resolving merge conflicts

*fingers crossed*

I admit I’m not exactly sure how this works… Let’s see if I did the
right thing.

LICENSE

@@ -549,3 +549,4 @@ The following components are provided under the MIT License. See project link fo
      (MIT License) pyrolite (org.spark-project:pyrolite:2.0.1 - http://pythonhosted.org/Pyro4/)
      (MIT License) scopt (com.github.scopt:scopt_2.10:3.2.0 - https://github.com/scopt/scopt)
      (The MIT License) Mockito (org.mockito:mockito-all:1.8.5 - http://www.mockito.org)
+     (MIT License) jquery (https://jquery.org/license/)

bin/run-example

@@ -29,7 +29,8 @@ if [ -n "$1" ]; then
 else
   echo "Usage: ./bin/run-example <example-class> [example-args]" 1>&2
   echo "  - set MASTER=XX to use a specific master" 1>&2
-  echo "  - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)" 1>&2
+  echo "  - can use abbreviated example class name relative to com.apache.spark.examples" 1>&2
+  echo "     (e.g. SparkPi, mllib.LinearRegression, streaming.KinesisWordCountASL)" 1>&2
   exit 1
 fi
 

bin/run-example2.cmd

@@ -32,7 +32,8 @@ rem Test that an argument was given
 if not "x%1"=="x" goto arg_given
   echo Usage: run-example ^<example-class^> [example-args]
   echo   - set MASTER=XX to use a specific master
-  echo   - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)
+  echo   - can use abbreviated example class name relative to com.apache.spark.examples
+  echo      (e.g. SparkPi, mllib.LinearRegression, streaming.KinesisWordCountASL)
   goto exit
 :arg_given
 

core/src/main/scala/org/apache/spark/Logging.scala

@@ -39,13 +39,17 @@ trait Logging {
   // be serialized and used on another machine
   @transient private var log_ : Logger = null
 
+  // Method to get the logger name for this object
+  protected def logName = {
+    // Ignore trailing $'s in the class names for Scala objects
+    this.getClass.getName.stripSuffix("$")
+  }
+
   // Method to get or create the logger for this object
   protected def log: Logger = {
     if (log_ == null) {
       initializeIfNecessary()
-      var className = this.getClass.getName
-      // Ignore trailing $'s in the class names for Scala objects
-      log_ = LoggerFactory.getLogger(className.stripSuffix("$"))
+      log_ = LoggerFactory.getLogger(logName)
     }
     log_
   }

core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala

@@ -71,7 +71,7 @@ private[spark] class Worker(
   // TTL for app folders/data;  after TTL expires it will be cleaned up
   val APP_DATA_RETENTION_SECS = conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600)
 
-
+  val testing: Boolean = sys.props.contains("spark.testing")
   val masterLock: Object = new Object()
   var master: ActorSelection = null
   var masterAddress: Address = null
@@ -82,7 +82,12 @@ private[spark] class Worker(
   @volatile var connected = false
   val workerId = generateWorkerId()
   val sparkHome =
-    new File(sys.props.get("spark.test.home").orElse(sys.env.get("SPARK_HOME")).getOrElse("."))
+    if (testing) {
+      assert(sys.props.contains("spark.test.home"), "spark.test.home is not set!")
+      new File(sys.props("spark.test.home"))
+    } else {
+      new File(sys.env.get("SPARK_HOME").getOrElse("."))
+    }
   var workDir: File = null
   val executors = new HashMap[String, ExecutorRunner]
   val finishedExecutors = new HashMap[String, ExecutorRunner]

core/src/main/scala/org/apache/spark/storage/BlockManagerSource.scala

@@ -46,9 +46,8 @@ private[spark] class BlockManagerSource(val blockManager: BlockManager, sc: Spar
   metricRegistry.register(MetricRegistry.name("memory", "memUsed_MB"), new Gauge[Long] {
     override def getValue: Long = {
       val storageStatusList = blockManager.master.getStorageStatus
-      val maxMem = storageStatusList.map(_.maxMem).sum
-      val remainingMem = storageStatusList.map(_.memRemaining).sum
-      (maxMem - remainingMem) / 1024 / 1024
+      val memUsed = storageStatusList.map(_.memUsed).sum
+      memUsed / 1024 / 1024
     }
   })
 

core/src/main/scala/org/apache/spark/storage/StorageUtils.scala

@@ -172,16 +172,13 @@ class StorageStatus(val blockManagerId: BlockManagerId, val maxMem: Long) {
   def memRemaining: Long = maxMem - memUsed
 
   /** Return the memory used by this block manager. */
-  def memUsed: Long =
-    _nonRddStorageInfo._1 + _rddBlocks.keys.toSeq.map(memUsedByRdd).sum
+  def memUsed: Long = _nonRddStorageInfo._1 + _rddBlocks.keys.toSeq.map(memUsedByRdd).sum
 
   /** Return the disk space used by this block manager. */
-  def diskUsed: Long =
-    _nonRddStorageInfo._2 + _rddBlocks.keys.toSeq.map(diskUsedByRdd).sum
+  def diskUsed: Long = _nonRddStorageInfo._2 + _rddBlocks.keys.toSeq.map(diskUsedByRdd).sum
 
   /** Return the off-heap space used by this block manager. */
-  def offHeapUsed: Long =
-    _nonRddStorageInfo._3 + _rddBlocks.keys.toSeq.map(offHeapUsedByRdd).sum
+  def offHeapUsed: Long = _nonRddStorageInfo._3 + _rddBlocks.keys.toSeq.map(offHeapUsedByRdd).sum
 
   /** Return the memory used by the given RDD in this block manager in O(1) time. */
   def memUsedByRdd(rddId: Int): Long = _rddStorageInfo.get(rddId).map(_._1).getOrElse(0L)
@@ -246,7 +243,7 @@ private[spark] object StorageUtils {
       val rddId = rddInfo.id
       // Assume all blocks belonging to the same RDD have the same storage level
       val storageLevel = statuses
-        .map(_.rddStorageLevel(rddId)).flatMap(s => s).headOption.getOrElse(StorageLevel.NONE)
+        .flatMap(_.rddStorageLevel(rddId)).headOption.getOrElse(StorageLevel.NONE)
       val numCachedPartitions = statuses.map(_.numRddBlocksById(rddId)).sum
       val memSize = statuses.map(_.memUsedByRdd(rddId)).sum
       val diskSize = statuses.map(_.diskUsedByRdd(rddId)).sum

core/src/test/scala/org/apache/spark/DriverSuite.scala

@@ -34,7 +34,7 @@ import scala.language.postfixOps
 class DriverSuite extends FunSuite with Timeouts {
 
   test("driver should exit after finishing") {
-    val sparkHome = sys.props("spark.test.home")
+    val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
     // Regression test for SPARK-530: "Spark driver process doesn't exit after finishing"
     val masters = Table(("master"), ("local"), ("local-cluster[2,1,512]"))
     forAll(masters) { (master: String) =>

core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala

@@ -295,7 +295,7 @@ class SparkSubmitSuite extends FunSuite with Matchers {
 
   // NOTE: This is an expensive operation in terms of time (10 seconds+). Use sparingly.
   def runSparkSubmit(args: Seq[String]): String = {
-    val sparkHome = sys.props("spark.test.home")
+    val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
     Utils.executeAndGetOutput(
       Seq("./bin/spark-submit") ++ args,
       new File(sparkHome),

core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala

@@ -27,7 +27,7 @@ import org.apache.spark.SparkConf
 class ExecutorRunnerTest extends FunSuite {
   test("command includes appId") {
     def f(s:String) = new File(s)
-    val sparkHome = sys.props("spark.test.home")
+    val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
     val appDesc = new ApplicationDescription("app name", Some(8), 500,
       Command("foo", Seq(), Map(), Seq(), Seq(), Seq()), "appUiUrl")
     val appId = "12345-worker321-9876"

dev/audit-release/audit_release.py

@@ -105,7 +105,7 @@ def get_url(url):
     "spark-core", "spark-bagel", "spark-mllib", "spark-streaming", "spark-repl",
     "spark-graphx", "spark-streaming-flume", "spark-streaming-kafka",
     "spark-streaming-mqtt", "spark-streaming-twitter", "spark-streaming-zeromq",
-    "spark-catalyst", "spark-sql", "spark-hive"
+    "spark-catalyst", "spark-sql", "spark-hive", "spark-streaming-kinesis-asl"
 ]
 modules = map(lambda m: "%s_%s" % (m, SCALA_BINARY_VERSION), modules)
 
@@ -136,7 +136,7 @@ def ensure_path_not_present(x):
 os.chdir(original_dir)
 
 # SBT application tests
-for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive"]:
+for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive", "sbt_app_kinesis"]:
     os.chdir(app)
     ret = run_cmd("sbt clean run", exit_on_failure=False)
     test(ret == 0, "sbt application (%s)" % app)

dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala

@@ -50,5 +50,12 @@ object SimpleApp {
       println("Ganglia sink was loaded via spark-core")
       System.exit(-1)
     }
+
+    // Remove kinesis from default build due to ASL license issue
+    val foundKinesis = Try(Class.forName("org.apache.spark.streaming.kinesis.KinesisUtils")).isSuccess
+    if (foundKinesis) {
+      println("Kinesis was loaded via spark-core")
+      System.exit(-1)
+    }
   }
 }

dev/audit-release/sbt_app_kinesis/build.sbt

@@ -0,0 +1,28 @@
+//
+// 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.
+//
+
+name := "Kinesis Test"
+
+version := "1.0"
+
+scalaVersion := System.getenv.get("SCALA_VERSION")
+
+libraryDependencies += "org.apache.spark" %% "spark-streaming-kinesis-asl" % System.getenv.get("SPARK_VERSION")
+
+resolvers ++= Seq(
+  "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"),
+  "Spray Repository" at "http://repo.spray.cc/")

dev/audit-release/sbt_app_kinesis/src/main/scala/SparkApp.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 main.scala
+
+import scala.util.Try
+
+import org.apache.spark.SparkContext
+import org.apache.spark.SparkContext._
+
+object SimpleApp {
+  def main(args: Array[String]) {
+    val foundKinesis = Try(Class.forName("org.apache.spark.streaming.kinesis.KinesisUtils")).isSuccess
+    if (!foundKinesis) {
+      println("Kinesis not loaded via kinesis-asl")
+      System.exit(-1)
+    }
+  }
+}

dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala

@@ -38,7 +38,7 @@ object SparkSqlExample {
 
     import sqlContext._
     val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x))
-    people.registerAsTable("people")
+    people.registerTempTable("people")
     val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
     val teenagerNames = teenagers.map(t => "Name: " + t(0)).collect()
     teenagerNames.foreach(println)

dev/create-release/create-release.sh

@@ -53,15 +53,15 @@ if [[ ! "$@" =~ --package-only ]]; then
     -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \
     -Dmaven.javadoc.skip=true \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
     -Dtag=$GIT_TAG -DautoVersionSubmodules=true \
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     --batch-mode release:prepare
 
   mvn -DskipTests \
     -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \
     -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \
     -Dmaven.javadoc.skip=true \
-    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl\
+    -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
     release:perform
 
   cd ..

dev/run-tests

@@ -36,6 +36,9 @@ fi
 if [ -z "$SBT_MAVEN_PROFILES_ARGS" ]; then
   export SBT_MAVEN_PROFILES_ARGS="-Pyarn -Phadoop-2.3 -Dhadoop.version=2.3.0"
 fi
+
+export SBT_MAVEN_PROFILES_ARGS="$SBT_MAVEN_PROFILES_ARGS -Pkinesis-asl"
+
 echo "SBT_MAVEN_PROFILES_ARGS=\"$SBT_MAVEN_PROFILES_ARGS\""
 
 # Remove work directory

docs/sql-programming-guide.md

@@ -142,7 +142,7 @@ case class Person(name: String, age: Int)
 
 // Create an RDD of Person objects and register it as a table.
 val people = sc.textFile("examples/src/main/resources/people.txt").map(_.split(",")).map(p => Person(p(0), p(1).trim.toInt))
-people.registerAsTable("people")
+people.registerTempTable("people")
 
 // SQL statements can be run by using the sql methods provided by sqlContext.
 val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
@@ -210,7 +210,7 @@ JavaRDD<Person> people = sc.textFile("examples/src/main/resources/people.txt").m
 
 // Apply a schema to an RDD of JavaBeans and register it as a table.
 JavaSchemaRDD schemaPeople = sqlContext.applySchema(people, Person.class);
-schemaPeople.registerAsTable("people");
+schemaPeople.registerTempTable("people");
 
 // SQL can be run over RDDs that have been registered as tables.
 JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
@@ -248,7 +248,7 @@ people = parts.map(lambda p: {"name": p[0], "age": int(p[1])})
 # In future versions of PySpark we would like to add support for registering RDDs with other
 # datatypes as tables
 schemaPeople = sqlContext.inferSchema(people)
-schemaPeople.registerAsTable("people")
+schemaPeople.registerTempTable("people")
 
 # SQL can be run over SchemaRDDs that have been registered as a table.
 teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
@@ -292,7 +292,7 @@ people.saveAsParquetFile("people.parquet")
 val parquetFile = sqlContext.parquetFile("people.parquet")
 
 //Parquet files can also be registered as tables and then used in SQL statements.
-parquetFile.registerAsTable("parquetFile")
+parquetFile.registerTempTable("parquetFile")
 val teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
 teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
 {% endhighlight %}
@@ -314,7 +314,7 @@ schemaPeople.saveAsParquetFile("people.parquet");
 JavaSchemaRDD parquetFile = sqlContext.parquetFile("people.parquet");
 
 //Parquet files can also be registered as tables and then used in SQL statements.
-parquetFile.registerAsTable("parquetFile");
+parquetFile.registerTempTable("parquetFile");
 JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19");
 List<String> teenagerNames = teenagers.map(new Function<Row, String>() {
   public String call(Row row) {
@@ -340,7 +340,7 @@ schemaPeople.saveAsParquetFile("people.parquet")
 parquetFile = sqlContext.parquetFile("people.parquet")
 
 # Parquet files can also be registered as tables and then used in SQL statements.
-parquetFile.registerAsTable("parquetFile");
+parquetFile.registerTempTable("parquetFile");
 teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
 teenNames = teenagers.map(lambda p: "Name: " + p.name)
 for teenName in teenNames.collect():
@@ -378,7 +378,7 @@ people.printSchema()
 //  |-- name: StringType
 
 // Register this SchemaRDD as a table.
-people.registerAsTable("people")
+people.registerTempTable("people")
 
 // SQL statements can be run by using the sql methods provided by sqlContext.
 val teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")
@@ -416,7 +416,7 @@ people.printSchema();
 //  |-- name: StringType
 
 // Register this JavaSchemaRDD as a table.
-people.registerAsTable("people");
+people.registerTempTable("people");
 
 // SQL statements can be run by using the sql methods provided by sqlContext.
 JavaSchemaRDD teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19");
@@ -455,7 +455,7 @@ people.printSchema()
 #  |-- name: StringType
 
 # Register this SchemaRDD as a table.
-people.registerAsTable("people")
+people.registerTempTable("people")
 
 # SQL statements can be run by using the sql methods provided by sqlContext.
 teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")

docs/streaming-custom-receivers.md

@@ -4,7 +4,7 @@ title: Spark Streaming Custom Receivers
 ---
 
 Spark Streaming can receive streaming data from any arbitrary data source beyond
-the one's for which it has in-built support (that is, beyond Flume, Kafka, files, sockets, etc.).
+the one's for which it has in-built support (that is, beyond Flume, Kafka, Kinesis, files, sockets, etc.).
 This requires the developer to implement a *receiver* that is customized for receiving data from
 the concerned data source. This guide walks through the process of implementing a custom receiver
 and using it in a Spark Streaming application.
@@ -174,7 +174,7 @@ val words = lines.flatMap(_.split(" "))
 ...
 {% endhighlight %}
 
-The full source code is in the example [CustomReceiver.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/streaming/examples/CustomReceiver.scala).
+The full source code is in the example [CustomReceiver.scala](https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala).
 
 </div>
 <div data-lang="java" markdown="1">