars used by the hive specific classloader. */
+  protected def allJars = execJars.map(_.toURI.toURL).toArray
+
+  protected def isSharedClass(name: String): Boolean =
+    name.contains("slf4j") ||
+    name.contains("log4j") ||
+    name.startsWith("org.apache.spark.") ||
+    name.startsWith("scala.") ||
+    name.startsWith("com.google") ||
+    name.startsWith("java.lang.") ||
+    name.startsWith("java.net")
+
+  /** True if `name` refers to a spark class that must see specific version of Hive. */
+  protected def isBarrierClass(name: String): Boolean =
+    name.startsWith("org.apache.spark.sql.hive.execution.PairSerDe") ||
+    name.startsWith(classOf[ClientWrapper].getName) ||
+    name.startsWith(classOf[ReflectionMagic].getName)
+
+  protected def classToPath(name: String): String =
+    name.replaceAll("\\.", "/") + ".class"
+
+  /** The classloader that is used to load an isolated version of Hive. */
+  protected val classLoader: ClassLoader = new URLClassLoader(allJars, rootClassLoader) {
+    override def loadClass(name: String, resolve: Boolean): Class[_] = {
+      val loaded = findLoadedClass(name)
+      if (loaded == null) doLoadClass(name, resolve) else loaded
+    }
+
+    def doLoadClass(name: String, resolve: Boolean): Class[_] = {
+      val classFileName = name.replaceAll("\\.", "/") + ".class"
+      if (isBarrierClass(name) && isolationOn) {
+        val bytes = IOUtils.toByteArray(baseClassLoader.getResourceAsStream(classFileName))
+        logDebug(s"custom defining: $name - ${util.Arrays.hashCode(bytes)}")
+        defineClass(name, bytes, 0, bytes.length)
+      } else if (!isSharedClass(name)) {
+        logDebug(s"hive class: $name - ${getResource(classToPath(name))}")
+        super.loadClass(name, resolve)
+      } else {
+        logDebug(s"shared class: $name")
+        baseClassLoader.loadClass(name)
+      }
+    }
+  }
+
+  // Pre-reflective instantiation setup.
+  logDebug("Initializing the logger to avoid disaster...")
+  Thread.currentThread.setContextClassLoader(classLoader)
+
+  /** The isolated client interface to Hive. */
+  val client: ClientInterface = try {
+    classLoader
+      .loadClass(classOf[ClientWrapper].getName)
+      .getConstructors.head
+      .newInstance(version, config)
+      .asInstanceOf[ClientInterface]
+  } finally {
+    Thread.currentThread.setContextClassLoader(baseClassLoader)
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala
new file mode 100644
index 0000000000000..90d03049356b5
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala
@@ -0,0 +1,200 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import scala.reflect._
+
+/**
+ * Provides implicit functions on any object for calling methods reflectively.
+ */
+protected trait ReflectionMagic {
+    /** code for InstanceMagic
+        println(
+    (1 to 22).map { n =>
+      def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ")
+      val types = repeat(n => s"A$n <: AnyRef : ClassTag")
+      val inArgs = repeat(n => s"a$n: A$n")
+      val erasure = repeat(n => s"classTag[A$n].erasure")
+      val outArgs = repeat(n => s"a$n")
+      s"""|def call[$types, R](name: String, $inArgs): R = {
+         |  clazz.getMethod(name, $erasure).invoke(a, $outArgs).asInstanceOf[R]
+         |}""".stripMargin
+    }.mkString("\n")
+    )
+   */
+
+  // scalastyle:off
+  protected implicit class InstanceMagic(a: Any) {
+    private val clazz = a.getClass
+
+    def call[R](name: String): R = {
+      clazz.getMethod(name).invoke(a).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = {
+      clazz.getMethod(name, classTag[A1].erasure).invoke(a, a1).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(a, a1, a2).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(a, a1, a2, a3).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(a, a1, a2, a3, a4).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(a, a1, a2, a3, a4, a5).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(a, a1, a2, a3, a4, a5, a6).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R]
+    }
+    def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = {
+      clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R]
+    }
+  }
+
+  /** code for StaticMagic
+        println(
+    (1 to 22).map { n =>
+      def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ")
+      val types = repeat(n => s"A$n <: AnyRef : ClassTag")
+      val inArgs = repeat(n => s"a$n: A$n")
+      val erasure = repeat(n => s"classTag[A$n].erasure")
+      val outArgs = repeat(n => s"a$n")
+      s"""|def callStatic[$types, R](name: String, $inArgs): R = {
+         |  c.getDeclaredMethod(name, $erasure).invoke(c, $outArgs).asInstanceOf[R]
+         |}""".stripMargin
+    }.mkString("\n")
+    )
+   */
+
+  protected implicit class StaticMagic(c: Class[_]) {
+    def callStatic[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure).invoke(c, a1).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(c, a1, a2).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(c, a1, a2, a3).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(c, a1, a2, a3, a4).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(c, a1, a2, a3, a4, a5).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(c, a1, a2, a3, a4, a5, a6).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R]
+    }
+    def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = {
+      c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R]
+    }
+  }
+  // scalastyle:on
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
new file mode 100644
index 0000000000000..7db9200d47440
--- /dev/null
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
@@ -0,0 +1,33 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+/** Support for interacting with different versions of the HiveMetastoreClient */
+package object client {
+  private[client] abstract class HiveVersion(val fullVersion: String, val hasBuiltinsJar: Boolean)
+
+  // scalastyle:off
+  private[client] object hive {
+    case object v10 extends HiveVersion("0.10.0", true)
+    case object v11 extends HiveVersion("0.11.0", false)
+    case object v12 extends HiveVersion("0.12.0", false)
+    case object v13 extends HiveVersion("0.13.1", false)
+  }
+  // scalastyle:on
+  
+}
\ No newline at end of file
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
new file mode 100644
index 0000000000000..81e77ba257bf1
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive.client
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.util.Utils
+import org.scalatest.FunSuite
+
+class VersionsSuite extends FunSuite with Logging {
+  val testType = "derby"
+
+  private def buildConf() = {
+    lazy val warehousePath = Utils.createTempDir()
+    lazy val metastorePath = Utils.createTempDir()
+    metastorePath.delete()
+    Map(
+      "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$metastorePath;create=true",
+      "hive.metastore.warehouse.dir" -> warehousePath.toString)
+  }
+
+  test("success sanity check") {
+    val badClient = IsolatedClientLoader.forVersion("13", buildConf()).client
+    val db = new HiveDatabase("default", "")
+    badClient.createDatabase(db)
+  }
+
+  private def getNestedMessages(e: Throwable): String = {
+    var causes = ""
+    var lastException = e
+    while (lastException != null) {
+      causes += lastException.toString + "\n"
+      lastException = lastException.getCause
+    }
+    causes
+  }
+
+  // Its actually pretty easy to mess things up and have all of your tests "pass" by accidentally
+  // connecting to an auto-populated, in-process metastore.  Let's make sure we are getting the
+  // versions right by forcing a known compatibility failure.
+  // TODO: currently only works on mysql where we manually create the schema...
+  ignore("failure sanity check") {
+    val e = intercept[Throwable] {
+      val badClient = quietly { IsolatedClientLoader.forVersion("13", buildConf()).client }
+    }
+    assert(getNestedMessages(e) contains "Unknown column 'A0.OWNER_NAME' in 'field list'")
+  }
+
+  private val versions = Seq("12", "13")
+
+  private var client: ClientInterface = null
+
+  versions.foreach { version =>
+    test(s"$version: listTables") {
+      client = null
+      client = IsolatedClientLoader.forVersion(version, buildConf()).client
+      client.listTables("default")
+    }
+
+    test(s"$version: createDatabase") {
+      val db = HiveDatabase("default", "")
+      client.createDatabase(db)
+    }
+
+    test(s"$version: createTable") {
+      val table =
+        HiveTable(
+          specifiedDatabase = Option("default"),
+          name = "src",
+          schema = Seq(HiveColumn("key", "int", "")),
+          partitionColumns = Seq.empty,
+          properties = Map.empty,
+          serdeProperties = Map.empty,
+          tableType = ManagedTable,
+          location = None,
+          inputFormat =
+            Some(classOf[org.apache.hadoop.mapred.TextInputFormat].getName),
+          outputFormat =
+            Some(classOf[org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat[_, _]].getName),
+          serde =
+            Some(classOf[org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe].getName()))
+
+      client.createTable(table)
+    }
+
+    test(s"$version: getTable") {
+      client.getTable("default", "src")
+    }
+  }
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala
index 175140481e5ae..9c7f698840778 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala
@@ -110,6 +110,10 @@ final private[streaming] class DStreamGraph extends Serializable with Logging {
       .toArray
   }
 
+  def getInputStreamName(streamId: Int): Option[String] = synchronized {
+    inputStreams.find(_.id == streamId).map(_.name)
+  }
+
   def generateJobs(time: Time): Seq[Job] = {
     logDebug("Generating jobs for time " + time)
     val jobs = this.synchronized {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 117cb59fb61c9..b1ad0d42ec8a6 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -527,6 +527,7 @@ class StreamingContext private[streaming] (
     validate()
     sparkContext.setCallSite(DStream.getCreationSite())
     scheduler.start()
+    uiTab.foreach(_.attach())
     state = Started
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
index 83d41f5762444..f1f8a70655996 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala
@@ -553,7 +553,8 @@ abstract class DStream[T: ClassTag] (
     // because the DStream is reachable from the outer object here, and because 
     // DStreams can't be serialized with closures, we can't proactively check 
     // it for serializability and so we pass the optional false to SparkContext.clean
-    transform((r: RDD[T], t: Time) => context.sparkContext.clean(transformFunc(r), false))
+    val cleanedF = context.sparkContext.clean(transformFunc, false)
+    transform((r: RDD[T], t: Time) => cleanedF(r))
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
index e4ad4b509d8d8..9716adb62817c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
@@ -44,6 +44,11 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext)
   /** This is an unique identifier for the input stream. */
   val id = ssc.getNewInputStreamId()
 
+  /**
+   * The name of this InputDStream. By default, it's the class name with its id.
+   */
+  private[streaming] def name: String = s"${getClass.getSimpleName}-$id"
+
   /**
    * Checks whether the 'time' is valid wrt slideDuration for generating RDD.
    * Additionally it also ensures valid times are in strictly increasing order.
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index ba88416ef4009..15d9710d37cd4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -20,11 +20,11 @@ package org.apache.spark.streaming.dstream
 import scala.reflect.ClassTag
 
 import org.apache.spark.rdd.{BlockRDD, RDD}
-import org.apache.spark.storage.{BlockId, StorageLevel}
+import org.apache.spark.storage.BlockId
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
-import org.apache.spark.streaming.receiver.{Receiver, WriteAheadLogBasedStoreResult}
-import org.apache.spark.streaming.scheduler.{InputInfo, ReceivedBlockInfo}
+import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.util.WriteAheadLogUtils
 
 /**
  * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]]
@@ -64,31 +64,30 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
       } else {
         // Otherwise, ask the tracker for all the blocks that have been allocated to this stream
         // for this batch
-        val blockInfos =
-          ssc.scheduler.receiverTracker.getBlocksOfBatch(validTime).get(id).getOrElse(Seq.empty)
-        val blockStoreResults = blockInfos.map { _.blockStoreResult }
-        val blockIds = blockStoreResults.map { _.blockId.asInstanceOf[BlockId] }.toArray
+        val receiverTracker = ssc.scheduler.receiverTracker
+        val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)
+        val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
 
-        // Register the input blocks information into InputInfoTracker
-        val inputInfo = InputInfo(id, blockInfos.map(_.numRecords).sum)
-        ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
+        // Are WAL record handles present with all the blocks
+        val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }
 
-        // Check whether all the results are of the same type
-        val resultTypes = blockStoreResults.map { _.getClass }.distinct
-        if (resultTypes.size > 1) {
-          logWarning("Multiple result types in block information, WAL information will be ignored.")
-        }
-
-        // If all the results are of type WriteAheadLogBasedStoreResult, then create
-        // WriteAheadLogBackedBlockRDD else create simple BlockRDD.
-        if (resultTypes.size == 1 && resultTypes.head == classOf[WriteAheadLogBasedStoreResult]) {
-          val logSegments = blockStoreResults.map {
-            _.asInstanceOf[WriteAheadLogBasedStoreResult].walRecordHandle
-          }.toArray
-          // Since storeInBlockManager = false, the storage level does not matter.
-          new WriteAheadLogBackedBlockRDD[T](ssc.sparkContext,
-            blockIds, logSegments, storeInBlockManager = false, StorageLevel.MEMORY_ONLY_SER)
+        if (areWALRecordHandlesPresent) {
+          // If all the blocks have WAL record handle, then create a WALBackedBlockRDD
+          val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
+          val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray
+          new WriteAheadLogBackedBlockRDD[T](
+            ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)
         } else {
+          // Else, create a BlockRDD. However, if there are some blocks with WAL info but not others
+          // then that is unexpected and log a warning accordingly.
+          if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {
+            if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
+              logError("Some blocks do not have Write Ahead Log information; " +
+                "this is unexpected and data may not be recoverable after driver failures")
+            } else {
+              logWarning("Some blocks have Write Ahead Log information; this is unexpected")
+            }
+          }
           new BlockRDD[T](ssc.sc, blockIds)
         }
       }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index f4c8046e8a1a8..ffce6a4c3c74c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -23,6 +23,8 @@ import java.util.UUID
 import scala.reflect.ClassTag
 import scala.util.control.NonFatal
 
+import org.apache.commons.io.FileUtils
+
 import org.apache.spark._
 import org.apache.spark.rdd.BlockRDD
 import org.apache.spark.storage.{BlockId, StorageLevel}
@@ -31,30 +33,42 @@ import org.apache.spark.streaming.util._
 /**
  * Partition class for [[org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD]].
  * It contains information about the id of the blocks having this partition's data and
- * the segment of the write ahead log that backs the partition.
+ * the corresponding record handle in the write ahead log that backs the partition.
  * @param index index of the partition
  * @param blockId id of the block having the partition data
+ * @param isBlockIdValid Whether the block Ids are valid (i.e., the blocks are present in the Spark
+ *                         executors). If not, then block lookups by the block ids will be skipped.
+ *                         By default, this is an empty array signifying true for all the blocks.
  * @param walRecordHandle Handle of the record in a write ahead log having the partition data
  */
 private[streaming]
 class WriteAheadLogBackedBlockRDDPartition(
     val index: Int,
     val blockId: BlockId,
-    val walRecordHandle: WriteAheadLogRecordHandle)
-  extends Partition
+    val isBlockIdValid: Boolean,
+    val walRecordHandle: WriteAheadLogRecordHandle
+  ) extends Partition
 
 
 /**
  * This class represents a special case of the BlockRDD where the data blocks in
  * the block manager are also backed by data in write ahead logs. For reading
  * the data, this RDD first looks up the blocks by their ids in the block manager.
- * If it does not find them, it looks up the corresponding data in the write ahead log.
+ * If it does not find them, it looks up the WAL using the corresponding record handle.
+ * The lookup of the blocks from the block manager can be skipped by setting the corresponding
+ * element in isBlockIdValid to false. This is a performance optimization which does not affect
+ * correctness, and it can be used in situations where it is known that the block
+ * does not exist in the Spark executors (e.g. after a failed driver is restarted).
+ *
  *
  * @param sc SparkContext
  * @param blockIds Ids of the blocks that contains this RDD's data
  * @param walRecordHandles Record handles in write ahead logs that contain this RDD's data
- * @param storeInBlockManager Whether to store in the block manager after reading
- *                            from the WAL record
+ * @param isBlockIdValid Whether the block Ids are valid (i.e., the blocks are present in the Spark
+ *                         executors). If not, then block lookups by the block ids will be skipped.
+ *                         By default, this is an empty array signifying true for all the blocks.
+ * @param storeInBlockManager Whether to store a block in the block manager
+ *                            after reading it from the WAL
  * @param storageLevel storage level to store when storing in block manager
  *                     (applicable when storeInBlockManager = true)
  */
@@ -63,23 +77,32 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
     @transient sc: SparkContext,
     @transient blockIds: Array[BlockId],
     @transient walRecordHandles: Array[WriteAheadLogRecordHandle],
-    storeInBlockManager: Boolean,
-    storageLevel: StorageLevel)
+    @transient isBlockIdValid: Array[Boolean] = Array.empty,
+    storeInBlockManager: Boolean = false,
+    storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER)
   extends BlockRDD[T](sc, blockIds) {
 
   require(
     blockIds.length == walRecordHandles.length,
-    s"Number of block ids (${blockIds.length}) must be " +
-      s"the same as number of WAL record handles (${walRecordHandles.length}})!")
+    s"Number of block Ids (${blockIds.length}) must be " +
+      s" same as number of WAL record handles (${walRecordHandles.length}})")
+
+  require(
+    isBlockIdValid.isEmpty || isBlockIdValid.length == blockIds.length,
+    s"Number of elements in isBlockIdValid (${isBlockIdValid.length}) must be " +
+      s" same as number of block Ids (${blockIds.length})")
 
   // Hadoop configuration is not serializable, so broadcast it as a serializable.
   @transient private val hadoopConfig = sc.hadoopConfiguration
   private val broadcastedHadoopConf = new SerializableWritable(hadoopConfig)
 
+  override def isValid(): Boolean = true
+
   override def getPartitions: Array[Partition] = {
     assertValid()
-    Array.tabulate(blockIds.size) { i =>
-      new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), walRecordHandles(i))
+    Array.tabulate(blockIds.length) { i =>
+      val isValid = if (isBlockIdValid.length == 0) true else isBlockIdValid(i)
+      new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), isValid, walRecordHandles(i))
     }
   }
 
@@ -94,51 +117,57 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
     val blockManager = SparkEnv.get.blockManager
     val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition]
     val blockId = partition.blockId
-    blockManager.get(blockId) match {
-      case Some(block) => // Data is in Block Manager
-        val iterator = block.data.asInstanceOf[Iterator[T]]
-        logDebug(s"Read partition data of $this from block manager, block $blockId")
-        iterator
-      case None => // Data not found in Block Manager, grab it from write ahead log file
-        var dataRead: ByteBuffer = null
-        var writeAheadLog: WriteAheadLog = null
-        try {
-          // The WriteAheadLogUtils.createLog*** method needs a directory to create a
-          // WriteAheadLog object as the default FileBasedWriteAheadLog needs a directory for
-          // writing log data. However, the directory is not needed if data needs to be read, hence
-          // a dummy path is provided to satisfy the method parameter requirements.
-          // FileBasedWriteAheadLog will not create any file or directory at that path.
-          // FileBasedWriteAheadLog will not create any file or directory at that path. Also,
-          // this dummy directory should not already exist otherwise the WAL will try to recover
-          // past events from the directory and throw errors.
-          val nonExistentDirectory = new File(
-            System.getProperty("java.io.tmpdir"), UUID.randomUUID().toString).getAbsolutePath
-          writeAheadLog = WriteAheadLogUtils.createLogForReceiver(
-            SparkEnv.get.conf, nonExistentDirectory, hadoopConf)
-          dataRead = writeAheadLog.read(partition.walRecordHandle)
-        } catch {
-          case NonFatal(e) =>
-            throw new SparkException(
-              s"Could not read data from write ahead log record ${partition.walRecordHandle}", e)
-        } finally {
-          if (writeAheadLog != null) {
-            writeAheadLog.close()
-            writeAheadLog = null
-          }
-        }
-        if (dataRead == null) {
+
+    def getBlockFromBlockManager(): Option[Iterator[T]] = {
+      blockManager.get(blockId).map(_.data.asInstanceOf[Iterator[T]])
+    }
+
+    def getBlockFromWriteAheadLog(): Iterator[T] = {
+      var dataRead: ByteBuffer = null
+      var writeAheadLog: WriteAheadLog = null
+      try {
+        // The WriteAheadLogUtils.createLog*** method needs a directory to create a
+        // WriteAheadLog object as the default FileBasedWriteAheadLog needs a directory for
+        // writing log data. However, the directory is not needed if data needs to be read, hence
+        // a dummy path is provided to satisfy the method parameter requirements.
+        // FileBasedWriteAheadLog will not create any file or directory at that path.
+        // FileBasedWriteAheadLog will not create any file or directory at that path. Also,
+        // this dummy directory should not already exist otherwise the WAL will try to recover
+        // past events from the directory and throw errors.
+        val nonExistentDirectory = new File(
+          System.getProperty("java.io.tmpdir"), UUID.randomUUID().toString).getAbsolutePath
+        writeAheadLog = WriteAheadLogUtils.createLogForReceiver(
+          SparkEnv.get.conf, nonExistentDirectory, hadoopConf)
+        dataRead = writeAheadLog.read(partition.walRecordHandle)
+      } catch {
+        case NonFatal(e) =>
           throw new SparkException(
-            s"Could not read data from write ahead log record ${partition.walRecordHandle}, " +
-              s"read returned null")
+            s"Could not read data from write ahead log record ${partition.walRecordHandle}", e)
+      } finally {
+        if (writeAheadLog != null) {
+          writeAheadLog.close()
+          writeAheadLog = null
         }
-        logInfo(s"Read partition data of $this from write ahead log, record handle " +
-          partition.walRecordHandle)
-        if (storeInBlockManager) {
-          blockManager.putBytes(blockId, dataRead, storageLevel)
-          logDebug(s"Stored partition data of $this into block manager with level $storageLevel")
-          dataRead.rewind()
-        }
-        blockManager.dataDeserialize(blockId, dataRead).asInstanceOf[Iterator[T]]
+      }
+      if (dataRead == null) {
+        throw new SparkException(
+          s"Could not read data from write ahead log record ${partition.walRecordHandle}, " +
+            s"read returned null")
+      }
+      logInfo(s"Read partition data of $this from write ahead log, record handle " +
+        partition.walRecordHandle)
+      if (storeInBlockManager) {
+        blockManager.putBytes(blockId, dataRead, storageLevel)
+        logDebug(s"Stored partition data of $this into block manager with level $storageLevel")
+        dataRead.rewind()
+      }
+      blockManager.dataDeserialize(blockId, dataRead).asInstanceOf[Iterator[T]]
+    }
+
+    if (partition.isBlockIdValid) {
+      getBlockFromBlockManager().getOrElse { getBlockFromWriteAheadLog() }
+    } else {
+      getBlockFromWriteAheadLog()
     }
   }
 
@@ -149,12 +178,23 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
    */
   override def getPreferredLocations(split: Partition): Seq[String] = {
     val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition]
-    val blockLocations = getBlockIdLocations().get(partition.blockId)
+    val blockLocations = if (partition.isBlockIdValid) {
+      getBlockIdLocations().get(partition.blockId)
+    } else {
+      None
+    }
+
     blockLocations.getOrElse {
       partition.walRecordHandle match {
         case fileSegment: FileBasedWriteAheadLogSegment =>
-          HdfsUtils.getFileSegmentLocations(
-            fileSegment.path, fileSegment.offset, fileSegment.length, hadoopConfig)
+          try {
+            HdfsUtils.getFileSegmentLocations(
+              fileSegment.path, fileSegment.offset, fileSegment.length, hadoopConfig)
+          } catch {
+            case NonFatal(e) =>
+              logError("Error getting WAL file segment locations", e)
+              Seq.empty
+          }
         case _ =>
           Seq.empty
       }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index 93f047b91018f..92938379b9c17 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -146,7 +146,7 @@ private[streaming] class ReceiverSupervisorImpl(
     val blockStoreResult = receivedBlockHandler.storeBlock(blockId, receivedBlock)
     logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms")
 
-    val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult)
+    val blockInfo = ReceivedBlockInfo(streamId, numRecords, metadataOption, blockStoreResult)
     trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo))
     logDebug(s"Reported block $blockId")
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala
index 94beb590f52d6..dc11e84f29965 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockInfo.scala
@@ -17,12 +17,38 @@
 
 package org.apache.spark.streaming.scheduler
 
-import org.apache.spark.streaming.receiver.ReceivedBlockStoreResult
+import org.apache.spark.storage.StreamBlockId
+import org.apache.spark.streaming.receiver.{ReceivedBlockStoreResult, WriteAheadLogBasedStoreResult}
+import org.apache.spark.streaming.util.WriteAheadLogRecordHandle
 
 /** Information about blocks received by the receiver */
 private[streaming] case class ReceivedBlockInfo(
     streamId: Int,
     numRecords: Long,
+    metadataOption: Option[Any],
     blockStoreResult: ReceivedBlockStoreResult
-  )
+  ) {
+
+  @volatile private var _isBlockIdValid = true
+
+  def blockId: StreamBlockId = blockStoreResult.blockId
+
+  def walRecordHandleOption: Option[WriteAheadLogRecordHandle] = {
+    blockStoreResult match {
+      case walStoreResult: WriteAheadLogBasedStoreResult => Some(walStoreResult.walRecordHandle)
+      case _ => None
+    }
+  }
+
+  /** Is the block ID valid, that is, is the block present in the Spark executors. */
+  def isBlockIdValid(): Boolean = _isBlockIdValid
+
+  /**
+   * Set the block ID as invalid. This is useful when it is known that the block is not present
+   * in the Spark executors.
+   */
+  def setBlockIdInvalid(): Unit = {
+    _isBlockIdValid = false
+  }
+}
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
index 14e769a281f51..a9f4147a5f020 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
@@ -45,7 +45,7 @@ private[streaming] case class BatchCleanupEvent(times: Seq[Time])
 private[streaming]
 case class AllocatedBlocks(streamIdToAllocatedBlocks: Map[Int, Seq[ReceivedBlockInfo]]) {
   def getBlocksOfStream(streamId: Int): Seq[ReceivedBlockInfo] = {
-    streamIdToAllocatedBlocks.get(streamId).getOrElse(Seq.empty)
+    streamIdToAllocatedBlocks.getOrElse(streamId, Seq.empty)
   }
 }
 
@@ -63,6 +63,7 @@ private[streaming] class ReceivedBlockTracker(
     hadoopConf: Configuration,
     streamIds: Seq[Int],
     clock: Clock,
+    recoverFromWriteAheadLog: Boolean,
     checkpointDirOption: Option[String])
   extends Logging {
 
@@ -75,7 +76,9 @@ private[streaming] class ReceivedBlockTracker(
   private var lastAllocatedBatchTime: Time = null
 
   // Recover block information from write ahead logs
-  recoverFromWriteAheadLogs()
+  if (recoverFromWriteAheadLog) {
+    recoverPastEvents()
+  }
 
   /** Add received block. This event will get written to the write ahead log (if enabled). */
   def addBlock(receivedBlockInfo: ReceivedBlockInfo): Boolean = synchronized {
@@ -167,10 +170,11 @@ private[streaming] class ReceivedBlockTracker(
    * Recover all the tracker actions from the write ahead logs to recover the state (unallocated
    * and allocated block info) prior to failure.
    */
-  private def recoverFromWriteAheadLogs(): Unit = synchronized {
+  private def recoverPastEvents(): Unit = synchronized {
     // Insert the recovered block information
     def insertAddedBlock(receivedBlockInfo: ReceivedBlockInfo) {
       logTrace(s"Recovery: Inserting added block $receivedBlockInfo")
+      receivedBlockInfo.setBlockIdInvalid()
       getReceivedBlockQueue(receivedBlockInfo.streamId) += receivedBlockInfo
     }
 
@@ -224,19 +228,9 @@ private[streaming] class ReceivedBlockTracker(
 
   /** Optionally create the write ahead log manager only if the feature is enabled */
   private def createWriteAheadLog(): Option[WriteAheadLog] = {
-    if (WriteAheadLogUtils.enableReceiverLog(conf)) {
-      if (checkpointDirOption.isEmpty) {
-        throw new SparkException(
-          "Cannot enable receiver write-ahead log without checkpoint directory set. " +
-            "Please use streamingContext.checkpoint() to set the checkpoint directory. " +
-            "See documentation for more details.")
-      }
+    checkpointDirOption.map { checkpointDir =>
       val logDir = ReceivedBlockTracker.checkpointDirToLogDir(checkpointDirOption.get)
-
-      val log = WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf)
-      Some(log)
-    } else {
-      None
+      WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf)
     }
   }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
index 52f08b9c9de68..de85f24dd988d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala
@@ -32,6 +32,7 @@ case class ReceiverInfo(
     active: Boolean,
     location: String,
     lastErrorMessage: String = "",
-    lastError: String = ""
+    lastError: String = "",
+    lastErrorTime: Long = -1L
    ) {
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 1af65716d3003..f73f7e705ee0d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -62,6 +62,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     ssc.sparkContext.hadoopConfiguration,
     receiverInputStreamIds,
     ssc.scheduler.clock,
+    ssc.isCheckpointPresent,
     Option(ssc.checkpointDir)
   )
   private val listenerBus = ssc.scheduler.listenerBus
@@ -154,10 +155,16 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   private def deregisterReceiver(streamId: Int, message: String, error: String) {
     val newReceiverInfo = receiverInfo.get(streamId) match {
       case Some(oldInfo) =>
-        oldInfo.copy(endpoint = null, active = false, lastErrorMessage = message, lastError = error)
+        val lastErrorTime =
+          if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis()
+        oldInfo.copy(endpoint = null, active = false, lastErrorMessage = message,
+          lastError = error, lastErrorTime = lastErrorTime)
       case None =>
         logWarning("No prior receiver info")
-        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error)
+        val lastErrorTime =
+          if (error == null || error == "") -1 else ssc.scheduler.clock.getTimeMillis()
+        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message,
+          lastError = error, lastErrorTime = lastErrorTime)
     }
     receiverInfo -= streamId
     listenerBus.post(StreamingListenerReceiverStopped(newReceiverInfo))
@@ -181,7 +188,8 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         oldInfo.copy(lastErrorMessage = message, lastError = error)
       case None =>
         logWarning("No prior receiver info")
-        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error)
+        ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message,
+          lastError = error, lastErrorTime = ssc.scheduler.clock.getTimeMillis())
     }
     receiverInfo(streamId) = newReceiverInfo
     listenerBus.post(StreamingListenerReceiverError(receiverInfo(streamId)))
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala
index e219e27785533..2960b528d4c5e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.ui
 
 import scala.xml.Node
 
-import org.apache.spark.ui.UIUtils
+import org.apache.spark.ui.{UIUtils => SparkUIUtils}
 
 private[ui] abstract class BatchTableBase(tableId: String) {
 
@@ -32,12 +32,12 @@ private[ui] abstract class BatchTableBase(tableId: String) {
 
   protected def baseRow(batch: BatchUIData): Seq[Node] = {
     val batchTime = batch.batchTime.milliseconds
-    val formattedBatchTime = UIUtils.formatDate(batch.batchTime.milliseconds)
+    val formattedBatchTime = SparkUIUtils.formatDate(batch.batchTime.milliseconds)
     val eventCount = batch.numRecords
     val schedulingDelay = batch.schedulingDelay
-    val formattedSchedulingDelay = schedulingDelay.map(UIUtils.formatDuration).getOrElse("-")
+    val formattedSchedulingDelay = schedulingDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
     val processingTime = batch.processingDelay
-    val formattedProcessingTime = processingTime.map(UIUtils.formatDuration).getOrElse("-")
+    val formattedProcessingTime = processingTime.map(SparkUIUtils.formatDuration).getOrElse("-")
 
     <td sorttable_customkey={batchTime.toString}>
       <a href={s"batch?id=$batchTime"}>
@@ -107,7 +107,7 @@ private[ui] class CompletedBatchTable(batches: Seq[BatchUIData])
 
   private def completedBatchRow(batch: BatchUIData): Seq[Node] = {
     val totalDelay = batch.totalDelay
-    val formattedTotalDelay = totalDelay.map(UIUtils.formatDuration).getOrElse("-")
+    val formattedTotalDelay = totalDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
     baseRow(batch) ++
       <td sorttable_customkey={totalDelay.getOrElse(Long.MaxValue).toString}>
         {formattedTotalDelay}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
index 2da9a29e2529e..3f1cab69068dc 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
@@ -24,7 +24,7 @@ import scala.xml.{NodeSeq, Node}
 import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.streaming.Time
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.ui.{UIUtils => SparkUIUtils, WebUIPage}
 import org.apache.spark.streaming.ui.StreamingJobProgressListener.{SparkJobId, OutputOpId}
 import org.apache.spark.ui.jobs.UIData.JobUIData
 
@@ -73,8 +73,8 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
       sparkJob.stageIds.sorted.reverse.flatMap(sparkListener.stageIdToInfo.get).
       dropWhile(_.failureReason == None).take(1). // get the first info that contains failure
       flatMap(info => info.failureReason).headOption.getOrElse("")
-    val formattedDuration = duration.map(d => UIUtils.formatDuration(d)).getOrElse("-")
-    val detailUrl = s"${UIUtils.prependBaseUri(parent.basePath)}/jobs/job?id=${sparkJob.jobId}"
+    val formattedDuration = duration.map(d => SparkUIUtils.formatDuration(d)).getOrElse("-")
+    val detailUrl = s"${SparkUIUtils.prependBaseUri(parent.basePath)}/jobs/job?id=${sparkJob.jobId}"
 
     // In the first row, output op id and its information needs to be shown. In other rows, these
     // cells will be taken up due to "rowspan".
@@ -110,7 +110,7 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
       </td>
       <td class="progress-cell">
         {
-          UIUtils.makeProgressBar(
+          SparkUIUtils.makeProgressBar(
             started = sparkJob.numActiveTasks,
             completed = sparkJob.numCompletedTasks,
             failed = sparkJob.numFailedTasks,
@@ -135,7 +135,7 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
         // If any job does not finish, set "formattedOutputOpDuration" to "-"
         "-"
       } else {
-        UIUtils.formatDuration(sparkjobDurations.flatMap(x => x).sum)
+        SparkUIUtils.formatDuration(sparkjobDurations.flatMap(x => x).sum)
       }
     generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, true, sparkJobs.head) ++
       sparkJobs.tail.map { sparkJob =>
@@ -212,24 +212,24 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
     val batchTime = Option(request.getParameter("id")).map(id => Time(id.toLong)).getOrElse {
       throw new IllegalArgumentException(s"Missing id parameter")
     }
-    val formattedBatchTime = UIUtils.formatDate(batchTime.milliseconds)
+    val formattedBatchTime = SparkUIUtils.formatDate(batchTime.milliseconds)
 
     val batchUIData = streamingListener.getBatchUIData(batchTime).getOrElse {
       throw new IllegalArgumentException(s"Batch $formattedBatchTime does not exist")
     }
 
     val formattedSchedulingDelay =
-      batchUIData.schedulingDelay.map(UIUtils.formatDuration).getOrElse("-")
+      batchUIData.schedulingDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
     val formattedProcessingTime =
-      batchUIData.processingDelay.map(UIUtils.formatDuration).getOrElse("-")
-    val formattedTotalDelay = batchUIData.totalDelay.map(UIUtils.formatDuration).getOrElse("-")
+      batchUIData.processingDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
+    val formattedTotalDelay = batchUIData.totalDelay.map(SparkUIUtils.formatDuration).getOrElse("-")
 
     val summary: NodeSeq =
       <div>
         <ul class="unstyled">
           <li>
             <strong>Batch Duration: </strong>
-            {UIUtils.formatDuration(streamingListener.batchDuration)}
+            {SparkUIUtils.formatDuration(streamingListener.batchDuration)}
           </li>
           <li>
             <strong>Input data size: </strong>
@@ -259,6 +259,6 @@ private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
 
     val content = summary ++ jobTable
 
-    UIUtils.headerSparkPage(s"Details of batch at $formattedBatchTime", content, parent)
+    SparkUIUtils.headerSparkPage(s"Details of batch at $formattedBatchTime", content, parent)
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala
index 99e10d2b0be12..a5514dfd71c9f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala
@@ -26,7 +26,7 @@ private[ui] case class OutputOpIdAndSparkJobId(outputOpId: OutputOpId, sparkJobI
 
 private[ui] case class BatchUIData(
     val batchTime: Time,
-    val receiverNumRecords: Map[Int, Long],
+    val streamIdToNumRecords: Map[Int, Long],
     val submissionTime: Long,
     val processingStartTime: Option[Long],
     val processingEndTime: Option[Long],
@@ -58,7 +58,7 @@ private[ui] case class BatchUIData(
   /**
    * The number of recorders received by the receivers in this batch.
    */
-  def numRecords: Long = receiverNumRecords.map(_._2).sum
+  def numRecords: Long = streamIdToNumRecords.values.sum
 }
 
 private[ui] object BatchUIData {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
index d2729fa70d6d2..68e8ce98945e0 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
@@ -29,7 +29,6 @@ import org.apache.spark.streaming.scheduler._
 import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
 import org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted
 import org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted
-import org.apache.spark.util.Distribution
 
 
 private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
@@ -38,7 +37,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
   private val waitingBatchUIData = new HashMap[Time, BatchUIData]
   private val runningBatchUIData = new HashMap[Time, BatchUIData]
   private val completedBatchUIData = new Queue[BatchUIData]
-  private val batchUIDataLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100)
+  private val batchUIDataLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
   private var totalCompletedBatches = 0L
   private var totalReceivedRecords = 0L
   private var totalProcessedRecords = 0L
@@ -145,7 +144,9 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
     }
   }
 
-  def numReceivers: Int = ssc.graph.getReceiverInputStreams().size
+  def numReceivers: Int = synchronized {
+    receiverInfos.size
+  }
 
   def numTotalCompletedBatches: Long = synchronized {
     totalCompletedBatches
@@ -175,39 +176,42 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
     completedBatchUIData.toSeq
   }
 
-  def processingDelayDistribution: Option[Distribution] = synchronized {
-    extractDistribution(_.processingDelay)
+  def streamName(streamId: Int): Option[String] = {
+    ssc.graph.getInputStreamName(streamId)
   }
 
-  def schedulingDelayDistribution: Option[Distribution] = synchronized {
-    extractDistribution(_.schedulingDelay)
-  }
+  /**
+   * Return all InputDStream Ids
+   */
+  def streamIds: Seq[Int] = ssc.graph.getInputStreams().map(_.id)
 
-  def totalDelayDistribution: Option[Distribution] = synchronized {
-    extractDistribution(_.totalDelay)
-  }
-
-  def receivedRecordsDistributions: Map[Int, Option[Distribution]] = synchronized {
-    val latestBatchInfos = retainedBatches.reverse.take(batchUIDataLimit)
-    val latestReceiverNumRecords = latestBatchInfos.map(_.receiverNumRecords)
-    val streamIds = ssc.graph.getInputStreams().map(_.id)
-    streamIds.map { id =>
-     val recordsOfParticularReceiver =
-       latestReceiverNumRecords.map(v => v.getOrElse(id, 0L).toDouble * 1000 / batchDuration)
-      val distribution = Distribution(recordsOfParticularReceiver)
-      (id, distribution)
+  /**
+   * Return all of the event rates for each InputDStream in each batch. The key of the return value
+   * is the stream id, and the value is a sequence of batch time with its event rate.
+   */
+  def receivedEventRateWithBatchTime: Map[Int, Seq[(Long, Double)]] = synchronized {
+    val _retainedBatches = retainedBatches
+    val latestBatches = _retainedBatches.map { batchUIData =>
+      (batchUIData.batchTime.milliseconds, batchUIData.streamIdToNumRecords)
+    }
+    streamIds.map { streamId =>
+      val eventRates = latestBatches.map {
+        case (batchTime, streamIdToNumRecords) =>
+          val numRecords = streamIdToNumRecords.getOrElse(streamId, 0L)
+          (batchTime, numRecords * 1000.0 / batchDuration)
+      }
+      (streamId, eventRates)
     }.toMap
   }
 
   def lastReceivedBatchRecords: Map[Int, Long] = synchronized {
-    val lastReceiverNumRecords = lastReceivedBatch.map(_.receiverNumRecords)
-    val streamIds = ssc.graph.getInputStreams().map(_.id)
-    lastReceiverNumRecords.map { receiverNumRecords =>
-      streamIds.map { id =>
-        (id, receiverNumRecords.getOrElse(id, 0L))
+    val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.streamIdToNumRecords)
+    lastReceivedBlockInfoOption.map { lastReceivedBlockInfo =>
+      streamIds.map { streamId =>
+        (streamId, lastReceivedBlockInfo.getOrElse(streamId, 0L))
       }.toMap
     }.getOrElse {
-      streamIds.map(id => (id, 0L)).toMap
+      streamIds.map(streamId => (streamId, 0L)).toMap
     }
   }
 
@@ -215,10 +219,6 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
     receiverInfos.get(receiverId)
   }
 
-  def receiverIds(): Iterable[Int] = synchronized {
-    receiverInfos.keys
-  }
-
   def lastCompletedBatch: Option[BatchUIData] = synchronized {
     completedBatchUIData.sortBy(_.batchTime)(Time.ordering).lastOption
   }
@@ -227,15 +227,11 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
     retainedBatches.lastOption
   }
 
-  private def retainedBatches: Seq[BatchUIData] = {
+  def retainedBatches: Seq[BatchUIData] = synchronized {
     (waitingBatchUIData.values.toSeq ++
       runningBatchUIData.values.toSeq ++ completedBatchUIData).sortBy(_.batchTime)(Time.ordering)
   }
 
-  private def extractDistribution(getMetric: BatchUIData => Option[Long]): Option[Distribution] = {
-    Distribution(completedBatchUIData.flatMap(getMetric(_)).map(_.toDouble))
-  }
-
   def getBatchUIData(batchTime: Time): Option[BatchUIData] = synchronized {
     val batchUIData = waitingBatchUIData.get(batchTime).orElse {
       runningBatchUIData.get(batchTime).orElse {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
index db37ae815bdf5..ecbebe5c6c97b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
@@ -17,181 +17,454 @@
 
 package org.apache.spark.streaming.ui
 
-import java.util.Calendar
+import java.text.SimpleDateFormat
+import java.util.Date
+import java.util.concurrent.TimeUnit
 import javax.servlet.http.HttpServletRequest
 
-import scala.xml.Node
+import scala.collection.mutable.ArrayBuffer
+import scala.xml.{Node, Unparsed}
 
 import org.apache.spark.Logging
 import org.apache.spark.ui._
-import org.apache.spark.ui.UIUtils._
-import org.apache.spark.util.Distribution
+import org.apache.spark.ui.{UIUtils => SparkUIUtils}
+
+/**
+ * A helper class to generate JavaScript and HTML for both timeline and histogram graphs.
+ *
+ * @param timelineDivId the timeline `id` used in the html `div` tag
+ * @param histogramDivId the timeline `id` used in the html `div` tag
+ * @param data the data for the graph
+ * @param minX the min value of X axis
+ * @param maxX the max value of X axis
+ * @param minY the min value of Y axis
+ * @param maxY the max value of Y axis
+ * @param unitY the unit of Y axis
+ * @param batchInterval if `batchInterval` is not None, we will draw a line for `batchInterval` in
+ *                      the graph
+ */
+private[ui] class GraphUIData(
+    timelineDivId: String,
+    histogramDivId: String,
+    data: Seq[(Long, Double)],
+    minX: Long,
+    maxX: Long,
+    minY: Double,
+    maxY: Double,
+    unitY: String,
+    batchInterval: Option[Double] = None) {
+
+  private var dataJavaScriptName: String = _
+
+  def generateDataJs(jsCollector: JsCollector): Unit = {
+    val jsForData = data.map { case (x, y) =>
+      s"""{"x": $x, "y": $y}"""
+    }.mkString("[", ",", "]")
+    dataJavaScriptName = jsCollector.nextVariableName
+    jsCollector.addPreparedStatement(s"var $dataJavaScriptName = $jsForData;")
+  }
+
+  def generateTimelineHtml(jsCollector: JsCollector): Seq[Node] = {
+    jsCollector.addPreparedStatement(s"registerTimeline($minY, $maxY);")
+    if (batchInterval.isDefined) {
+      jsCollector.addStatement(
+        "drawTimeline(" +
+          s"'#$timelineDivId', $dataJavaScriptName, $minX, $maxX, $minY, $maxY, '$unitY'," +
+          s" ${batchInterval.get}" +
+          ");")
+    } else {
+      jsCollector.addStatement(
+        s"drawTimeline('#$timelineDivId', $dataJavaScriptName, $minX, $maxX, $minY, $maxY," +
+          s" '$unitY');")
+    }
+    <div id={timelineDivId}></div>
+  }
+
+  def generateHistogramHtml(jsCollector: JsCollector): Seq[Node] = {
+    val histogramData = s"$dataJavaScriptName.map(function(d) { return d.y; })"
+    jsCollector.addPreparedStatement(s"registerHistogram($histogramData, $minY, $maxY);")
+    if (batchInterval.isDefined) {
+      jsCollector.addStatement(
+        "drawHistogram(" +
+          s"'#$histogramDivId', $histogramData, $minY, $maxY, '$unitY', ${batchInterval.get}" +
+          ");")
+    } else {
+      jsCollector.addStatement(
+        s"drawHistogram('#$histogramDivId', $histogramData, $minY, $maxY, '$unitY');")
+    }
+    <div id={histogramDivId}></div>
+  }
+}
+
+/**
+ * A helper class for "scheduling delay", "processing time" and "total delay" to generate data that
+ * will be used in the timeline and histogram graphs.
+ *
+ * @param data (batchTime, milliseconds). "milliseconds" is something like "processing time".
+ */
+private[ui] class MillisecondsStatUIData(data: Seq[(Long, Long)]) {
+
+  /**
+   * Converting the original data as per `unit`.
+   */
+  def timelineData(unit: TimeUnit): Seq[(Long, Double)] =
+    data.map(x => x._1 -> UIUtils.convertToTimeUnit(x._2, unit))
+
+  /**
+   * Converting the original data as per `unit`.
+   */
+  def histogramData(unit: TimeUnit): Seq[Double] =
+    data.map(x => UIUtils.convertToTimeUnit(x._2, unit))
+
+  val avg: Option[Long] = if (data.isEmpty) None else Some(data.map(_._2).sum / data.size)
+
+  val formattedAvg: String = StreamingPage.formatDurationOption(avg)
+
+  val max: Option[Long] = if (data.isEmpty) None else Some(data.map(_._2).max)
+}
+
+/**
+ * A helper class for "input rate" to generate data that will be used in the timeline and histogram
+ * graphs.
+ *
+ * @param data (batchTime, event-rate).
+ */
+private[ui] class EventRateUIData(val data: Seq[(Long, Double)]) {
+
+  val avg: Option[Double] = if (data.isEmpty) None else Some(data.map(_._2).sum / data.size)
+
+  val formattedAvg: String = avg.map(_.formatted("%.2f")).getOrElse("-")
+
+  val max: Option[Double] = if (data.isEmpty) None else Some(data.map(_._2).max)
+}
 
 /** Page for Spark Web UI that shows statistics of a streaming job */
 private[ui] class StreamingPage(parent: StreamingTab)
   extends WebUIPage("") with Logging {
 
+  import StreamingPage._
+
   private val listener = parent.listener
   private val startTime = System.currentTimeMillis()
-  private val emptyCell = "-"
 
   /** Render the page */
   def render(request: HttpServletRequest): Seq[Node] = {
-    val content = listener.synchronized {
-      generateBasicStats() ++ <br></br> ++
-      <h4>Statistics over last {listener.retainedCompletedBatches.size} processed batches</h4> ++
-      generateReceiverStats() ++
-      generateBatchStatsTable() ++
-      generateBatchListTables()
-    }
-    UIUtils.headerSparkPage("Streaming", content, parent, Some(5000))
+    val resources = generateLoadResources()
+    val basicInfo = generateBasicInfo()
+    val content = resources ++
+      basicInfo ++
+      listener.synchronized {
+        generateStatTable() ++
+          generateBatchListTables()
+      }
+    SparkUIUtils.headerSparkPage("Streaming Statistics", content, parent, Some(5000))
   }
 
-  /** Generate basic stats of the streaming program */
-  private def generateBasicStats(): Seq[Node] = {
-    val timeSinceStart = System.currentTimeMillis() - startTime
+  /**
+   * Generate html that will load css/js files for StreamingPage
+   */
+  private def generateLoadResources(): Seq[Node] = {
     // scalastyle:off
-    <ul class ="unstyled">
-      <li>
-        <strong>Started at: </strong> {UIUtils.formatDate(startTime)}
-      </li>
-      <li>
-        <strong>Time since start: </strong>{formatDurationVerbose(timeSinceStart)}
-      </li>
-      <li>
-        <strong>Network receivers: </strong>{listener.numReceivers}
-      </li>
-      <li>
-        <strong>Batch interval: </strong>{formatDurationVerbose(listener.batchDuration)}
-      </li>
-      <li>
-        <a href="#completed"><strong>Completed batches: </strong></a>{listener.numTotalCompletedBatches}
-      </li>
-      <li>
-        <a href="#active"><strong>Active batches: </strong></a>{listener.numUnprocessedBatches}
-      </li>
-      <li>
-        <strong>Received events: </strong>{listener.numTotalReceivedRecords}
-      </li>
-      <li>
-        <strong>Processed events: </strong>{listener.numTotalProcessedRecords}
-      </li>
-    </ul>
+    <script src={SparkUIUtils.prependBaseUri("/static/d3.min.js")}></script>
+      <link rel="stylesheet" href={SparkUIUtils.prependBaseUri("/static/streaming-page.css")} type="text/css"/>
+      <script src={SparkUIUtils.prependBaseUri("/static/streaming-page.js")}></script>
     // scalastyle:on
   }
 
-  /** Generate stats of data received by the receivers in the streaming program */
-  private def generateReceiverStats(): Seq[Node] = {
-    val receivedRecordDistributions = listener.receivedRecordsDistributions
-    val lastBatchReceivedRecord = listener.lastReceivedBatchRecords
-    val table = if (receivedRecordDistributions.size > 0) {
-      val headerRow = Seq(
-        "Receiver",
-        "Status",
-        "Location",
-        "Events in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]",
-        "Minimum rate\n[events/sec]",
-        "Median rate\n[events/sec]",
-        "Maximum rate\n[events/sec]",
-        "Last Error"
-      )
-      val dataRows = listener.receiverIds().map { receiverId =>
-        val receiverInfo = listener.receiverInfo(receiverId)
-        val receiverName = receiverInfo.map(_.name).getOrElse(s"Receiver-$receiverId")
-        val receiverActive = receiverInfo.map { info =>
-          if (info.active) "ACTIVE" else "INACTIVE"
-        }.getOrElse(emptyCell)
-        val receiverLocation = receiverInfo.map(_.location).getOrElse(emptyCell)
-        val receiverLastBatchRecords = formatNumber(lastBatchReceivedRecord(receiverId))
-        val receivedRecordStats = receivedRecordDistributions(receiverId).map { d =>
-          d.getQuantiles(Seq(0.0, 0.5, 1.0)).map(r => formatNumber(r.toLong))
-        }.getOrElse {
-          Seq(emptyCell, emptyCell, emptyCell, emptyCell, emptyCell)
-        }
-        val receiverLastError = listener.receiverInfo(receiverId).map { info =>
-          val msg = s"${info.lastErrorMessage} - ${info.lastError}"
-          if (msg.size > 100) msg.take(97) + "..." else msg
-        }.getOrElse(emptyCell)
-        Seq(receiverName, receiverActive, receiverLocation, receiverLastBatchRecords) ++
-          receivedRecordStats ++ Seq(receiverLastError)
-      }.toSeq
-      Some(listingTable(headerRow, dataRows))
-    } else {
-      None
-    }
+  /** Generate basic information of the streaming program */
+  private def generateBasicInfo(): Seq[Node] = {
+    val timeSinceStart = System.currentTimeMillis() - startTime
+    <div>Running batches of
+      <strong>
+        {SparkUIUtils.formatDurationVerbose(listener.batchDuration)}
+      </strong>
+      for
+      <strong>
+        {SparkUIUtils.formatDurationVerbose(timeSinceStart)}
+      </strong>
+      since
+      <strong>
+        {SparkUIUtils.formatDate(startTime)}
+      </strong>
+    </div>
+    <br />
+  }
 
-    val content =
-      <h5>Receiver Statistics</h5> ++
-      <div>{table.getOrElse("No receivers")}</div>
+  /**
+   * Generate a global "timeFormat" dictionary in the JavaScript to store the time and its formatted
+   * string. Because we cannot specify a timezone in JavaScript, to make sure the server and client
+   * use the same timezone, we use the "timeFormat" dictionary to format all time values used in the
+   * graphs.
+   *
+   * @param times all time values that will be used in the graphs.
+   */
+  private def generateTimeMap(times: Seq[Long]): Seq[Node] = {
+    val dateFormat = new SimpleDateFormat("HH:mm:ss")
+    val js = "var timeFormat = {};\n" + times.map { time =>
+      val formattedTime = dateFormat.format(new Date(time))
+      s"timeFormat[$time] = '$formattedTime';"
+    }.mkString("\n")
 
-    content
+    <script>{Unparsed(js)}</script>
   }
 
-  /** Generate stats of batch jobs of the streaming program */
-  private def generateBatchStatsTable(): Seq[Node] = {
-    val numBatches = listener.retainedCompletedBatches.size
-    val lastCompletedBatch = listener.lastCompletedBatch
-    val table = if (numBatches > 0) {
-      val processingDelayQuantilesRow = {
-        Seq(
-          "Processing Time",
-          formatDurationOption(lastCompletedBatch.flatMap(_.processingDelay))
-        ) ++ getQuantiles(listener.processingDelayDistribution)
-      }
-      val schedulingDelayQuantilesRow = {
-        Seq(
-          "Scheduling Delay",
-          formatDurationOption(lastCompletedBatch.flatMap(_.schedulingDelay))
-        ) ++ getQuantiles(listener.schedulingDelayDistribution)
-      }
-      val totalDelayQuantilesRow = {
-        Seq(
-          "Total Delay",
-          formatDurationOption(lastCompletedBatch.flatMap(_.totalDelay))
-        ) ++ getQuantiles(listener.totalDelayDistribution)
-      }
-      val headerRow = Seq("Metric", "Last batch", "Minimum", "25th percentile",
-        "Median", "75th percentile", "Maximum")
-      val dataRows: Seq[Seq[String]] = Seq(
-        processingDelayQuantilesRow,
-        schedulingDelayQuantilesRow,
-        totalDelayQuantilesRow
-      )
-      Some(listingTable(headerRow, dataRows))
-    } else {
-      None
-    }
+  private def generateStatTable(): Seq[Node] = {
+    val batches = listener.retainedBatches
 
-    val content =
-      <h5>Batch Processing Statistics</h5> ++
-      <div>
-        <ul class="unstyled">
-          {table.getOrElse("No statistics have been generated yet.")}
-        </ul>
-      </div>
+    val batchTimes = batches.map(_.batchTime.milliseconds)
+    val minBatchTime = if (batchTimes.isEmpty) startTime else batchTimes.min
+    val maxBatchTime = if (batchTimes.isEmpty) startTime else batchTimes.max
 
-    content
-  }
+    val eventRateForAllStreams = new EventRateUIData(batches.map { batchInfo =>
+      (batchInfo.batchTime.milliseconds, batchInfo.numRecords * 1000.0 / listener.batchDuration)
+    })
 
+    val schedulingDelay = new MillisecondsStatUIData(batches.flatMap { batchInfo =>
+      batchInfo.schedulingDelay.map(batchInfo.batchTime.milliseconds -> _)
+    })
+    val processingTime = new MillisecondsStatUIData(batches.flatMap { batchInfo =>
+      batchInfo.processingDelay.map(batchInfo.batchTime.milliseconds -> _)
+    })
+    val totalDelay = new MillisecondsStatUIData(batches.flatMap { batchInfo =>
+      batchInfo.totalDelay.map(batchInfo.batchTime.milliseconds -> _)
+    })
 
-  /**
-   * Returns a human-readable string representing a duration such as "5 second 35 ms"
-   */
-  private def formatDurationOption(msOption: Option[Long]): String = {
-    msOption.map(formatDurationVerbose).getOrElse(emptyCell)
+    // Use the max value of "schedulingDelay", "processingTime", and "totalDelay" to make the
+    // Y axis ranges same.
+    val _maxTime =
+      (for (m1 <- schedulingDelay.max; m2 <- processingTime.max; m3 <- totalDelay.max) yield
+        m1 max m2 max m3).getOrElse(0L)
+    // Should start at 0
+    val minTime = 0L
+    val (maxTime, normalizedUnit) = UIUtils.normalizeDuration(_maxTime)
+    val formattedUnit = UIUtils.shortTimeUnitString(normalizedUnit)
+
+    // Use the max input rate for all InputDStreams' graphs to make the Y axis ranges same.
+    // If it's not an integral number, just use its ceil integral number.
+    val maxEventRate = eventRateForAllStreams.max.map(_.ceil.toLong).getOrElse(0L)
+    val minEventRate = 0L
+
+    // JavaScript to show/hide the InputDStreams sub table.
+    val triangleJs =
+      s"""$$('#inputs-table').toggle('collapsed');
+         |var status = false;
+         |if ($$(this).html() == '$BLACK_RIGHT_TRIANGLE_HTML') {
+         |$$(this).html('$BLACK_DOWN_TRIANGLE_HTML');status = true;}
+         |else {$$(this).html('$BLACK_RIGHT_TRIANGLE_HTML');status  = false;}
+         |window.history.pushState('',
+         |    document.title, window.location.pathname + '?show-streams-detail=' + status);"""
+        .stripMargin.replaceAll("\\n", "") // it must be only one single line
+
+    val batchInterval = UIUtils.convertToTimeUnit(listener.batchDuration, normalizedUnit)
+
+    val jsCollector = new JsCollector
+
+    val graphUIDataForEventRateOfAllStreams =
+      new GraphUIData(
+        "all-stream-events-timeline",
+        "all-stream-events-histogram",
+        eventRateForAllStreams.data,
+        minBatchTime,
+        maxBatchTime,
+        minEventRate,
+        maxEventRate,
+        "events/sec")
+    graphUIDataForEventRateOfAllStreams.generateDataJs(jsCollector)
+
+    val graphUIDataForSchedulingDelay =
+      new GraphUIData(
+        "scheduling-delay-timeline",
+        "scheduling-delay-histogram",
+        schedulingDelay.timelineData(normalizedUnit),
+        minBatchTime,
+        maxBatchTime,
+        minTime,
+        maxTime,
+        formattedUnit)
+    graphUIDataForSchedulingDelay.generateDataJs(jsCollector)
+
+    val graphUIDataForProcessingTime =
+      new GraphUIData(
+        "processing-time-timeline",
+        "processing-time-histogram",
+        processingTime.timelineData(normalizedUnit),
+        minBatchTime,
+        maxBatchTime,
+        minTime,
+        maxTime,
+        formattedUnit, Some(batchInterval))
+    graphUIDataForProcessingTime.generateDataJs(jsCollector)
+
+    val graphUIDataForTotalDelay =
+      new GraphUIData(
+        "total-delay-timeline",
+        "total-delay-histogram",
+        totalDelay.timelineData(normalizedUnit),
+        minBatchTime,
+        maxBatchTime,
+        minTime,
+        maxTime,
+        formattedUnit)
+    graphUIDataForTotalDelay.generateDataJs(jsCollector)
+
+    // It's false before the user registers the first InputDStream
+    val hasStream = listener.streamIds.nonEmpty
+
+    val numCompletedBatches = listener.retainedCompletedBatches.size
+    val numActiveBatches = batchTimes.length - numCompletedBatches
+    val table =
+      // scalastyle:off
+      <table id="stat-table" class="table table-bordered" style="width: auto">
+      <thead>
+        <tr>
+          <th style="width: 160px;"></th>
+          <th style="width: 492px;">Timelines (Last {batchTimes.length} batches, {numActiveBatches} active, {numCompletedBatches} completed)</th>
+          <th style="width: 300px;">Histograms</th></tr>
+      </thead>
+      <tbody>
+        <tr>
+          <td style="vertical-align: middle;">
+            <div style="width: 160px;">
+              <div>
+              {if (hasStream) {
+                <span id="triangle" onclick={Unparsed(triangleJs)}>{Unparsed(BLACK_RIGHT_TRIANGLE_HTML)}</span>
+              }}
+                <strong>Input Rate</strong>
+              </div>
+              <div>Avg: {eventRateForAllStreams.formattedAvg} events/sec</div>
+            </div>
+          </td>
+          <td class="timeline">{graphUIDataForEventRateOfAllStreams.generateTimelineHtml(jsCollector)}</td>
+          <td class="histogram">{graphUIDataForEventRateOfAllStreams.generateHistogramHtml(jsCollector)}</td>
+        </tr>
+      {if (hasStream) {
+        <tr id="inputs-table" style="display: none;" >
+          <td colspan="3">
+            {generateInputDStreamsTable(jsCollector, minBatchTime, maxBatchTime, minEventRate, maxEventRate)}
+          </td>
+        </tr>
+      }}
+        <tr>
+          <td style="vertical-align: middle;">
+            <div style="width: 160px;">
+              <div><strong>Scheduling Delay</strong></div>
+              <div>Avg: {schedulingDelay.formattedAvg}</div>
+            </div>
+          </td>
+          <td class="timeline">{graphUIDataForSchedulingDelay.generateTimelineHtml(jsCollector)}</td>
+          <td class="histogram">{graphUIDataForSchedulingDelay.generateHistogramHtml(jsCollector)}</td>
+        </tr>
+        <tr>
+          <td style="vertical-align: middle;">
+            <div style="width: 160px;">
+              <div><strong>Processing Time</strong></div>
+              <div>Avg: {processingTime.formattedAvg}</div>
+            </div>
+          </td>
+          <td class="timeline">{graphUIDataForProcessingTime.generateTimelineHtml(jsCollector)}</td>
+          <td class="histogram">{graphUIDataForProcessingTime.generateHistogramHtml(jsCollector)}</td>
+        </tr>
+        <tr>
+          <td style="vertical-align: middle;">
+            <div style="width: 160px;">
+              <div><strong>Total Delay</strong></div>
+              <div>Avg: {totalDelay.formattedAvg}</div>
+            </div>
+          </td>
+          <td class="timeline">{graphUIDataForTotalDelay.generateTimelineHtml(jsCollector)}</td>
+          <td class="histogram">{graphUIDataForTotalDelay.generateHistogramHtml(jsCollector)}</td>
+        </tr>
+      </tbody>
+    </table>
+    // scalastyle:on
+
+    generateTimeMap(batchTimes) ++ table ++ jsCollector.toHtml
   }
 
-  /** Get quantiles for any time distribution */
-  private def getQuantiles(timeDistributionOption: Option[Distribution]) = {
-    timeDistributionOption.get.getQuantiles().map { ms => formatDurationVerbose(ms.toLong) }
+  private def generateInputDStreamsTable(
+      jsCollector: JsCollector,
+      minX: Long,
+      maxX: Long,
+      minY: Double,
+      maxY: Double): Seq[Node] = {
+    val content = listener.receivedEventRateWithBatchTime.map { case (streamId, eventRates) =>
+      generateInputDStreamRow(jsCollector, streamId, eventRates, minX, maxX, minY, maxY)
+    }.foldLeft[Seq[Node]](Nil)(_ ++ _)
+
+    // scalastyle:off
+    <table class="table table-bordered" style="width: auto">
+      <thead>
+        <tr>
+          <th style="width: 151px;"></th>
+          <th style="width: 167px; padding: 8px 0 8px 0"><div style="margin: 0 8px 0 8px">Status</div></th>
+          <th style="width: 167px; padding: 8px 0 8px 0"><div style="margin: 0 8px 0 8px">Location</div></th>
+          <th style="width: 166px; padding: 8px 0 8px 0"><div style="margin: 0 8px 0 8px">Last Error Time</div></th>
+          <th>Last Error Message</th>
+        </tr>
+      </thead>
+      <tbody>
+        {content}
+      </tbody>
+    </table>
+    // scalastyle:on
   }
 
-  /** Generate HTML table from string data */
-  private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = {
-    def generateDataRow(data: Seq[String]): Seq[Node] = {
-      <tr> {data.map(d => <td>{d}</td>)} </tr>
-    }
-    UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true)
+  private def generateInputDStreamRow(
+      jsCollector: JsCollector,
+      streamId: Int,
+      eventRates: Seq[(Long, Double)],
+      minX: Long,
+      maxX: Long,
+      minY: Double,
+      maxY: Double): Seq[Node] = {
+    // If this is a ReceiverInputDStream, we need to show the receiver info. Or we only need the
+    // InputDStream name.
+    val receiverInfo = listener.receiverInfo(streamId)
+    val receiverName = receiverInfo.map(_.name).
+      orElse(listener.streamName(streamId)).getOrElse(s"Stream-$streamId")
+    val receiverActive = receiverInfo.map { info =>
+      if (info.active) "ACTIVE" else "INACTIVE"
+    }.getOrElse(emptyCell)
+    val receiverLocation = receiverInfo.map(_.location).getOrElse(emptyCell)
+    val receiverLastError = receiverInfo.map { info =>
+      val msg = s"${info.lastErrorMessage} - ${info.lastError}"
+      if (msg.size > 100) msg.take(97) + "..." else msg
+    }.getOrElse(emptyCell)
+    val receiverLastErrorTime = receiverInfo.map {
+      r => if (r.lastErrorTime < 0) "-" else SparkUIUtils.formatDate(r.lastErrorTime)
+    }.getOrElse(emptyCell)
+    val receivedRecords = new EventRateUIData(eventRates)
+
+    val graphUIDataForEventRate =
+      new GraphUIData(
+        s"stream-$streamId-events-timeline",
+        s"stream-$streamId-events-histogram",
+        receivedRecords.data,
+        minX,
+        maxX,
+        minY,
+        maxY,
+        "events/sec")
+    graphUIDataForEventRate.generateDataJs(jsCollector)
+
+    <tr>
+      <td rowspan="2" style="vertical-align: middle; width: 151px;">
+        <div style="width: 151px;">
+          <div><strong>{receiverName}</strong></div>
+          <div>Avg: {receivedRecords.formattedAvg} events/sec</div>
+        </div>
+      </td>
+      <td>{receiverActive}</td>
+      <td>{receiverLocation}</td>
+      <td>{receiverLastErrorTime}</td>
+      <td><div style="width: 292px;">{receiverLastError}</div></td>
+    </tr>
+    <tr>
+      <td colspan="3" class="timeline">
+        {graphUIDataForEventRate.generateTimelineHtml(jsCollector)}
+      </td>
+      <td class="histogram">{graphUIDataForEventRate.generateHistogramHtml(jsCollector)}</td>
+    </tr>
   }
 
   private def generateBatchListTables(): Seq[Node] = {
@@ -216,3 +489,67 @@ private[ui] class StreamingPage(parent: StreamingTab)
   }
 }
 
+private[ui] object StreamingPage {
+  val BLACK_RIGHT_TRIANGLE_HTML = "&#9654;"
+  val BLACK_DOWN_TRIANGLE_HTML = "&#9660;"
+
+  val emptyCell = "-"
+
+  /**
+   * Returns a human-readable string representing a duration such as "5 second 35 ms"
+   */
+  def formatDurationOption(msOption: Option[Long]): String = {
+    msOption.map(SparkUIUtils.formatDurationVerbose).getOrElse(emptyCell)
+  }
+
+}
+
+/**
+ * A helper class that allows the user to add JavaScript statements which will be executed when the
+ * DOM has finished loading.
+ */
+private[ui] class JsCollector {
+
+  private var variableId = 0
+
+  /**
+   * Return the next unused JavaScript variable name
+   */
+  def nextVariableName: String = {
+    variableId += 1
+    "v" + variableId
+  }
+
+  /**
+   * JavaScript statements that will execute before `statements`
+   */
+  private val preparedStatements = ArrayBuffer[String]()
+
+  /**
+   * JavaScript statements that will execute after `preparedStatements`
+   */
+  private val statements = ArrayBuffer[String]()
+
+  def addPreparedStatement(js: String): Unit = {
+    preparedStatements += js
+  }
+
+  def addStatement(js: String): Unit = {
+    statements += js
+  }
+
+  /**
+   * Generate a html snippet that will execute all scripts when the DOM has finished loading.
+   */
+  def toHtml: Seq[Node] = {
+    val js =
+      s"""
+         |$$(document).ready(function(){
+         |    ${preparedStatements.mkString("\n")}
+         |    ${statements.mkString("\n")}
+         |});""".stripMargin
+
+   <script>{Unparsed(js)}</script>
+  }
+}
+
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
index e4039639adbad..f307b54bb9630 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala
@@ -37,7 +37,10 @@ private[spark] class StreamingTab(val ssc: StreamingContext)
   ssc.sc.addSparkListener(listener)
   attachPage(new StreamingPage(this))
   attachPage(new BatchPage(this))
-  parent.attachTab(this)
+
+  def attach() {
+    getSparkUI(ssc).attachTab(this)
+  }
 
   def detach() {
     getSparkUI(ssc).detachTab(this)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala
new file mode 100644
index 0000000000000..c206f973b2c66
--- /dev/null
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/UIUtils.scala
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.ui
+
+import java.util.concurrent.TimeUnit
+
+object UIUtils {
+
+  /**
+   * Return the short string for a `TimeUnit`.
+   */
+  def shortTimeUnitString(unit: TimeUnit): String = unit match {
+    case TimeUnit.NANOSECONDS => "ns"
+    case TimeUnit.MICROSECONDS => "us"
+    case TimeUnit.MILLISECONDS => "ms"
+    case TimeUnit.SECONDS => "sec"
+    case TimeUnit.MINUTES => "min"
+    case TimeUnit.HOURS => "hrs"
+    case TimeUnit.DAYS => "days"
+  }
+
+  /**
+   * Find the best `TimeUnit` for converting milliseconds to a friendly string. Return the value
+   * after converting, also with its TimeUnit.
+   */
+  def normalizeDuration(milliseconds: Long): (Double, TimeUnit) = {
+    if (milliseconds < 1000) {
+      return (milliseconds, TimeUnit.MILLISECONDS)
+    }
+    val seconds = milliseconds.toDouble / 1000
+    if (seconds < 60) {
+      return (seconds, TimeUnit.SECONDS)
+    }
+    val minutes = seconds / 60
+    if (minutes < 60) {
+      return (minutes, TimeUnit.MINUTES)
+    }
+    val hours = minutes / 60
+    if (hours < 24) {
+      return (hours, TimeUnit.HOURS)
+    }
+    val days = hours / 24
+    (days, TimeUnit.DAYS)
+  }
+
+  /**
+   * Convert `milliseconds` to the specified `unit`. We cannot use `TimeUnit.convert` because it
+   * will discard the fractional part.
+   */
+  def convertToTimeUnit(milliseconds: Long, unit: TimeUnit): Double =  unit match {
+    case TimeUnit.NANOSECONDS => milliseconds * 1000 * 1000
+    case TimeUnit.MICROSECONDS => milliseconds * 1000
+    case TimeUnit.MILLISECONDS => milliseconds
+    case TimeUnit.SECONDS => milliseconds / 1000.0
+    case TimeUnit.MINUTES => milliseconds / 1000.0 / 60.0
+    case TimeUnit.HOURS => milliseconds / 1000.0 / 60.0 / 60.0
+    case TimeUnit.DAYS => milliseconds / 1000.0 / 60.0 / 60.0 / 24.0
+  }
+}
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala
index 858ba3c9eb4e5..f60688f173c44 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/HdfsUtils.scala
@@ -27,7 +27,7 @@ private[streaming] object HdfsUtils {
     // If the file exists and we have append support, append instead of creating a new file
     val stream: FSDataOutputStream = {
       if (dfs.isFile(dfsPath)) {
-        if (conf.getBoolean("hdfs.append.support", false)) {
+        if (conf.getBoolean("hdfs.append.support", false) || dfs.isInstanceOf[RawLocalFileSystem]) {
           dfs.append(dfsPath)
         } else {
           throw new IllegalStateException("File exists and there is no append support!")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index eb136758249d5..60745325029f6 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -18,9 +18,9 @@
 package org.apache.spark.streaming
 
 import java.io.{File, BufferedWriter, OutputStreamWriter}
-import java.net.{SocketException, ServerSocket}
+import java.net.{Socket, SocketException, ServerSocket}
 import java.nio.charset.Charset
-import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue}
+import java.util.concurrent.{CountDownLatch, Executors, TimeUnit, ArrayBlockingQueue}
 import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer, SynchronizedQueue}
@@ -36,6 +36,7 @@ import org.scalatest.concurrent.Eventually._
 import org.apache.spark.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.scheduler.{StreamingListenerBatchCompleted, StreamingListener}
 import org.apache.spark.util.{ManualClock, Utils}
 import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
 import org.apache.spark.streaming.receiver.Receiver
@@ -43,51 +44,57 @@ import org.apache.spark.streaming.receiver.Receiver
 class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
 
   test("socket input stream") {
-    // Start the server
-    val testServer = new TestServer()
-    testServer.start()
+    withTestServer(new TestServer()) { testServer =>
+      // Start the server
+      testServer.start()
 
-    // Set up the streaming context and input streams
-    val ssc = new StreamingContext(conf, batchDuration)
-    val networkStream = ssc.socketTextStream(
-      "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK)
-    val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
-    val outputStream = new TestOutputStream(networkStream, outputBuffer)
-    def output: ArrayBuffer[String] = outputBuffer.flatMap(x => x)
-    outputStream.register()
-    ssc.start()
-
-    // Feed data to the server to send to the network receiver
-    val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
-    val input = Seq(1, 2, 3, 4, 5)
-    val expectedOutput = input.map(_.toString)
-    Thread.sleep(1000)
-    for (i <- 0 until input.size) {
-      testServer.send(input(i).toString + "\n")
-      Thread.sleep(500)
-      clock.advance(batchDuration.milliseconds)
-    }
-    Thread.sleep(1000)
-    logInfo("Stopping server")
-    testServer.stop()
-    logInfo("Stopping context")
-    ssc.stop()
-
-    // Verify whether data received was as expected
-    logInfo("--------------------------------")
-    logInfo("output.size = " + outputBuffer.size)
-    logInfo("output")
-    outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]"))
-    logInfo("expected output.size = " + expectedOutput.size)
-    logInfo("expected output")
-    expectedOutput.foreach(x => logInfo("[" + x.mkString(",") + "]"))
-    logInfo("--------------------------------")
+      // Set up the streaming context and input streams
+      withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc =>
+        val input = Seq(1, 2, 3, 4, 5)
+        // Use "batchCount" to make sure we check the result after all batches finish
+        val batchCounter = new BatchCounter(ssc)
+        val networkStream = ssc.socketTextStream(
+          "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK)
+        val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]]
+        val outputStream = new TestOutputStream(networkStream, outputBuffer)
+        outputStream.register()
+        ssc.start()
 
-    // Verify whether all the elements received are as expected
-    // (whether the elements were received one in each interval is not verified)
-    assert(output.size === expectedOutput.size)
-    for (i <- 0 until output.size) {
-      assert(output(i) === expectedOutput(i))
+        // Feed data to the server to send to the network receiver
+        val clock = ssc.scheduler.clock.asInstanceOf[ManualClock]
+        val expectedOutput = input.map(_.toString)
+        for (i <- 0 until input.size) {
+          testServer.send(input(i).toString + "\n")
+          Thread.sleep(500)
+          clock.advance(batchDuration.milliseconds)
+        }
+        // Make sure we finish all batches before "stop"
+        if (!batchCounter.waitUntilBatchesCompleted(input.size, 30000)) {
+          fail("Timeout: cannot finish all batches in 30 seconds")
+        }
+        logInfo("Stopping server")
+        testServer.stop()
+        logInfo("Stopping context")
+        ssc.stop()
+
+        // Verify whether data received was as expected
+        logInfo("--------------------------------")
+        logInfo("output.size = " + outputBuffer.size)
+        logInfo("output")
+        outputBuffer.foreach(x => logInfo("[" + x.mkString(",") + "]"))
+        logInfo("expected output.size = " + expectedOutput.size)
+        logInfo("expected output")
+        expectedOutput.foreach(x => logInfo("[" + x.mkString(",") + "]"))
+        logInfo("--------------------------------")
+
+        // Verify whether all the elements received are as expected
+        // (whether the elements were received one in each interval is not verified)
+        val output: ArrayBuffer[String] = outputBuffer.flatMap(x => x)
+        assert(output.size === expectedOutput.size)
+        for (i <- 0 until output.size) {
+          assert(output(i) === expectedOutput(i))
+        }
+      }
     }
   }
 
@@ -368,31 +375,45 @@ class TestServer(portToBind: Int = 0) extends Logging {
 
   val serverSocket = new ServerSocket(portToBind)
 
+  private val startLatch = new CountDownLatch(1)
+
   val servingThread = new Thread() {
     override def run() {
       try {
         while(true) {
           logInfo("Accepting connections on port " + port)
           val clientSocket = serverSocket.accept()
-          logInfo("New connection")
-          try {
-            clientSocket.setTcpNoDelay(true)
-            val outputStream = new BufferedWriter(
-              new OutputStreamWriter(clientSocket.getOutputStream))
-
-            while(clientSocket.isConnected) {
-              val msg = queue.poll(100, TimeUnit.MILLISECONDS)
-              if (msg != null) {
-                outputStream.write(msg)
-                outputStream.flush()
-                logInfo("Message '" + msg + "' sent")
+          if (startLatch.getCount == 1) {
+            // The first connection is a test connection to implement "waitForStart", so skip it
+            // and send a signal
+            if (!clientSocket.isClosed) {
+              clientSocket.close()
+            }
+            startLatch.countDown()
+          } else {
+            // Real connections
+            logInfo("New connection")
+            try {
+              clientSocket.setTcpNoDelay(true)
+              val outputStream = new BufferedWriter(
+                new OutputStreamWriter(clientSocket.getOutputStream))
+
+              while (clientSocket.isConnected) {
+                val msg = queue.poll(100, TimeUnit.MILLISECONDS)
+                if (msg != null) {
+                  outputStream.write(msg)
+                  outputStream.flush()
+                  logInfo("Message '" + msg + "' sent")
+                }
+              }
+            } catch {
+              case e: SocketException => logError("TestServer error", e)
+            } finally {
+              logInfo("Connection closed")
+              if (!clientSocket.isClosed) {
+                clientSocket.close()
               }
             }
-          } catch {
-            case e: SocketException => logError("TestServer error", e)
-          } finally {
-            logInfo("Connection closed")
-            if (!clientSocket.isClosed) clientSocket.close()
           }
         }
       } catch {
@@ -404,7 +425,29 @@ class TestServer(portToBind: Int = 0) extends Logging {
     }
   }
 
-  def start() { servingThread.start() }
+  def start(): Unit = {
+    servingThread.start()
+    if (!waitForStart(10000)) {
+      stop()
+      throw new AssertionError("Timeout: TestServer cannot start in 10 seconds")
+    }
+  }
+
+  /**
+   * Wait until the server starts. Return true if the server starts in "millis" milliseconds.
+   * Otherwise, return false to indicate it's timeout.
+   */
+  private def waitForStart(millis: Long): Boolean = {
+    // We will create a test connection to the server so that we can make sure it has started.
+    val socket = new Socket("localhost", port)
+    try {
+      startLatch.await(millis, TimeUnit.MILLISECONDS)
+    } finally {
+      if (!socket.isClosed) {
+        socket.close()
+      }
+    }
+  }
 
   def send(msg: String) { queue.put(msg) }
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala
index 8317fb9720416..b1af8d5eaacfb 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala
@@ -67,15 +67,20 @@ class ReceivedBlockTrackerSuite
 
     // Verify added blocks are unallocated blocks
     receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual blockInfos
+    receivedBlockTracker.hasUnallocatedReceivedBlocks should be (true)
+
 
     // Allocate the blocks to a batch and verify that all of them have been allocated
     receivedBlockTracker.allocateBlocksToBatch(1)
     receivedBlockTracker.getBlocksOfBatchAndStream(1, streamId) shouldEqual blockInfos
+    receivedBlockTracker.getBlocksOfBatch(1) shouldEqual Map(streamId -> blockInfos)
     receivedBlockTracker.getUnallocatedBlocks(streamId) shouldBe empty
+    receivedBlockTracker.hasUnallocatedReceivedBlocks should be (false)
 
     // Allocate no blocks to another batch
     receivedBlockTracker.allocateBlocksToBatch(2)
     receivedBlockTracker.getBlocksOfBatchAndStream(2, streamId) shouldBe empty
+    receivedBlockTracker.getBlocksOfBatch(2) shouldEqual Map(streamId -> Seq.empty)
 
     // Verify that older batches have no operation on batch allocation,
     // will return the same blocks as previously allocated.
@@ -88,7 +93,7 @@ class ReceivedBlockTrackerSuite
     receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual blockInfos
   }
 
-  test("block addition, block to batch allocation and clean up with write ahead log") {
+  test("recovery and cleanup with write ahead logs") {
     val manualClock = new ManualClock
     // Set the time increment level to twice the rotation interval so that every increment creates
     // a new log file
@@ -114,9 +119,7 @@ class ReceivedBlockTrackerSuite
     }
 
     // Set WAL configuration
-    conf.set("spark.streaming.receiver.writeAheadLog.enable", "true")
     conf.set("spark.streaming.driver.writeAheadLog.rollingIntervalSecs", "1")
-    require(WriteAheadLogUtils.enableReceiverLog(conf))
     require(WriteAheadLogUtils.getRollingIntervalSecs(conf, isDriver = true) === 1)
 
     // Start tracker and add blocks
@@ -131,15 +134,27 @@ class ReceivedBlockTrackerSuite
     getWrittenLogData() shouldEqual expectedWrittenData1
     getWriteAheadLogFiles() should have size 1
 
-    // Restart tracker and verify recovered list of unallocated blocks
     incrementTime()
-    val tracker2 = createTracker(clock = manualClock)
-    tracker2.getUnallocatedBlocks(streamId).toList shouldEqual blockInfos1
+
+    // Recovery without recovery from WAL and verify list of unallocated blocks is empty
+    val tracker1_ = createTracker(clock = manualClock, recoverFromWriteAheadLog = false)
+    tracker1_.getUnallocatedBlocks(streamId) shouldBe empty
+    tracker1_.hasUnallocatedReceivedBlocks should be (false)
+
+    // Restart tracker and verify recovered list of unallocated blocks
+    val tracker2 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true)
+    val unallocatedBlocks = tracker2.getUnallocatedBlocks(streamId).toList
+    unallocatedBlocks shouldEqual blockInfos1
+    unallocatedBlocks.foreach { block =>
+      block.isBlockIdValid() should be (false)
+    }
+
 
     // Allocate blocks to batch and verify whether the unallocated blocks got allocated
     val batchTime1 = manualClock.getTimeMillis()
     tracker2.allocateBlocksToBatch(batchTime1)
     tracker2.getBlocksOfBatchAndStream(batchTime1, streamId) shouldEqual blockInfos1
+    tracker2.getBlocksOfBatch(batchTime1) shouldEqual Map(streamId -> blockInfos1)
 
     // Add more blocks and allocate to another batch
     incrementTime()
@@ -157,7 +172,7 @@ class ReceivedBlockTrackerSuite
 
     // Restart tracker and verify recovered state
     incrementTime()
-    val tracker3 = createTracker(clock = manualClock)
+    val tracker3 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true)
     tracker3.getBlocksOfBatchAndStream(batchTime1, streamId) shouldEqual blockInfos1
     tracker3.getBlocksOfBatchAndStream(batchTime2, streamId) shouldEqual blockInfos2
     tracker3.getUnallocatedBlocks(streamId) shouldBe empty
@@ -180,28 +195,16 @@ class ReceivedBlockTrackerSuite
     // Restart tracker and verify recovered state, specifically whether info about the first
     // batch has been removed, but not the second batch
     incrementTime()
-    val tracker4 = createTracker(clock = manualClock)
+    val tracker4 = createTracker(clock = manualClock, recoverFromWriteAheadLog = true)
     tracker4.getUnallocatedBlocks(streamId) shouldBe empty
     tracker4.getBlocksOfBatchAndStream(batchTime1, streamId) shouldBe empty  // should be cleaned
     tracker4.getBlocksOfBatchAndStream(batchTime2, streamId) shouldEqual blockInfos2
   }
 
-  test("enabling write ahead log but not setting checkpoint dir") {
-    conf.set("spark.streaming.receiver.writeAheadLog.enable", "true")
-    intercept[SparkException] {
-      createTracker(setCheckpointDir = false)
-    }
-  }
-
-  test("setting checkpoint dir but not enabling write ahead log") {
-    // When WAL config is not set, log manager should not be enabled
-    val tracker1 = createTracker(setCheckpointDir = true)
+  test("disable write ahead log when checkpoint directory is not set") {
+    // When checkpoint is disabled, then the write ahead log is disabled
+    val tracker1 = createTracker(setCheckpointDir = false)
     tracker1.isWriteAheadLogEnabled should be (false)
-
-    // When WAL is explicitly disabled, log manager should not be enabled
-    conf.set("spark.streaming.receiver.writeAheadLog.enable", "false")
-    val tracker2 = createTracker(setCheckpointDir = true)
-    tracker2.isWriteAheadLogEnabled should be(false)
   }
 
   /**
@@ -210,16 +213,18 @@ class ReceivedBlockTrackerSuite
    */
   def createTracker(
       setCheckpointDir: Boolean = true,
+      recoverFromWriteAheadLog: Boolean = false,
       clock: Clock = new SystemClock): ReceivedBlockTracker = {
     val cpDirOption = if (setCheckpointDir) Some(checkpointDirectory.toString) else None
-    val tracker = new ReceivedBlockTracker(conf, hadoopConf, Seq(streamId), clock, cpDirOption)
+    val tracker = new ReceivedBlockTracker(
+      conf, hadoopConf, Seq(streamId), clock, recoverFromWriteAheadLog, cpDirOption)
     allReceivedBlockTrackers += tracker
     tracker
   }
 
   /** Generate blocks infos using random ids */
   def generateBlockInfos(): Seq[ReceivedBlockInfo] = {
-    List.fill(5)(ReceivedBlockInfo(streamId, 0,
+    List.fill(5)(ReceivedBlockInfo(streamId, 0, None,
       BlockManagerBasedStoreResult(StreamBlockId(streamId, math.abs(Random.nextInt)))))
   }
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
index 393a360cfe150..5d7127627eea5 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
@@ -256,8 +256,8 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable {
     }
 
     withStreamingContext(new StreamingContext(sparkConf, batchDuration)) { ssc =>
-      val receiver1 = ssc.sparkContext.clean(new FakeReceiver(sendData = true))
-      val receiver2 = ssc.sparkContext.clean(new FakeReceiver(sendData = true))
+      val receiver1 = new FakeReceiver(sendData = true)
+      val receiver2 = new FakeReceiver(sendData = true)
       val receiverStream1 = ssc.receiverStream(receiver1)
       val receiverStream2 = ssc.receiverStream(receiver2)
       receiverStream1.register()
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
index 2ba86aeaf9d51..4d0cd7516f42e 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
@@ -146,6 +146,40 @@ class BatchCounter(ssc: StreamingContext) {
   def getNumStartedBatches: Int = this.synchronized {
     numStartedBatches
   }
+
+  /**
+   * Wait until `expectedNumCompletedBatches` batches are completed, or timeout. Return true if
+   * `expectedNumCompletedBatches` batches are completed. Otherwise, return false to indicate it's
+   * timeout.
+   *
+   * @param expectedNumCompletedBatches the `expectedNumCompletedBatches` batches to wait
+   * @param timeout the maximum time to wait in milliseconds.
+   */
+  def waitUntilBatchesCompleted(expectedNumCompletedBatches: Int, timeout: Long): Boolean =
+    waitUntilConditionBecomeTrue(numCompletedBatches >= expectedNumCompletedBatches, timeout)
+
+  /**
+   * Wait until `expectedNumStartedBatches` batches are completed, or timeout. Return true if
+   * `expectedNumStartedBatches` batches are completed. Otherwise, return false to indicate it's
+   * timeout.
+   *
+   * @param expectedNumStartedBatches the `expectedNumStartedBatches` batches to wait
+   * @param timeout the maximum time to wait in milliseconds.
+   */
+  def waitUntilBatchesStarted(expectedNumStartedBatches: Int, timeout: Long): Boolean =
+    waitUntilConditionBecomeTrue(numStartedBatches >= expectedNumStartedBatches, timeout)
+
+  private def waitUntilConditionBecomeTrue(condition: => Boolean, timeout: Long): Boolean = {
+    synchronized {
+      var now = System.currentTimeMillis()
+      val timeoutTick = now + timeout
+      while (!condition && timeoutTick > now) {
+        wait(timeoutTick - now)
+        now = System.currentTimeMillis()
+      }
+      condition
+    }
+  }
 }
 
 /**
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
index 8de43baabc21d..2211f62383ce8 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
@@ -94,19 +94,34 @@ class UISeleniumSuite
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         // check whether streaming page exists
         go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming")
-        val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq
-        statisticText should contain("Network receivers:")
-        statisticText should contain("Batch interval:")
-
+        val h3Text = findAll(cssSelector("h3")).map(_.text).toSeq
+        h3Text should contain("Streaming Statistics")
+
+        // Check stat table
+        val statTableHeaders = findAll(cssSelector("#stat-table th")).map(_.text).toSeq
+        statTableHeaders.exists(
+          _.matches("Timelines \\(Last \\d+ batches, \\d+ active, \\d+ completed\\)")) should be
+          (true)
+        statTableHeaders should contain ("Histograms")
+
+        val statTableCells = findAll(cssSelector("#stat-table td")).map(_.text).toSeq
+        statTableCells.exists(_.contains("Input Rate")) should be (true)
+        statTableCells.exists(_.contains("Scheduling Delay")) should be (true)
+        statTableCells.exists(_.contains("Processing Time")) should be (true)
+        statTableCells.exists(_.contains("Total Delay")) should be (true)
+
+        // Check batch tables
         val h4Text = findAll(cssSelector("h4")).map(_.text).toSeq
         h4Text.exists(_.matches("Active Batches \\(\\d+\\)")) should be (true)
         h4Text.exists(_.matches("Completed Batches \\(last \\d+ out of \\d+\\)")) should be (true)
 
         findAll(cssSelector("""#active-batches-table th""")).map(_.text).toSeq should be {
-          List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Status")
+          List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time",
+            "Status")
         }
         findAll(cssSelector("""#completed-batches-table th""")).map(_.text).toSeq should be {
-          List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Total Delay")
+          List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time",
+            "Total Delay")
         }
 
         val batchLinks =
@@ -176,9 +191,8 @@ class UISeleniumSuite
 
       eventually(timeout(10 seconds), interval(50 milliseconds)) {
         go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming")
-        val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq
-        statisticText should not contain ("Network receivers:")
-        statisticText should not contain ("Batch interval:")
+        val h3Text = findAll(cssSelector("h3")).map(_.text).toSeq
+        h3Text should not contain("Streaming Statistics")
       }
     }
   }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala
index 8b300d8dd3fbe..6859b65c7165f 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala
@@ -26,7 +26,7 @@ import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite}
 import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBlockId}
 import org.apache.spark.streaming.util.{FileBasedWriteAheadLogSegment, FileBasedWriteAheadLogWriter}
 import org.apache.spark.util.Utils
-import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.{SparkConf, SparkContext, SparkException}
 
 class WriteAheadLogBackedBlockRDDSuite
   extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach {
@@ -60,24 +60,35 @@ class WriteAheadLogBackedBlockRDDSuite
     System.clearProperty("spark.driver.port")
   }
 
-  test("Read data available in block manager and write ahead log") {
-    testRDD(5, 5)
+  test("Read data available in both block manager and write ahead log") {
+    testRDD(numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 5)
   }
 
   test("Read data available only in block manager, not in write ahead log") {
-    testRDD(5, 0)
+    testRDD(numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 0)
   }
 
   test("Read data available only in write ahead log, not in block manager") {
-    testRDD(0, 5)
+    testRDD(numPartitions = 5, numPartitionsInBM = 0, numPartitionsInWAL = 5)
   }
 
-  test("Read data available only in write ahead log, and test storing in block manager") {
-    testRDD(0, 5, testStoreInBM = true)
+  test("Read data with partially available in block manager, and rest in write ahead log") {
+    testRDD(numPartitions = 5, numPartitionsInBM = 3, numPartitionsInWAL = 2)
   }
 
-  test("Read data with partially available in block manager, and rest in write ahead log") {
-    testRDD(3, 2)
+  test("Test isBlockValid skips block fetching from BlockManager") {
+    testRDD(
+      numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 0, testIsBlockValid = true)
+  }
+
+  test("Test whether RDD is valid after removing blocks from block manager") {
+    testRDD(
+      numPartitions = 5, numPartitionsInBM = 5, numPartitionsInWAL = 5, testBlockRemove = true)
+  }
+
+  test("Test storing of blocks recovered from write ahead log back into block manager") {
+    testRDD(
+      numPartitions = 5, numPartitionsInBM = 0, numPartitionsInWAL = 5, testStoreInBM = true)
   }
 
   /**
@@ -85,23 +96,52 @@ class WriteAheadLogBackedBlockRDDSuite
    * and the rest to a write ahead log, and then reading reading it all back using the RDD.
    * It can also test if the partitions that were read from the log were again stored in
    * block manager.
-   * @param numPartitionsInBM Number of partitions to write to the Block Manager
-   * @param numPartitionsInWAL Number of partitions to write to the Write Ahead Log
-   * @param testStoreInBM Test whether blocks read from log are stored back into block manager
+   *
+   *
+   *
+   * @param numPartitions Number of partitions in RDD
+   * @param numPartitionsInBM Number of partitions to write to the BlockManager.
+   *                          Partitions 0 to (numPartitionsInBM-1) will be written to BlockManager
+   * @param numPartitionsInWAL Number of partitions to write to the Write Ahead Log.
+   *                           Partitions (numPartitions - 1 - numPartitionsInWAL) to
+   *                           (numPartitions - 1) will be written to WAL
+   * @param testIsBlockValid Test whether setting isBlockValid to false skips block fetching
+   * @param testBlockRemove Test whether calling rdd.removeBlock() makes the RDD still usable with
+   *                        reads falling back to the WAL
+   * @param testStoreInBM   Test whether blocks read from log are stored back into block manager
+   *
+   * Example with numPartitions = 5, numPartitionsInBM = 3, and numPartitionsInWAL = 4
+   *
+   *   numPartitionsInBM = 3
+   *   |------------------|
+   *   |                  |
+   *    0       1       2       3       4
+   *           |                         |
+   *           |-------------------------|
+   *              numPartitionsInWAL = 4
    */
   private def testRDD(
-      numPartitionsInBM: Int, numPartitionsInWAL: Int, testStoreInBM: Boolean = false) {
-    val numBlocks = numPartitionsInBM + numPartitionsInWAL
-    val data = Seq.fill(numBlocks, 10)(scala.util.Random.nextString(50))
+      numPartitions: Int,
+      numPartitionsInBM: Int,
+      numPartitionsInWAL: Int,
+      testIsBlockValid: Boolean = false,
+      testBlockRemove: Boolean = false,
+      testStoreInBM: Boolean = false
+    ) {
+    require(numPartitionsInBM <= numPartitions,
+      "Can't put more partitions in BlockManager than that in RDD")
+    require(numPartitionsInWAL <= numPartitions,
+      "Can't put more partitions in write ahead log than that in RDD")
+    val data = Seq.fill(numPartitions, 10)(scala.util.Random.nextString(50))
 
     // Put the necessary blocks in the block manager
-    val blockIds = Array.fill(numBlocks)(StreamBlockId(Random.nextInt(), Random.nextInt()))
+    val blockIds = Array.fill(numPartitions)(StreamBlockId(Random.nextInt(), Random.nextInt()))
     data.zip(blockIds).take(numPartitionsInBM).foreach { case(block, blockId) =>
       blockManager.putIterator(blockId, block.iterator, StorageLevel.MEMORY_ONLY_SER)
     }
 
-    // Generate write ahead log file segments
-    val recordHandles = generateFakeRecordHandles(numPartitionsInBM) ++
+    // Generate write ahead log record handles
+    val recordHandles = generateFakeRecordHandles(numPartitions - numPartitionsInWAL) ++
       generateWALRecordHandles(data.takeRight(numPartitionsInWAL),
         blockIds.takeRight(numPartitionsInWAL))
 
@@ -111,7 +151,7 @@ class WriteAheadLogBackedBlockRDDSuite
       "Expected blocks not in BlockManager"
     )
     require(
-      blockIds.takeRight(numPartitionsInWAL).forall(blockManager.get(_).isEmpty),
+      blockIds.takeRight(numPartitions - numPartitionsInBM).forall(blockManager.get(_).isEmpty),
       "Unexpected blocks in BlockManager"
     )
 
@@ -122,19 +162,42 @@ class WriteAheadLogBackedBlockRDDSuite
       "Expected blocks not in write ahead log"
     )
     require(
-      recordHandles.take(numPartitionsInBM).forall(s =>
+      recordHandles.take(numPartitions - numPartitionsInWAL).forall(s =>
         !new File(s.path.stripPrefix("file://")).exists()),
       "Unexpected blocks in write ahead log"
     )
 
     // Create the RDD and verify whether the returned data is correct
     val rdd = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray,
-      recordHandles.toArray, storeInBlockManager = false, StorageLevel.MEMORY_ONLY)
+      recordHandles.toArray, storeInBlockManager = false)
     assert(rdd.collect() === data.flatten)
 
+    // Verify that the block fetching is skipped when isBlockValid is set to false.
+    // This is done by using a RDD whose data is only in memory but is set to skip block fetching
+    // Using that RDD will throw exception, as it skips block fetching even if the blocks are in
+    // in BlockManager.
+    if (testIsBlockValid) {
+      require(numPartitionsInBM === numPartitions, "All partitions must be in BlockManager")
+      require(numPartitionsInWAL === 0, "No partitions must be in WAL")
+      val rdd2 = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray,
+        recordHandles.toArray, isBlockIdValid = Array.fill(blockIds.length)(false))
+      intercept[SparkException] {
+        rdd2.collect()
+      }
+    }
+
+    // Verify that the RDD is not invalid after the blocks are removed and can still read data
+    // from write ahead log
+    if (testBlockRemove) {
+      require(numPartitions === numPartitionsInWAL, "All partitions must be in WAL for this test")
+      require(numPartitionsInBM > 0, "Some partitions must be in BlockManager for this test")
+      rdd.removeBlocks()
+      assert(rdd.collect() === data.flatten)
+    }
+
     if (testStoreInBM) {
       val rdd2 = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray,
-        recordHandles.toArray, storeInBlockManager = true, StorageLevel.MEMORY_ONLY)
+        recordHandles.toArray, storeInBlockManager = true, storageLevel = StorageLevel.MEMORY_ONLY)
       assert(rdd2.collect() === data.flatten)
       assert(
         blockIds.forall(blockManager.get(_).nonEmpty),
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
index e874536e63518..2a0f45830e03c 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala
@@ -94,7 +94,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     batchUIData.get.schedulingDelay should be (batchInfoStarted.schedulingDelay)
     batchUIData.get.processingDelay should be (batchInfoStarted.processingDelay)
     batchUIData.get.totalDelay should be (batchInfoStarted.totalDelay)
-    batchUIData.get.receiverNumRecords should be (Map(0 -> 300L, 1 -> 300L))
+    batchUIData.get.streamIdToNumRecords should be (Map(0 -> 300L, 1 -> 300L))
     batchUIData.get.numRecords should be(600)
     batchUIData.get.outputOpIdSparkJobIdPairs should be
       Seq(OutputOpIdAndSparkJobId(0, 0),
@@ -138,7 +138,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
 
   test("Remove the old completed batches when exceeding the limit") {
     val ssc = setupStreams(input, operation)
-    val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100)
+    val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
     val listener = new StreamingJobProgressListener(ssc)
 
     val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L)
@@ -155,7 +155,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
 
   test("out-of-order onJobStart and onBatchXXX") {
     val ssc = setupStreams(input, operation)
-    val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100)
+    val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
     val listener = new StreamingJobProgressListener(ssc)
 
     // fulfill completedBatchInfos
@@ -182,7 +182,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
     batchUIData.get.schedulingDelay should be (batchInfoSubmitted.schedulingDelay)
     batchUIData.get.processingDelay should be (batchInfoSubmitted.processingDelay)
     batchUIData.get.totalDelay should be (batchInfoSubmitted.totalDelay)
-    batchUIData.get.receiverNumRecords should be (Map.empty)
+    batchUIData.get.streamIdToNumRecords should be (Map.empty)
     batchUIData.get.numRecords should be (0)
     batchUIData.get.outputOpIdSparkJobIdPairs should be (Seq(OutputOpIdAndSparkJobId(0, 0)))
 
@@ -203,4 +203,48 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers {
       (listener.waitingBatches.size + listener.runningBatches.size +
         listener.retainedCompletedBatches.size + 10)
   }
+
+  test("detect memory leak") {
+    val ssc = setupStreams(input, operation)
+    val listener = new StreamingJobProgressListener(ssc)
+
+    val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000)
+
+    for (_ <- 0 until 2 * limit) {
+      val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L)
+
+      // onBatchSubmitted
+      val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, None, None)
+      listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted))
+
+      // onBatchStarted
+      val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+      listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted))
+
+      // onJobStart
+      val jobStart1 = createJobStart(Time(1000), outputOpId = 0, jobId = 0)
+      listener.onJobStart(jobStart1)
+
+      val jobStart2 = createJobStart(Time(1000), outputOpId = 0, jobId = 1)
+      listener.onJobStart(jobStart2)
+
+      val jobStart3 = createJobStart(Time(1000), outputOpId = 1, jobId = 0)
+      listener.onJobStart(jobStart3)
+
+      val jobStart4 = createJobStart(Time(1000), outputOpId = 1, jobId = 1)
+      listener.onJobStart(jobStart4)
+
+      // onBatchCompleted
+      val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None)
+      listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted))
+    }
+
+    listener.waitingBatches.size should be (0)
+    listener.runningBatches.size should be (0)
+    listener.retainedCompletedBatches.size should be (limit)
+    listener.batchTimeToOutputOpIdSparkJobIdPair.size() should be <=
+      (listener.waitingBatches.size + listener.runningBatches.size +
+        listener.retainedCompletedBatches.size + 10)
+  }
+
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala
new file mode 100644
index 0000000000000..6df1a63ab2e37
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/UIUtilsSuite.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.ui
+
+import java.util.concurrent.TimeUnit
+
+import org.scalatest.FunSuite
+import org.scalatest.Matchers
+
+class UIUtilsSuite extends FunSuite with Matchers{
+
+  test("shortTimeUnitString") {
+    assert("ns" === UIUtils.shortTimeUnitString(TimeUnit.NANOSECONDS))
+    assert("us" === UIUtils.shortTimeUnitString(TimeUnit.MICROSECONDS))
+    assert("ms" === UIUtils.shortTimeUnitString(TimeUnit.MILLISECONDS))
+    assert("sec" === UIUtils.shortTimeUnitString(TimeUnit.SECONDS))
+    assert("min" === UIUtils.shortTimeUnitString(TimeUnit.MINUTES))
+    assert("hrs" === UIUtils.shortTimeUnitString(TimeUnit.HOURS))
+    assert("days" === UIUtils.shortTimeUnitString(TimeUnit.DAYS))
+  }
+
+  test("normalizeDuration") {
+    verifyNormalizedTime(900, TimeUnit.MILLISECONDS, 900)
+    verifyNormalizedTime(1.0, TimeUnit.SECONDS, 1000)
+    verifyNormalizedTime(1.0, TimeUnit.MINUTES, 60 * 1000)
+    verifyNormalizedTime(1.0, TimeUnit.HOURS, 60 * 60 * 1000)
+    verifyNormalizedTime(1.0, TimeUnit.DAYS, 24 * 60 * 60 * 1000)
+  }
+
+  private def verifyNormalizedTime(
+      expectedTime: Double, expectedUnit: TimeUnit, input: Long): Unit = {
+    val (time, unit) = UIUtils.normalizeDuration(input)
+    time should be (expectedTime +- 1E-6)
+    unit should be (expectedUnit)
+  }
+
+  test("convertToTimeUnit") {
+    verifyConvertToTimeUnit(60.0 * 1000 * 1000 * 1000, 60 * 1000, TimeUnit.NANOSECONDS)
+    verifyConvertToTimeUnit(60.0 * 1000 * 1000, 60 * 1000, TimeUnit.MICROSECONDS)
+    verifyConvertToTimeUnit(60 * 1000, 60 * 1000, TimeUnit.MILLISECONDS)
+    verifyConvertToTimeUnit(60, 60 * 1000, TimeUnit.SECONDS)
+    verifyConvertToTimeUnit(1, 60 * 1000, TimeUnit.MINUTES)
+    verifyConvertToTimeUnit(1.0 / 60, 60 * 1000, TimeUnit.HOURS)
+    verifyConvertToTimeUnit(1.0 / 60 / 24, 60 * 1000, TimeUnit.DAYS)
+  }
+
+  private def verifyConvertToTimeUnit(
+      expectedTime: Double, milliseconds: Long, unit: TimeUnit): Unit = {
+    val convertedTime = UIUtils.convertToTimeUnit(milliseconds, unit)
+    convertedTime should be (expectedTime +- 1E-6)
+  }
+}
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 4e5ebc402be35..19d6a169fd2ad 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -351,9 +351,9 @@ public int getValueLength() {
      * As an example usage, here's the proper way to store a new key:
      * <p>
      * <pre>
-     *   Location loc = map.lookup(keyBaseOffset, keyBaseObject, keyLengthInBytes);
+     *   Location loc = map.lookup(keyBaseObject, keyBaseOffset, keyLengthInBytes);
      *   if (!loc.isDefined()) {
-     *     loc.putNewKey(keyBaseOffset, keyBaseObject, keyLengthInBytes, ...)
+     *     loc.putNewKey(keyBaseObject, keyBaseOffset, keyLengthInBytes, ...)
      *   }
      * </pre>
      * <p>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index e1694c1f64a9f..29752969e6152 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -285,7 +285,8 @@ private[spark] class ApplicationMaster(
   }
 
   private def runExecutorLauncher(securityMgr: SecurityManager): Unit = {
-    rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, 0, sparkConf, securityMgr)
+    val port = sparkConf.getInt("spark.yarn.am.port", 0)
+    rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, port, sparkConf, securityMgr)
     waitForSparkDriver()
     addAmIpFilter()
     registerAM(sparkConf.get("spark.driver.appUIAddress", ""), securityMgr)