diff --git a/.rat-excludes b/.rat-excludes index 50766954ef070..15589702c5599 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -43,3 +43,9 @@ test.out/* .*iml service.properties db.lck +build/* +dist/* +.*out +.*ipr +.*iws +logs diff --git a/LICENSE b/LICENSE index 1c1c2c0255fa9..383f079df8c8b 100644 --- a/LICENSE +++ b/LICENSE @@ -428,3 +428,106 @@ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON A 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 colt: +======================================================================== + +Copyright (c) 1999 CERN - European Organization for Nuclear Research. +Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation. CERN makes no representations about the suitability of this software for any purpose. It is provided "as is" without expressed or implied warranty. + +Packages hep.aida.* + +Written by Pavel Binko, Dino Ferrero Merlino, Wolfgang Hoschek, Tony Johnson, Andreas Pfeiffer, and others. Check the FreeHEP home page for more info. Permission to use and/or redistribute this work is granted under the terms of the LGPL License, with the exception that any usage related to military applications is expressly forbidden. The software and documentation made available under the terms of this license are provided with no warranty. + + +======================================================================== +Fo SnapTree: +======================================================================== + +SNAPTREE LICENSE + +Copyright (c) 2009-2012 Stanford University, unless otherwise specified. +All rights reserved. + +This software was developed by the Pervasive Parallelism Laboratory of +Stanford University, California, USA. + +Permission to use, copy, modify, and distribute this software in source +or binary form for any purpose with or without fee is hereby granted, +provided that the following conditions are met: + + 1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + 2. 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See project link for details. + + (Eclipse Public License - Version 1.0) mqtt-client (org.eclipse.paho:mqtt-client:0.4.0 - http://www.eclipse.org/paho/mqtt-client) + (Eclipse Public License v1.0) Eclipse JDT Core (org.eclipse.jdt:core:3.1.1 - http://www.eclipse.org/jdt/) + +======================================================================== +Mozilla Public License 1.0 +======================================================================== + +The following components are provided under the Mozilla Public License 1.0. 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Oliphant for the SciPy library (http://www.scipy.org/) +Copyright © 2003-2009 SciPy Developers. +=============================================================================== + +The LinearConstraint, LinearObjectiveFunction, LinearOptimizer, +RelationShip, SimplexSolver and SimplexTableau classes in package +org.apache.commons.math3.optimization.linear include software developed by +Benjamin McCann (http://www.benmccann.com) and distributed with +the following copyright: Copyright 2009 Google Inc. +=============================================================================== + +This product includes software developed by the +University of Chicago, as Operator of Argonne National +Laboratory. +The LevenbergMarquardtOptimizer class in package +org.apache.commons.math3.optimization.general includes software +translated from the lmder, lmpar and qrsolv Fortran routines +from the Minpack package +Minpack Copyright Notice (1999) University of Chicago. All rights reserved +=============================================================================== + +The GraggBulirschStoerIntegrator class in package +org.apache.commons.math3.ode.nonstiff includes software translated +from the odex Fortran routine developed by E. Hairer and G. Wanner. +Original source copyright: +Copyright (c) 2004, Ernst Hairer +=============================================================================== + +The EigenDecompositionImpl class in package +org.apache.commons.math3.linear includes software translated +from some LAPACK Fortran routines. Original source copyright: +Copyright (c) 1992-2008 The University of Tennessee. All rights reserved. +=============================================================================== + +The MersenneTwister class in package org.apache.commons.math3.random +includes software translated from the 2002-01-26 version of +the Mersenne-Twister generator written in C by Makoto Matsumoto and Takuji +Nishimura. Original source copyright: +Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura, +All rights reserved +=============================================================================== + +The LocalizedFormatsTest class in the unit tests is an adapted version of +the OrekitMessagesTest class from the orekit library distributed under the +terms of the Apache 2 licence. Original source copyright: +Copyright 2010 CS Systèmes d'Information +=============================================================================== + +The HermiteInterpolator class and its corresponding test have been imported from +the orekit library distributed under the terms of the Apache 2 licence. Original +source copyright: +Copyright 2010-2012 CS Systèmes d'Information +=============================================================================== + +The creation of the package "o.a.c.m.analysis.integration.gauss" was inspired +by an original code donated by Sébastien Brisard. +=============================================================================== + +The complete text of licenses and disclaimers associated with the the original +sources enumerated above at the time of code translation are in the LICENSE.txt +file. + +This product currently only contains code developed by authors +of specific components, as identified by the source code files; +if such notes are missing files have been created by +Tatu Saloranta. + +For additional credits (generally to people who reported problems) +see CREDITS file. + +Apache Commons Lang +Copyright 2001-2011 The Apache Software Foundation + +Apache Commons Compress +Copyright 2002-2012 The Apache Software Foundation + +Apache Commons CLI +Copyright 2001-2009 The Apache Software Foundation + +Google Guice - Extensions - Servlet +Copyright 2006-2011 Google, Inc. + +Google Guice - Core Library +Copyright 2006-2011 Google, Inc. + +Apache Jakarta HttpClient +Copyright 1999-2007 The Apache Software Foundation + +Apache Hive +Copyright 2008-2013 The Apache Software Foundation + +This product includes software developed by The Apache Software +Foundation (http://www.apache.org/). + +This product includes software developed by The JDBM Project +(http://jdbm.sourceforge.net/). + +This product includes/uses ANTLR (http://www.antlr.org/), +Copyright (c) 2003-2011, Terrence Parr. + +This product includes/uses StringTemplate (http://www.stringtemplate.org/), +Copyright (c) 2011, Terrence Parr. + +This product includes/uses ASM (http://asm.ow2.org/), +Copyright (c) 2000-2007 INRIA, France Telecom. + +This product includes/uses org.json (http://www.json.org/java/index.html), +Copyright (c) 2002 JSON.org + +This product includes/uses JLine (http://jline.sourceforge.net/), +Copyright (c) 2002-2006, Marc Prud'hommeaux . + +This product includes/uses SQLLine (http://sqlline.sourceforge.net), +Copyright (c) 2002, 2003, 2004, 2005 Marc Prud'hommeaux . + +This product includes/uses SLF4J (http://www.slf4j.org/), +Copyright (c) 2004-2010 QOS.ch + +This product includes/uses Bootstrap (http://twitter.github.com/bootstrap/), +Copyright (c) 2012 Twitter, Inc. + +This product includes/uses Glyphicons (http://glyphicons.com/), +Copyright (c) 2010 - 2012 Jan Kovarík + +This product includes DataNucleus (http://www.datanucleus.org/) +Copyright 2008-2008 DataNucleus + +This product includes Guava (http://code.google.com/p/guava-libraries/) +Copyright (C) 2006 Google Inc. + +This product includes JavaEWAH (http://code.google.com/p/javaewah/) +Copyright (C) 2011 Google Inc. + +Apache Commons Pool +Copyright 1999-2009 The Apache Software Foundation + +========================================================================= +== NOTICE file corresponding to section 4(d) of the Apache License, == +== Version 2.0, in this case for the DataNucleus distribution. == +========================================================================= + +=================================================================== +This product includes software developed by many individuals, +including the following: +=================================================================== +Erik Bengtson +Andy Jefferson + +=================================================================== +This product has included contributions from some individuals, +including the following: +=================================================================== + +=================================================================== +This product has included contributions from some individuals, +including the following: +=================================================================== +Joerg von Frantzius +Thomas Marti +Barry Haddow +Marco Schulze +Ralph Ullrich +David Ezzio +Brendan de Beer +David Eaves +Martin Taal +Tony Lai +Roland Szabo +Marcus Mennemeier +Xuan Baldauf +Eric Sultan + +=================================================================== +This product also includes software developed by the TJDO project +(http://tjdo.sourceforge.net/). +=================================================================== + +=================================================================== +This product includes software developed by many individuals, +including the following: +=================================================================== +Andy Jefferson +Erik Bengtson +Joerg von Frantzius +Marco Schulze + +=================================================================== +This product has included contributions from some individuals, +including the following: +=================================================================== +Barry Haddow +Ralph Ullrich +David Ezzio +Brendan de Beer +David Eaves +Martin Taal +Tony Lai +Roland Szabo +Anton Troshin (Timesten) + +=================================================================== +This product also includes software developed by the Apache Commons project +(http://commons.apache.org/). +=================================================================== + +Apache Java Data Objects (JDO) +Copyright 2005-2006 The Apache Software Foundation + +========================================================================= +== NOTICE file corresponding to section 4(d) of the Apache License, == +== Version 2.0, in this case for the Apache Derby distribution. == +========================================================================= + +Apache Derby +Copyright 2004-2008 The Apache Software Foundation + +Portions of Derby were originally developed by +International Business Machines Corporation and are +licensed to the Apache Software Foundation under the +"Software Grant and Corporate Contribution License Agreement", +informally known as the "Derby CLA". +The following copyright notice(s) were affixed to portions of the code +with which this file is now or was at one time distributed +and are placed here unaltered. + +(C) Copyright 1997,2004 International Business Machines Corporation. All rights reserved. + +(C) Copyright IBM Corp. 2003. + +The portion of the functionTests under 'nist' was originally +developed by the National Institute of Standards and Technology (NIST), +an agency of the United States Department of Commerce, and adapted by +International Business Machines Corporation in accordance with the NIST +Software Acknowledgment and Redistribution document at +http://www.itl.nist.gov/div897/ctg/sql_form.htm + +Apache Commons Collections +Copyright 2001-2008 The Apache Software Foundation + +Apache Commons Configuration +Copyright 2001-2008 The Apache Software Foundation + +Apache Jakarta Commons Digester +Copyright 2001-2006 The Apache Software Foundation + +Apache Commons BeanUtils +Copyright 2000-2008 The Apache Software Foundation + +Apache Avro Mapred API +Copyright 2009-2013 The Apache Software Foundation + +Apache Avro IPC +Copyright 2009-2013 The Apache Software Foundation diff --git a/README.md b/README.md index 9c2e32b90f162..6211a5889a3f5 100644 --- a/README.md +++ b/README.md @@ -9,13 +9,14 @@ You can find the latest Spark documentation, including a programming guide, on the project webpage at . This README file only contains basic setup instructions. - ## Building Spark Spark is built on Scala 2.10. To build Spark and its example programs, run: ./sbt/sbt assembly +(You do not need to do this if you downloaded a pre-built package.) + ## Interactive Scala Shell The easiest way to start using Spark is through the Scala shell: @@ -41,9 +42,9 @@ And run the following command, which should also return 1000: Spark also comes with several sample programs in the `examples` directory. To run one of them, use `./bin/run-example [params]`. For example: - ./bin/run-example org.apache.spark.examples.SparkLR + ./bin/run-example SparkPi -will run the Logistic Regression example locally. +will run the Pi example locally. You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, diff --git a/assembly/pom.xml b/assembly/pom.xml index 6c4d46aeb67bd..963357b9ab167 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -96,7 +96,7 @@ *:* - org.datanucleus:* + org/datanucleus/** META-INF/*.SF META-INF/*.DSA META-INF/*.RSA @@ -122,6 +122,8 @@ log4j.properties + + diff --git a/bagel/src/main/scala/org/apache/spark/bagel/package-info.java b/bagel/src/main/scala/org/apache/spark/bagel/package-info.java new file mode 100644 index 0000000000000..81f26f276549f --- /dev/null +++ b/bagel/src/main/scala/org/apache/spark/bagel/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Bagel: An implementation of Pregel in Spark. THIS IS DEPRECATED - use Spark's GraphX library. + */ +package org.apache.spark.bagel; \ No newline at end of file diff --git a/bagel/src/main/scala/org/apache/spark/bagel/package.scala b/bagel/src/main/scala/org/apache/spark/bagel/package.scala new file mode 100644 index 0000000000000..2fb1934579781 --- /dev/null +++ b/bagel/src/main/scala/org/apache/spark/bagel/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Bagel: An implementation of Pregel in Spark. THIS IS DEPRECATED - use Spark's GraphX library. + */ +package object bagel diff --git a/bagel/src/test/resources/log4j.properties b/bagel/src/test/resources/log4j.properties index 5cdcf35b23a6c..30b4baa4d714a 100644 --- a/bagel/src/test/resources/log4j.properties +++ b/bagel/src/test/resources/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=bagel/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/bin/compute-classpath.cmd b/bin/compute-classpath.cmd index 065553eb31939..58710cd1bd548 100644 --- a/bin/compute-classpath.cmd +++ b/bin/compute-classpath.cmd @@ -20,6 +20,13 @@ rem rem This script computes Spark's classpath and prints it to stdout; it's used by both the "run" rem script and the ExecutorRunner in standalone cluster mode. +rem If we're called from spark-class2.cmd, it already set enabledelayedexpansion and setting +rem it here would stop us from affecting its copy of the CLASSPATH variable; otherwise we +rem need to set it here because we use !datanucleus_jars! below. +if "%DONT_PRINT_CLASSPATH%"=="1" goto skip_delayed_expansion +setlocal enabledelayedexpansion +:skip_delayed_expansion + set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed @@ -31,7 +38,7 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" rem Build up classpath set CLASSPATH=%FWDIR%conf if exist "%FWDIR%RELEASE" ( - for %%d in ("%FWDIR%jars\spark-assembly*.jar") do ( + for %%d in ("%FWDIR%lib\spark-assembly*.jar") do ( set ASSEMBLY_JAR=%%d ) ) else ( @@ -42,6 +49,21 @@ if exist "%FWDIR%RELEASE" ( set CLASSPATH=%CLASSPATH%;%ASSEMBLY_JAR% +rem When Hive support is needed, Datanucleus jars must be included on the classpath. +rem Datanucleus jars do not work if only included in the uber jar as plugin.xml metadata is lost. +rem Both sbt and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is +rem built with Hive, so look for them there. +if exist "%FWDIR%RELEASE" ( + set datanucleus_dir=%FWDIR%lib +) else ( + set datanucleus_dir=%FWDIR%lib_managed\jars +) +set "datanucleus_jars=" +for %%d in ("%datanucleus_dir%\datanucleus-*.jar") do ( + set datanucleus_jars=!datanucleus_jars!;%%d +) +set CLASSPATH=%CLASSPATH%;%datanucleus_jars% + set SPARK_CLASSES=%FWDIR%core\target\scala-%SCALA_VERSION%\classes set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%repl\target\scala-%SCALA_VERSION%\classes set SPARK_CLASSES=%SPARK_CLASSES%;%FWDIR%mllib\target\scala-%SCALA_VERSION%\classes diff --git a/bin/pyspark b/bin/pyspark index 10e35e0f1734e..d0fa56f31913f 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -17,7 +17,7 @@ # limitations under the License. # -# Figure out where the Scala framework is installed +# Figure out where Spark is installed FWDIR="$(cd `dirname $0`/..; pwd)" # Export this as SPARK_HOME @@ -25,6 +25,12 @@ export SPARK_HOME="$FWDIR" SCALA_VERSION=2.10 +if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then + echo "Usage: ./bin/pyspark [options]" + $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + exit 0 +fi + # Exit if the user hasn't compiled Spark if [ ! -f "$FWDIR/RELEASE" ]; then # Exit if the user hasn't compiled Spark @@ -52,13 +58,34 @@ export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.1-src.zip:$PYTHONPATH export OLD_PYTHONSTARTUP=$PYTHONSTARTUP export PYTHONSTARTUP=$FWDIR/python/pyspark/shell.py +# If IPython options are specified, assume user wants to run IPython if [ -n "$IPYTHON_OPTS" ]; then IPYTHON=1 fi -# Only use ipython if no command line arguments were provided [SPARK-1134] -if [[ "$IPYTHON" = "1" && $# = 0 ]] ; then - exec ipython $IPYTHON_OPTS +# Build up arguments list manually to preserve quotes and backslashes. +# We export Spark submit arguments as an environment variable because shell.py must run as a +# PYTHONSTARTUP script, which does not take in arguments. This is required for IPython notebooks. + +PYSPARK_SUBMIT_ARGS="" +whitespace="[[:space:]]" +for i in "$@"; do + if [[ $i =~ \" ]]; then i=$(echo $i | sed 's/\"/\\\"/g'); fi + if [[ $i =~ $whitespace ]]; then i=\"$i\"; fi + PYSPARK_SUBMIT_ARGS="$PYSPARK_SUBMIT_ARGS $i" +done +export PYSPARK_SUBMIT_ARGS + +# If a python file is provided, directly run spark-submit. +if [[ "$1" =~ \.py$ ]]; then + echo -e "\nWARNING: Running python applications through ./bin/pyspark is deprecated as of Spark 1.0." 1>&2 + echo -e "Use ./bin/spark-submit \n" 1>&2 + exec $FWDIR/bin/spark-submit "$@" else - exec "$PYSPARK_PYTHON" "$@" + # Only use ipython if no command line arguments were provided [SPARK-1134] + if [[ "$IPYTHON" = "1" ]]; then + exec ipython $IPYTHON_OPTS + else + exec "$PYSPARK_PYTHON" + fi fi diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index d7cfd5eec501c..0ef9eea95342e 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -31,7 +31,7 @@ set FOUND_JAR=0 for %%d in ("%FWDIR%assembly\target\scala-%SCALA_VERSION%\spark-assembly*hadoop*.jar") do ( set FOUND_JAR=1 ) -if "%FOUND_JAR%"=="0" ( +if [%FOUND_JAR%] == [0] ( echo Failed to find Spark assembly JAR. echo You need to build Spark with sbt\sbt assembly before running this program. goto exit @@ -42,15 +42,30 @@ rem Load environment variables from conf\spark-env.cmd, if it exists if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" rem Figure out which Python to use. -if "x%PYSPARK_PYTHON%"=="x" set PYSPARK_PYTHON=python +if [%PYSPARK_PYTHON%] == [] set PYSPARK_PYTHON=python set PYTHONPATH=%FWDIR%python;%PYTHONPATH% set PYTHONPATH=%FWDIR%python\lib\py4j-0.8.1-src.zip;%PYTHONPATH% set OLD_PYTHONSTARTUP=%PYTHONSTARTUP% set PYTHONSTARTUP=%FWDIR%python\pyspark\shell.py +set PYSPARK_SUBMIT_ARGS=%* echo Running %PYSPARK_PYTHON% with PYTHONPATH=%PYTHONPATH% -"%PYSPARK_PYTHON%" %* +rem Check whether the argument is a file +for /f %%i in ('echo %1^| findstr /R "\.py"') do ( + set PYTHON_FILE=%%i +) + +if [%PYTHON_FILE%] == [] ( + %PYSPARK_PYTHON% +) else ( + echo. + echo WARNING: Running python applications through ./bin/pyspark.cmd is deprecated as of Spark 1.0. + echo Use ./bin/spark-submit ^ + echo. + "%FWDIR%\bin\spark-submit.cmd" %PYSPARK_SUBMIT_ARGS% +) + :exit diff --git a/bin/run-example b/bin/run-example index 146951ac0ee56..7caab31daef39 100755 --- a/bin/run-example +++ b/bin/run-example @@ -23,6 +23,16 @@ FWDIR="$(cd `dirname $0`/..; pwd)" export SPARK_HOME="$FWDIR" EXAMPLES_DIR="$FWDIR"/examples +if [ -n "$1" ]; then + EXAMPLE_CLASS="$1" + shift +else + echo "Usage: ./bin/run-example [example-args]" + echo " - set MASTER=XX to use a specific master" + echo " - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression)" + exit 1 +fi + if [ -f "$FWDIR/RELEASE" ]; then export SPARK_EXAMPLES_JAR=`ls "$FWDIR"/lib/spark-examples-*hadoop*.jar` elif [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/spark-examples-*hadoop*.jar ]; then @@ -37,17 +47,6 @@ fi EXAMPLE_MASTER=${MASTER:-"local[*]"} -if [ -n "$1" ]; then - EXAMPLE_CLASS="$1" - shift -else - echo "usage: ./bin/run-example [example-args]" - echo " - set MASTER=XX to use a specific master" - echo " - can use abbreviated example class name (e.g. SparkPi, mllib.MovieLensALS)" - echo - exit -1 -fi - if [[ ! $EXAMPLE_CLASS == org.apache.spark.examples* ]]; then EXAMPLE_CLASS="org.apache.spark.examples.$EXAMPLE_CLASS" fi @@ -55,5 +54,5 @@ fi ./bin/spark-submit \ --master $EXAMPLE_MASTER \ --class $EXAMPLE_CLASS \ - $SPARK_EXAMPLES_JAR \ + "$SPARK_EXAMPLES_JAR" \ "$@" diff --git a/bin/run-example2.cmd b/bin/run-example2.cmd index 40abb9af74246..eadedd7fa61ff 100644 --- a/bin/run-example2.cmd +++ b/bin/run-example2.cmd @@ -30,7 +30,9 @@ if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" rem Test that an argument was given if not "x%1"=="x" goto arg_given - echo Usage: run-example ^ [^] + echo Usage: run-example ^ [example-args] + echo - set MASTER=XX to use a specific master + echo - can use abbreviated example class name (e.g. SparkPi, mllib.LinearRegression) goto exit :arg_given @@ -38,8 +40,14 @@ set EXAMPLES_DIR=%FWDIR%examples rem Figure out the JAR file that our examples were packaged into. set SPARK_EXAMPLES_JAR= -for %%d in ("%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\spark-examples*assembly*.jar") do ( - set SPARK_EXAMPLES_JAR=%%d +if exist "%FWDIR%RELEASE" ( + for %%d in ("%FWDIR%lib\spark-examples*.jar") do ( + set SPARK_EXAMPLES_JAR=%%d + ) +) else ( + for %%d in ("%EXAMPLES_DIR%\target\scala-%SCALA_VERSION%\spark-examples*.jar") do ( + set SPARK_EXAMPLES_JAR=%%d + ) ) if "x%SPARK_EXAMPLES_JAR%"=="x" ( echo Failed to find Spark examples assembly JAR. @@ -47,15 +55,34 @@ if "x%SPARK_EXAMPLES_JAR%"=="x" ( goto exit ) -rem Compute Spark classpath using external script -set DONT_PRINT_CLASSPATH=1 -call "%FWDIR%bin\compute-classpath.cmd" -set DONT_PRINT_CLASSPATH=0 -set CLASSPATH=%SPARK_EXAMPLES_JAR%;%CLASSPATH% +rem Set master from MASTER environment variable if given +if "x%MASTER%"=="x" ( + set EXAMPLE_MASTER=local[*] +) else ( + set EXAMPLE_MASTER=%MASTER% +) + +rem If the EXAMPLE_CLASS does not start with org.apache.spark.examples, add that +set EXAMPLE_CLASS=%1 +set PREFIX=%EXAMPLE_CLASS:~0,25% +if not %PREFIX%==org.apache.spark.examples ( + set EXAMPLE_CLASS=org.apache.spark.examples.%EXAMPLE_CLASS% +) + +rem Get the tail of the argument list, to skip the first one. This is surprisingly +rem complicated on Windows. +set "ARGS=" +:top +shift +if "%~1" neq "" ( + set ARGS=%ARGS% "%~1" + goto :top +) +if defined ARGS set ARGS=%ARGS:~1% -rem Figure out where java is. -set RUNNER=java -if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java +call "%FWDIR%bin\spark-submit.cmd" ^ + --master %EXAMPLE_MASTER% ^ + --class %EXAMPLE_CLASS% ^ + "%SPARK_EXAMPLES_JAR%" %ARGS% -"%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %* :exit diff --git a/bin/spark-class b/bin/spark-class index 6480ccb58d6aa..e884511010c6c 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -24,7 +24,7 @@ esac SCALA_VERSION=2.10 -# Figure out where the Scala framework is installed +# Figure out where Spark is installed FWDIR="$(cd `dirname $0`/..; pwd)" # Export this as SPARK_HOME @@ -99,14 +99,14 @@ else fi # Set JAVA_OPTS to be able to load native libraries and to set heap size -JAVA_OPTS="$OUR_JAVA_OPTS" +JAVA_OPTS="-XX:MaxPermSize=128m $OUR_JAVA_OPTS" JAVA_OPTS="$JAVA_OPTS -Xms$OUR_JAVA_MEM -Xmx$OUR_JAVA_MEM" # Load extra JAVA_OPTS from conf/java-opts, if it exists if [ -e "$FWDIR/conf/java-opts" ] ; then JAVA_OPTS="$JAVA_OPTS `cat $FWDIR/conf/java-opts`" fi export JAVA_OPTS -# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala! +# Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala! if [ ! -f "$FWDIR/RELEASE" ]; then # Exit if the user hasn't compiled Spark diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 4302c1b6b7ff4..e420eb409e529 100755 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -17,6 +17,8 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem +setlocal enabledelayedexpansion + set SCALA_VERSION=2.10 rem Figure out where the Spark framework is installed @@ -75,8 +77,8 @@ rem All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SP ) rem Set JAVA_OPTS to be able to load native libraries and to set heap size -set JAVA_OPTS=%OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM% -rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in ExecutorRunner.scala! +set JAVA_OPTS=-XX:MaxPermSize=128m %OUR_JAVA_OPTS% -Djava.library.path=%SPARK_LIBRARY_PATH% -Xms%OUR_JAVA_MEM% -Xmx%OUR_JAVA_MEM% +rem Attention: when changing the way the JAVA_OPTS are assembled, the change must be reflected in CommandUtils.scala! rem Test whether the user has built Spark if exist "%FWDIR%RELEASE" goto skip_build_test diff --git a/bin/spark-shell b/bin/spark-shell index 7f03349c5e910..850e9507ec38f 100755 --- a/bin/spark-shell +++ b/bin/spark-shell @@ -28,15 +28,15 @@ esac # Enter posix mode for bash set -o posix -if [[ "$@" == *--help* ]]; then +## Global script variables +FWDIR="$(cd `dirname $0`/..; pwd)" + +if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then echo "Usage: ./bin/spark-shell [options]" - ./bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + $FWDIR/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 exit 0 fi -## Global script variables -FWDIR="$(cd `dirname $0`/..; pwd)" - function main(){ if $cygwin; then # Workaround for issue involving JLine and Cygwin @@ -46,11 +46,11 @@ function main(){ # (see https://github.com/sbt/sbt/issues/562). stty -icanon min 1 -echo > /dev/null 2>&1 export SPARK_SUBMIT_OPTS="$SPARK_SUBMIT_OPTS -Djline.terminal=unix" - $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main + $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main stty icanon echo > /dev/null 2>&1 else export SPARK_SUBMIT_OPTS - $FWDIR/bin/spark-submit spark-internal "$@" --class org.apache.spark.repl.Main + $FWDIR/bin/spark-submit spark-shell "$@" --class org.apache.spark.repl.Main fi } diff --git a/bin/spark-shell.cmd b/bin/spark-shell.cmd index 99799128eb734..4b9708a8c03f3 100755 --- a/bin/spark-shell.cmd +++ b/bin/spark-shell.cmd @@ -17,7 +17,6 @@ rem See the License for the specific language governing permissions and rem limitations under the License. rem -rem Find the path of sbin -set BIN=%~dp0..\bin\ +set SPARK_HOME=%~dp0.. -cmd /V /E /C %BIN%spark-class2.cmd org.apache.spark.repl.Main %* +cmd /V /E /C %SPARK_HOME%\bin\spark-submit.cmd spark-shell %* --class org.apache.spark.repl.Main diff --git a/bin/spark-submit.cmd b/bin/spark-submit.cmd new file mode 100644 index 0000000000000..6eb702ed8c561 --- /dev/null +++ b/bin/spark-submit.cmd @@ -0,0 +1,56 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +set SPARK_HOME=%~dp0.. +set ORIG_ARGS=%* + +rem Clear the values of all variables used +set DEPLOY_MODE= +set DRIVER_MEMORY= +set SPARK_SUBMIT_LIBRARY_PATH= +set SPARK_SUBMIT_CLASSPATH= +set SPARK_SUBMIT_OPTS= +set SPARK_DRIVER_MEMORY= + +:loop +if [%1] == [] goto continue + if [%1] == [--deploy-mode] ( + set DEPLOY_MODE=%2 + ) else if [%1] == [--driver-memory] ( + set DRIVER_MEMORY=%2 + ) else if [%1] == [--driver-library-path] ( + set SPARK_SUBMIT_LIBRARY_PATH=%2 + ) else if [%1] == [--driver-class-path] ( + set SPARK_SUBMIT_CLASSPATH=%2 + ) else if [%1] == [--driver-java-options] ( + set SPARK_SUBMIT_OPTS=%2 + ) + shift +goto loop +:continue + +if [%DEPLOY_MODE%] == [] ( + set DEPLOY_MODE=client +) + +if not [%DRIVER_MEMORY%] == [] if [%DEPLOY_MODE%] == [client] ( + set SPARK_DRIVER_MEMORY=%DRIVER_MEMORY% +) + +cmd /V /E /C %SPARK_HOME%\bin\spark-class.cmd org.apache.spark.deploy.SparkSubmit %ORIG_ARGS% diff --git a/conf/spark-defaults.conf.template b/conf/spark-defaults.conf.template index f840ff681d019..2779342769c14 100644 --- a/conf/spark-defaults.conf.template +++ b/conf/spark-defaults.conf.template @@ -2,6 +2,7 @@ # This is useful for setting default environmental settings. # Example: -# spark.master spark://master:7077 +# spark.master spark://master:7077 # spark.eventLog.enabled true # spark.eventLog.dir hdfs://namenode:8021/directory +# spark.serializer org.apache.spark.serializer.KryoSerializer diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index f906be611a931..f8ffbf64278fb 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -30,14 +30,14 @@ # Options for the daemons used in the standalone deploy mode: # - SPARK_MASTER_IP, to bind the master to a different IP address or hostname -# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports +# - SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports for the master # - SPARK_MASTER_OPTS, to set config properties only for the master (e.g. "-Dx=y") # - SPARK_WORKER_CORES, to set the number of cores to use on this machine # - SPARK_WORKER_MEMORY, to set how much total memory workers have to give executors (e.g. 1000m, 2g) -# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT +# - SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT, to use non-default ports for the worker # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node # - SPARK_WORKER_DIR, to set the working directory of worker processes # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y") # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y") -# - SPARK_DAEMON_OPTS, to set config properties for all daemons (e.g. "-Dx=y") +# - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y") # - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers diff --git a/core/pom.xml b/core/pom.xml index 8fe215ab24289..bab50f5ce2888 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -140,6 +140,7 @@ org.apache.mesos mesos + ${mesos.classifier} io.netty @@ -322,7 +323,7 @@ - + src/main/resources diff --git a/core/src/main/java/org/apache/spark/api/java/function/package-info.java b/core/src/main/java/org/apache/spark/api/java/function/package-info.java new file mode 100644 index 0000000000000..463a42f23342c --- /dev/null +++ b/core/src/main/java/org/apache/spark/api/java/function/package-info.java @@ -0,0 +1,23 @@ +/* + * 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. + */ + +/** + * Set of interfaces to represent functions in Spark's Java API. Users create implementations of + * these interfaces to pass functions to various Java API methods for Spark. Please visit Spark's + * Java programming guide for more details. + */ +package org.apache.spark.api.java.function; \ No newline at end of file diff --git a/core/src/main/java/org/apache/spark/api/java/function/package.scala b/core/src/main/java/org/apache/spark/api/java/function/package.scala new file mode 100644 index 0000000000000..7f91de653a64a --- /dev/null +++ b/core/src/main/java/org/apache/spark/api/java/function/package.scala @@ -0,0 +1,25 @@ +/* + * 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.api.java + +/** + * Set of interfaces to represent functions in Spark's Java API. Users create implementations of + * these interfaces to pass functions to various Java API methods for Spark. Please visit Spark's + * Java programming guide for more details. + */ +package object function \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala index 54e08d7866f75..e2d2250982daa 100644 --- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala +++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala @@ -23,6 +23,7 @@ import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.RDD +import org.apache.spark.util.Utils /** * Classes that represent cleaning tasks. @@ -110,7 +111,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Keep cleaning RDD, shuffle, and broadcast state. */ - private def keepCleaning() { + private def keepCleaning(): Unit = Utils.logUncaughtExceptions { while (!stopped) { try { val reference = Option(referenceQueue.remove(ContextCleaner.REF_QUEUE_POLL_TIMEOUT)) @@ -128,7 +129,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } } } catch { - case t: Throwable => logError("Error in cleaning thread", t) + case e: Exception => logError("Error in cleaning thread", e) } } } @@ -141,7 +142,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { listeners.foreach(_.rddCleaned(rddId)) logInfo("Cleaned RDD " + rddId) } catch { - case t: Throwable => logError("Error cleaning RDD " + rddId, t) + case e: Exception => logError("Error cleaning RDD " + rddId, e) } } @@ -154,7 +155,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { listeners.foreach(_.shuffleCleaned(shuffleId)) logInfo("Cleaned shuffle " + shuffleId) } catch { - case t: Throwable => logError("Error cleaning shuffle " + shuffleId, t) + case e: Exception => logError("Error cleaning shuffle " + shuffleId, e) } } @@ -166,7 +167,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { listeners.foreach(_.broadcastCleaned(broadcastId)) logInfo("Cleaned broadcast " + broadcastId) } catch { - case t: Throwable => logError("Error cleaning broadcast " + broadcastId, t) + case e: Exception => logError("Error cleaning broadcast " + broadcastId, e) } } diff --git a/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala b/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala index ec11dbbffaaf8..f40baa8e43592 100644 --- a/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala +++ b/core/src/main/scala/org/apache/spark/InterruptibleIterator.scala @@ -17,11 +17,15 @@ package org.apache.spark +import org.apache.spark.annotation.DeveloperApi + /** + * :: DeveloperApi :: * An iterator that wraps around an existing iterator to provide task killing functionality. * It works by checking the interrupted flag in [[TaskContext]]. */ -private[spark] class InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator[T]) +@DeveloperApi +class InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator[T]) extends Iterator[T] { def hasNext: Boolean = { diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index bd21fdc5a18e4..8ce4b91cae8ae 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -238,18 +238,18 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { // Check for legacy configs sys.env.get("SPARK_JAVA_OPTS").foreach { value => - val error = + val warning = s""" |SPARK_JAVA_OPTS was detected (set to '$value'). - |This has undefined behavior when running on a cluster and is deprecated in Spark 1.0+. + |This is deprecated in Spark 1.0+. | |Please instead use: | - ./spark-submit with conf/spark-defaults.conf to set defaults for an application | - ./spark-submit with --driver-java-options to set -X options for a driver | - spark.executor.extraJavaOptions to set -X options for executors - | - SPARK_DAEMON_OPTS to set java options for standalone daemons (i.e. master, worker) + | - SPARK_DAEMON_JAVA_OPTS to set java options for standalone daemons (master or worker) """.stripMargin - logError(error) + logWarning(warning) for (key <- Seq(executorOptsKey, driverOptsKey)) { if (getOption(key).isDefined) { @@ -262,16 +262,16 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { } sys.env.get("SPARK_CLASSPATH").foreach { value => - val error = + val warning = s""" |SPARK_CLASSPATH was detected (set to '$value'). - | This has undefined behavior when running on a cluster and is deprecated in Spark 1.0+. + |This is deprecated in Spark 1.0+. | |Please instead use: | - ./spark-submit with --driver-class-path to augment the driver classpath | - spark.executor.extraClassPath to augment the executor classpath """.stripMargin - logError(error) + logWarning(warning) for (key <- Seq(executorClasspathKey, driverClassPathKey)) { if (getOption(key).isDefined) { diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 71bab295442fc..49737fa4be56b 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -17,15 +17,17 @@ package org.apache.spark +import scala.language.implicitConversions + import java.io._ import java.net.URI import java.util.concurrent.atomic.AtomicInteger import java.util.{Properties, UUID} import java.util.UUID.randomUUID import scala.collection.{Map, Set} +import scala.collection.JavaConversions._ import scala.collection.generic.Growable -import scala.collection.mutable.{ArrayBuffer, HashMap} -import scala.language.implicitConversions +import scala.collection.mutable.HashMap import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path @@ -50,7 +52,6 @@ import org.apache.spark.ui.SparkUI import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, TimeStampedWeakValueHashMap, Utils} /** - * :: DeveloperApi :: * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. * @@ -58,7 +59,6 @@ import org.apache.spark.util.{ClosureCleaner, MetadataCleaner, MetadataCleanerTy * this config overrides the default configs as well as system properties. */ -@DeveloperApi class SparkContext(config: SparkConf) extends Logging { // This is used only by YARN for now, but should be relevant to other cluster types (Mesos, @@ -66,6 +66,12 @@ class SparkContext(config: SparkConf) extends Logging { // contains a map from hostname to a list of input format splits on the host. private[spark] var preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map() + /** + * Create a SparkContext that loads settings from system properties (for instance, when + * launching with ./bin/spark-submit). + */ + def this() = this(new SparkConf()) + /** * :: DeveloperApi :: * Alternative constructor for setting preferred locations where Spark will create executors. @@ -276,19 +282,18 @@ class SparkContext(config: SparkConf) extends Logging { .getOrElse(512) // Environment variables to pass to our executors. - // NOTE: This should only be used for test related settings. - private[spark] val testExecutorEnvs = HashMap[String, String]() + private[spark] val executorEnvs = HashMap[String, String]() // Convert java options to env vars as a work around // since we can't set env vars directly in sbt. for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing")) value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} { - testExecutorEnvs(envKey) = value + executorEnvs(envKey) = value } // The Mesos scheduler backend relies on this environment variable to set executor memory. // TODO: Set this only in the Mesos scheduler. - testExecutorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m" - testExecutorEnvs ++= conf.getExecutorEnv + executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m" + executorEnvs ++= conf.getExecutorEnv // Set SPARK_USER for user who is running SparkContext. val sparkUser = Option { @@ -296,7 +301,7 @@ class SparkContext(config: SparkConf) extends Logging { }.getOrElse { SparkContext.SPARK_UNKNOWN_USER } - testExecutorEnvs("SPARK_USER") = sparkUser + executorEnvs("SPARK_USER") = sparkUser // Create and start the scheduler private[spark] var taskScheduler = SparkContext.createTaskScheduler(this, master) @@ -713,7 +718,7 @@ class SparkContext(config: SparkConf) extends Logging { minPartitions: Int = defaultMinPartitions ): RDD[T] = { sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions) - .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes)) + .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader)) } protected[spark] def checkpointFile[T: ClassTag]( @@ -839,18 +844,22 @@ class SparkContext(config: SparkConf) extends Logging { } /** - * Return pools for fair scheduler - * TODO(xiajunluan): We should take nested pools into account + * :: DeveloperApi :: + * Return pools for fair scheduler */ - def getAllPools: ArrayBuffer[Schedulable] = { - taskScheduler.rootPool.schedulableQueue + @DeveloperApi + def getAllPools: Seq[Schedulable] = { + // TODO(xiajunluan): We should take nested pools into account + taskScheduler.rootPool.schedulableQueue.toSeq } /** + * :: DeveloperApi :: * Return the pool associated with the given name, if one exists */ + @DeveloperApi def getPoolForName(pool: String): Option[Schedulable] = { - taskScheduler.rootPool.schedulableNameToSchedulable.get(pool) + Option(taskScheduler.rootPool.schedulableNameToSchedulable.get(pool)) } /** @@ -1494,8 +1503,8 @@ object SparkContext extends Logging { } catch { // TODO: Enumerate the exact reasons why it can fail // But irrespective of it, it means we cannot proceed ! - case th: Throwable => { - throw new SparkException("YARN mode not available ?", th) + case e: Exception => { + throw new SparkException("YARN mode not available ?", e) } } val backend = new CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) @@ -1510,8 +1519,8 @@ object SparkContext extends Logging { cons.newInstance(sc).asInstanceOf[TaskSchedulerImpl] } catch { - case th: Throwable => { - throw new SparkException("YARN mode not available ?", th) + case e: Exception => { + throw new SparkException("YARN mode not available ?", e) } } @@ -1521,8 +1530,8 @@ object SparkContext extends Logging { val cons = clazz.getConstructor(classOf[TaskSchedulerImpl], classOf[SparkContext]) cons.newInstance(scheduler, sc).asInstanceOf[CoarseGrainedSchedulerBackend] } catch { - case th: Throwable => { - throw new SparkException("YARN mode not available ?", th) + case e: Exception => { + throw new SparkException("YARN mode not available ?", e) } } diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 19d507c0cf860..720151a6b0f84 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -278,10 +278,11 @@ object SparkEnv extends Logging { addedJars: Seq[String], addedFiles: Seq[String]): Map[String, Seq[(String, String)]] = { + import Properties._ val jvmInformation = Seq( - ("Java Version", "%s (%s)".format(Properties.javaVersion, Properties.javaVendor)), - ("Java Home", Properties.javaHome), - ("Scala Version", Properties.versionString) + ("Java Version", s"$javaVersion ($javaVendor)"), + ("Java Home", javaHome), + ("Scala Version", versionString) ).sorted // Spark properties @@ -296,18 +297,15 @@ object SparkEnv extends Logging { // System properties that are not java classpaths val systemProperties = System.getProperties.iterator.toSeq - val otherProperties = systemProperties.filter { case (k, v) => + val otherProperties = systemProperties.filter { case (k, _) => k != "java.class.path" && !k.startsWith("spark.") }.sorted // Class paths including all added jars and files - val classPathProperty = systemProperties.find { case (k, v) => - k == "java.class.path" - }.getOrElse(("", "")) - val classPathEntries = classPathProperty._2 + val classPathEntries = javaClassPath .split(File.pathSeparator) - .filterNot(e => e.isEmpty) - .map(e => (e, "System Classpath")) + .filterNot(_.isEmpty) + .map((_, "System Classpath")) val addedJarsAndFiles = (addedJars ++ addedFiles).map((_, "Added By User")) val classPaths = (addedJarsAndFiles ++ classPathEntries).sorted diff --git a/core/src/main/scala/org/apache/spark/TaskKilledException.scala b/core/src/main/scala/org/apache/spark/TaskKilledException.scala index cbd6b2866e4f9..ad487c4efb87a 100644 --- a/core/src/main/scala/org/apache/spark/TaskKilledException.scala +++ b/core/src/main/scala/org/apache/spark/TaskKilledException.scala @@ -17,7 +17,11 @@ package org.apache.spark +import org.apache.spark.annotation.DeveloperApi + /** - * Exception for a task getting killed. + * :: DeveloperApi :: + * Exception thrown when a task is explicitly killed (i.e., task failure is expected). */ -private[spark] class TaskKilledException extends RuntimeException +@DeveloperApi +class TaskKilledException extends RuntimeException diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala index 8ae02154823ee..885c6829a2d72 100644 --- a/core/src/main/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -43,6 +43,7 @@ private[spark] object TestUtils { */ def createJarWithClasses(classNames: Seq[String], value: String = ""): URL = { val tempDir = Files.createTempDir() + tempDir.deleteOnExit() val files = for (name <- classNames) yield createCompiledClass(name, tempDir, value) val jarFile = new File(tempDir, "testJar-%s.jar".format(System.currentTimeMillis())) createJar(files, jarFile) diff --git a/core/src/main/scala/org/apache/spark/annotation/package-info.java b/core/src/main/scala/org/apache/spark/annotation/package-info.java new file mode 100644 index 0000000000000..12c7afe6f108a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/annotation/package-info.java @@ -0,0 +1,23 @@ +/* + * 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. + */ + +/** + * Spark annotations to mark an API experimental or intended only for advanced usages by developers. + * This package consist of these annotations, which are used project wide and are reflected in + * Scala and Java docs. + */ +package org.apache.spark.annotation; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/annotation/package.scala b/core/src/main/scala/org/apache/spark/annotation/package.scala new file mode 100644 index 0000000000000..c3f4026a29d44 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/annotation/package.scala @@ -0,0 +1,25 @@ +/* + * 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 + +/** + * Spark annotations to mark an API experimental or intended only for advanced usages by developers. + * This package consist of these annotations, which are used project wide and are reflected in + * Scala and Java docs. + */ +package object annotation diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 554c065358648..4c8f9ed6fbc02 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -263,6 +263,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) /** * Group the values for each key in the RDD into a single sequence. Allows controlling the * partitioning of the resulting key-value pair RDD by passing a Partitioner. + * + * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over + * each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]] + * will provide much better performance. */ def groupByKey(partitioner: Partitioner): JavaPairRDD[K, JIterable[V]] = fromRDD(groupByResultToJava(rdd.groupByKey(partitioner))) @@ -270,6 +274,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with into `numPartitions` partitions. + * + * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over + * each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]] + * will provide much better performance. */ def groupByKey(numPartitions: Int): JavaPairRDD[K, JIterable[V]] = fromRDD(groupByResultToJava(rdd.groupByKey(numPartitions))) @@ -380,6 +388,10 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with the existing partitioner/parallelism level. + * + * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over + * each key, using [[JavaPairRDD.reduceByKey]] or [[JavaPairRDD.combineByKey]] + * will provide much better performance. */ def groupByKey(): JavaPairRDD[K, JIterable[V]] = fromRDD(groupByResultToJava(rdd.groupByKey())) diff --git a/core/src/main/scala/org/apache/spark/api/java/package-info.java b/core/src/main/scala/org/apache/spark/api/java/package-info.java new file mode 100644 index 0000000000000..10a480fc78e44 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/java/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Spark Java programming APIs. + */ +package org.apache.spark.api.java; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 2971c277aa863..57b28b9972366 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -171,7 +171,7 @@ private[spark] class PythonRDD[T: ClassTag]( this.interrupt() } - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { try { SparkEnv.set(env) val stream = new BufferedOutputStream(worker.getOutputStream, bufferSize) @@ -282,7 +282,6 @@ private[spark] object PythonRDD { } } catch { case eof: EOFException => {} - case e: Throwable => throw e } JavaRDD.fromRDD(sc.sc.parallelize(objs, parallelism)) } diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala index 002f2acd94dee..759cbe2c46c52 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala @@ -71,7 +71,6 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String stopDaemon() startDaemon() new Socket(daemonHost, daemonPort) - case e: Throwable => throw e } } } diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index 78fc286e5192c..4f6cabaff2b99 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -112,7 +112,7 @@ private[spark] object HttpBroadcast extends Logging { private var securityManager: SecurityManager = null // TODO: This shouldn't be a global variable so that multiple SparkContexts can coexist - private val files = new TimeStampedHashSet[String] + private val files = new TimeStampedHashSet[File] private val httpReadTimeout = TimeUnit.MILLISECONDS.convert(5, TimeUnit.MINUTES).toInt private var compressionCodec: CompressionCodec = null private var cleaner: MetadataCleaner = null @@ -173,7 +173,7 @@ private[spark] object HttpBroadcast extends Logging { val serOut = ser.serializeStream(out) serOut.writeObject(value) serOut.close() - files += file.getAbsolutePath + files += file } def read[T: ClassTag](id: Long): T = { @@ -216,7 +216,7 @@ private[spark] object HttpBroadcast extends Logging { SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) if (removeFromDriver) { val file = getFile(id) - files.remove(file.toString) + files.remove(file) deleteBroadcastFile(file) } } @@ -232,7 +232,7 @@ private[spark] object HttpBroadcast extends Logging { val (file, time) = (entry.getKey, entry.getValue) if (time < cleanupTime) { iterator.remove() - deleteBroadcastFile(new File(file.toString)) + deleteBroadcastFile(file) } } } diff --git a/core/src/main/scala/org/apache/spark/broadcast/package-info.java b/core/src/main/scala/org/apache/spark/broadcast/package-info.java new file mode 100644 index 0000000000000..1510e6e84c7a8 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/broadcast/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Spark's broadcast variables, used to broadcast immutable datasets to all nodes. + */ +package org.apache.spark.broadcast; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/broadcast/package.scala b/core/src/main/scala/org/apache/spark/broadcast/package.scala index 01bf88629a7dd..4f2c3c07161a3 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/package.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/package.scala @@ -18,7 +18,7 @@ package org.apache.spark /** - * Package for broadcast variables. See [[broadcast.Broadcast]] for details. + * Spark's broadcast variables, used to broadcast immutable datasets to all nodes. */ package object broadcast { // For package docs only diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index 7ead1171525d2..aeb159adc31d9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -157,7 +157,7 @@ object Client { // TODO: See if we can initialize akka so return messages are sent back using the same TCP // flow. Else, this (sadly) requires the DriverClient be routable from the Master. val (actorSystem, _) = AkkaUtils.createActorSystem( - "driverClient", Utils.localHostName(), 0, false, conf, new SecurityManager(conf)) + "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf)) actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf)) diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala index e20d4486c8f0c..2dfa02bd26f13 100644 --- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala @@ -42,7 +42,7 @@ object PythonRunner { // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the // python directories in SPARK_HOME (if set), and any files in the pyFiles argument val pathElements = new ArrayBuffer[String] - pathElements ++= pyFiles.split(",") + pathElements ++= Option(pyFiles).getOrElse("").split(",") pathElements += PythonUtils.sparkPythonPath pathElements += sys.env.getOrElse("PYTHONPATH", "") val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*) diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index e2df1b8954124..148115d3ed351 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -103,7 +103,7 @@ object SparkHadoopUtil { .newInstance() .asInstanceOf[SparkHadoopUtil] } catch { - case th: Throwable => throw new SparkException("Unable to load YARN support", th) + case e: Exception => throw new SparkException("Unable to load YARN support", e) } } else { new SparkHadoopUtil 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 c6d3cbd2e728b..c54331c00fab8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -41,10 +41,10 @@ object SparkSubmit { private var clusterManager: Int = LOCAL /** - * A special jar name that indicates the class being run is inside of Spark itself, - * and therefore no user jar is needed. + * Special primary resource names that represent shells rather than application jars. */ - private val RESERVED_JAR_NAME = "spark-internal" + private val SPARK_SHELL = "spark-shell" + private val PYSPARK_SHELL = "pyspark-shell" def main(args: Array[String]) { val appArgs = new SparkSubmitArguments(args) @@ -68,11 +68,11 @@ object SparkSubmit { /** * @return a tuple containing the arguments for the child, a list of classpath - * entries for the child, a list of system propertes, a list of env vars + * entries for the child, a list of system properties, a list of env vars * and the main class for the child */ - private[spark] def createLaunchEnv(args: SparkSubmitArguments): (ArrayBuffer[String], - ArrayBuffer[String], Map[String, String], String) = { + private[spark] def createLaunchEnv(args: SparkSubmitArguments) + : (ArrayBuffer[String], ArrayBuffer[String], Map[String, String], String) = { if (args.master.startsWith("local")) { clusterManager = LOCAL } else if (args.master.startsWith("yarn")) { @@ -121,24 +121,30 @@ object SparkSubmit { printErrorAndExit("Cannot currently run driver on the cluster in Mesos") } - // If we're running a Python app, set the Java class to run to be our PythonRunner, add - // Python files to deployment list, and pass the main file and Python path to PythonRunner + // If we're running a python app, set the main class to our specific python runner if (isPython) { if (deployOnCluster) { printErrorAndExit("Cannot currently run Python driver programs on cluster") } - args.mainClass = "org.apache.spark.deploy.PythonRunner" - args.files = mergeFileLists(args.files, args.pyFiles, args.primaryResource) + if (args.primaryResource == PYSPARK_SHELL) { + args.mainClass = "py4j.GatewayServer" + args.childArgs = ArrayBuffer("--die-on-broken-pipe", "0") + } else { + // If a python file is provided, add it to the child arguments and list of files to deploy. + // Usage: PythonAppRunner
[app arguments] + args.mainClass = "org.apache.spark.deploy.PythonRunner" + args.childArgs = ArrayBuffer(args.primaryResource, args.pyFiles) ++ args.childArgs + args.files = mergeFileLists(args.files, args.primaryResource) + } val pyFiles = Option(args.pyFiles).getOrElse("") - args.childArgs = ArrayBuffer(args.primaryResource, pyFiles) ++ args.childArgs - args.primaryResource = RESERVED_JAR_NAME + args.files = mergeFileLists(args.files, pyFiles) sysProps("spark.submit.pyFiles") = pyFiles } // If we're deploying into YARN, use yarn.Client as a wrapper around the user class if (!deployOnCluster) { childMainClass = args.mainClass - if (args.primaryResource != RESERVED_JAR_NAME) { + if (isUserJar(args.primaryResource)) { childClasspath += args.primaryResource } } else if (clusterManager == YARN) { @@ -219,7 +225,7 @@ object SparkSubmit { // For python files, the primary resource is already distributed as a regular file if (!isYarnCluster && !isPython) { var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq()) - if (args.primaryResource != RESERVED_JAR_NAME) { + if (isUserJar(args.primaryResource)) { jars = jars ++ Seq(args.primaryResource) } sysProps.put("spark.jars", jars.mkString(",")) @@ -293,7 +299,7 @@ object SparkSubmit { } private def addJarToClasspath(localJar: String, loader: ExecutorURLClassLoader) { - val localJarFile = new File(new URI(localJar).getPath()) + val localJarFile = new File(localJar) if (!localJarFile.exists()) { printWarning(s"Jar $localJar does not exist, skipping.") } @@ -302,6 +308,27 @@ object SparkSubmit { loader.addURL(url) } + /** + * Return whether the given primary resource represents a user jar. + */ + private def isUserJar(primaryResource: String): Boolean = { + !isShell(primaryResource) && !isPython(primaryResource) + } + + /** + * Return whether the given primary resource represents a shell. + */ + private def isShell(primaryResource: String): Boolean = { + primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL + } + + /** + * Return whether the given primary resource requires running python. + */ + private[spark] def isPython(primaryResource: String): Boolean = { + primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL + } + /** * Merge a sequence of comma-separated file lists, some of which may be null to indicate * no files, into a single comma-separated string. diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index 2d327aa3fb27f..0cc05fb95aef0 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -298,11 +298,13 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { case v => primaryResource = v inSparkOpts = false - isPython = v.endsWith(".py") + isPython = SparkSubmit.isPython(v) parse(tail) } } else { - childArgs += value + if (!value.isEmpty) { + childArgs += value + } parse(tail) } @@ -324,8 +326,7 @@ private[spark] class SparkSubmitArguments(args: Seq[String]) { | --class CLASS_NAME Your application's main class (for Java / Scala apps). | --name NAME A name of your application. | --jars JARS Comma-separated list of local jars to include on the driver - | and executor classpaths. Doesn't work for drivers in - | standalone mode with "cluster" deploy mode. + | and executor classpaths. | --py-files PY_FILES Comma-separated list of .zip or .egg files to place on the | PYTHONPATH for Python apps. | --files FILES Comma-separated list of files to be placed in the working diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala index 896913d796604..d38e9e79204c2 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala @@ -30,7 +30,7 @@ import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.Master -import org.apache.spark.util.AkkaUtils +import org.apache.spark.util.{Utils, AkkaUtils} /** * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL, @@ -88,13 +88,15 @@ private[spark] class AppClient( var retries = 0 registrationRetryTimer = Some { context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) { - retries += 1 - if (registered) { - registrationRetryTimer.foreach(_.cancel()) - } else if (retries >= REGISTRATION_RETRIES) { - markDead("All masters are unresponsive! Giving up.") - } else { - tryRegisterAllMasters() + Utils.tryOrExit { + retries += 1 + if (registered) { + registrationRetryTimer.foreach(_.cancel()) + } else if (retries >= REGISTRATION_RETRIES) { + markDead("All masters are unresponsive! Giving up.") + } else { + tryRegisterAllMasters() + } } } } 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 1238bbf9da2fd..a9c11dca5678e 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 @@ -70,7 +70,7 @@ class HistoryServer( * TODO: Add a mechanism to update manually. */ private val logCheckingThread = new Thread { - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { while (!stopped) { val now = System.currentTimeMillis if (now - lastLogCheckTime > UPDATE_INTERVAL_MS) { @@ -154,7 +154,7 @@ class HistoryServer( numCompletedApplications = logInfos.size } catch { - case t: Throwable => logError("Exception in checking for event log updates", t) + case e: Exception => logError("Exception in checking for event log updates", e) } } else { logWarning("Attempted to check for event log updates before binding the server.") @@ -231,8 +231,8 @@ class HistoryServer( dir.getModificationTime } } catch { - case t: Throwable => - logError("Exception in accessing modification time of %s".format(dir.getPath), t) + case e: Exception => + logError("Exception in accessing modification time of %s".format(dir.getPath), e) -1L } } 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 f254f5585ba25..c6dec305bffcb 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 @@ -684,8 +684,8 @@ private[spark] class Master( webUi.attachSparkUI(ui) return true } catch { - case t: Throwable => - logError("Exception in replaying log for application %s (%s)".format(appName, app.id), t) + case e: Exception => + logError("Exception in replaying log for application %s (%s)".format(appName, app.id), e) } } else { logWarning("Application %s (%s) has no valid logs: %s".format(appName, app.id, eventLogDir)) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala index c7f0f244ea2fc..4af5bc3afad6c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala @@ -65,6 +65,8 @@ object CommandUtils extends Logging { Seq() } + val permGenOpt = Seq("-XX:MaxPermSize=128m") + // Figure out our classpath with the external compute-classpath script val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh" val classPath = Utils.executeAndGetOutput( @@ -73,7 +75,7 @@ object CommandUtils extends Logging { val userClassPath = command.classPathEntries ++ Seq(classPath) Seq("-cp", userClassPath.filterNot(_.isEmpty).mkString(File.pathSeparator)) ++ - libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts + permGenOpt ++ libraryOpts ++ extraOpts ++ workerLocalOpts ++ memoryOpts } /** Spawn a thread that will redirect a given stream to a file */ diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala index be15138f62406..05e242e6df702 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala @@ -31,7 +31,7 @@ object DriverWrapper { case workerUrl :: mainClass :: extraArgs => val conf = new SparkConf() val (actorSystem, _) = AkkaUtils.createActorSystem("Driver", - Utils.localHostName(), 0, false, conf, new SecurityManager(conf)) + Utils.localHostName(), 0, conf, new SecurityManager(conf)) actorSystem.actorOf(Props(classOf[WorkerWatcher], workerUrl), name = "workerWatcher") // Delegate to supplied main class diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 85d25dc7dbfa4..fb9cc116cd08b 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -65,7 +65,7 @@ private[spark] class Worker( val REGISTRATION_TIMEOUT = 20.seconds val REGISTRATION_RETRIES = 3 - val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", true) + val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false) // How often worker will clean up old app folders val CLEANUP_INTERVAL_MILLIS = conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000 // TTL for app folders/data; after TTL expires it will be cleaned up @@ -166,14 +166,16 @@ private[spark] class Worker( var retries = 0 registrationRetryTimer = Some { context.system.scheduler.schedule(REGISTRATION_TIMEOUT, REGISTRATION_TIMEOUT) { - retries += 1 - if (registered) { - registrationRetryTimer.foreach(_.cancel()) - } else if (retries >= REGISTRATION_RETRIES) { - logError("All masters are unresponsive! Giving up.") - System.exit(1) - } else { - tryRegisterAllMasters() + Utils.tryOrExit { + retries += 1 + if (registered) { + registrationRetryTimer.foreach(_.cancel()) + } else if (retries >= REGISTRATION_RETRIES) { + logError("All masters are unresponsive! Giving up.") + System.exit(1) + } else { + tryRegisterAllMasters() + } } } } diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index e912ae8a5d3c5..84aec65b7765d 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -105,7 +105,7 @@ private[spark] object CoarseGrainedExecutorBackend { // Create a new ActorSystem to run the backend, because we can't create a // SparkEnv / Executor before getting started with all our system properties, etc val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0, - indestructible = true, conf = conf, new SecurityManager(conf)) + conf, new SecurityManager(conf)) // set it val sparkHostPort = hostname + ":" + boundPort actorSystem.actorOf( diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 98e7e0be813be..baee7a216a7c3 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -74,28 +74,7 @@ private[spark] class Executor( // Setup an uncaught exception handler for non-local mode. // Make any thread terminations due to uncaught exceptions kill the entire // executor process to avoid surprising stalls. - Thread.setDefaultUncaughtExceptionHandler( - new Thread.UncaughtExceptionHandler { - override def uncaughtException(thread: Thread, exception: Throwable) { - try { - logError("Uncaught exception in thread " + thread, exception) - - // We may have been called from a shutdown hook. If so, we must not call System.exit(). - // (If we do, we will deadlock.) - if (!Utils.inShutdown()) { - if (exception.isInstanceOf[OutOfMemoryError]) { - System.exit(ExecutorExitCode.OOM) - } else { - System.exit(ExecutorExitCode.UNCAUGHT_EXCEPTION) - } - } - } catch { - case oom: OutOfMemoryError => Runtime.getRuntime.halt(ExecutorExitCode.OOM) - case t: Throwable => Runtime.getRuntime.halt(ExecutorExitCode.UNCAUGHT_EXCEPTION_TWICE) - } - } - } - ) + Thread.setDefaultUncaughtExceptionHandler(ExecutorUncaughtExceptionHandler) } val executorSource = new ExecutorSource(this, executorId) @@ -259,6 +238,11 @@ private[spark] class Executor( } case t: Throwable => { + // Attempt to exit cleanly by informing the driver of our failure. + // If anything goes wrong (or this was a fatal exception), we will delegate to + // the default uncaught exception handler, which will terminate the Executor. + logError("Exception in task ID " + taskId, t) + val serviceTime = System.currentTimeMillis() - taskStart val metrics = attemptedTask.flatMap(t => t.metrics) for (m <- metrics) { @@ -268,10 +252,11 @@ private[spark] class Executor( val reason = ExceptionFailure(t.getClass.getName, t.toString, t.getStackTrace, metrics) execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) - // TODO: Should we exit the whole executor here? On the one hand, the failed task may - // have left some weird state around depending on when the exception was thrown, but on - // the other hand, maybe we could detect that when future tasks fail and exit then. - logError("Exception in task ID " + taskId, t) + // Don't forcibly exit unless the exception was inherently fatal, to avoid + // stopping other tasks unnecessarily. + if (Utils.isFatalError(t)) { + ExecutorUncaughtExceptionHandler.uncaughtException(t) + } } } finally { // TODO: Unregister shuffle memory only for ResultTask diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala new file mode 100644 index 0000000000000..b0e984c03964c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorUncaughtExceptionHandler.scala @@ -0,0 +1,53 @@ +/* + * 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.executor + +import org.apache.spark.Logging +import org.apache.spark.util.Utils + +/** + * The default uncaught exception handler for Executors terminates the whole process, to avoid + * getting into a bad state indefinitely. Since Executors are relatively lightweight, it's better + * to fail fast when things go wrong. + */ +private[spark] object ExecutorUncaughtExceptionHandler + extends Thread.UncaughtExceptionHandler with Logging { + + override def uncaughtException(thread: Thread, exception: Throwable) { + try { + logError("Uncaught exception in thread " + thread, exception) + + // We may have been called from a shutdown hook. If so, we must not call System.exit(). + // (If we do, we will deadlock.) + if (!Utils.inShutdown()) { + if (exception.isInstanceOf[OutOfMemoryError]) { + System.exit(ExecutorExitCode.OOM) + } else { + System.exit(ExecutorExitCode.UNCAUGHT_EXCEPTION) + } + } + } catch { + case oom: OutOfMemoryError => Runtime.getRuntime.halt(ExecutorExitCode.OOM) + case t: Throwable => Runtime.getRuntime.halt(ExecutorExitCode.UNCAUGHT_EXCEPTION_TWICE) + } + } + + def uncaughtException(exception: Throwable) { + uncaughtException(Thread.currentThread(), exception) + } +} diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala index 9b56f711e0e0b..74100498bb2bd 100644 --- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala @@ -19,7 +19,7 @@ package org.apache.spark.executor import java.nio.ByteBuffer -import com.google.protobuf.ByteString +import org.apache.mesos.protobuf.ByteString import org.apache.mesos.{Executor => MesosExecutor, ExecutorDriver, MesosExecutorDriver, MesosNativeLibrary} import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _} diff --git a/core/src/main/scala/org/apache/spark/executor/package-info.java b/core/src/main/scala/org/apache/spark/executor/package-info.java new file mode 100644 index 0000000000000..dd3b6815fb45f --- /dev/null +++ b/core/src/main/scala/org/apache/spark/executor/package-info.java @@ -0,0 +1,21 @@ +/* + * 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 for executor components used with various cluster managers. + */ +package org.apache.spark.executor; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/executor/package.scala b/core/src/main/scala/org/apache/spark/executor/package.scala new file mode 100644 index 0000000000000..ef471d761d4b3 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/executor/package.scala @@ -0,0 +1,24 @@ +/* + * 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 + +/** + * Executor components used with various cluster managers. + * See [[org.apache.spark.executor.Executor]]. + */ +package object executor diff --git a/core/src/main/scala/org/apache/spark/io/package-info.java b/core/src/main/scala/org/apache/spark/io/package-info.java new file mode 100644 index 0000000000000..bea1bfdb63751 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/io/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * IO codecs used for compression. + */ +package org.apache.spark.io; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/io/package.scala b/core/src/main/scala/org/apache/spark/io/package.scala new file mode 100644 index 0000000000000..f987e66a5dc44 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/io/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * IO codecs used for compression. See [[org.apache.spark.io.CompressionCodec]]. + */ +package object io diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/package.scala b/core/src/main/scala/org/apache/spark/metrics/sink/package.scala new file mode 100644 index 0000000000000..90e3aa70b99ef --- /dev/null +++ b/core/src/main/scala/org/apache/spark/metrics/sink/package.scala @@ -0,0 +1,23 @@ +/* + * 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.metrics + +/** + * Sinks used in Spark's metrics system. + */ +package object sink diff --git a/core/src/main/scala/org/apache/spark/metrics/source/package.scala b/core/src/main/scala/org/apache/spark/metrics/source/package.scala new file mode 100644 index 0000000000000..1e59fed6f7d7a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/metrics/source/package.scala @@ -0,0 +1,23 @@ +/* + * 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.metrics + +/** + * Sources used in Spark's metrics system. + */ +package object source diff --git a/core/src/main/scala/org/apache/spark/partial/package.scala b/core/src/main/scala/org/apache/spark/partial/package.scala new file mode 100644 index 0000000000000..62dc5cd25a164 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/partial/package.scala @@ -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. + */ + +package org.apache.spark + +/** + * :: Experimental :: + * + * Support for approximate results. This provides convenient api and also implementation for + * approximate calculation. + * + * @see [[org.apache.spark.rdd.RDD.countApprox]] + */ +package object partial 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 5efb4388f6c71..223fef79261d0 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -217,7 +217,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Return approximate number of distinct values for each key in this RDD. * The accuracy of approximation can be controlled through the relative standard deviation * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in - * more accurate counts but increase the memory footprint and vise versa. Uses the provided + * more accurate counts but increase the memory footprint and vice versa. Uses the provided * Partitioner to partition the output RDD. */ def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = { @@ -232,7 +232,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Return approximate number of distinct values for each key in this RDD. * The accuracy of approximation can be controlled through the relative standard deviation * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in - * more accurate counts but increase the memory footprint and vise versa. HashPartitions the + * more accurate counts but increase the memory footprint and vice versa. HashPartitions the * output RDD into numPartitions. * */ @@ -244,7 +244,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Return approximate number of distinct values for each key this RDD. * The accuracy of approximation can be controlled through the relative standard deviation * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in - * more accurate counts but increase the memory footprint and vise versa. The default value of + * more accurate counts but increase the memory footprint and vice versa. The default value of * relativeSD is 0.05. Hash-partitions the output RDD using the existing partitioner/parallelism * level. */ @@ -264,6 +264,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** * Group the values for each key in the RDD into a single sequence. Allows controlling the * partitioning of the resulting key-value pair RDD by passing a Partitioner. + * + * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over + * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]] + * will provide much better performance. */ def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = { // groupByKey shouldn't use map side combine because map side combine does not @@ -280,6 +284,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with into `numPartitions` partitions. + * + * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over + * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]] + * will provide much better performance. */ def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = { groupByKey(new HashPartitioner(numPartitions)) @@ -365,6 +373,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** * Group the values for each key in the RDD into a single sequence. Hash-partitions the * resulting RDD with the existing partitioner/parallelism level. + * + * Note: If you are grouping in order to perform an aggregation (such as a sum or average) over + * each key, using [[PairRDDFunctions.reduceByKey]] or [[PairRDDFunctions.combineByKey]] + * will provide much better performance, */ def groupByKey(): RDD[(K, Iterable[V])] = { groupByKey(defaultPartitioner(self)) diff --git a/core/src/main/scala/org/apache/spark/rdd/package-info.java b/core/src/main/scala/org/apache/spark/rdd/package-info.java new file mode 100644 index 0000000000000..176cc58179fb0 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Provides implementation's of various RDDs. + */ +package org.apache.spark.rdd; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/rdd/package.scala b/core/src/main/scala/org/apache/spark/rdd/package.scala new file mode 100644 index 0000000000000..55fc6e4d2b4df --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Provides several RDD implementations. See [[org.apache.spark.rdd.RDD]]. + */ +package object rdd diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala index 7968a0691db10..a90b0d475c04e 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala @@ -206,8 +206,8 @@ private[spark] object EventLoggingListener extends Logging { applicationComplete = filePaths.exists { path => isApplicationCompleteFile(path.getName) } ) } catch { - case t: Throwable => - logError("Exception in parsing logging info from directory %s".format(logDir), t) + case e: Exception => + logError("Exception in parsing logging info from directory %s".format(logDir), e) EventLoggingInfo.empty } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala index dec3316bf7745..36a6e6338faa6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/LiveListenerBus.scala @@ -20,6 +20,7 @@ package org.apache.spark.scheduler import java.util.concurrent.{LinkedBlockingQueue, Semaphore} import org.apache.spark.Logging +import org.apache.spark.util.Utils /** * Asynchronously passes SparkListenerEvents to registered SparkListeners. @@ -42,7 +43,7 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging { private val listenerThread = new Thread("SparkListenerBus") { setDaemon(true) - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { while (true) { eventLock.acquire() // Atomically remove and process this event @@ -77,11 +78,8 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging { val eventAdded = eventQueue.offer(event) if (eventAdded) { eventLock.release() - } else if (!queueFullErrorMessageLogged) { - logError("Dropping SparkListenerEvent because no remaining room in event queue. " + - "This likely means one of the SparkListeners is too slow and cannot keep up with the " + - "rate at which tasks are being started by the scheduler.") - queueFullErrorMessageLogged = true + } else { + logQueueFullErrorMessage() } } @@ -96,13 +94,18 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging { if (System.currentTimeMillis > finishTime) { return false } - /* Sleep rather than using wait/notify, because this is used only for testing and wait/notify - * add overhead in the general case. */ + /* Sleep rather than using wait/notify, because this is used only for testing and + * wait/notify add overhead in the general case. */ Thread.sleep(10) } true } + /** + * For testing only. Return whether the listener daemon thread is still alive. + */ + def listenerThreadIsAlive: Boolean = synchronized { listenerThread.isAlive } + /** * Return whether the event queue is empty. * @@ -111,6 +114,23 @@ private[spark] class LiveListenerBus extends SparkListenerBus with Logging { */ def queueIsEmpty: Boolean = synchronized { eventQueue.isEmpty } + /** + * Log an error message to indicate that the event queue is full. Do this only once. + */ + private def logQueueFullErrorMessage(): Unit = { + if (!queueFullErrorMessageLogged) { + if (listenerThread.isAlive) { + logError("Dropping SparkListenerEvent because no remaining room in event queue. " + + "This likely means one of the SparkListeners is too slow and cannot keep up with" + + "the rate at which tasks are being started by the scheduler.") + } else { + logError("SparkListenerBus thread is dead! This means SparkListenerEvents have not" + + "been (and will no longer be) propagated to listeners for some time.") + } + queueFullErrorMessageLogged = true + } + } + def stop() { if (!started) { throw new IllegalStateException("Attempted to stop a listener bus that has not yet started!") diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala index 187672c4e19e7..174b73221afc0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala @@ -17,8 +17,10 @@ package org.apache.spark.scheduler +import java.util.concurrent.{ConcurrentHashMap, ConcurrentLinkedQueue} + +import scala.collection.JavaConversions._ import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap import org.apache.spark.Logging import org.apache.spark.scheduler.SchedulingMode.SchedulingMode @@ -35,18 +37,15 @@ private[spark] class Pool( extends Schedulable with Logging { - var schedulableQueue = new ArrayBuffer[Schedulable] - var schedulableNameToSchedulable = new HashMap[String, Schedulable] - + val schedulableQueue = new ConcurrentLinkedQueue[Schedulable] + val schedulableNameToSchedulable = new ConcurrentHashMap[String, Schedulable] var weight = initWeight var minShare = initMinShare var runningTasks = 0 - var priority = 0 // A pool's stage id is used to break the tie in scheduling. var stageId = -1 - var name = poolName var parent: Pool = null @@ -60,19 +59,20 @@ private[spark] class Pool( } override def addSchedulable(schedulable: Schedulable) { - schedulableQueue += schedulable - schedulableNameToSchedulable(schedulable.name) = schedulable + require(schedulable != null) + schedulableQueue.add(schedulable) + schedulableNameToSchedulable.put(schedulable.name, schedulable) schedulable.parent = this } override def removeSchedulable(schedulable: Schedulable) { - schedulableQueue -= schedulable - schedulableNameToSchedulable -= schedulable.name + schedulableQueue.remove(schedulable) + schedulableNameToSchedulable.remove(schedulable.name) } override def getSchedulableByName(schedulableName: String): Schedulable = { - if (schedulableNameToSchedulable.contains(schedulableName)) { - return schedulableNameToSchedulable(schedulableName) + if (schedulableNameToSchedulable.containsKey(schedulableName)) { + return schedulableNameToSchedulable.get(schedulableName) } for (schedulable <- schedulableQueue) { val sched = schedulable.getSchedulableByName(schedulableName) @@ -95,11 +95,12 @@ private[spark] class Pool( shouldRevive } - override def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] = { + override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = { var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager] - val sortedSchedulableQueue = schedulableQueue.sortWith(taskSetSchedulingAlgorithm.comparator) + val sortedSchedulableQueue = + schedulableQueue.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator) for (schedulable <- sortedSchedulableQueue) { - sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue() + sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue } sortedTaskSetQueue } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala index ed24eb6a549dd..a87ef030e69c2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala @@ -17,6 +17,8 @@ package org.apache.spark.scheduler +import java.util.concurrent.ConcurrentLinkedQueue + import scala.collection.mutable.ArrayBuffer import org.apache.spark.scheduler.SchedulingMode.SchedulingMode @@ -28,7 +30,7 @@ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode private[spark] trait Schedulable { var parent: Pool // child queues - def schedulableQueue: ArrayBuffer[Schedulable] + def schedulableQueue: ConcurrentLinkedQueue[Schedulable] def schedulingMode: SchedulingMode def weight: Int def minShare: Int @@ -42,5 +44,5 @@ private[spark] trait Schedulable { def getSchedulableByName(name: String): Schedulable def executorLost(executorId: String, host: String): Unit def checkSpeculatableTasks(): Boolean - def getSortedTaskSetQueue(): ArrayBuffer[TaskSetManager] + def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala index 0286aac8769b2..ed9fb24bc8ce8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListenerBus.scala @@ -20,10 +20,13 @@ package org.apache.spark.scheduler import scala.collection.mutable import scala.collection.mutable.ArrayBuffer +import org.apache.spark.Logging +import org.apache.spark.util.Utils + /** * A SparkListenerEvent bus that relays events to its listeners */ -private[spark] trait SparkListenerBus { +private[spark] trait SparkListenerBus extends Logging { // SparkListeners attached to this event bus protected val sparkListeners = new ArrayBuffer[SparkListener] @@ -34,38 +37,53 @@ private[spark] trait SparkListenerBus { } /** - * Post an event to all attached listeners. This does nothing if the event is - * SparkListenerShutdown. + * Post an event to all attached listeners. + * This does nothing if the event is SparkListenerShutdown. */ def postToAll(event: SparkListenerEvent) { event match { case stageSubmitted: SparkListenerStageSubmitted => - sparkListeners.foreach(_.onStageSubmitted(stageSubmitted)) + foreachListener(_.onStageSubmitted(stageSubmitted)) case stageCompleted: SparkListenerStageCompleted => - sparkListeners.foreach(_.onStageCompleted(stageCompleted)) + foreachListener(_.onStageCompleted(stageCompleted)) case jobStart: SparkListenerJobStart => - sparkListeners.foreach(_.onJobStart(jobStart)) + foreachListener(_.onJobStart(jobStart)) case jobEnd: SparkListenerJobEnd => - sparkListeners.foreach(_.onJobEnd(jobEnd)) + foreachListener(_.onJobEnd(jobEnd)) case taskStart: SparkListenerTaskStart => - sparkListeners.foreach(_.onTaskStart(taskStart)) + foreachListener(_.onTaskStart(taskStart)) case taskGettingResult: SparkListenerTaskGettingResult => - sparkListeners.foreach(_.onTaskGettingResult(taskGettingResult)) + foreachListener(_.onTaskGettingResult(taskGettingResult)) case taskEnd: SparkListenerTaskEnd => - sparkListeners.foreach(_.onTaskEnd(taskEnd)) + foreachListener(_.onTaskEnd(taskEnd)) case environmentUpdate: SparkListenerEnvironmentUpdate => - sparkListeners.foreach(_.onEnvironmentUpdate(environmentUpdate)) + foreachListener(_.onEnvironmentUpdate(environmentUpdate)) case blockManagerAdded: SparkListenerBlockManagerAdded => - sparkListeners.foreach(_.onBlockManagerAdded(blockManagerAdded)) + foreachListener(_.onBlockManagerAdded(blockManagerAdded)) case blockManagerRemoved: SparkListenerBlockManagerRemoved => - sparkListeners.foreach(_.onBlockManagerRemoved(blockManagerRemoved)) + foreachListener(_.onBlockManagerRemoved(blockManagerRemoved)) case unpersistRDD: SparkListenerUnpersistRDD => - sparkListeners.foreach(_.onUnpersistRDD(unpersistRDD)) + foreachListener(_.onUnpersistRDD(unpersistRDD)) case applicationStart: SparkListenerApplicationStart => - sparkListeners.foreach(_.onApplicationStart(applicationStart)) + foreachListener(_.onApplicationStart(applicationStart)) case applicationEnd: SparkListenerApplicationEnd => - sparkListeners.foreach(_.onApplicationEnd(applicationEnd)) + foreachListener(_.onApplicationEnd(applicationEnd)) case SparkListenerShutdown => } } + + /** + * Apply the given function to all attached listeners, catching and logging any exception. + */ + private def foreachListener(f: SparkListener => Unit): Unit = { + sparkListeners.foreach { listener => + try { + f(listener) + } catch { + case e: Exception => + logError(s"Listener ${Utils.getFormattedClassName(listener)} threw an exception", e) + } + } + } + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 2ca3479c80efc..5871edeb856ad 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -33,7 +33,7 @@ import org.apache.spark.util.ByteBufferInputStream * - [[org.apache.spark.scheduler.ResultTask]] * * A Spark job consists of one or more stages. The very last stage in a job consists of multiple - * ResultTask's, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task + * ResultTasks, while earlier stages consist of ShuffleMapTasks. A ResultTask executes the task * and sends the task output back to the driver application. A ShuffleMapTask executes the task * and divides the task output to multiple buckets (based on the task's partitioner). * diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala index c9ad2b151daf0..99d305b36a959 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -43,7 +43,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul def enqueueSuccessfulTask( taskSetManager: TaskSetManager, tid: Long, serializedData: ByteBuffer) { getTaskResultExecutor.execute(new Runnable { - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { try { val result = serializer.get().deserialize[TaskResult[_]](serializedData) match { case directResult: DirectTaskResult[_] => directResult @@ -70,7 +70,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul case cnf: ClassNotFoundException => val loader = Thread.currentThread.getContextClassLoader taskSetManager.abort("ClassNotFound with classloader: " + loader) - case ex: Throwable => + case ex: Exception => taskSetManager.abort("Exception while deserializing and fetching task: %s".format(ex)) } } @@ -81,7 +81,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul serializedData: ByteBuffer) { var reason : TaskEndReason = UnknownReason getTaskResultExecutor.execute(new Runnable { - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { try { if (serializedData != null && serializedData.limit() > 0) { reason = serializer.get().deserialize[TaskEndReason]( @@ -94,7 +94,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul val loader = Utils.getContextOrSparkClassLoader logError( "Could not deserialize TaskEndReason: ClassNotFound with classloader " + loader) - case ex: Throwable => {} + case ex: Exception => {} } scheduler.handleFailedTask(taskSetManager, tid, taskState, reason) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 5a68f38bc5844..17292b4c15b8b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -31,6 +31,7 @@ import scala.util.Random import org.apache.spark._ import org.apache.spark.TaskState.TaskState import org.apache.spark.scheduler.SchedulingMode.SchedulingMode +import org.apache.spark.util.Utils /** * Schedules tasks for multiple types of clusters by acting through a SchedulerBackend. @@ -104,7 +105,7 @@ private[spark] class TaskSchedulerImpl( SchedulingMode.withName(schedulingModeConf.toUpperCase) } catch { case e: java.util.NoSuchElementException => - throw new SparkException(s"Urecognized spark.scheduler.mode: $schedulingModeConf") + throw new SparkException(s"Unrecognized spark.scheduler.mode: $schedulingModeConf") } // This is a var so that we can reset it for testing purposes. @@ -139,7 +140,7 @@ private[spark] class TaskSchedulerImpl( import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, SPECULATION_INTERVAL milliseconds) { - checkSpeculatableTasks() + Utils.tryOrExit { checkSpeculatableTasks() } } } } @@ -222,7 +223,7 @@ private[spark] class TaskSchedulerImpl( // Build a list of tasks to assign to each worker. val tasks = shuffledOffers.map(o => new ArrayBuffer[TaskDescription](o.cores)) val availableCpus = shuffledOffers.map(o => o.cores).toArray - val sortedTaskSets = rootPool.getSortedTaskSetQueue() + val sortedTaskSets = rootPool.getSortedTaskSetQueue for (taskSet <- sortedTaskSets) { logDebug("parentName: %s, name: %s, runningTasks: %s".format( taskSet.parent.name, taskSet.name, taskSet.runningTasks)) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index cefa41729964a..9c07b3f7b695a 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -46,15 +46,16 @@ private[spark] class SparkDeploySchedulerBackend( CoarseGrainedSchedulerBackend.ACTOR_NAME) val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}") val extraJavaOpts = sc.conf.getOption("spark.executor.extraJavaOptions") - val classPathEntries = sys.props.get("spark.executor.extraClassPath").toSeq.flatMap { cp => - cp.split(java.io.File.pathSeparator) - } - val libraryPathEntries = sys.props.get("spark.executor.extraLibraryPath").toSeq.flatMap { cp => + val classPathEntries = sc.conf.getOption("spark.executor.extraClassPath").toSeq.flatMap { cp => cp.split(java.io.File.pathSeparator) } + val libraryPathEntries = + sc.conf.getOption("spark.executor.extraLibraryPath").toSeq.flatMap { cp => + cp.split(java.io.File.pathSeparator) + } val command = Command( - "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.testExecutorEnvs, + "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs, classPathEntries, libraryPathEntries, extraJavaOpts) val sparkHome = sc.getSparkHome() val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command, @@ -87,6 +88,8 @@ private[spark] class SparkDeploySchedulerBackend( if (!stopping) { logError("Application has been killed. Reason: " + reason) scheduler.error(reason) + // Ensure the application terminates, as we can no longer run jobs. + sc.stop() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index 2cd9d6c12eaf7..cbe9bb093d1c9 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -122,7 +122,7 @@ private[spark] class CoarseMesosSchedulerBackend( val extraLibraryPath = conf.getOption(libraryPathOption).map(p => s"-Djava.library.path=$p") val extraOpts = Seq(extraJavaOpts, extraLibraryPath).flatten.mkString(" ") - sc.testExecutorEnvs.foreach { case (key, value) => + sc.executorEnvs.foreach { case (key, value) => environment.addVariables(Environment.Variable.newBuilder() .setName(key) .setValue(value) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index c975f312324ed..f08b19e6782e3 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -24,7 +24,7 @@ import java.util.Collections import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import com.google.protobuf.ByteString +import org.apache.mesos.protobuf.ByteString import org.apache.mesos.{Scheduler => MScheduler} import org.apache.mesos._ import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} @@ -90,7 +90,7 @@ private[spark] class MesosSchedulerBackend( "Spark home is not set; set it through the spark.home system " + "property, the SPARK_HOME environment variable or the SparkContext constructor")) val environment = Environment.newBuilder() - sc.testExecutorEnvs.foreach { case (key, value) => + sc.executorEnvs.foreach { case (key, value) => environment.addVariables(Environment.Variable.newBuilder() .setName(key) .setValue(value) @@ -175,7 +175,7 @@ private[spark] class MesosSchedulerBackend( override def reregistered(d: SchedulerDriver, masterInfo: MasterInfo) {} /** - * Method called by Mesos to offer resources on slaves. We resond by asking our active task sets + * Method called by Mesos to offer resources on slaves. We respond by asking our active task sets * for tasks in order of priority. We fill each node with tasks in a round-robin manner so that * tasks are balanced across the cluster. */ diff --git a/core/src/main/scala/org/apache/spark/scheduler/package-info.java b/core/src/main/scala/org/apache/spark/scheduler/package-info.java new file mode 100644 index 0000000000000..5b4a628d3cee4 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Spark's DAG scheduler. + */ +package org.apache.spark.scheduler; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/scheduler/package.scala b/core/src/main/scala/org/apache/spark/scheduler/package.scala new file mode 100644 index 0000000000000..f0dbfc2ac5f48 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/package.scala @@ -0,0 +1,24 @@ +/* + * 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 + +/** + * Spark's scheduling components. This includes the [[org.apache.spark.scheduler.DAGScheduler]] and + * lower level [[org.apache.spark.scheduler.TaskScheduler]]. + */ +package object scheduler diff --git a/core/src/main/scala/org/apache/spark/serializer/package-info.java b/core/src/main/scala/org/apache/spark/serializer/package-info.java new file mode 100644 index 0000000000000..4c0b73ab36a00 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/serializer/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Pluggable serializers for RDD and shuffle data. + */ +package org.apache.spark.serializer; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/serializer/package.scala b/core/src/main/scala/org/apache/spark/serializer/package.scala new file mode 100644 index 0000000000000..8f09ef6f5b34c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/serializer/package.scala @@ -0,0 +1,25 @@ +/* + * 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 + +/** + * Pluggable serializers for RDD and shuffle data. + * + * @see [[org.apache.spark.serializer.Serializer]] + */ +package object serializer diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 6d7d4f922e1fa..6534095811907 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -155,7 +155,7 @@ private[spark] class BlockManager( BlockManagerWorker.startBlockManagerWorker(this) if (!BlockManager.getDisableHeartBeatsForTesting(conf)) { heartBeatTask = actorSystem.scheduler.schedule(0.seconds, heartBeatFrequency.milliseconds) { - heartBeat() + Utils.tryOrExit { heartBeat() } } } } diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index cf6ef0029a861..3a7243a1ba19c 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -148,7 +148,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD private def addShutdownHook() { localDirs.foreach(localDir => Utils.registerShutdownDeleteDir(localDir)) Runtime.getRuntime.addShutdownHook(new Thread("delete Spark local dirs") { - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { logDebug("Shutdown hook called") DiskBlockManager.this.stop() } @@ -162,8 +162,8 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD try { if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir) } catch { - case t: Throwable => - logError("Exception while deleting local spark dir: " + localDir, t) + case e: Exception => + logError("Exception while deleting local spark dir: " + localDir, e) } } } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index b0b9674856568..a6cbe3aa440ff 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -25,7 +25,6 @@ import tachyon.client.TachyonFile import org.apache.spark.Logging import org.apache.spark.executor.ExecutorExitCode -import org.apache.spark.network.netty.ShuffleSender import org.apache.spark.util.Utils @@ -137,7 +136,7 @@ private[spark] class TachyonBlockManager( private def addShutdownHook() { tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) Runtime.getRuntime.addShutdownHook(new Thread("delete Spark tachyon dirs") { - override def run() { + override def run(): Unit = Utils.logUncaughtExceptions { logDebug("Shutdown hook called") tachyonDirs.foreach { tachyonDir => try { @@ -145,8 +144,8 @@ private[spark] class TachyonBlockManager( Utils.deleteRecursively(tachyonDir, client) } } catch { - case t: Throwable => - logError("Exception while deleting tachyon spark dir: " + tachyonDir, t) + case e: Exception => + logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) } } } 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 a3d6a1821245b..a43314f48112f 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -36,7 +36,13 @@ private[spark] object UIUtils extends Logging { def formatDate(timestamp: Long): String = dateFormat.get.format(new Date(timestamp)) def formatDuration(milliseconds: Long): String = { + if (milliseconds < 100) { + return "%d ms".format(milliseconds) + } val seconds = milliseconds.toDouble / 1000 + if (seconds < 1) { + return "%.1f s".format(seconds) + } if (seconds < 60) { return "%.0f s".format(seconds) } 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 8afe09a117ebc..a8d12bb2a0165 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -20,7 +20,7 @@ package org.apache.spark.util import scala.collection.JavaConversions.mapAsJavaMap import scala.concurrent.duration.{Duration, FiniteDuration} -import akka.actor.{ActorSystem, ExtendedActorSystem, IndestructibleActorSystem} +import akka.actor.{ActorSystem, ExtendedActorSystem} import com.typesafe.config.ConfigFactory import org.apache.log4j.{Level, Logger} @@ -41,7 +41,7 @@ private[spark] object AkkaUtils extends Logging { * If indestructible is set to true, the Actor System will continue running in the event * of a fatal exception. This is used by [[org.apache.spark.executor.Executor]]. */ - def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false, + def createActorSystem(name: String, host: String, port: Int, conf: SparkConf, securityManager: SecurityManager): (ActorSystem, Int) = { val akkaThreads = conf.getInt("spark.akka.threads", 4) @@ -101,12 +101,7 @@ private[spark] object AkkaUtils extends Logging { |akka.log-dead-letters-during-shutdown = $lifecycleEvents """.stripMargin)) - val actorSystem = if (indestructible) { - IndestructibleActorSystem(name, akkaConf) - } else { - ActorSystem(name, akkaConf) - } - + val actorSystem = ActorSystem(name, akkaConf) val provider = actorSystem.asInstanceOf[ExtendedActorSystem].provider val boundPort = provider.getDefaultAddress.port.get (actorSystem, boundPort) 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 2d05e09b10948..4916d9b86cca5 100644 --- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala @@ -25,7 +25,7 @@ import scala.collection.mutable.Set import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type} import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._ -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkException} private[spark] object ClosureCleaner extends Logging { // Get an ASM class reader for a given class from the JAR that loaded it @@ -108,6 +108,9 @@ private[spark] object ClosureCleaner extends Logging { val outerObjects = getOuterObjects(func) val accessedFields = Map[Class[_], Set[String]]() + + getClassReader(func.getClass).accept(new ReturnStatementFinder(), 0) + for (cls <- outerClasses) accessedFields(cls) = Set[String]() for (cls <- func.getClass :: innerClasses) @@ -180,6 +183,24 @@ private[spark] object ClosureCleaner extends Logging { } } +private[spark] +class ReturnStatementFinder extends ClassVisitor(ASM4) { + override def visitMethod(access: Int, name: String, desc: String, + sig: String, exceptions: Array[String]): MethodVisitor = { + if (name.contains("apply")) { + 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") + } + } + } + } else { + new MethodVisitor(ASM4) {} + } + } +} + private[spark] class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor(ASM4) { override def visitMethod(access: Int, name: String, desc: String, diff --git a/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala b/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala deleted file mode 100644 index 4188a869c13da..0000000000000 --- a/core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala +++ /dev/null @@ -1,68 +0,0 @@ -/* - * 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. - */ - -// Must be in akka.actor package as ActorSystemImpl is protected[akka]. -package akka.actor - -import scala.util.control.{ControlThrowable, NonFatal} - -import com.typesafe.config.Config - -/** - * An akka.actor.ActorSystem which refuses to shut down in the event of a fatal exception - * This is necessary as Spark Executors are allowed to recover from fatal exceptions - * (see org.apache.spark.executor.Executor) - */ -object IndestructibleActorSystem { - def apply(name: String, config: Config): ActorSystem = - apply(name, config, ActorSystem.findClassLoader()) - - def apply(name: String, config: Config, classLoader: ClassLoader): ActorSystem = - new IndestructibleActorSystemImpl(name, config, classLoader).start() -} - -private[akka] class IndestructibleActorSystemImpl( - override val name: String, - applicationConfig: Config, - classLoader: ClassLoader) - extends ActorSystemImpl(name, applicationConfig, classLoader) { - - protected override def uncaughtExceptionHandler: Thread.UncaughtExceptionHandler = { - val fallbackHandler = super.uncaughtExceptionHandler - - new Thread.UncaughtExceptionHandler() { - def uncaughtException(thread: Thread, cause: Throwable): Unit = { - if (isFatalError(cause) && !settings.JvmExitOnFatalError) { - log.error(cause, "Uncaught fatal error from thread [{}] not shutting down " + - "ActorSystem [{}] tolerating and continuing.... ", thread.getName, name) - // shutdown() //TODO make it configurable - } else { - fallbackHandler.uncaughtException(thread, cause) - } - } - } - } - - def isFatalError(e: Throwable): Boolean = { - e match { - case NonFatal(_) | _: InterruptedException | _: NotImplementedError | _: ControlThrowable => - false - case _ => - true - } - } -} diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 95777fbf57d8b..0c7cff019fce1 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -29,6 +29,7 @@ import scala.collection.mutable.ArrayBuffer import scala.io.Source import scala.reflect.ClassTag import scala.util.Try +import scala.util.control.{ControlThrowable, NonFatal} import com.google.common.io.Files import com.google.common.util.concurrent.ThreadFactoryBuilder @@ -39,9 +40,9 @@ import tachyon.client.{TachyonFile,TachyonFS} import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.executor.ExecutorUncaughtExceptionHandler import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} - /** * Various utility methods used by Spark. */ @@ -586,15 +587,17 @@ private[spark] object Utils extends Logging { * Don't follow directories if they are symlinks. */ def deleteRecursively(file: File) { - if ((file.isDirectory) && !isSymlink(file)) { - for (child <- listFilesSafely(file)) { - deleteRecursively(child) + if (file != null) { + if ((file.isDirectory) && !isSymlink(file)) { + for (child <- listFilesSafely(file)) { + deleteRecursively(child) + } } - } - if (!file.delete()) { - // Delete can also fail if the file simply did not exist - if (file.exists()) { - throw new IOException("Failed to delete: " + file.getAbsolutePath) + if (!file.delete()) { + // Delete can also fail if the file simply did not exist + if (file.exists()) { + throw new IOException("Failed to delete: " + file.getAbsolutePath) + } } } } @@ -778,6 +781,18 @@ private[spark] object Utils extends Logging { output.toString } + /** + * Execute a block of code that evaluates to Unit, forwarding any uncaught exceptions to the + * default UncaughtExceptionHandler + */ + def tryOrExit(block: => Unit) { + try { + block + } catch { + case t: Throwable => ExecutorUncaughtExceptionHandler.uncaughtException(t) + } + } + /** * A regular expression to match classes of the "core" Spark API that we want to skip when * finding the call site of a method. @@ -1086,7 +1101,7 @@ private[spark] object Utils extends Logging { * Strip the directory from a path name */ def stripDirectory(path: String): String = { - path.split(File.separator).last + new File(path).getName } /** @@ -1125,4 +1140,30 @@ private[spark] object Utils extends Logging { } } + /** + * Execute the given block, logging and re-throwing any uncaught exception. + * This is particularly useful for wrapping code that runs in a thread, to ensure + * that exceptions are printed, and to avoid having to catch Throwable. + */ + def logUncaughtExceptions[T](f: => T): T = { + try { + f + } catch { + case ct: ControlThrowable => + throw ct + case t: Throwable => + logError(s"Uncaught exception in thread ${Thread.currentThread().getName}", t) + throw t + } + } + + /** Returns true if the given exception was fatal. See docs for scala.util.control.NonFatal. */ + def isFatalError(e: Throwable): Boolean = { + e match { + case NonFatal(_) | _: InterruptedException | _: NotImplementedError | _: ControlThrowable => + false + case _ => + true + } + } } diff --git a/core/src/main/scala/org/apache/spark/util/package-info.java b/core/src/main/scala/org/apache/spark/util/package-info.java new file mode 100644 index 0000000000000..819f54ee41a79 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Spark utilities. + */ +package org.apache.spark.util; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/util/package.scala b/core/src/main/scala/org/apache/spark/util/package.scala new file mode 100644 index 0000000000000..c7099b85d225f --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Spark utilities. + */ +package object util diff --git a/core/src/main/scala/org/apache/spark/util/random/package-info.java b/core/src/main/scala/org/apache/spark/util/random/package-info.java new file mode 100644 index 0000000000000..62c3762dd11b6 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/random/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Utilities for random number generation. + */ +package org.apache.spark.util.random; \ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/util/random/package.scala b/core/src/main/scala/org/apache/spark/util/random/package.scala new file mode 100644 index 0000000000000..f361125fe2e8a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/random/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Utilities for random number generation. + */ +package object random diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index 1912015827927..3dd79243ab5bd 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -18,7 +18,6 @@ package org.apache.spark; import java.io.*; -import java.lang.StringBuilder; import java.util.*; import scala.Tuple2; @@ -49,16 +48,20 @@ import org.apache.spark.partial.PartialResult; import org.apache.spark.storage.StorageLevel; import org.apache.spark.util.StatCounter; +import org.apache.spark.util.Utils; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; // see http://stackoverflow.com/questions/758570/. public class JavaAPISuite implements Serializable { private transient JavaSparkContext sc; + private transient File tempDir; @Before public void setUp() { sc = new JavaSparkContext("local", "JavaAPISuite"); + tempDir = Files.createTempDir(); + tempDir.deleteOnExit(); } @After @@ -67,6 +70,7 @@ public void tearDown() { sc = null; // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown System.clearProperty("spark.driver.port"); + Utils.deleteRecursively(tempDir); } static class ReverseIntComparator implements Comparator, Serializable { @@ -611,7 +615,6 @@ public void glom() { @Test public void textFiles() throws IOException { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); rdd.saveAsTextFile(outputDir); @@ -630,7 +633,6 @@ public void wholeTextFiles() throws IOException { byte[] content1 = "spark is easy to use.\n".getBytes("utf-8"); byte[] content2 = "spark is also easy to use.\n".getBytes("utf-8"); - File tempDir = Files.createTempDir(); String tempDirName = tempDir.getAbsolutePath(); DataOutputStream ds = new DataOutputStream(new FileOutputStream(tempDirName + "/part-00000")); ds.write(content1); @@ -653,7 +655,6 @@ public void wholeTextFiles() throws IOException { @Test public void textFilesCompressed() throws IOException { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); rdd.saveAsTextFile(outputDir, DefaultCodec.class); @@ -667,7 +668,6 @@ public void textFilesCompressed() throws IOException { @SuppressWarnings("unchecked") @Test public void sequenceFile() { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -697,7 +697,6 @@ public Tuple2 call(Tuple2 pair) { @SuppressWarnings("unchecked") @Test public void writeWithNewAPIHadoopFile() { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -728,7 +727,6 @@ public String call(Tuple2 x) { @SuppressWarnings("unchecked") @Test public void readWithNewAPIHadoopFile() throws IOException { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -758,7 +756,6 @@ public String call(Tuple2 x) { @Test public void objectFilesOfInts() { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); rdd.saveAsObjectFile(outputDir); @@ -771,7 +768,6 @@ public void objectFilesOfInts() { @SuppressWarnings("unchecked") @Test public void objectFilesOfComplexTypes() { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -788,7 +784,6 @@ public void objectFilesOfComplexTypes() { @SuppressWarnings("unchecked") @Test public void hadoopFile() { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -818,7 +813,6 @@ public String call(Tuple2 x) { @SuppressWarnings("unchecked") @Test public void hadoopFileCompressed() { - File tempDir = Files.createTempDir(); String outputDir = new File(tempDir, "output_compressed").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -948,7 +942,6 @@ public String call(Integer t) throws Exception { @Test public void checkpointAndComputation() { - File tempDir = Files.createTempDir(); JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); sc.setCheckpointDir(tempDir.getAbsolutePath()); Assert.assertEquals(false, rdd.isCheckpointed()); @@ -960,7 +953,6 @@ public void checkpointAndComputation() { @Test public void checkpointAndRestore() { - File tempDir = Files.createTempDir(); JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); sc.setCheckpointDir(tempDir.getAbsolutePath()); Assert.assertEquals(false, rdd.isCheckpointed()); diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties index f6fef03689a7c..26b73a1b39744 100644 --- a/core/src/test/resources/log4j.properties +++ b/core/src/test/resources/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=core/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala index d2555b7c052c1..64933f4b1046d 100644 --- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala +++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala @@ -35,6 +35,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { override def beforeEach() { super.beforeEach() checkpointDir = File.createTempFile("temp", "") + checkpointDir.deleteOnExit() checkpointDir.delete() sc = new SparkContext("local", "test") sc.setCheckpointDir(checkpointDir.toString) @@ -42,9 +43,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { override def afterEach() { super.afterEach() - if (checkpointDir != null) { - checkpointDir.delete() - } + Utils.deleteRecursively(checkpointDir) } test("basic checkpointing") { diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala index d651fbbac4e97..7e18f45de7b5b 100644 --- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala @@ -24,9 +24,11 @@ import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.spark.SparkContext._ +import org.apache.spark.util.Utils class FileServerSuite extends FunSuite with LocalSparkContext { + @transient var tmpDir: File = _ @transient var tmpFile: File = _ @transient var tmpJarUrl: String = _ @@ -38,15 +40,18 @@ class FileServerSuite extends FunSuite with LocalSparkContext { override def beforeAll() { super.beforeAll() - val tmpDir = new File(Files.createTempDir(), "test") - tmpDir.mkdir() - val textFile = new File(tmpDir, "FileServerSuite.txt") + tmpDir = Files.createTempDir() + tmpDir.deleteOnExit() + val testTempDir = new File(tmpDir, "test") + testTempDir.mkdir() + + val textFile = new File(testTempDir, "FileServerSuite.txt") val pw = new PrintWriter(textFile) pw.println("100") pw.close() - val jarFile = new File(tmpDir, "test.jar") + val jarFile = new File(testTempDir, "test.jar") val jarStream = new FileOutputStream(jarFile) val jar = new JarOutputStream(jarStream, new java.util.jar.Manifest()) System.setProperty("spark.authenticate", "false") @@ -70,6 +75,11 @@ class FileServerSuite extends FunSuite with LocalSparkContext { tmpJarUrl = jarFile.toURI.toURL.toString } + override def afterAll() { + super.afterAll() + Utils.deleteRecursively(tmpDir) + } + test("Distributing files locally") { sc = new SparkContext("local[4]", "test") sc.addFile(tmpFile.toString) diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala index b9b668d3cc62a..1f2206b1f0379 100644 --- a/core/src/test/scala/org/apache/spark/FileSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileSuite.scala @@ -30,12 +30,24 @@ import org.apache.hadoop.mapreduce.Job import org.scalatest.FunSuite import org.apache.spark.SparkContext._ +import org.apache.spark.util.Utils class FileSuite extends FunSuite with LocalSparkContext { + var tempDir: File = _ + + override def beforeEach() { + super.beforeEach() + tempDir = Files.createTempDir() + tempDir.deleteOnExit() + } + + override def afterEach() { + super.afterEach() + Utils.deleteRecursively(tempDir) + } test("text files") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 4) nums.saveAsTextFile(outputDir) @@ -49,7 +61,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("text files (compressed)") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val normalDir = new File(tempDir, "output_normal").getAbsolutePath val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath val codec = new DefaultCodec() @@ -71,7 +82,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("SequenceFiles") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) // (1,a), (2,aa), (3,aaa) nums.saveAsSequenceFile(outputDir) @@ -82,7 +92,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("SequenceFile (compressed)") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val normalDir = new File(tempDir, "output_normal").getAbsolutePath val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath val codec = new DefaultCodec() @@ -104,7 +113,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("SequenceFile with writable key") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), "a" * x)) nums.saveAsSequenceFile(outputDir) @@ -115,7 +123,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("SequenceFile with writable value") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (x, new Text("a" * x))) nums.saveAsSequenceFile(outputDir) @@ -126,7 +133,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("SequenceFile with writable key and value") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) nums.saveAsSequenceFile(outputDir) @@ -137,7 +143,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("implicit conversions in reading SequenceFiles") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) // (1,a), (2,aa), (3,aaa) nums.saveAsSequenceFile(outputDir) @@ -154,7 +159,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("object files of ints") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 4) nums.saveAsObjectFile(outputDir) @@ -165,7 +169,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("object files of complex types") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) nums.saveAsObjectFile(outputDir) @@ -177,7 +180,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("write SequenceFile using new Hadoop API") { import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) nums.saveAsNewAPIHadoopFile[SequenceFileOutputFormat[IntWritable, Text]]( @@ -189,7 +191,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("read SequenceFile using new Hadoop API") { import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val outputDir = new File(tempDir, "output").getAbsolutePath val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) nums.saveAsSequenceFile(outputDir) @@ -200,7 +201,6 @@ class FileSuite extends FunSuite with LocalSparkContext { test("file caching") { sc = new SparkContext("local", "test") - val tempDir = Files.createTempDir() val out = new FileWriter(tempDir + "/input") out.write("Hello world!\n") out.write("What's up?\n") @@ -214,67 +214,61 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("prevent user from overwriting the empty directory (old Hadoop API)") { sc = new SparkContext("local", "test") - val tempdir = Files.createTempDir() val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1) intercept[FileAlreadyExistsException] { - randomRDD.saveAsTextFile(tempdir.getPath) + randomRDD.saveAsTextFile(tempDir.getPath) } } test ("prevent user from overwriting the non-empty directory (old Hadoop API)") { sc = new SparkContext("local", "test") - val tempdir = Files.createTempDir() val randomRDD = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 1) - randomRDD.saveAsTextFile(tempdir.getPath + "/output") - assert(new File(tempdir.getPath + "/output/part-00000").exists() === true) + randomRDD.saveAsTextFile(tempDir.getPath + "/output") + assert(new File(tempDir.getPath + "/output/part-00000").exists() === true) intercept[FileAlreadyExistsException] { - randomRDD.saveAsTextFile(tempdir.getPath + "/output") + randomRDD.saveAsTextFile(tempDir.getPath + "/output") } } test ("prevent user from overwriting the empty directory (new Hadoop API)") { sc = new SparkContext("local", "test") - val tempdir = Files.createTempDir() val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) intercept[FileAlreadyExistsException] { - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath) } } test ("prevent user from overwriting the non-empty directory (new Hadoop API)") { sc = new SparkContext("local", "test") - val tempdir = Files.createTempDir() val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath + "/output") - assert(new File(tempdir.getPath + "/output/part-r-00000").exists() === true) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) intercept[FileAlreadyExistsException] { - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempdir.getPath) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath) } } test ("save Hadoop Dataset through old Hadoop API") { sc = new SparkContext("local", "test") - val tempdir = Files.createTempDir() val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) val job = new JobConf() job.setOutputKeyClass(classOf[String]) job.setOutputValueClass(classOf[String]) job.set("mapred.output.format.class", classOf[TextOutputFormat[String, String]].getName) - job.set("mapred.output.dir", tempdir.getPath + "/outputDataset_old") + job.set("mapred.output.dir", tempDir.getPath + "/outputDataset_old") randomRDD.saveAsHadoopDataset(job) - assert(new File(tempdir.getPath + "/outputDataset_old/part-00000").exists() === true) + assert(new File(tempDir.getPath + "/outputDataset_old/part-00000").exists() === true) } test ("save Hadoop Dataset through new Hadoop API") { sc = new SparkContext("local", "test") - val tempdir = Files.createTempDir() val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) val job = new Job(sc.hadoopConfiguration) job.setOutputKeyClass(classOf[String]) job.setOutputValueClass(classOf[String]) job.setOutputFormatClass(classOf[NewTextOutputFormat[String, String]]) - job.getConfiguration.set("mapred.output.dir", tempdir.getPath + "/outputDataset_new") + job.getConfiguration.set("mapred.output.dir", tempDir.getPath + "/outputDataset_new") randomRDD.saveAsNewAPIHadoopDataset(job.getConfiguration) - assert(new File(tempdir.getPath + "/outputDataset_new/part-r-00000").exists() === true) + assert(new File(tempDir.getPath + "/outputDataset_new/part-r-00000").exists() === true) } } diff --git a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala index 33d6de9a76405..d5ebfb3f3fae1 100644 --- a/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala +++ b/core/src/test/scala/org/apache/spark/input/WholeTextFileRecordReaderSuite.scala @@ -31,6 +31,7 @@ import org.scalatest.FunSuite import org.apache.hadoop.io.Text import org.apache.spark.SparkContext +import org.apache.spark.util.Utils /** * Tests the correctness of @@ -67,6 +68,7 @@ class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll { test("Correctness of WholeTextFileRecordReader.") { val dir = Files.createTempDir() + dir.deleteOnExit() println(s"Local disk address is ${dir.toString}.") WholeTextFileRecordReaderSuite.files.foreach { case (filename, contents) => @@ -86,7 +88,7 @@ class WholeTextFileRecordReaderSuite extends FunSuite with BeforeAndAfterAll { s"file $filename contents can not match.") } - dir.delete() + Utils.deleteRecursively(dir) } } diff --git a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala index 7c7f69b261a0a..76e317d754ba3 100644 --- a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala @@ -15,13 +15,13 @@ * limitations under the License. */ -package org.apache.spark +package org.apache.spark.rdd import java.sql._ import org.scalatest.{BeforeAndAfter, FunSuite} -import org.apache.spark.rdd.JdbcRDD +import org.apache.spark.{LocalSparkContext, SparkContext} class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { @@ -35,18 +35,18 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1), DATA INTEGER )""") - create.close + create.close() val insert = conn.prepareStatement("INSERT INTO FOO(DATA) VALUES(?)") (1 to 100).foreach { i => insert.setInt(1, i * 2) insert.executeUpdate } - insert.close + insert.close() } catch { case e: SQLException if e.getSQLState == "X0Y32" => // table exists } finally { - conn.close + conn.close() } } @@ -57,7 +57,7 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { () => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") }, "SELECT DATA FROM FOO WHERE ? <= ID AND ID <= ?", 1, 100, 3, - (r: ResultSet) => { r.getInt(1) } ).cache + (r: ResultSet) => { r.getInt(1) } ).cache() assert(rdd.count === 100) assert(rdd.reduce(_+_) === 10100) diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala index 8f3e6bd21b752..1230565ea5b7e 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala @@ -236,11 +236,13 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("zero-partition RDD") { val emptyDir = Files.createTempDir() + emptyDir.deleteOnExit() val file = sc.textFile(emptyDir.getAbsolutePath) assert(file.partitions.size == 0) assert(file.collect().toList === Nil) // Test that a shuffle on the file works, because this used to be a bug assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil) + emptyDir.delete() } test("keys and values") { diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala index 95f5bcd855665..21e3db34b8b7a 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala @@ -19,7 +19,6 @@ package org.apache.spark.scheduler import scala.collection.mutable import scala.io.Source -import scala.util.Try import com.google.common.io.Files import org.apache.hadoop.fs.{FileStatus, Path} @@ -30,6 +29,8 @@ import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.io.CompressionCodec import org.apache.spark.util.{JsonProtocol, Utils} +import java.io.File + /** * Test whether EventLoggingListener logs events properly. * @@ -43,11 +44,17 @@ class EventLoggingListenerSuite extends FunSuite with BeforeAndAfter { "org.apache.spark.io.LZFCompressionCodec", "org.apache.spark.io.SnappyCompressionCodec" ) - private val testDir = Files.createTempDir() - private val logDirPath = Utils.getFilePath(testDir, "spark-events") + private var testDir: File = _ + private var logDirPath: Path = _ + + before { + testDir = Files.createTempDir() + testDir.deleteOnExit() + logDirPath = Utils.getFilePath(testDir, "spark-events") + } after { - Try { fileSystem.delete(logDirPath, true) } + Utils.deleteRecursively(testDir) } test("Parse names of special files") { diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala index d1fe1fc348961..d81499ac6abef 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala @@ -17,9 +17,7 @@ package org.apache.spark.scheduler -import java.io.PrintWriter - -import scala.util.Try +import java.io.{File, PrintWriter} import com.google.common.io.Files import org.json4s.jackson.JsonMethods._ @@ -39,11 +37,15 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { "org.apache.spark.io.LZFCompressionCodec", "org.apache.spark.io.SnappyCompressionCodec" ) - private val testDir = Files.createTempDir() + private var testDir: File = _ + + before { + testDir = Files.createTempDir() + testDir.deleteOnExit() + } after { - Try { fileSystem.delete(Utils.getFilePath(testDir, "events.txt"), true) } - Try { fileSystem.delete(Utils.getFilePath(testDir, "test-replay"), true) } + Utils.deleteRecursively(testDir) } test("Simple replay") { diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala index 4e9fd07e68a21..5426e578a9ddd 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala @@ -331,16 +331,47 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc } } - def checkNonZeroAvg(m: Traversable[Long], msg: String) { + test("SparkListener moves on if a listener throws an exception") { + val badListener = new BadListener + val jobCounter1 = new BasicJobCounter + val jobCounter2 = new BasicJobCounter + val bus = new LiveListenerBus + + // Propagate events to bad listener first + bus.addListener(badListener) + bus.addListener(jobCounter1) + bus.addListener(jobCounter2) + bus.start() + + // Post events to all listeners, and wait until the queue is drained + (1 to 5).foreach { _ => bus.post(SparkListenerJobEnd(0, JobSucceeded)) } + assert(bus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + + // The exception should be caught, and the event should be propagated to other listeners + assert(bus.listenerThreadIsAlive) + assert(jobCounter1.count === 5) + assert(jobCounter2.count === 5) + } + + /** + * Assert that the given list of numbers has an average that is greater than zero. + */ + private def checkNonZeroAvg(m: Traversable[Long], msg: String) { assert(m.sum / m.size.toDouble > 0.0, msg) } - class BasicJobCounter extends SparkListener { + /** + * A simple listener that counts the number of jobs observed. + */ + private class BasicJobCounter extends SparkListener { var count = 0 override def onJobEnd(job: SparkListenerJobEnd) = count += 1 } - class SaveStageAndTaskInfo extends SparkListener { + /** + * A simple listener that saves all task infos and task metrics. + */ + private class SaveStageAndTaskInfo extends SparkListener { val stageInfos = mutable.Map[StageInfo, Seq[(TaskInfo, TaskMetrics)]]() var taskInfoMetrics = mutable.Buffer[(TaskInfo, TaskMetrics)]() @@ -358,7 +389,10 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc } } - class SaveTaskEvents extends SparkListener { + /** + * A simple listener that saves the task indices for all task events. + */ + private class SaveTaskEvents extends SparkListener { val startedTasks = new mutable.HashSet[Int]() val startedGettingResultTasks = new mutable.HashSet[Int]() val endedTasks = new mutable.HashSet[Int]() @@ -377,4 +411,12 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatc startedGettingResultTasks += taskGettingResult.taskInfo.index } } + + /** + * A simple listener that throws an exception on job end. + */ + private class BadListener extends SparkListener { + override def onJobEnd(jobEnd: SparkListenerJobEnd) = { throw new Exception } + } + } diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala index a8b605c5b212a..7532da88c6065 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala @@ -117,7 +117,7 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin } def resourceOffer(rootPool: Pool): Int = { - val taskSetQueue = rootPool.getSortedTaskSetQueue() + val taskSetQueue = rootPool.getSortedTaskSetQueue /* Just for Test*/ for (manager <- taskSetQueue) { logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format( diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala index 42bfbf1bdfc74..aaa7714049732 100644 --- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala @@ -23,18 +23,16 @@ import scala.collection.mutable import scala.language.reflectiveCalls import com.google.common.io.Files -import org.scalatest.{BeforeAndAfterEach, FunSuite} +import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} import org.apache.spark.SparkConf +import org.apache.spark.util.Utils -class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach { +class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with BeforeAndAfterAll { private val testConf = new SparkConf(false) - val rootDir0 = Files.createTempDir() - rootDir0.deleteOnExit() - val rootDir1 = Files.createTempDir() - rootDir1.deleteOnExit() - val rootDirs = rootDir0.getName + "," + rootDir1.getName - println("Created root dirs: " + rootDirs) + private var rootDir0: File = _ + private var rootDir1: File = _ + private var rootDirs: String = _ // This suite focuses primarily on consolidation features, // so we coerce consolidation if not already enabled. @@ -48,6 +46,22 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach { var diskBlockManager: DiskBlockManager = _ + override def beforeAll() { + super.beforeAll() + rootDir0 = Files.createTempDir() + rootDir0.deleteOnExit() + rootDir1 = Files.createTempDir() + rootDir1.deleteOnExit() + rootDirs = rootDir0.getAbsolutePath + "," + rootDir1.getAbsolutePath + println("Created root dirs: " + rootDirs) + } + + override def afterAll() { + super.afterAll() + Utils.deleteRecursively(rootDir0) + Utils.deleteRecursively(rootDir1) + } + override def beforeEach() { diskBlockManager = new DiskBlockManager(shuffleBlockManager, rootDirs) shuffleBlockManager.idToSegmentMap.clear() diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala index fff8020ade6d1..038746d2eda4b 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala @@ -94,7 +94,7 @@ class UISuite extends FunSuite { } } - test("jetty port increases under contention") { + test("jetty selects different port under contention") { val startPort = 4040 val server = new Server(startPort) @@ -110,8 +110,9 @@ class UISuite extends FunSuite { // Allow some wiggle room in case ports on the machine are under contention val boundPort1 = serverInfo1.boundPort val boundPort2 = serverInfo2.boundPort - assert(boundPort1 > startPort && boundPort1 < startPort + 10) - assert(boundPort2 > boundPort1 && boundPort2 < boundPort1 + 10) + assert(boundPort1 != startPort) + assert(boundPort2 != startPort) + assert(boundPort1 != boundPort2) } test("jetty binds to port 0 correctly") { 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 d7e48e633e0ee..054ef54e746a5 100644 --- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.util import org.scalatest.FunSuite import org.apache.spark.LocalSparkContext._ -import org.apache.spark.SparkContext +import org.apache.spark.{SparkContext, SparkException} class ClosureCleanerSuite extends FunSuite { test("closures inside an object") { @@ -50,6 +50,19 @@ class ClosureCleanerSuite extends FunSuite { val obj = new TestClassWithNesting(1) assert(obj.run() === 96) // 4 * (1+2+3+4) + 4 * (1+2+3+4) + 16 * 1 } + + test("toplevel return statements in closures are identified at cleaning time") { + val ex = intercept[SparkException] { + 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) + } } // A non-serializable class we create in closures to make sure that we aren't @@ -108,6 +121,30 @@ class TestClassWithoutFieldAccess { } } +object TestObjectWithBogusReturns { + def run(): Int = { + withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + // this return is invalid since it will transfer control outside the closure + nums.map {x => return 1 ; x * 2} + 1 + } + } +} + +object TestObjectWithNestedReturns { + def run(): Int = { + withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + nums.map {x => + // this return is fine since it will not transfer control outside the closure + def foo(): Int = { return 5; 1 } + foo() + } + 1 + } + } +} object TestObjectWithNesting { def run(): Int = { diff --git a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala index f675e1e5b4981..44332fc8dbc23 100644 --- a/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/FileLoggerSuite.scala @@ -17,10 +17,9 @@ package org.apache.spark.util -import java.io.IOException +import java.io.{File, IOException} import scala.io.Source -import scala.util.Try import com.google.common.io.Files import org.apache.hadoop.fs.Path @@ -38,12 +37,18 @@ class FileLoggerSuite extends FunSuite with BeforeAndAfter { "org.apache.spark.io.LZFCompressionCodec", "org.apache.spark.io.SnappyCompressionCodec" ) - private val testDir = Files.createTempDir() - private val logDirPath = Utils.getFilePath(testDir, "test-file-logger") - private val logDirPathString = logDirPath.toString + private var testDir: File = _ + private var logDirPath: Path = _ + private var logDirPathString: String = _ + + before { + testDir = Files.createTempDir() + logDirPath = Utils.getFilePath(testDir, "test-file-logger") + logDirPathString = logDirPath.toString + } after { - Try { fileSystem.delete(logDirPath, true) } + Utils.deleteRecursively(testDir) } test("Simple logging") { diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index eb7fb6318262b..cf9e20d347ddd 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -112,6 +112,7 @@ class UtilsSuite extends FunSuite { test("reading offset bytes of a file") { val tmpDir2 = Files.createTempDir() + tmpDir2.deleteOnExit() val f1Path = tmpDir2 + "/f1" val f1 = new FileOutputStream(f1Path) f1.write("1\n2\n3\n4\n5\n6\n7\n8\n9\n".getBytes(Charsets.UTF_8)) diff --git a/data/mllib/sample_binary_classification_data.txt b/data/mllib/sample_binary_classification_data.txt new file mode 100644 index 0000000000000..861c70cde7fd2 --- /dev/null +++ b/data/mllib/sample_binary_classification_data.txt @@ -0,0 +1,100 @@ +0 128:51 129:159 130:253 131:159 132:50 155:48 156:238 157:252 158:252 159:252 160:237 182:54 183:227 184:253 185:252 186:239 187:233 188:252 189:57 190:6 208:10 209:60 210:224 211:252 212:253 213:252 214:202 215:84 216:252 217:253 218:122 236:163 237:252 238:252 239:252 240:253 241:252 242:252 243:96 244:189 245:253 246:167 263:51 264:238 265:253 266:253 267:190 268:114 269:253 270:228 271:47 272:79 273:255 274:168 290:48 291:238 292:252 293:252 294:179 295:12 296:75 297:121 298:21 301:253 302:243 303:50 317:38 318:165 319:253 320:233 321:208 322:84 329:253 330:252 331:165 344:7 345:178 346:252 347:240 348:71 349:19 350:28 357:253 358:252 359:195 372:57 373:252 374:252 375:63 385:253 386:252 387:195 400:198 401:253 402:190 413:255 414:253 415:196 427:76 428:246 429:252 430:112 441:253 442:252 443:148 455:85 456:252 457:230 458:25 467:7 468:135 469:253 470:186 471:12 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+29::80::1 +29::81::2 +29::83::1 +29::85::1 +29::86::1 +29::90::4 +29::93::1 +29::94::4 +29::97::1 +29::99::1 diff --git a/dev/audit-release/audit_release.py b/dev/audit-release/audit_release.py index 4a816d4101e57..8c7573b91f688 100755 --- a/dev/audit-release/audit_release.py +++ b/dev/audit-release/audit_release.py @@ -93,9 +93,12 @@ def get_url(url): # For each of these modules, we'll test an 'empty' application in sbt and # maven that links against them. This will catch issues with messed up # dependencies within those projects. -modules = ["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"] +modules = [ + "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" +] modules = map(lambda m: "%s_%s" % (m, SCALA_BINARY_VERSION), modules) # Check for directories that might interfere with tests @@ -122,7 +125,7 @@ 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"]: +for app in ["sbt_app_core", "sbt_app_graphx", "sbt_app_streaming", "sbt_app_sql", "sbt_app_hive"]: os.chdir(app) ret = run_cmd("sbt clean run", exit_on_failure=False) test(ret == 0, "sbt application (%s)" % app) diff --git a/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java b/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java index 6b65dda39b1a2..5217689e7c092 100644 --- a/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java +++ b/dev/audit-release/maven_app_core/src/main/java/SimpleApp.java @@ -37,5 +37,6 @@ public static void main(String[] args) { System.exit(-1); } System.out.println("Test succeeded"); + sc.stop(); } } diff --git a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala index a89b0d7d38bf1..77bbd167b199a 100644 --- a/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala +++ b/dev/audit-release/sbt_app_core/src/main/scala/SparkApp.scala @@ -19,6 +19,7 @@ package main.scala import scala.util.Try +import org.apache.spark.SparkConf import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ @@ -30,7 +31,6 @@ object SimpleApp { } val logFile = "input.txt" val sc = new SparkContext(conf) - SparkContext.jarOfClass(this.getClass).foreach(sc.addJar) val logData = sc.textFile(logFile, 2).cache() val numAs = logData.filter(line => line.contains("a")).count() val numBs = logData.filter(line => line.contains("b")).count() diff --git a/dev/audit-release/sbt_app_hive/build.sbt b/dev/audit-release/sbt_app_hive/build.sbt new file mode 100644 index 0000000000000..7ac1be729c561 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/build.sbt @@ -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. +// + +name := "Simple Project" + +version := "1.0" + +scalaVersion := System.getenv.get("SCALA_VERSION") + +libraryDependencies += "org.apache.spark" %% "spark-hive" % System.getenv.get("SPARK_VERSION") + +resolvers ++= Seq( + "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"), + "Akka Repository" at "http://repo.akka.io/releases/", + "Spray Repository" at "http://repo.spray.cc/") diff --git a/dev/audit-release/sbt_app_hive/data.txt b/dev/audit-release/sbt_app_hive/data.txt new file mode 100644 index 0000000000000..0229e67f51e01 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/data.txt @@ -0,0 +1,9 @@ +0val_0 +1val_1 +2val_2 +3val_3 +4val_4 +5val_5 +6val_6 +7val_7 +9val_9 diff --git a/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml b/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml new file mode 100644 index 0000000000000..93b835813d535 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/src/main/resources/hive-site.xml @@ -0,0 +1,213 @@ + + + + + + + + + + + + + + + + + + build.dir + ${user.dir}/build + + + + build.dir.hive + ${build.dir}/hive + + + + hadoop.tmp.dir + ${build.dir.hive}/test/hadoop-${user.name} + A base for other temporary directories. + + + + + + hive.exec.scratchdir + ${build.dir}/scratchdir + Scratch space for Hive jobs + + + + hive.exec.local.scratchdir + ${build.dir}/localscratchdir/ + Local scratch space for Hive jobs + + + + javax.jdo.option.ConnectionURL + + jdbc:derby:;databaseName=../build/test/junit_metastore_db;create=true + + + + javax.jdo.option.ConnectionDriverName + org.apache.derby.jdbc.EmbeddedDriver + + + + javax.jdo.option.ConnectionUserName + APP + + + + javax.jdo.option.ConnectionPassword + mine + + + + + hive.metastore.warehouse.dir + ${test.warehouse.dir} + + + + + hive.metastore.metadb.dir + ${build.dir}/test/data/metadb/ + + Required by metastore server or if the uris argument below is not supplied + + + + + test.log.dir + ${build.dir}/test/logs + + + + + test.src.dir + ${build.dir}/src/test + + + + + + + hive.jar.path + ${build.dir.hive}/ql/hive-exec-${version}.jar + + + + + hive.metastore.rawstore.impl + org.apache.hadoop.hive.metastore.ObjectStore + Name of the class that implements org.apache.hadoop.hive.metastore.rawstore interface. This class is used to store and retrieval of raw metadata objects such as table, database + + + + hive.querylog.location + ${build.dir}/tmp + Location of the structured hive logs + + + + + + hive.task.progress + false + Track progress of a task + + + + hive.support.concurrency + false + Whether hive supports concurrency or not. A zookeeper instance must be up and running for the default hive lock manager to support read-write locks. + + + + fs.pfile.impl + org.apache.hadoop.fs.ProxyLocalFileSystem + A proxy for local file system used for cross file system testing + + + + hive.exec.mode.local.auto + false + + Let hive determine whether to run in local mode automatically + Disabling this for tests so that minimr is not affected + + + + + hive.auto.convert.join + false + Whether Hive enable the optimization about converting common join into mapjoin based on the input file size + + + + hive.ignore.mapjoin.hint + false + Whether Hive ignores the mapjoin hint + + + + hive.input.format + org.apache.hadoop.hive.ql.io.CombineHiveInputFormat + The default input format, if it is not specified, the system assigns it. It is set to HiveInputFormat for hadoop versions 17, 18 and 19, whereas it is set to CombineHiveInputFormat for hadoop 20. The user can always overwrite it - if there is a bug in CombineHiveInputFormat, it can always be manually set to HiveInputFormat. + + + + hive.default.rcfile.serde + org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe + The default SerDe hive will use for the rcfile format + + + diff --git a/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala new file mode 100644 index 0000000000000..7257d17d10116 --- /dev/null +++ b/dev/audit-release/sbt_app_hive/src/main/scala/HiveApp.scala @@ -0,0 +1,57 @@ +/* + * 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.collection.mutable.{ListBuffer, Queue} + +import org.apache.spark.SparkConf +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.hive.LocalHiveContext + +case class Person(name: String, age: Int) + +object SparkSqlExample { + + def main(args: Array[String]) { + val conf = sys.env.get("SPARK_AUDIT_MASTER") match { + case Some(master) => new SparkConf().setAppName("Simple Sql App").setMaster(master) + case None => new SparkConf().setAppName("Simple Sql App") + } + val sc = new SparkContext(conf) + val hiveContext = new LocalHiveContext(sc) + + import hiveContext._ + hql("DROP TABLE IF EXISTS src") + hql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)") + hql("LOAD DATA LOCAL INPATH 'data.txt' INTO TABLE src") + val results = hql("FROM src SELECT key, value WHERE key >= 0 AND KEY < 5").collect() + results.foreach(println) + + def test(f: => Boolean, failureMsg: String) = { + if (!f) { + println(failureMsg) + System.exit(-1) + } + } + + test(results.size == 5, "Unexpected number of selected elements: " + results) + println("Test succeeded") + sc.stop() + } +} diff --git a/dev/audit-release/sbt_app_sql/build.sbt b/dev/audit-release/sbt_app_sql/build.sbt new file mode 100644 index 0000000000000..6e0ad3b4b2960 --- /dev/null +++ b/dev/audit-release/sbt_app_sql/build.sbt @@ -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. +// + +name := "Simple Project" + +version := "1.0" + +scalaVersion := System.getenv.get("SCALA_VERSION") + +libraryDependencies += "org.apache.spark" %% "spark-sql" % System.getenv.get("SPARK_VERSION") + +resolvers ++= Seq( + "Spark Release Repository" at System.getenv.get("SPARK_RELEASE_REPOSITORY"), + "Akka Repository" at "http://repo.akka.io/releases/", + "Spray Repository" at "http://repo.spray.cc/") diff --git a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala new file mode 100644 index 0000000000000..50af90c213b5a --- /dev/null +++ b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala @@ -0,0 +1,57 @@ +/* + * 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.collection.mutable.{ListBuffer, Queue} + +import org.apache.spark.SparkConf +import org.apache.spark.SparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.SQLContext + +case class Person(name: String, age: Int) + +object SparkSqlExample { + + def main(args: Array[String]) { + val conf = sys.env.get("SPARK_AUDIT_MASTER") match { + case Some(master) => new SparkConf().setAppName("Simple Sql App").setMaster(master) + case None => new SparkConf().setAppName("Simple Sql App") + } + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + + import sqlContext._ + val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x)) + people.registerAsTable("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) + + def test(f: => Boolean, failureMsg: String) = { + if (!f) { + println(failureMsg) + System.exit(-1) + } + } + + test(teenagerNames.size == 7, "Unexpected number of selected elements: " + teenagerNames) + println("Test succeeded") + sc.stop() + } +} diff --git a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala index a1d8971abe9a4..58a662bd9b2e8 100644 --- a/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala +++ b/dev/audit-release/sbt_app_streaming/src/main/scala/StreamingApp.scala @@ -32,7 +32,6 @@ object SparkStreamingExample { case None => new SparkConf().setAppName("Simple Streaming App") } val ssc = new StreamingContext(conf, Seconds(1)) - SparkContext.jarOfClass(this.getClass).foreach(ssc.sparkContext.addJar) val seen = ListBuffer[RDD[Int]]() val rdd1 = ssc.sparkContext.makeRDD(1 to 100, 10) diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh index 33552a74920a7..c96afe822c6a9 100755 --- a/dev/create-release/create-release.sh +++ b/dev/create-release/create-release.sh @@ -49,17 +49,19 @@ if [[ ! "$@" =~ --package-only ]]; then mvn -Pyarn release:clean mvn -DskipTests \ - -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \ + -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \ -Dusername=$GIT_USERNAME -Dpassword=$GIT_PASSWORD \ + -Dmaven.javadoc.skip=true \ -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ - -Pyarn -Phive -Pspark-ganglia-lgpl\ + -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\ -Dtag=$GIT_TAG -DautoVersionSubmodules=true \ --batch-mode release:prepare mvn -DskipTests \ - -Darguments="-DskipTests=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \ + -Darguments="-DskipTests=true -Dmaven.javadoc.skip=true -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 -Dgpg.passphrase=${GPG_PASSPHRASE}" \ -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ - -Pyarn -Phive -Pspark-ganglia-lgpl\ + -Dmaven.javadoc.skip=true \ + -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl\ release:perform cd .. @@ -109,9 +111,9 @@ make_binary_release() { spark-$RELEASE_VERSION-bin-$NAME.tgz.sha } -make_binary_release "hadoop1" "--hadoop 1.0.4" -make_binary_release "cdh4" "--hadoop 2.0.0-mr1-cdh4.2.0" -make_binary_release "hadoop2" "--with-yarn --hadoop 2.2.0" +make_binary_release "hadoop1" "--with-hive --hadoop 1.0.4" +make_binary_release "cdh4" "--with-hive --hadoop 2.0.0-mr1-cdh4.2.0" +make_binary_release "hadoop2" "--with-hive --with-yarn --hadoop 2.2.0" # Copy data echo "Copying release tarballs" diff --git a/dev/create-release/generate-changelist.py b/dev/create-release/generate-changelist.py new file mode 100755 index 0000000000000..13b744ec1b37e --- /dev/null +++ b/dev/create-release/generate-changelist.py @@ -0,0 +1,144 @@ +#!/usr/bin/python + +# +# 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. +# +# Creates CHANGES.txt from git history. +# +# Usage: +# First set the new release version and old CHANGES.txt version in this file. +# Make sure you have SPARK_HOME set. +# $ python generate-changelist.py + + +import os +import sys +import subprocess +import time +import traceback + +SPARK_HOME = os.environ["SPARK_HOME"] +NEW_RELEASE_VERSION = "1.0.0" +PREV_RELEASE_GIT_TAG = "v0.9.1" + +CHANGELIST = "CHANGES.txt" +OLD_CHANGELIST = "%s.old" % (CHANGELIST) +NEW_CHANGELIST = "%s.new" % (CHANGELIST) +TMP_CHANGELIST = "%s.tmp" % (CHANGELIST) + +# date before first PR in TLP Spark repo +SPARK_REPO_CHANGE_DATE1 = time.strptime("2014-02-26", "%Y-%m-%d") +# date after last PR in incubator Spark repo +SPARK_REPO_CHANGE_DATE2 = time.strptime("2014-03-01", "%Y-%m-%d") +# Threshold PR number that differentiates PRs to TLP +# and incubator repos +SPARK_REPO_PR_NUM_THRESH = 200 + +LOG_FILE_NAME = "changes_%s" % time.strftime("%h_%m_%Y_%I_%M_%S") +LOG_FILE = open(LOG_FILE_NAME, 'w') + +def run_cmd(cmd): + try: + print >> LOG_FILE, "Running command: %s" % cmd + output = subprocess.check_output(cmd, shell=True, stderr=LOG_FILE) + print >> LOG_FILE, "Output: %s" % output + return output + except: + traceback.print_exc() + cleanup() + sys.exit(1) + +def append_to_changelist(string): + with open(TMP_CHANGELIST, "a") as f: + print >> f, string + +def cleanup(ask = True): + if ask == True: + print "OK to delete temporary and log files? (y/N): " + response = raw_input() + if ask == False or (ask == True and response == "y"): + if os.path.isfile(TMP_CHANGELIST): + os.remove(TMP_CHANGELIST) + if os.path.isfile(OLD_CHANGELIST): + os.remove(OLD_CHANGELIST) + LOG_FILE.close() + os.remove(LOG_FILE_NAME) + +print "Generating new %s for Spark release %s" % (CHANGELIST, NEW_RELEASE_VERSION) +os.chdir(SPARK_HOME) +if os.path.isfile(TMP_CHANGELIST): + os.remove(TMP_CHANGELIST) +if os.path.isfile(OLD_CHANGELIST): + os.remove(OLD_CHANGELIST) + +append_to_changelist("Spark Change Log") +append_to_changelist("----------------") +append_to_changelist("") +append_to_changelist("Release %s" % NEW_RELEASE_VERSION) +append_to_changelist("") + +print "Getting commits between tag %s and HEAD" % PREV_RELEASE_GIT_TAG +hashes = run_cmd("git log %s..HEAD --pretty='%%h'" % PREV_RELEASE_GIT_TAG).split() + +print "Getting details of %s commits" % len(hashes) +for h in hashes: + date = run_cmd("git log %s -1 --pretty='%%ad' --date=iso | head -1" % h).strip() + subject = run_cmd("git log %s -1 --pretty='%%s' | head -1" % h).strip() + body = run_cmd("git log %s -1 --pretty='%%b'" % h) + committer = run_cmd("git log %s -1 --pretty='%%cn <%%ce>' | head -1" % h).strip() + body_lines = body.split("\n") + + if "Merge pull" in subject: + ## Parse old format commit message + append_to_changelist(" %s %s" % (h, date)) + append_to_changelist(" %s" % subject) + append_to_changelist(" [%s]" % body_lines[0]) + append_to_changelist("") + + elif "maven-release" not in subject: + ## Parse new format commit message + # Get authors from commit message, committer otherwise + authors = [committer] + if "Author:" in body: + authors = [line.split(":")[1].strip() for line in body_lines if "Author:" in line] + + # Generate GitHub PR URL for easy access if possible + github_url = "" + if "Closes #" in body: + pr_num = [line.split()[1].lstrip("#") for line in body_lines if "Closes #" in line][0] + github_url = "github.com/apache/spark/pull/%s" % pr_num + day = time.strptime(date.split()[0], "%Y-%m-%d") + if day < SPARK_REPO_CHANGE_DATE1 or (day < SPARK_REPO_CHANGE_DATE2 and pr_num < SPARK_REPO_PR_NUM_THRESH): + github_url = "github.com/apache/incubator-spark/pull/%s" % pr_num + + append_to_changelist(" %s" % subject) + append_to_changelist(" %s" % ', '.join(authors)) + # for author in authors: + # append_to_changelist(" %s" % author) + append_to_changelist(" %s" % date) + if len(github_url) > 0: + append_to_changelist(" Commit: %s, %s" % (h, github_url)) + else: + append_to_changelist(" Commit: %s" % h) + append_to_changelist("") + +# Append old change list +print "Appending changelist from tag %s" % PREV_RELEASE_GIT_TAG +run_cmd("git show %s:%s | tail -n +3 >> %s" % (PREV_RELEASE_GIT_TAG, CHANGELIST, TMP_CHANGELIST)) +run_cmd("cp %s %s" % (TMP_CHANGELIST, NEW_CHANGELIST)) +print "New change list generated as %s" % NEW_CHANGELIST +cleanup(False) + diff --git a/docs/_config.yml b/docs/_config.yml index d177e38f884ca..45b78fe724a50 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -7,6 +7,6 @@ SPARK_VERSION: 1.0.0-SNAPSHOT SPARK_VERSION_SHORT: 1.0.0 SCALA_BINARY_VERSION: "2.10" SCALA_VERSION: "2.10.4" -MESOS_VERSION: 0.13.0 +MESOS_VERSION: 0.18.1 SPARK_ISSUE_TRACKER_URL: https://issues.apache.org/jira/browse/SPARK SPARK_GITHUB_URL: https://github.com/apache/spark diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index 8b543de574622..fb808129bb65d 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -114,7 +114,11 @@
-

{{ page.title }}

+ {% if page.displayTitle %} +

{{ page.displayTitle }}

+ {% else %} +

{{ page.title }}

+ {% endif %} {{ content }} diff --git a/docs/building-with-maven.md b/docs/building-with-maven.md index b6dd553bbe06b..8b44535d82404 100644 --- a/docs/building-with-maven.md +++ b/docs/building-with-maven.md @@ -129,6 +129,13 @@ Java 8 tests are run when -Pjava8-tests profile is enabled, they will run in spi For these tests to run your system must have a JDK 8 installation. If you have JDK 8 installed but it is not the system default, you can set JAVA_HOME to point to JDK 8 before running the tests. +## Building for PySpark on YARN ## + +PySpark on YARN is only supported if the jar is built with maven. Further, there is a known problem +with building this assembly jar on Red Hat based operating systems (see SPARK-1753). If you wish to +run PySpark on a YARN cluster with Red Hat installed, we recommend that you build the jar elsewhere, +then ship it over to the cluster. We are investigating the exact cause for this. + ## Packaging without Hadoop dependencies for deployment on YARN ## The assembly jar produced by "mvn package" will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath. The "hadoop-provided" profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself. diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index 162c415b5883e..f05a755de7fec 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -66,62 +66,76 @@ script as shown here while passing your jar. For Python, you can use the `pyFiles` argument of SparkContext or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed. -### Launching Applications with ./bin/spark-submit +### Launching Applications with Spark submit Once a user application is bundled, it can be launched using the `spark-submit` script located in the bin directory. This script takes care of setting up the classpath with Spark and its -dependencies, and can support different cluster managers and deploy modes that Spark supports. -It's usage is +dependencies, and can support different cluster managers and deploy modes that Spark supports: - ./bin/spark-submit --class path.to.your.Class [options] [app options] + ./bin/spark-submit \ + --class + --master \ + --deploy-mode \ + ... // other options + + [application-arguments] -When calling `spark-submit`, `[app options]` will be passed along to your application's -main class. To enumerate all options available to `spark-submit` run it with -the `--help` flag. Here are a few examples of common options: + main-class: The entry point for your application (e.g. org.apache.spark.examples.SparkPi) + master-url: The URL of the master node (e.g. spark://23.195.26.187:7077) + deploy-mode: Whether to deploy this application within the cluster or from an external client (e.g. client) + application-jar: Path to a bundled jar including your application and all dependencies. The URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. + application-arguments: Space delimited arguments passed to the main method of , if any + +To enumerate all options available to `spark-submit` run it with the `--help` flag. Here are a few +examples of common options: {% highlight bash %} # Run application locally ./bin/spark-submit \ - --class my.main.ClassName + --class org.apache.spark.examples.SparkPi --master local[8] \ - my-app.jar + /path/to/examples.jar \ + 100 # Run on a Spark standalone cluster ./bin/spark-submit \ - --class my.main.ClassName - --master spark://mycluster:7077 \ + --class org.apache.spark.examples.SparkPi + --master spark://207.184.161.138:7077 \ --executor-memory 20G \ --total-executor-cores 100 \ - my-app.jar + /path/to/examples.jar \ + 1000 # Run on a YARN cluster -HADOOP_CONF_DIR=XX /bin/spark-submit \ - --class my.main.ClassName +HADOOP_CONF_DIR=XX ./bin/spark-submit \ + --class org.apache.spark.examples.SparkPi --master yarn-cluster \ # can also be `yarn-client` for client mode --executor-memory 20G \ --num-executors 50 \ - my-app.jar + /path/to/examples.jar \ + 1000 {% endhighlight %} ### Loading Configurations from a File -The `spark-submit` script can load default `SparkConf` values from a properties file and pass them -onto your application. By default it will read configuration options from -`conf/spark-defaults.conf`. Any values specified in the file will be passed on to the -application when run. They can obviate the need for certain flags to `spark-submit`: for -instance, if `spark.master` property is set, you can safely omit the +The `spark-submit` script can load default [Spark configuration values](configuration.html) from a +properties file and pass them on to your application. By default it will read configuration options +from `conf/spark-defaults.conf`. For more detail, see the section on +[loading default configurations](configuration.html#loading-default-configurations). + +Loading default Spark configurations this way can obviate the need for certain flags to +`spark-submit`. For instance, if the `spark.master` property is set, you can safely omit the `--master` flag from `spark-submit`. In general, configuration values explicitly set on a -`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values -in the defaults file. +`SparkConf` take the highest precedence, then flags passed to `spark-submit`, then values in the +defaults file. -If you are ever unclear where configuration options are coming from. fine-grained debugging -information can be printed by adding the `--verbose` option to `./spark-submit`. +If you are ever unclear where configuration options are coming from, you can print out fine-grained +debugging information by running `spark-submit` with the `--verbose` option. ### Advanced Dependency Management -When using `./bin/spark-submit` the app jar along with any jars included with the `--jars` option -will be automatically transferred to the cluster. `--jars` can also be used to distribute .egg and .zip -libraries for Python to executors. Spark uses the following URL scheme to allow different -strategies for disseminating jars: +When using `spark-submit`, the application jar along with any jars included with the `--jars` option +will be automatically transferred to the cluster. Spark uses the following URL scheme to allow +different strategies for disseminating jars: - **file:** - Absolute paths and `file:/` URIs are served by the driver's HTTP file server, and every executor pulls the file from the driver HTTP server. @@ -135,6 +149,9 @@ This can use up a significant amount of space over time and will need to be clea is handled automatically, and with Spark standalone, automatic cleanup can be configured with the `spark.worker.cleanup.appDataTtl` property. +For python, the equivalent `--py-files` option can be used to distribute .egg and .zip libraries +to executors. + # Monitoring Each driver program has a web UI, typically on port 4040, that displays information about running diff --git a/docs/configuration.md b/docs/configuration.md index 5b034e3cb3d47..4d41c36e38e26 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -5,9 +5,9 @@ title: Spark Configuration Spark provides three locations to configure the system: -* [Spark properties](#spark-properties) control most application parameters and can be set by passing - a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java - system properties. +* [Spark properties](#spark-properties) control most application parameters and can be set by + passing a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) object to SparkContext, + or through the `conf/spark-defaults.conf` properties file. * [Environment variables](#environment-variables) can be used to set per-machine settings, such as the IP address, through the `conf/spark-env.sh` script on each node. * [Logging](#configuring-logging) can be configured through `log4j.properties`. @@ -15,38 +15,61 @@ Spark provides three locations to configure the system: # Spark Properties -Spark properties control most application settings and are configured separately for each application. -The preferred way to set them is by passing a [SparkConf](api/scala/index.html#org.apache.spark.SparkConf) -class to your SparkContext constructor. -Alternatively, Spark will also load them from Java system properties, for compatibility with old versions -of Spark. - -SparkConf lets you configure most of the common properties to initialize a cluster (e.g., master URL and -application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could -initialize an application as follows: +Spark properties control most application settings and are configured separately for each +application. The preferred way is to set them through +[SparkConf](api/scala/index.html#org.apache.spark.SparkConf) and passing it as an argument to your +SparkContext. SparkConf allows you to configure most of the common properties to initialize a +cluster (e.g. master URL and application name), as well as arbitrary key-value pairs through the +`set()` method. For example, we could initialize an application as follows: {% highlight scala %} -val conf = new SparkConf(). - setMaster("local"). - setAppName("My application"). - set("spark.executor.memory", "1g") +val conf = new SparkConf() + .setMaster("local") + .setAppName("CountingSheep") + .set("spark.executor.memory", "1g") val sc = new SparkContext(conf) {% endhighlight %} -Most of the properties control internal settings that have reasonable default values. However, +## Loading Default Configurations + +In the case of `spark-shell`, a SparkContext has already been created for you, so you cannot control +the configuration properties through SparkConf. However, you can still set configuration properties +through a default configuration file. By default, `spark-shell` (and more generally `spark-submit`) +will read configuration options from `conf/spark-defaults.conf`, in which each line consists of a +key and a value separated by whitespace. For example, + + spark.master spark://5.6.7.8:7077 + spark.executor.memory 512m + spark.eventLog.enabled true + spark.serializer org.apache.spark.serializer.KryoSerializer + +Any values specified in the file will be passed on to the application, and merged with those +specified through SparkConf. If the same configuration property exists in both `spark-defaults.conf` +and SparkConf, then the latter will take precedence as it is the most application-specific. + +## Viewing Spark Properties + +The application web UI at `http://:4040` lists Spark properties in the "Environment" tab. +This is a useful place to check to make sure that your properties have been set correctly. Note +that only values explicitly specified through either `spark-defaults.conf` or SparkConf will +appear. For all other configuration properties, you can assume the default value is used. + +## All Configuration Properties + +Most of the properties that control internal settings have reasonable default values. However, there are at least five properties that you will commonly want to control: - + - + - + - + - +
Property NameDefaultMeaning
spark.executor.memoryspark.executor.memory 512m Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. 512m, 2g).
spark.serializerspark.serializer org.apache.spark.serializer.
JavaSerializer		 Class to use for serializing objects that will be sent over the network or need to be cached @@ -57,7 +80,7 @@ there are at least five properties that you will commonly want to control:
spark.kryo.registratorspark.kryo.registrator (none) If you use Kryo serialization, set this class to register your custom classes with Kryo. @@ -67,7 +90,7 @@ there are at least five properties that you will commonly want to control:
spark.local.dirspark.local.dir /tmp Directory to use for "scratch" space in Spark, including map output files and RDDs that get stored @@ -79,7 +102,7 @@ there are at least five properties that you will commonly want to control:
spark.cores.maxspark.cores.max (not set) When running on a standalone deploy cluster or a @@ -98,12 +121,12 @@ Apart from these, the following properties are also available, and may be useful - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - - + + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
Property NameDefaultMeaning
spark.default.parallelismspark.default.parallelism
    +
  • Local mode: number of cores on the local machine
  • Mesos fine grained mode: 8
    • -
  • Local mode: core number of the local machine
    • -
  • Others: total core number of all executor nodes or 2, whichever is larger
    • +
  • Others: total number of cores on all executor nodes or 2, whichever is larger
 @@ -112,7 +135,7 @@ Apart from these, the following properties are also available, and may be useful
spark.storage.memoryFractionspark.storage.memoryFraction 0.6 Fraction of Java heap to use for Spark's memory cache. This should not be larger than the "old" @@ -121,7 +144,7 @@ Apart from these, the following properties are also available, and may be useful
spark.shuffle.memoryFractionspark.shuffle.memoryFraction 0.3 Fraction of Java heap to use for aggregation and cogroups during shuffles, if @@ -132,7 +155,7 @@ Apart from these, the following properties are also available, and may be useful
spark.storage.memoryMapThresholdspark.storage.memoryMapThreshold 8192 Size of a block, in bytes, above which Spark memory maps when reading a block from disk. @@ -141,7 +164,7 @@ Apart from these, the following properties are also available, and may be useful
spark.tachyonStore.baseDirspark.tachyonStore.baseDir System.getProperty("java.io.tmpdir") Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by spark.tachyonStore.url. @@ -149,14 +172,14 @@ Apart from these, the following properties are also available, and may be useful
spark.tachyonStore.urlspark.tachyonStore.url tachyon://localhost:19998 The URL of the underlying Tachyon file system in the TachyonStore.
spark.mesos.coarsespark.mesos.coarse false If set to "true", runs over Mesos clusters in @@ -167,31 +190,31 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.portspark.ui.port 4040 Port for your application's dashboard, which shows memory and workload data
spark.ui.retainedStagesspark.ui.retainedStages 1000 How many stages the Spark UI remembers before garbage collecting.
spark.ui.filtersspark.ui.filters None Comma separated list of filter class names to apply to the Spark web ui. The filter should be a standard javax servlet Filter. Parameters to each filter can also be specified by setting a java system property of spark.<class name of filter>.params='param1=value1,param2=value2' - (e.g.-Dspark.ui.filters=com.test.filter1 -Dspark.com.test.filter1.params='param1=foo,param2=testing') + (e.g. -Dspark.ui.filters=com.test.filter1 -Dspark.com.test.filter1.params='param1=foo,param2=testing')
spark.ui.acls.enablespark.ui.acls.enable false Whether spark web ui acls should are enabled. If enabled, this checks to see if the user has @@ -201,7 +224,7 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.view.aclsspark.ui.view.acls Empty Comma separated list of users that have view access to the spark web ui. By default only the @@ -209,35 +232,35 @@ Apart from these, the following properties are also available, and may be useful
spark.ui.killEnabledspark.ui.killEnabled true Allows stages and corresponding jobs to be killed from the web ui.
spark.shuffle.compressspark.shuffle.compress true Whether to compress map output files. Generally a good idea.
spark.shuffle.spill.compressspark.shuffle.spill.compress true Whether to compress data spilled during shuffles.
spark.broadcast.compressspark.broadcast.compress true Whether to compress broadcast variables before sending them. Generally a good idea.
spark.rdd.compressspark.rdd.compress false Whether to compress serialized RDD partitions (e.g. for StorageLevel.MEMORY_ONLY_SER). @@ -245,7 +268,7 @@ Apart from these, the following properties are also available, and may be useful
spark.io.compression.codecspark.io.compression.codec org.apache.spark.io.
LZFCompressionCodec		 The codec used to compress internal data such as RDD partitions and shuffle outputs. By default, Spark provides two @@ -253,14 +276,14 @@ Apart from these, the following properties are also available, and may be useful
spark.io.compression.snappy.block.sizespark.io.compression.snappy.block.size 32768 Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec is used.
spark.scheduler.modespark.scheduler.mode FIFO The scheduling mode between @@ -270,14 +293,14 @@ Apart from these, the following properties are also available, and may be useful
spark.scheduler.revive.intervalspark.scheduler.revive.interval 1000 The interval length for the scheduler to revive the worker resource offers to run tasks. (in milliseconds)
spark.reducer.maxMbInFlightspark.reducer.maxMbInFlight 48 Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since @@ -286,14 +309,14 @@ Apart from these, the following properties are also available, and may be useful
spark.closure.serializerspark.closure.serializer org.apache.spark.serializer.
JavaSerializer		 Serializer class to use for closures. Currently only the Java serializer is supported.
spark.kryo.referenceTrackingspark.kryo.referenceTracking true Whether to track references to the same object when serializing data with Kryo, which is @@ -303,7 +326,7 @@ Apart from these, the following properties are also available, and may be useful
spark.kryoserializer.buffer.mbspark.kryoserializer.buffer.mb 2 Maximum object size to allow within Kryo (the library needs to create a buffer at least as @@ -312,7 +335,7 @@ Apart from these, the following properties are also available, and may be useful
spark.serializer.objectStreamResetspark.serializer.objectStreamReset 10000 When serializing using org.apache.spark.serializer.JavaSerializer, the serializer caches @@ -323,14 +346,14 @@ Apart from these, the following properties are also available, and may be useful
spark.broadcast.factoryspark.broadcast.factory org.apache.spark.broadcast.
HttpBroadcastFactory		 Which broadcast implementation to use.
spark.locality.waitspark.locality.wait 3000 Number of milliseconds to wait to launch a data-local task before giving up and launching it @@ -342,7 +365,7 @@ Apart from these, the following properties are also available, and may be useful
spark.locality.wait.processspark.locality.wait.process spark.locality.wait Customize the locality wait for process locality. This affects tasks that attempt to access @@ -350,7 +373,7 @@ Apart from these, the following properties are also available, and may be useful
spark.locality.wait.nodespark.locality.wait.node spark.locality.wait Customize the locality wait for node locality. For example, you can set this to 0 to skip @@ -358,14 +381,14 @@ Apart from these, the following properties are also available, and may be useful
spark.locality.wait.rackspark.locality.wait.rack spark.locality.wait Customize the locality wait for rack locality.
spark.worker.timeoutspark.worker.timeout 60 Number of seconds after which the standalone deploy master considers a worker lost if it @@ -373,15 +396,16 @@ Apart from these, the following properties are also available, and may be useful
spark.worker.cleanup.enabledtruespark.worker.cleanup.enabledfalse Enable periodic cleanup of worker / application directories. Note that this only affects standalone - mode, as YARN works differently. + mode, as YARN works differently. Applications directories are cleaned up regardless of whether + the application is still running.
spark.worker.cleanup.intervalspark.worker.cleanup.interval 1800 (30 minutes) Controls the interval, in seconds, at which the worker cleans up old application work dirs @@ -389,7 +413,7 @@ Apart from these, the following properties are also available, and may be useful
spark.worker.cleanup.appDataTtlspark.worker.cleanup.appDataTtl 7 * 24 * 3600 (7 days) The number of seconds to retain application work directories on each worker. This is a Time To Live @@ -399,7 +423,7 @@ Apart from these, the following properties are also available, and may be useful
spark.akka.frameSizespark.akka.frameSize 10 Maximum message size to allow in "control plane" communication (for serialized tasks and task @@ -408,7 +432,7 @@ Apart from these, the following properties are also available, and may be useful
spark.akka.threadsspark.akka.threads 4 Number of actor threads to use for communication. Can be useful to increase on large clusters @@ -416,49 +440,49 @@ Apart from these, the following properties are also available, and may be useful
spark.akka.timeoutspark.akka.timeout 100 Communication timeout between Spark nodes, in seconds.
spark.akka.heartbeat.pausesspark.akka.heartbeat.pauses 600 This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart beat pause in seconds for akka. This can be used to control sensitivity to gc pauses. Tune this in combination of `spark.akka.heartbeat.interval` and `spark.akka.failure-detector.threshold` if you need to.
spark.akka.failure-detector.thresholdspark.akka.failure-detector.threshold 300.0 This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). This maps to akka's `akka.remote.transport-failure-detector.threshold`. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
spark.akka.heartbeat.intervalspark.akka.heartbeat.interval 1000 This is set to a larger value to disable failure detector that comes inbuilt akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more informative for akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` and `spark.akka.failure-detector.threshold` if you need to. Only positive use case for using failure detector can be, a sensistive failure detector can help evict rogue executors really quick. However this is usually not the case as gc pauses and network lags are expected in a real spark cluster. Apart from that enabling this leads to a lot of exchanges of heart beats between nodes leading to flooding the network with those.
spark.driver.hostspark.driver.host (local hostname) Hostname or IP address for the driver to listen on.
spark.driver.portspark.driver.port (random) Port for the driver to listen on.
spark.cleaner.ttlspark.cleaner.ttl (infinite) Duration (seconds) of how long Spark will remember any metadata (stages generated, tasks generated, etc.). @@ -468,7 +492,7 @@ Apart from these, the following properties are also available, and may be useful
spark.streaming.blockIntervalspark.streaming.blockInterval 200 Interval (milliseconds) at which data received by Spark Streaming receivers is coalesced @@ -476,7 +500,7 @@ Apart from these, the following properties are also available, and may be useful
spark.streaming.unpersistspark.streaming.unpersist true Force RDDs generated and persisted by Spark Streaming to be automatically unpersisted from @@ -487,7 +511,7 @@ Apart from these, the following properties are also available, and may be useful
spark.task.maxFailuresspark.task.maxFailures 4 Number of individual task failures before giving up on the job. @@ -495,7 +519,7 @@ Apart from these, the following properties are also available, and may be useful
spark.broadcast.blockSizespark.broadcast.blockSize 4096 Size of each piece of a block in kilobytes for TorrentBroadcastFactory. @@ -504,14 +528,14 @@ Apart from these, the following properties are also available, and may be useful
spark.shuffle.consolidateFilesspark.shuffle.consolidateFiles false If set to "true", consolidates intermediate files created during a shuffle. Creating fewer files can improve filesystem performance for shuffles with large numbers of reduce tasks. It is recommended to set this to "true" when using ext4 or xfs filesystems. On ext3, this option might degrade performance on machines with many (>8) cores due to filesystem limitations.
spark.shuffle.file.buffer.kbspark.shuffle.file.buffer.kb 100 Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers @@ -519,7 +543,7 @@ Apart from these, the following properties are also available, and may be useful
spark.shuffle.spillspark.shuffle.spill true If set to "true", limits the amount of memory used during reduces by spilling data out to disk. This spilling @@ -527,56 +551,56 @@ Apart from these, the following properties are also available, and may be useful
spark.speculationspark.speculation false If set to "true", performs speculative execution of tasks. This means if one or more tasks are running slowly in a stage, they will be re-launched.
spark.speculation.intervalspark.speculation.interval 100 How often Spark will check for tasks to speculate, in milliseconds.
spark.speculation.quantilespark.speculation.quantile 0.75 Percentage of tasks which must be complete before speculation is enabled for a particular stage.
spark.speculation.multiplierspark.speculation.multiplier 1.5 How many times slower a task is than the median to be considered for speculation.
spark.logConfspark.logConf false Whether to log the supplied SparkConf as INFO at start of spark context.
spark.eventLog.enabledspark.eventLog.enabled false Whether to log spark events, useful for reconstructing the Web UI after the application has finished.
spark.eventLog.compressspark.eventLog.compress false Whether to compress logged events, if spark.eventLog.enabled is true.
spark.eventLog.dirspark.eventLog.dir file:///tmp/spark-events Base directory in which spark events are logged, if spark.eventLog.enabled is true. @@ -585,7 +609,7 @@ Apart from these, the following properties are also available, and may be useful
spark.deploy.spreadOutspark.deploy.spreadOut true Whether the standalone cluster manager should spread applications out across nodes or try @@ -596,7 +620,7 @@ Apart from these, the following properties are also available, and may be useful
spark.deploy.defaultCoresspark.deploy.defaultCores (infinite) Default number of cores to give to applications in Spark's standalone mode if they don't @@ -609,14 +633,14 @@ Apart from these, the following properties are also available, and may be useful
spark.files.overwritespark.files.overwrite false Whether to overwrite files added through SparkContext.addFile() when the target file exists and its contents do not match those of the source.
spark.files.fetchTimeoutspark.files.fetchTimeout false Communication timeout to use when fetching files added through SparkContext.addFile() from @@ -624,7 +648,7 @@ Apart from these, the following properties are also available, and may be useful
spark.files.userClassPathFirstspark.files.userClassPathFirst false (Experimental) Whether to give user-added jars precedence over Spark's own jars when @@ -633,7 +657,7 @@ Apart from these, the following properties are also available, and may be useful
spark.authenticatespark.authenticate false Whether spark authenticates its internal connections. See spark.authenticate.secret if not @@ -641,7 +665,7 @@ Apart from these, the following properties are also available, and may be useful
spark.authenticate.secretspark.authenticate.secret None Set the secret key used for Spark to authenticate between components. This needs to be set if @@ -649,7 +673,7 @@ Apart from these, the following properties are also available, and may be useful
spark.core.connection.auth.wait.timeoutspark.core.connection.auth.wait.timeout 30 Number of seconds for the connection to wait for authentication to occur before timing @@ -657,14 +681,14 @@ Apart from these, the following properties are also available, and may be useful
spark.task.cpusspark.task.cpus 1 Number of cores to allocate for each task.
spark.executor.extraJavaOptionsspark.executor.extraJavaOptions (none) A string of extra JVM options to pass to executors. For instance, GC settings or other @@ -675,7 +699,7 @@ Apart from these, the following properties are also available, and may be useful
spark.executor.extraClassPathspark.executor.extraClassPath (none) Extra classpath entries to append to the classpath of executors. This exists primarily @@ -684,7 +708,7 @@ Apart from these, the following properties are also available, and may be useful
spark.executor.extraLibraryPathspark.executor.extraLibraryPath (none) Set a special library path to use when launching executor JVM's. @@ -693,11 +717,6 @@ Apart from these, the following properties are also available, and may be useful
-## Viewing Spark Properties - -The application web UI at `http://:4040` lists Spark properties in the "Environment" tab. -This is a useful place to check to make sure that your properties have been set correctly. - # Environment Variables Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh` @@ -710,12 +729,28 @@ Note that `conf/spark-env.sh` does not exist by default when Spark is installed. The following variables can be set in `spark-env.sh`: -* `JAVA_HOME`, the location where Java is installed (if it's not on your default `PATH`) -* `PYSPARK_PYTHON`, the Python binary to use for PySpark -* `SPARK_LOCAL_IP`, to configure which IP address of the machine to bind to. -* `SPARK_PUBLIC_DNS`, the hostname your Spark program will advertise to other machines. -* Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores - to use on each machine and maximum memory. + + + + + + + + + + + + + + + + + + + +
Environment VariableMeaning
JAVA_HOMELocation where Java is installed (if it's not on your default `PATH`).
PYSPARK_PYTHONPython binary executable to use for PySpark.
SPARK_LOCAL_IPIP address of the machine to bind to.
SPARK_PUBLIC_DNSHostname your Spark program will advertise to other machines.
+ +In addition to the above, there are also options for setting up the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory. Since `spark-env.sh` is a shell script, some of these can be set programmatically -- for example, you might compute `SPARK_LOCAL_IP` by looking up the IP of a specific network interface. diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md index 454877a7fa8a5..a0aeab5727bde 100644 --- a/docs/hadoop-third-party-distributions.md +++ b/docs/hadoop-third-party-distributions.md @@ -9,12 +9,14 @@ with these distributions: # Compile-time Hadoop Version -When compiling Spark, you'll need to -[set the SPARK_HADOOP_VERSION flag](index.html#a-note-about-hadoop-versions): +When compiling Spark, you'll need to specify the Hadoop version by defining the `hadoop.version` +property. For certain versions, you will need to specify additional profiles. For more detail, +see the guide on [building with maven](building-with-maven.html#specifying-the-hadoop-version): - SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly + mvn -Dhadoop.version=1.0.4 -DskipTests clean package + mvn -Phadoop-2.2 -Dhadoop.version=2.2.0 -DskipTests clean package -The table below lists the corresponding `SPARK_HADOOP_VERSION` code for each CDH/HDP release. Note that +The table below lists the corresponding `hadoop.version` code for each CDH/HDP release. Note that some Hadoop releases are binary compatible across client versions. This means the pre-built Spark distribution may "just work" without you needing to compile. That said, we recommend compiling with the _exact_ Hadoop version you are running to avoid any compatibility errors. @@ -46,6 +48,10 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors.
+In SBT, the equivalent can be achieved by setting the SPARK_HADOOP_VERSION flag: + + SPARK_HADOOP_VERSION=1.0.4 sbt/sbt assembly + # Linking Applications to the Hadoop Version In addition to compiling Spark itself against the right version, you need to add a Maven dependency on that diff --git a/docs/index.md b/docs/index.md index a2f1a84371ff4..c9b10376cc809 100644 --- a/docs/index.md +++ b/docs/index.md @@ -24,21 +24,34 @@ right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/ # Running the Examples and Shell -Spark comes with several sample programs. Scala, Java and Python examples are in the `examples/src/main` directory. -To run one of the Java or Scala sample programs, use `./bin/run-example ` in the top-level Spark directory -(the `bin/run-example` script sets up the appropriate paths and launches that program). -For example, try `./bin/run-example org.apache.spark.examples.SparkPi local`. -To run a Python sample program, use `./bin/pyspark `. For example, try `./bin/pyspark ./examples/src/main/python/pi.py local`. +Spark comes with several sample programs. Scala, Java and Python examples are in the +`examples/src/main` directory. To run one of the Java or Scala sample programs, use +`bin/run-example [params]` in the top-level Spark directory. (Behind the scenes, this +invokes the more general +[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) for +launching applications). For example, -Each example prints usage help when run with no parameters. + ./bin/run-example SparkPi 10 -Note that all of the sample programs take a `` parameter specifying the cluster URL -to connect to. This can be a [URL for a distributed cluster](scala-programming-guide.html#master-urls), -or `local` to run locally with one thread, or `local[N]` to run locally with N threads. You should start by using -`local` for testing. +You can also run Spark interactively through modified versions of the Scala shell. This is a +great way to learn the framework. -Finally, you can run Spark interactively through modified versions of the Scala shell (`./bin/spark-shell`) or -Python interpreter (`./bin/pyspark`). These are a great way to learn the framework. + ./bin/spark-shell --master local[2] + +The `--master` option specifies the +[master URL for a distributed cluster](scala-programming-guide.html#master-urls), or `local` to run +locally with one thread, or `local[N]` to run locally with N threads. You should start by using +`local` for testing. For a full list of options, run Spark shell with the `--help` option. + +Spark also provides a Python interface. To run Spark interactively in a Python interpreter, use +`bin/pyspark`. As in Spark shell, you can also pass in the `--master` option to configure your +master URL. + + ./bin/pyspark --master local[2] + +Example applications are also provided in Python. For example, + + ./bin/spark-submit examples/src/main/python/pi.py 10 # Launching on a Cluster diff --git a/docs/java-programming-guide.md b/docs/java-programming-guide.md index c34eb28fc06a2..943fdd9d019ff 100644 --- a/docs/java-programming-guide.md +++ b/docs/java-programming-guide.md @@ -215,7 +215,4 @@ Spark includes several sample programs using the Java API in [`examples/src/main/java`](https://github.com/apache/spark/tree/master/examples/src/main/java/org/apache/spark/examples). You can run them by passing the class name to the `bin/run-example` script included in Spark; for example: - ./bin/run-example org.apache.spark.examples.JavaWordCount - -Each example program prints usage help when run -without any arguments. + ./bin/run-example JavaWordCount README.md diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md index aa9321a547097..5796e16e8f99c 100644 --- a/docs/mllib-basics.md +++ b/docs/mllib-basics.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Basics +title: Basics - MLlib +displayTitle: MLlib - Basics --- * Table of contents @@ -26,11 +27,11 @@ of the vector.
The base class of local vectors is -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two -implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and -[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend +[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two +implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseVector) and +[`SparseVector`](api/scala/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend using the factory methods implemented in -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. {% highlight scala %} import org.apache.spark.mllib.linalg.{Vector, Vectors} @@ -53,11 +54,11 @@ Scala imports `scala.collection.immutable.Vector` by default, so you have to imp
The base class of local vectors is -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector), and we provide two -implementations: [`DenseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseVector) and -[`SparseVector`](api/mllib/index.html#org.apache.spark.mllib.linalg.SparseVector). We recommend +[`Vector`](api/java/org/apache/spark/mllib/linalg/Vector.html), and we provide two +implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVector.html) and +[`SparseVector`](api/java/org/apache/spark/mllib/linalg/SparseVector.html). We recommend using the factory methods implemented in -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) to create local vectors. +[`Vectors`](api/java/org/apache/spark/mllib/linalg/Vector.html) to create local vectors. {% highlight java %} import org.apache.spark.mllib.linalg.Vector; @@ -78,13 +79,13 @@ MLlib recognizes the following types as dense vectors: and the following as sparse vectors: -* MLlib's [`SparseVector`](api/pyspark/pyspark.mllib.linalg.SparseVector-class.html). +* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html). * SciPy's [`csc_matrix`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html#scipy.sparse.csc_matrix) with a single column We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented -in [`Vectors`](api/pyspark/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. +in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. {% highlight python %} import numpy as np @@ -117,7 +118,7 @@ For multiclass classification, labels should be class indices staring from zero:
A labeled point is represented by the case class -[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint). +[`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint). {% highlight scala %} import org.apache.spark.mllib.linalg.Vectors @@ -134,7 +135,7 @@ val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
A labeled point is represented by -[`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint). +[`LabeledPoint`](api/java/org/apache/spark/mllib/regression/LabeledPoint.html). {% highlight java %} import org.apache.spark.mllib.linalg.Vectors; @@ -151,7 +152,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new
A labeled point is represented by -[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html). +[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html). {% highlight python %} from pyspark.mllib.linalg import SparseVector @@ -184,7 +185,7 @@ After loading, the feature indices are converted to zero-based.
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training +[`MLUtils.loadLibSVMFile`](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) reads training examples stored in LIBSVM format. {% highlight scala %} @@ -192,20 +193,32 @@ import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD -val training: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") +val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") {% endhighlight %}
-[`MLUtils.loadLibSVMFile`](api/mllib/index.html#org.apache.spark.mllib.util.MLUtils$) reads training +[`MLUtils.loadLibSVMFile`](api/java/org/apache/spark/mllib/util/MLUtils.html) reads training examples stored in LIBSVM format. {% highlight java %} import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.rdd.RDDimport; +import org.apache.spark.api.java.JavaRDD; + +JavaRDD examples = + MLUtils.loadLibSVMFile(jsc.sc(), "mllib/data/sample_libsvm_data.txt").toJavaRDD(); +{% endhighlight %} +
+ +
+[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.util.MLUtils-class.html) reads training +examples stored in LIBSVM format. -RDD training = MLUtils.loadLibSVMFile(jsc, "mllib/data/sample_libsvm_data.txt"); +{% highlight python %} +from pyspark.mllib.util import MLUtils + +examples = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") {% endhighlight %}
@@ -227,10 +240,10 @@ We are going to add sparse matrix in the next release.
The base class of local matrices is -[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one -implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix). +[`Matrix`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one +implementation: [`DenseMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.DenseMatrix). Sparse matrix will be added in the next release. We recommend using the factory methods implemented -in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local +in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) to create local matrices. {% highlight scala %} @@ -244,10 +257,10 @@ val dm: Matrix = Matrices.dense(3, 2, Array(1.0, 3.0, 5.0, 2.0, 4.0, 6.0))
The base class of local matrices is -[`Matrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrix), and we provide one -implementation: [`DenseMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.DenseMatrix). +[`Matrix`](api/java/org/apache/spark/mllib/linalg/Matrix.html), and we provide one +implementation: [`DenseMatrix`](api/java/org/apache/spark/mllib/linalg/DenseMatrix.html). Sparse matrix will be added in the next release. We recommend using the factory methods implemented -in [`Matrices`](api/mllib/index.html#org.apache.spark.mllib.linalg.Matrices) to create local +in [`Matrices`](api/java/org/apache/spark/mllib/linalg/Matrices.html) to create local matrices. {% highlight java %} @@ -269,6 +282,15 @@ and distributed matrices. Converting a distributed matrix to a different format global shuffle, which is quite expensive. We implemented three types of distributed matrices in this release and will add more types in the future. +The basic type is called `RowMatrix`. A `RowMatrix` is a row-oriented distributed +matrix without meaningful row indices, e.g., a collection of feature vectors. +It is backed by an RDD of its rows, where each row is a local vector. +We assume that the number of columns is not huge for a `RowMatrix`. +An `IndexedRowMatrix` is similar to a `RowMatrix` but with row indices, +which can be used for identifying rows and joins. +A `CoordinateMatrix` is a distributed matrix stored in [coordinate list (COO)](https://en.wikipedia.org/wiki/Sparse_matrix) format, +backed by an RDD of its entries. + ***Note*** The underlying RDDs of a distributed matrix must be deterministic, because we cache the matrix size. @@ -284,7 +306,7 @@ limited by the integer range but it should be much smaller in practice.
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be +A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be created from an `RDD[Vector]` instance. Then we can compute its column summary statistics. {% highlight scala %} @@ -303,7 +325,7 @@ val n = mat.numCols()
-A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be +A [`RowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html) can be created from a `JavaRDD` instance. Then we can compute its column summary statistics. {% highlight java %} @@ -333,8 +355,8 @@ which could be faster if the rows are sparse.
-`RowMatrix#computeColumnSummaryStatistics` returns an instance of -[`MultivariateStatisticalSummary`](api/mllib/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary), +[`RowMatrix#computeColumnSummaryStatistics`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) returns an instance of +[`MultivariateStatisticalSummary`](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary), which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the total count. @@ -355,6 +377,31 @@ println(summary.numNonzeros) // number of nonzeros in each column val cov: Matrix = mat.computeCovariance() {% endhighlight %}
+ +
+ +[`RowMatrix#computeColumnSummaryStatistics`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html#computeColumnSummaryStatistics()) returns an instance of +[`MultivariateStatisticalSummary`](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html), +which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the +total count. + +{% highlight java %} +import org.apache.spark.mllib.linalg.Matrix; +import org.apache.spark.mllib.linalg.distributed.RowMatrix; +import org.apache.spark.mllib.stat.MultivariateStatisticalSummary; + +RowMatrix mat = ... // a RowMatrix + +// Compute column summary statistics. +MultivariateStatisticalSummary summary = mat.computeColumnSummaryStatistics(); +System.out.println(summary.mean()); // a dense vector containing the mean value for each column +System.out.println(summary.variance()); // column-wise variance +System.out.println(summary.numNonzeros()); // number of nonzeros in each column + +// Compute the covariance matrix. +Matrix cov = mat.computeCovariance(); +{% endhighlight %} +
### IndexedRowMatrix @@ -366,9 +413,9 @@ an RDD of indexed rows, which each row is represented by its index (long-typed)
An -[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) +[`IndexedRowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) can be created from an `RDD[IndexedRow]` instance, where -[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a +[`IndexedRow`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a wrapper over `(Long, Vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. @@ -391,9 +438,9 @@ val rowMat: RowMatrix = mat.toRowMatrix()
An -[`IndexedRowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) +[`IndexedRowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.html) can be created from an `JavaRDD` instance, where -[`IndexedRow`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRow) is a +[`IndexedRow`](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRow.html) is a wrapper over `(long, Vector)`. An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping its row indices. @@ -427,9 +474,9 @@ dimensions of the matrix are huge and the matrix is very sparse.
A -[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) +[`CoordinateMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) can be created from an `RDD[MatrixEntry]` instance, where -[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a +[`MatrixEntry`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a wrapper over `(Long, Long, Double)`. A `CoordinateMatrix` can be converted to a `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. In this release, we do not provide other computation for `CoordinateMatrix`. @@ -453,13 +500,13 @@ val indexedRowMatrix = mat.toIndexedRowMatrix()
A -[`CoordinateMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) +[`CoordinateMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.html) can be created from a `JavaRDD` instance, where -[`MatrixEntry`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.MatrixEntry) is a +[`MatrixEntry`](api/java/org/apache/spark/mllib/linalg/distributed/MatrixEntry.html) is a wrapper over `(long, long, double)`. A `CoordinateMatrix` can be converted to a `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`. -{% highlight scala %} +{% highlight java %} import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; @@ -467,7 +514,7 @@ import org.apache.spark.mllib.linalg.distributed.MatrixEntry; JavaRDD entries = ... // a JavaRDD of matrix entries // Create a CoordinateMatrix from a JavaRDD. -CoordinateMatrix mat = new CoordinateMatrix(entries); +CoordinateMatrix mat = new CoordinateMatrix(entries.rdd()); // Get its size. long m = mat.numRows(); diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 276868fa8490d..429cdf8d40cec 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Clustering +title: Clustering - MLlib +displayTitle: MLlib - Clustering --- * Table of contents @@ -40,7 +41,7 @@ a given dataset, the algorithm returns the best clustering result). Following code snippets can be executed in `spark-shell`. In the following example after loading and parsing data, we use the -[`KMeans`](api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data +[`KMeans`](api/scala/index.html#org.apache.spark.mllib.clustering.KMeans) object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the optimal *k* is usually one where there is an "elbow" in the WSSSE graph. diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index f486c56e55907..d51002f015670 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Collaborative Filtering +title: Collaborative Filtering - MLlib +displayTitle: MLlib - Collaborative Filtering --- * Table of contents @@ -48,7 +49,7 @@ user for an item.
In the following example we load rating data. Each row consists of a user, a product and a rating. -We use the default [ALS.train()](api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS$) +We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$) method which assumes ratings are explicit. We evaluate the recommendation model by measuring the Mean Squared Error of rating prediction. @@ -58,9 +59,9 @@ import org.apache.spark.mllib.recommendation.Rating // Load and parse the data val data = sc.textFile("mllib/data/als/test.data") -val ratings = data.map(_.split(',') match { - case Array(user, item, rate) => Rating(user.toInt, item.toInt, rate.toDouble) -}) +val ratings = data.map(_.split(',') match { case Array(user, item, rate) => + Rating(user.toInt, item.toInt, rate.toDouble) + }) // Build the recommendation model using ALS val rank = 10 @@ -68,15 +69,19 @@ val numIterations = 20 val model = ALS.train(ratings, rank, numIterations, 0.01) // Evaluate the model on rating data -val usersProducts = ratings.map{ case Rating(user, product, rate) => (user, product)} -val predictions = model.predict(usersProducts).map{ - case Rating(user, product, rate) => ((user, product), rate) +val usersProducts = ratings.map { case Rating(user, product, rate) => + (user, product) } -val ratesAndPreds = ratings.map{ - case Rating(user, product, rate) => ((user, product), rate) +val predictions = + model.predict(usersProducts).map { case Rating(user, product, rate) => + ((user, product), rate) + } +val ratesAndPreds = ratings.map { case Rating(user, product, rate) => + ((user, product), rate) }.join(predictions) -val MSE = ratesAndPreds.map{ - case ((user, product), (r1, r2)) => math.pow((r1- r2), 2) +val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) => + val err = (r1 - r2) + err * err }.mean() println("Mean Squared Error = " + MSE) {% endhighlight %} diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md index 16e5d10ecbe7d..6089023fe67f2 100644 --- a/docs/mllib-decision-tree.md +++ b/docs/mllib-decision-tree.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Decision Tree +title: Decision Tree - MLlib +displayTitle: MLlib - Decision Tree --- * Table of contents diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md index ab24663cfe258..e3608075fbb13 100644 --- a/docs/mllib-dimensionality-reduction.md +++ b/docs/mllib-dimensionality-reduction.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Dimensionality Reduction +title: Dimensionality Reduction - MLlib +displayTitle: MLlib - Dimensionality Reduction --- * Table of contents diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 842ca5c8c6d8a..640ca83085387 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -27,8 +27,9 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv * stochastic gradient descent * limited-memory BFGS (L-BFGS) -MLlib is currently a *beta* component under active development. -The APIs may change in the future releases, and we will provide migration guide between releases. +MLlib is a new component under active development. +The APIs marked `Experimental`/`DeveloperApi` may change in future releases, +and we will provide migration guide between releases. ## Dependencies @@ -61,9 +62,9 @@ take advantage of sparsity in both storage and computation.
We used to represent a feature vector by `Array[Double]`, which is replaced by -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used +[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used to accept `RDD[Array[Double]]` now take -`RDD[Vector]`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) +`RDD[Vector]`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) is now a wrapper of `(Double, Vector)` instead of `(Double, Array[Double])`. Converting `Array[Double]` to `Vector` is straightforward: @@ -74,7 +75,7 @@ val array: Array[Double] = ... // a double array val vector: Vector = Vectors.dense(array) // a dense vector {% endhighlight %} -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors. +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors. *Note*. Scala imports `scala.collection.immutable.Vector` by default, so you have to import `org.apache.spark.mllib.linalg.Vector` explicitly to use MLlib's `Vector`. @@ -83,9 +84,9 @@ val vector: Vector = Vectors.dense(array) // a dense vector
We used to represent a feature vector by `double[]`, which is replaced by -[`Vector`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used +[`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) in v1.0. Algorithms that used to accept `RDD` now take -`RDD`. [`LabeledPoint`](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) +`RDD`. [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) is now a wrapper of `(double, Vector)` instead of `(double, double[])`. Converting `double[]` to `Vector` is straightforward: @@ -97,7 +98,7 @@ double[] array = ... // a double array Vector vector = Vectors.dense(array); // a dense vector {% endhighlight %} -[`Vectors`](api/mllib/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to +[`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) provides factory methods to create sparse vectors.
@@ -106,7 +107,7 @@ create sparse vectors. We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to the label and the rest are features. This representation is replaced by class -[`LabeledPoint`](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html), which takes both +[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html), which takes both dense and sparse feature vectors. {% highlight python %} diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index eff617d8641e2..4dfbebbcd04b7 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Linear Methods +title: Linear Methods - MLlib +displayTitle: MLlib - Linear Methods --- * Table of contents @@ -233,7 +234,7 @@ val modelL1 = svmAlg.run(training) {% endhighlight %} Similarly, you can use replace `SVMWithSGD` by -[`LogisticRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD). +[`LogisticRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD).
@@ -328,8 +329,8 @@ println("training Mean Squared Error = " + MSE) {% endhighlight %} Similarly you can use -[`RidgeRegressionWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) -and [`LassoWithSGD`](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD). +[`RidgeRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) +and [`LassoWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD).
@@ -380,11 +381,11 @@ all three possible regularizations (none, L1 or L2). Algorithms are all implemented in Scala: -* [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD) -* [LogisticRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD) -* [LinearRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD) -* [RidgeRegressionWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) -* [LassoWithSGD](api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD) +* [SVMWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD) +* [LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD) +* [LinearRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD) +* [RidgeRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD) +* [LassoWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LassoWithSGD) Python calls the Scala implementation via -[PythonMLLibAPI](api/mllib/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI). +[PythonMLLibAPI](api/scala/index.html#org.apache.spark.mllib.api.python.PythonMLLibAPI). diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index c47508b7daa2d..4b3a7cab32118 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Naive Bayes +title: Naive Bayes - MLlib +displayTitle: MLlib - Naive Bayes --- Naive Bayes is a simple multiclass classification algorithm with the assumption of independence @@ -27,11 +28,11 @@ sparsity. Since the training data is only used once, it is not necessary to cach
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements +[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional +[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which +[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. {% highlight scala %} @@ -59,11 +60,11 @@ val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test
-[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements +[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements multinomial naive Bayes. It takes a Scala RDD of -[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an +[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which +[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which can be used for evaluation and prediction. {% highlight java %} @@ -102,11 +103,11 @@ double accuracy = 1.0 * predictionAndLabel.filter(new Function -[NaiveBayes](api/pyspark/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial +[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally +[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be +[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be used for evaluation and prediction. diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md index bec3912b55dc7..a22980d03a2f0 100644 --- a/docs/mllib-optimization.md +++ b/docs/mllib-optimization.md @@ -1,6 +1,7 @@ --- layout: global -title: MLlib - Optimization +title: Optimization - MLlib +displayTitle: MLlib - Optimization --- * Table of contents @@ -28,7 +29,6 @@ title: MLlib - Optimization ## Mathematical description ### Gradient descent - The simplest method to solve optimization problems of the form `$\min_{\wv \in\R^d} \; f(\wv)$` is [gradient descent](http://en.wikipedia.org/wiki/Gradient_descent). Such first-order optimization methods (including gradient descent and stochastic variants @@ -128,10 +128,19 @@ is sampled, i.e. `$|S|=$ miniBatchFraction $\cdot n = 1$`, then the algorithm is standard SGD. In that case, the step direction depends from the uniformly random sampling of the point. - +### Limited-memory BFGS (L-BFGS) +[L-BFGS](http://en.wikipedia.org/wiki/Limited-memory_BFGS) is an optimization +algorithm in the family of quasi-Newton methods to solve the optimization problems of the form +`$\min_{\wv \in\R^d} \; f(\wv)$`. The L-BFGS method approximates the objective function locally as a +quadratic without evaluating the second partial derivatives of the objective function to construct the +Hessian matrix. The Hessian matrix is approximated by previous gradient evaluations, so there is no +vertical scalability issue (the number of training features) when computing the Hessian matrix +explicitly in Newton's method. As a result, L-BFGS often achieves rapider convergence compared with +other first-order optimization. ## Implementation in MLlib +### Gradient descent and stochastic gradient descent Gradient descent methods including stochastic subgradient descent (SGD) as included as a low-level primitive in `MLlib`, upon which various ML algorithms are developed, see the @@ -142,12 +151,12 @@ The SGD method [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) has the following parameters: -* `gradient` is a class that computes the stochastic gradient of the function +* `Gradient` is a class that computes the stochastic gradient of the function being optimized, i.e., with respect to a single training example, at the current parameter value. MLlib includes gradient classes for common loss functions, e.g., hinge, logistic, least-squares. The gradient class takes as input a training example, its label, and the current parameter value. -* `updater` is a class that performs the actual gradient descent step, i.e. +* `Updater` is a class that performs the actual gradient descent step, i.e. updating the weights in each iteration, for a given gradient of the loss part. The updater is also responsible to perform the update from the regularization part. MLlib includes updaters for cases without regularization, as well as @@ -162,4 +171,108 @@ each iteration, to compute the gradient direction. Available algorithms for gradient descent: -* [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) +* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) + +### L-BFGS +L-BFGS is currently only a low-level optimization primitive in `MLlib`. If you want to use L-BFGS in various +ML algorithms such as Linear Regression, and Logistic Regression, you have to pass the gradient of objective +function, and updater into optimizer yourself instead of using the training APIs like +[LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD). +See the example below. It will be addressed in the next release. + +The L1 regularization by using +[L1Updater](api/scala/index.html#org.apache.spark.mllib.optimization.L1Updater) will not work since the +soft-thresholding logic in L1Updater is designed for gradient descent. See the developer's note. + +The L-BFGS method +[LBFGS.runLBFGS](api/scala/index.html#org.apache.spark.mllib.optimization.LBFGS) +has the following parameters: + +* `Gradient` is a class that computes the gradient of the objective function +being optimized, i.e., with respect to a single training example, at the +current parameter value. MLlib includes gradient classes for common loss +functions, e.g., hinge, logistic, least-squares. The gradient class takes as +input a training example, its label, and the current parameter value. +* `Updater` is a class that computes the gradient and loss of objective function +of the regularization part for L-BFGS. MLlib includes updaters for cases without +regularization, as well as L2 regularizer. +* `numCorrections` is the number of corrections used in the L-BFGS update. 10 is +recommended. +* `maxNumIterations` is the maximal number of iterations that L-BFGS can be run. +* `regParam` is the regularization parameter when using regularization. + +The `return` is a tuple containing two elements. The first element is a column matrix +containing weights for every feature, and the second element is an array containing +the loss computed for every iteration. + +Here is an example to train binary logistic regression with L2 regularization using +L-BFGS optimizer. +{% highlight scala %} +import org.apache.spark.SparkContext +import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.classification.LogisticRegressionModel + +val data = MLUtils.loadLibSVMFile(sc, "mllib/data/sample_libsvm_data.txt") +val numFeatures = data.take(1)(0).features.size + +// Split data into training (60%) and test (40%). +val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) + +// Append 1 into the training data as intercept. +val training = splits(0).map(x => (x.label, MLUtils.appendBias(x.features))).cache() + +val test = splits(1) + +// Run training algorithm to build the model +val numCorrections = 10 +val convergenceTol = 1e-4 +val maxNumIterations = 20 +val regParam = 0.1 +val initialWeightsWithIntercept = Vectors.dense(new Array[Double](numFeatures + 1)) + +val (weightsWithIntercept, loss) = LBFGS.runLBFGS( + training, + new LogisticGradient(), + new SquaredL2Updater(), + numCorrections, + convergenceTol, + maxNumIterations, + regParam, + initialWeightsWithIntercept) + +val model = new LogisticRegressionModel( + Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)), + weightsWithIntercept(weightsWithIntercept.size - 1)) + +// Clear the default threshold. +model.clearThreshold() + +// Compute raw scores on the test set. +val scoreAndLabels = test.map { point => + val score = model.predict(point.features) + (score, point.label) +} + +// Get evaluation metrics. +val metrics = new BinaryClassificationMetrics(scoreAndLabels) +val auROC = metrics.areaUnderROC() + +println("Loss of each step in training process") +loss.foreach(println) +println("Area under ROC = " + auROC) +{% endhighlight %} + +#### Developer's note +Since the Hessian is constructed approximately from previous gradient evaluations, +the objective function can not be changed during the optimization process. +As a result, Stochastic L-BFGS will not work naively by just using miniBatch; +therefore, we don't provide this until we have better understanding. + +* `Updater` is a class originally designed for gradient decent which computes +the actual gradient descent step. However, we're able to take the gradient and +loss of objective function of regularization for L-BFGS by ignoring the part of logic +only for gradient decent such as adaptive step size stuff. We will refactorize +this into regularizer to replace updater to separate the logic between +regularization and step update later. diff --git a/docs/monitoring.md b/docs/monitoring.md index 6f35fc37c4075..fffc58ac39230 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -156,7 +156,7 @@ Each instance can report to zero or more _sinks_. Sinks are contained in the * `ConsoleSink`: Logs metrics information to the console. * `CSVSink`: Exports metrics data to CSV files at regular intervals. -* `JmxSink`: Registers metrics for viewing in a JXM console. +* `JmxSink`: Registers metrics for viewing in a JMX console. * `MetricsServlet`: Adds a servlet within the existing Spark UI to serve metrics data as JSON data. * `GraphiteSink`: Sends metrics to a Graphite node. diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index 39fb5f0c99ca3..b686bee1aebe2 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -60,13 +60,9 @@ By default, PySpark requires `python` to be available on the system `PATH` and u All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported. -Standalone PySpark applications should be run using the `bin/spark-submit` script, which automatically -configures the Java and Python environment for running Spark. - - # Interactive Use -The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options: +The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line: {% highlight bash %} $ sbt/sbt assembly @@ -83,20 +79,24 @@ The Python shell can be used explore data interactively and is a simple way to l {% endhighlight %} By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of -your machine's logical cores. -To connect to a non-local cluster, or to specify a number of cores, set the `MASTER` environment variable. -For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html): +your machine's logical cores. To connect to a non-local cluster, or to specify a number of cores, +set the `--master` flag. For example, to use the `bin/pyspark` shell with a +[standalone Spark cluster](spark-standalone.html): {% highlight bash %} -$ MASTER=spark://IP:PORT ./bin/pyspark +$ ./bin/pyspark --master spark://1.2.3.4:7077 {% endhighlight %} Or, to use exactly four cores on the local machine: {% highlight bash %} -$ MASTER=local[4] ./bin/pyspark +$ ./bin/pyspark --master local[4] {% endhighlight %} +Under the hood `bin/pyspark` is a wrapper around the +[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit), so these +two scripts share the same list of options. For a complete list of options, run `bin/pyspark` with +the `--help` option. ## IPython @@ -115,13 +115,14 @@ the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing sup $ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark {% endhighlight %} -IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable. +IPython also works on a cluster or on multiple cores if you set the `--master` flag. # Standalone Programs -PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/spark-submit`. -The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application. +PySpark can also be used from standalone Python scripts by creating a SparkContext in your script +and running the script using `bin/spark-submit`. The Quick Start guide includes a +[complete example](quick-start.html#standalone-applications) of a standalone Python application. Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`: @@ -138,6 +139,7 @@ You can set [configuration properties](configuration.html#spark-properties) by p {% highlight python %} from pyspark import SparkConf, SparkContext conf = (SparkConf() + .setMaster("local") .setAppName("My app") .set("spark.executor.memory", "1g")) sc = SparkContext(conf = conf) @@ -164,6 +166,6 @@ some example applications. PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python). You can run them by passing the files to `pyspark`; e.g.: - ./bin/spark-submit examples/src/main/python/wordcount.py + ./bin/spark-submit examples/src/main/python/wordcount.py README.md -Each program prints usage help when run without arguments. +Each program prints usage help when run without the sufficient arguments. diff --git a/docs/quick-start.md b/docs/quick-start.md index 478b790f92e17..33a0df1036424 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -6,7 +6,9 @@ title: Quick Start * This will become a table of contents (this text will be scraped). {:toc} -This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's interactive Scala shell (don't worry if you don't know Scala -- you will not need much for this), then show how to write standalone applications in Scala, Java, and Python. +This tutorial provides a quick introduction to using Spark. We will first introduce the API through Spark's +interactive shell (in Python or Scala), +then show how to write standalone applications in Java, Scala, and Python. See the [programming guide](scala-programming-guide.html) for a more complete reference. To follow along with this guide, first download a packaged release of Spark from the @@ -17,8 +19,14 @@ you can download a package for any version of Hadoop. ## Basics -Spark's interactive shell provides a simple way to learn the API, as well as a powerful tool to analyze datasets interactively. -Start the shell by running `./bin/spark-shell` in the Spark directory. +Spark's shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. +It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) +or Python. Start it by running the following in the Spark directory: + +
+
+ + ./bin/spark-shell Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a new RDD from the text of the README file in the Spark source directory: @@ -31,7 +39,7 @@ RDDs have _[actions](scala-programming-guide.html#actions)_, which return values {% highlight scala %} scala> textFile.count() // Number of items in this RDD -res0: Long = 74 +res0: Long = 126 scala> textFile.first() // First item in this RDD res1: String = # Apache Spark @@ -51,12 +59,53 @@ scala> textFile.filter(line => line.contains("Spark")).count() // How many lines res3: Long = 15 {% endhighlight %} +
+
+ + ./bin/pyspark + +Spark's primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a new RDD from the text of the README file in the Spark source directory: + +{% highlight python %} +>>> textFile = sc.textFile("README.md") +{% endhighlight %} + +RDDs have _[actions](scala-programming-guide.html#actions)_, which return values, and _[transformations](scala-programming-guide.html#transformations)_, which return pointers to new RDDs. Let's start with a few actions: + +{% highlight python %} +>>> textFile.count() # Number of items in this RDD +126 + +>>> textFile.first() # First item in this RDD +u'# Apache Spark' +{% endhighlight %} + +Now let's use a transformation. We will use the [`filter`](scala-programming-guide.html#transformations) transformation to return a new RDD with a subset of the items in the file. + +{% highlight python %} +>>> linesWithSpark = textFile.filter(lambda line: "Spark" in line) +{% endhighlight %} + +We can chain together transformations and actions: + +{% highlight python %} +>>> textFile.filter(lambda line: "Spark" in line).count() # How many lines contain "Spark"? +15 +{% endhighlight %} + +
+
+ + ## More on RDD Operations RDD actions and transformations can be used for more complex computations. Let's say we want to find the line with the most words: +
+
+ {% highlight scala %} scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b) -res4: Long = 16 +res4: Long = 15 {% endhighlight %} This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to `map` and `reduce` are Scala function literals (closures), and can use any language feature or Scala/Java library. For example, we can easily call functions declared elsewhere. We'll use `Math.max()` function to make this code easier to understand: @@ -66,26 +115,69 @@ scala> import java.lang.Math import java.lang.Math scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b)) -res5: Int = 16 +res5: Int = 15 {% endhighlight %} One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily: {% highlight scala %} scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b) -wordCounts: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8 +wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8 {% endhighlight %} Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action: {% highlight scala %} scala> wordCounts.collect() -res6: Array[(java.lang.String, Int)] = Array((need,2), ("",43), (Extra,3), (using,1), (passed,1), (etc.,1), (its,1), (`/usr/local/lib/libmesos.so`,1), (`SCALA_HOME`,1), (option,1), (these,1), (#,1), (`PATH`,,2), (200,1), (To,3),... +res6: Array[(String, Int)] = Array((means,1), (under,2), (this,3), (Because,1), (Python,2), (agree,1), (cluster.,1), ...) +{% endhighlight %} + +
+
+ +{% highlight python %} +>>> textFile.map(lambda line: len(line.split())).reduce(lambda a, b: a if (a > b) else b) +15 +{% endhighlight %} + +This first maps a line to an integer value, creating a new RDD. `reduce` is called on that RDD to find the largest line count. The arguments to `map` and `reduce` are Python [anonymous functions (lambdas)](https://docs.python.org/2/reference/expressions.html#lambda), +but we can also pass any top-level Python function we want. +For example, we'll define a `max` function to make this code easier to understand: + +{% highlight python %} +>>> def max(a, b): +... if a > b: +... return a +... else: +... return b +... + +>>> textFile.map(lambda line: len(line.split())).reduce(max) +15 +{% endhighlight %} + +One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can implement MapReduce flows easily: + +{% highlight python %} +>>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b) +{% endhighlight %} + +Here, we combined the [`flatMap`](scala-programming-guide.html#transformations), [`map`](scala-programming-guide.html#transformations) and [`reduceByKey`](scala-programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](scala-programming-guide.html#actions) action: + +{% highlight python %} +>>> wordCounts.collect() +[(u'and', 9), (u'A', 1), (u'webpage', 1), (u'README', 1), (u'Note', 1), (u'"local"', 1), (u'variable', 1), ...] {% endhighlight %} +
+
+ ## Caching Spark also supports pulling data sets into a cluster-wide in-memory cache. This is very useful when data is accessed repeatedly, such as when querying a small "hot" dataset or when running an iterative algorithm like PageRank. As a simple example, let's mark our `linesWithSpark` dataset to be cached: +
+
+ {% highlight scala %} scala> linesWithSpark.cache() res7: spark.RDD[String] = spark.FilteredRDD@17e51082 @@ -97,12 +189,33 @@ scala> linesWithSpark.count() res9: Long = 15 {% endhighlight %} -It may seem silly to use Spark to explore and cache a 30-line text file. The interesting part is +It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is that these same functions can be used on very large data sets, even when they are striped across tens or hundreds of nodes. You can also do this interactively by connecting `bin/spark-shell` to a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark). -# A Standalone Application +
+
+ +{% highlight python %} +>>> linesWithSpark.cache() + +>>> linesWithSpark.count() +15 + +>>> linesWithSpark.count() +15 +{% endhighlight %} + +It may seem silly to use Spark to explore and cache a 100-line text file. The interesting part is +that these same functions can be used on very large data sets, even when they are striped across +tens or hundreds of nodes. You can also do this interactively by connecting `bin/pyspark` to +a cluster, as described in the [programming guide](scala-programming-guide.html#initializing-spark). + +
+
+ +# Standalone Applications Now say we wanted to write a standalone application using the Spark API. We will walk through a simple application in both Scala (with SBT), Java (with Maven), and Python. @@ -113,7 +226,7 @@ We'll create a very simple Spark application in Scala. So simple, in fact, that named `SimpleApp.scala`: {% highlight scala %} -/*** SimpleApp.scala ***/ +/* SimpleApp.scala */ import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ import org.apache.spark.SparkConf @@ -192,7 +305,7 @@ This example will use Maven to compile an application jar, but any similar build We'll create a very simple Spark application, `SimpleApp.java`: {% highlight java %} -/*** SimpleApp.java ***/ +/* SimpleApp.java */ import org.apache.spark.api.java.*; import org.apache.spark.SparkConf; import org.apache.spark.api.java.function.Function; @@ -307,16 +420,18 @@ Note that you'll need to replace YOUR_SPARK_HOME with the location where Spark i As with the Scala and Java examples, we use a SparkContext to create RDDs. We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference. -For applications that use custom classes or third-party libraries, we can add those code -dependencies to SparkContext to ensure that they will be available on remote machines; this is -described in more detail in the [Python programming guide](python-programming-guide.html). +For applications that use custom classes or third-party libraries, we can also add code +dependencies to `spark-submit` through its `--py-files` argument by packaging them into a +.zip file (see `spark-submit --help` for details). `SimpleApp` is simple enough that we do not need to specify any code dependencies. -We can run this application using the `bin/pyspark` script: +We can run this application using the `bin/spark-submit` script: {% highlight python %} -$ cd $SPARK_HOME -$ ./bin/pyspark SimpleApp.py +# Use spark-submit to run your application +$ YOUR_SPARK_HOME/bin/spark-submit \ + --master local[4] \ + SimpleApp.py ... Lines with a: 46, Lines with b: 23 {% endhighlight python %} @@ -324,7 +439,7 @@ Lines with a: 46, Lines with b: 23
-# Where to go from here +# Where to Go from Here Congratulations on running your first Spark application! * For an in-depth overview of the API see "Programming Guides" menu section. diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 68259f0cb8ad1..df8687f81f3ef 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -3,19 +3,120 @@ layout: global title: Running Spark on Mesos --- -Spark can run on clusters managed by [Apache Mesos](http://mesos.apache.org/). Follow the steps below to install Mesos and Spark: - -1. Download and build Spark using the instructions [here](index.html). **Note:** Don't forget to consider what version of HDFS you might want to use! -2. Download, build, install, and start Mesos {{site.MESOS_VERSION}} on your cluster. You can download the Mesos distribution from a [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/). See the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for more information. **Note:** If you want to run Mesos without installing it into the default paths on your system (e.g., if you don't have administrative privileges to install it), you should also pass the `--prefix` option to `configure` to tell it where to install. For example, pass `--prefix=/home/user/mesos`. By default the prefix is `/usr/local`. -3. Create a Spark "distribution" using `make-distribution.sh`. -4. Rename the `dist` directory created from `make-distribution.sh` to `spark-{{site.SPARK_VERSION}}`. -5. Create a `tar` archive: `tar czf spark-{{site.SPARK_VERSION}}.tar.gz spark-{{site.SPARK_VERSION}}` -6. Upload this archive to HDFS or another place accessible from Mesos via `http://`, e.g., [Amazon Simple Storage Service](http://aws.amazon.com/s3): `hadoop fs -put spark-{{site.SPARK_VERSION}}.tar.gz /path/to/spark-{{site.SPARK_VERSION}}.tar.gz` -7. Create a file called `spark-env.sh` in Spark's `conf` directory, by copying `conf/spark-env.sh.template`, and add the following lines to it: - * `export MESOS_NATIVE_LIBRARY=`. This path is usually `/lib/libmesos.so` (where the prefix is `/usr/local` by default, see above). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`. - * `export SPARK_EXECUTOR_URI=`. - * `export MASTER=mesos://HOST:PORT` where HOST:PORT is the host and port (default: 5050) of your Mesos master (or `zk://...` if using Mesos with ZooKeeper). -8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the master URL. In addition, you'll need to set the `spark.executor.uri` property. For example: +Spark can run on hardware clusters managed by [Apache Mesos](http://mesos.apache.org/). + +The advantages of deploying Spark with Mesos include: + +- dynamic partitioning between Spark and other + [frameworks](https://mesos.apache.org/documentation/latest/mesos-frameworks/) +- scalable partitioning between multiple instances of Spark + +# How it Works + +In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master +instance. When using Mesos, the Mesos master replaces the Spark master as the cluster manager. + +

+ Spark cluster components +

+ +Now when a driver creates a job and starts issuing tasks for scheduling, Mesos determines what +machines handle what tasks. Because it takes into account other frameworks when scheduling these +many short-lived tasks, multiple frameworks can coexist on the same cluster without resorting to a +static partitioning of resources. + +To get started, follow the steps below to install Mesos and deploy Spark jobs via Mesos. + + +# Installing Mesos + +Spark {{site.SPARK_VERSION}} is designed for use with Mesos {{site.MESOS_VERSION}} and does not +require any special patches of Mesos. + +If you already have a Mesos cluster running, you can skip this Mesos installation step. + +Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other +frameworks. You can install Mesos either from source or using prebuilt packages. + +## From Source + +To install Apache Mesos from source, follow these steps: + +1. Download a Mesos release from a + [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/) +2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling and + installing Mesos + +**Note:** If you want to run Mesos without installing it into the default paths on your system +(e.g., if you lack administrative privileges to install it), pass the +`--prefix` option to `configure` to tell it where to install. For example, pass +`--prefix=/home/me/mesos`. By default the prefix is `/usr/local`. + +## Third-Party Packages + +The Apache Mesos project only publishes source releases, not binary packages. But other +third party projects publish binary releases that may be helpful in setting Mesos up. + +One of those is Mesosphere. To install Mesos using the binary releases provided by Mesosphere: + +1. Download Mesos installation package from [downloads page](http://mesosphere.io/downloads/) +2. Follow their instructions for installation and configuration + +The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master failover, +but Mesos can be run without ZooKeeper using a single master as well. + +## Verification + +To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at port +`:5050` Confirm that all expected machines are present in the slaves tab. + + +# Connecting Spark to Mesos + +To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and +a Spark driver program configured to connect to Mesos. + +## Uploading Spark Package + +When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary +package for running the Spark Mesos executor backend. +The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via `http://`, +[Amazon Simple Storage Service](http://aws.amazon.com/s3) via `s3n://`, or HDFS via `hdfs://`. + +To use a precompiled package: + +1. Download a Spark binary package from the Spark [download page](https://spark.apache.org/downloads.html) +2. Upload to hdfs/http/s3 + +To host on HDFS, use the Hadoop fs put command: `hadoop fs -put spark-{{site.SPARK_VERSION}}.tar.gz +/path/to/spark-{{site.SPARK_VERSION}}.tar.gz` + + +Or if you are using a custom-compiled version of Spark, you will need to create a package using +the `make-distribution.sh` script included in a Spark source tarball/checkout. + +1. Download and build Spark using the instructions [here](index.html) +2. Create a binary package using `make-distribution.sh --tgz`. +3. Upload archive to http/s3/hdfs + + +## Using a Mesos Master URL + +The Master URLs for Mesos are in the form `mesos://host:5050` for a single-master Mesos +cluster, or `zk://host:2181` for a multi-master Mesos cluster using ZooKeeper. + +The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos: + +1. In `spark.env.sh` set some environment variables: + * `export MESOS_NATIVE_LIBRARY=`. This path is typically + `/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos installation + instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of + `libmesos.so`. + * `export SPARK_EXECUTOR_URI=`. +2. Also set `spark.executor.uri` to ``. + +Now when starting a Spark application against the cluster, pass a `mesos://` +or `zk://` URL as the master when creating a `SparkContext`. For example: {% highlight scala %} val conf = new SparkConf() @@ -25,31 +126,73 @@ val conf = new SparkConf() val sc = new SparkContext(conf) {% endhighlight %} +When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so +it does not need to be redundantly passed in as a system property. + +{% highlight bash %} +./bin/spark-shell --master mesos://host:5050 +{% endhighlight %} + + # Mesos Run Modes -Spark can run over Mesos in two modes: "fine-grained" and "coarse-grained". In fine-grained mode, which is the default, -each Spark task runs as a separate Mesos task. This allows multiple instances of Spark (and other frameworks) to share -machines at a very fine granularity, where each application gets more or fewer machines as it ramps up, but it comes with an -additional overhead in launching each task, which may be inappropriate for low-latency applications (e.g. interactive queries or serving web requests). The coarse-grained mode will instead -launch only *one* long-running Spark task on each Mesos machine, and dynamically schedule its own "mini-tasks" within -it. The benefit is much lower startup overhead, but at the cost of reserving the Mesos resources for the complete duration -of the application. +Spark can run over Mesos in two modes: "fine-grained" (default) and "coarse-grained". + +In "fine-grained" mode (default), each Spark task runs as a separate Mesos task. This allows +multiple instances of Spark (and other frameworks) to share machines at a very fine granularity, +where each application gets more or fewer machines as it ramps up and down, but it comes with an +additional overhead in launching each task. This mode may be inappropriate for low-latency +requirements like interactive queries or serving web requests. + +The "coarse-grained" mode will instead launch only *one* long-running Spark task on each Mesos +machine, and dynamically schedule its own "mini-tasks" within it. The benefit is much lower startup +overhead, but at the cost of reserving the Mesos resources for the complete duration of the +application. -To run in coarse-grained mode, set the `spark.mesos.coarse` property in your [SparkConf](configuration.html#spark-properties): +To run in coarse-grained mode, set the `spark.mesos.coarse` property in your +[SparkConf](configuration.html#spark-properties): {% highlight scala %} conf.set("spark.mesos.coarse", "true") {% endhighlight %} -In addition, for coarse-grained mode, you can control the maximum number of resources Spark will acquire. By default, -it will acquire *all* cores in the cluster (that get offered by Mesos), which only makes sense if you run just one -application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). +In addition, for coarse-grained mode, you can control the maximum number of resources Spark will +acquire. By default, it will acquire *all* cores in the cluster (that get offered by Mesos), which +only makes sense if you run just one application at a time. You can cap the maximum number of cores +using `conf.set("spark.cores.max", "10")` (for example). # Running Alongside Hadoop -You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a separate service on the machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically `hdfs://:9000/path`, but you can find the right URL on your Hadoop Namenode's web UI). +You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a +separate service on the machines. To access Hadoop data from Spark, a full `hdfs://` URL is required +(typically `hdfs://:9000/path`, but you can find the right URL on your Hadoop Namenode web +UI). + +In addition, it is possible to also run Hadoop MapReduce on Mesos for better resource isolation and +sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to +either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each +node. Please refer to [Hadoop on Mesos](https://github.com/mesos/hadoop). + +In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos. + + +# Troubleshooting and Debugging + +A few places to look during debugging: + +- Mesos master on port `:5050` + - Slaves should appear in the slaves tab + - Spark applications should appear in the frameworks tab + - Tasks should appear in the details of a framework + - Check the stdout and stderr of the sandbox of failed tasks +- Mesos logs + - Master and slave logs are both in `/var/log/mesos` by default -In addition, it is possible to also run Hadoop MapReduce on Mesos, to get better resource isolation and sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each node. Please refer to [Hadoop on Mesos](https://github.com/mesos/hadoop). +And common pitfalls: -In either case, HDFS runs separately from Hadoop MapReduce, without going through Mesos. +- Spark assembly not reachable/accessible + - Slaves must be able to download the Spark binary package from the `http://`, `hdfs://` or `s3n://` URL you gave +- Firewall blocking communications + - Check for messages about failed connections + - Temporarily disable firewalls for debugging and then poke appropriate holes diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index c563594296802..66c330fdee734 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -54,13 +54,13 @@ For example: --executor-memory 2g \ --executor-cores 1 lib/spark-examples*.jar \ - yarn-cluster 5 + 10 The above starts a YARN client program which starts the default Application Master. Then SparkPi will be run as a child thread of Application Master. The client will periodically poll the Application Master for status updates and display them in the console. The client will exit once your application has finished running. Refer to the "Viewing Logs" section below for how to see driver and executor logs. To launch a Spark application in yarn-client mode, do the same, but replace "yarn-cluster" with "yarn-client". To run spark-shell: - $ MASTER=yarn-client ./bin/spark-shell + $ ./bin/spark-shell --master yarn-client ## Adding additional jars @@ -70,9 +70,9 @@ In yarn-cluster mode, the driver runs on a different machine than the client, so --master yarn-cluster \ --jars my-other-jar.jar,my-other-other-jar.jar my-main-jar.jar - yarn-cluster 5 + app_arg1 app_arg2 -# Viewing logs +# Debugging your Application In YARN terminology, executors and application masters run inside "containers". YARN has two modes for handling container logs after an application has completed. If log aggregation is turned on (with the yarn.log-aggregation-enable config), container logs are copied to HDFS and deleted on the local machine. These logs can be viewed from anywhere on the cluster with the "yarn logs" command. @@ -82,6 +82,13 @@ will print out the contents of all log files from all containers from the given When log aggregation isn't turned on, logs are retained locally on each machine under YARN_APP_LOGS_DIR, which is usually configured to /tmp/logs or $HADOOP_HOME/logs/userlogs depending on the Hadoop version and installation. Viewing logs for a container requires going to the host that contains them and looking in this directory. Subdirectories organize log files by application ID and container ID. +To review per-container launch environment, increase yarn.nodemanager.delete.debug-delay-sec to a +large value (e.g. 36000), and then access the application cache through yarn.nodemanager.local-dirs +on the nodes on which containers are launched. This directory contains the launch script, jars, and +all environment variables used for launching each container. This process is useful for debugging +classpath problems in particular. (Note that enabling this requires admin privileges on cluster +settings and a restart of all node managers. Thus, this is not applicable to hosted clusters). + # Important notes - Before Hadoop 2.2, YARN does not support cores in container resource requests. Thus, when running against an earlier version, the numbers of cores given via command line arguments cannot be passed to YARN. Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured. diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index f25e9cca88524..edaa7d0639a18 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -56,7 +56,7 @@ The `master` parameter is a string specifying a [Spark, Mesos or YARN cluster UR to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. It's also possible to set these variables [using a configuration file](cluster-overview.html#loading-configurations-from-a-file) -which avoids hard-coding the master name in your application. +which avoids hard-coding the master url in your application. In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the @@ -74,6 +74,11 @@ Or, to also add `code.jar` to its classpath, use: $ ./bin/spark-shell --master local[4] --jars code.jar {% endhighlight %} +For a complete list of options, run Spark shell with the `--help` option. Behind the scenes, +Spark shell invokes the more general [Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) +used for launching applications, and passes on all of its parameters. As a result, these two scripts +share the same parameters. + ### Master URLs The master URL passed to Spark can be in one of the following formats: @@ -98,7 +103,7 @@ cluster mode. The cluster location will be inferred based on the local Hadoop co -If no master URL is specified, the spark shell defaults to "local[*]". +If no master URL is specified, the spark shell defaults to `local[*]`. # Resilient Distributed Datasets (RDDs) @@ -191,6 +196,10 @@ The following tables list the transformations and actions currently supported (s groupByKey([numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, Seq[V]) pairs.
+Note: If you are grouping in order to perform an aggregation (such as a sum or + average) over each key, using `reduceByKey` or `combineByKey` will yield much better + performance. +
Note: By default, if the RDD already has a partitioner, the task number is decided by the partition number of the partitioner, or else relies on the value of spark.default.parallelism if the property is set , otherwise depends on the partition number of the RDD. You can pass an optional numTasks argument to set a different number of tasks. @@ -432,9 +441,7 @@ res2: Int = 10 You can see some [example Spark programs](http://spark.apache.org/examples.html) on the Spark website. In addition, Spark includes several samples in `examples/src/main/scala`. Some of them have both Spark versions and local (non-parallel) versions, allowing you to see what had to be changed to make the program run on a cluster. You can run them using by passing the class name to the `bin/run-example` script included in Spark; for example: - ./bin/run-example org.apache.spark.examples.SparkPi - -Each example program prints usage help when run without any arguments. + ./bin/run-example SparkPi For help on optimizing your program, the [configuration](configuration.html) and [tuning](tuning.html) guides provide information on best practices. They are especially important for diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index dc7f206e03996..eb3211b6b0e4e 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -70,7 +70,7 @@ Once you've set up this file, you can launch or stop your cluster with the follo - `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file. - `sbin/start-all.sh` - Starts both a master and a number of slaves as described above. - `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script. -- `sbin/stop-slaves.sh` - Stops the slave instances that were started via `bin/start-slaves.sh`. +- `sbin/stop-slaves.sh` - Stops all slave instances on the machines specified in the `conf/slaves` file. - `sbin/stop-all.sh` - Stops both the master and the slaves as described above. Note that these scripts must be executed on the machine you want to run the Spark master on, not your local machine. @@ -92,12 +92,8 @@ You can optionally configure the cluster further by setting environment variable Port for the master web UI (default: 8080). - SPARK_WORKER_PORT - Start the Spark worker on a specific port (default: random). - - - SPARK_WORKER_DIR - Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work). + SPARK_MASTER_OPTS + Configuration properties that apply only to the master in the form "-Dx=y" (default: none). SPARK_WORKER_CORES @@ -107,6 +103,10 @@ You can optionally configure the cluster further by setting environment variable SPARK_WORKER_MEMORY Total amount of memory to allow Spark applications to use on the machine, e.g. 1000m, 2g (default: total memory minus 1 GB); note that each application's individual memory is configured using its spark.executor.memory property. + + SPARK_WORKER_PORT + Start the Spark worker on a specific port (default: random). + SPARK_WORKER_WEBUI_PORT Port for the worker web UI (default: 8081). @@ -120,13 +120,25 @@ You can optionally configure the cluster further by setting environment variable or else each worker will try to use all the cores. + + SPARK_WORKER_DIR + Directory to run applications in, which will include both logs and scratch space (default: SPARK_HOME/work). + + + SPARK_WORKER_OPTS + Configuration properties that apply only to the worker in the form "-Dx=y" (default: none). + SPARK_DAEMON_MEMORY Memory to allocate to the Spark master and worker daemons themselves (default: 512m). SPARK_DAEMON_JAVA_OPTS - JVM options for the Spark master and worker daemons themselves (default: none). + JVM options for the Spark master and worker daemons themselves in the form "-Dx=y" (default: none). + + + SPARK_PUBLIC_DNS + The public DNS name of the Spark master and workers (default: none). @@ -148,38 +160,17 @@ You can also pass an option `--cores ` to control the number of cores # Launching Compiled Spark Applications -Spark supports two deploy modes. Spark applications may run with the driver inside the client process or entirely inside the cluster. - -The spark-submit script described in the [cluster mode overview](cluster-overview.html) provides the most straightforward way to submit a compiled Spark application to the cluster in either deploy mode. For info on the lower-level invocations used to launch an app inside the cluster, read ahead. - -## Launching Applications Inside the Cluster - - ./bin/spark-class org.apache.spark.deploy.Client launch - [client-options] \ - \ - [application-options] - - cluster-url: The URL of the master node. - application-jar-url: Path to a bundled jar including your application and all dependencies. Currently, the URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. - main-class: The entry point for your application. - - Client Options: - --memory (amount of memory, in MB, allocated for your driver program) - --cores (number of cores allocated for your driver program) - --supervise (whether to automatically restart your driver on application or node failure) - --verbose (prints increased logging output) - -Keep in mind that your driver program will be executed on a remote worker machine. You can control the execution environment in the following ways: - - * _Environment variables_: These will be captured from the environment in which you launch the client and applied when launching the driver program. - * _Java options_: You can add java options by setting `SPARK_JAVA_OPTS` in the environment in which you launch the submission client. - * _Dependencies_: You'll still need to call `sc.addJar` inside of your program to make your bundled application jar visible on all worker nodes. - -Once you submit a driver program, it will appear in the cluster management UI at port 8080 and -be assigned an identifier. If you'd like to prematurely terminate the program, you can do so using -the same client: +Spark supports two deploy modes: applications may run with the driver inside the client process or +entirely inside the cluster. The +[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit) provides the +most straightforward way to submit a compiled Spark application to the cluster in either deploy +mode. - ./bin/spark-class org.apache.spark.deploy.Client kill +If your application is launched through Spark submit, then the application jar is automatically +distributed to all worker nodes. For any additional jars that your application depends on, you +should specify them through the `--jars` flag using comma as a delimiter (e.g. `--jars jar1,jar2`). +To control the application's configuration or execution environment, see +[Spark Configuration](configuration.html). # Resource Scheduling @@ -203,7 +194,7 @@ default for applications that don't set `spark.cores.max` to something less than Do this by adding the following to `conf/spark-env.sh`: {% highlight bash %} -export SPARK_JAVA_OPTS="-Dspark.deploy.defaultCores=" +export SPARK_MASTER_OPTS="-Dspark.deploy.defaultCores=" {% endhighlight %} This is useful on shared clusters where users might not have configured a maximum number of cores diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 939599aa6855b..0c125eb693a8e 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -522,9 +522,9 @@ common ones are as follows. reduceByKey(func, [numTasks]) When called on a DStream of (K, V) pairs, return a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function. Note: By default, - this uses Spark's default number of parallel tasks (2 for local machine, 8 for a cluster) to - do the grouping. You can pass an optional numTasks argument to set a different - number of tasks. + this uses Spark's default number of parallel tasks (2 for local mode, and in cluster mode the number + is determined by the config property spark.default.parallelism) to do the grouping. + You can pass an optional numTasks argument to set a different number of tasks. join(otherStream, [numTasks]) @@ -743,8 +743,9 @@ said two parameters - windowLength and slideInterval. When called on a DStream of (K, V) pairs, returns a new DStream of (K, V) pairs where the values for each key are aggregated using the given reduce function func over batches in a sliding window. Note: By default, this uses Spark's default number of - parallel tasks (2 for local machine, 8 for a cluster) to do the grouping. You can pass an optional - numTasks argument to set a different number of tasks. + parallel tasks (2 for local mode, and in cluster mode the number is determined by the config + property spark.default.parallelism) to do the grouping. You can pass an optional + numTasks argument to set a different number of tasks. @@ -956,9 +957,10 @@ before further processing. ### Level of Parallelism in Data Processing Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` -and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of -parallelism as an argument (see the -[`PairDStreamFunctions`](api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions) +and `reduceByKeyAndWindow`, the default number of parallel tasks is decided by the [config property] +(configuration.html#spark-properties) `spark.default.parallelism`. You can pass the level of +parallelism as an argument (see [`PairDStreamFunctions`] +(api/scala/index.html#org.apache.spark.streaming.dstream.PairDStreamFunctions) documentation), or set the [config property](configuration.html#spark-properties) `spark.default.parallelism` to change the default. diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 0a8d6ca5d7519..4c5a99f5f6b2e 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -443,7 +443,7 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): # NOTE: We should clone the repository before running deploy_files to # prevent ec2-variables.sh from being overwritten - ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v2") + ssh(master, opts, "rm -rf spark-ec2 && git clone https://github.com/mesos/spark-ec2.git -b v3") print "Deploying files to master..." deploy_files(conn, "deploy.generic", opts, master_nodes, slave_nodes, modules) diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java index 7f558f3ee713a..5622df5ce03ff 100644 --- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaCustomReceiver.java @@ -19,6 +19,7 @@ import com.google.common.collect.Lists; +import org.apache.spark.SparkConf; import org.apache.spark.api.java.function.FlatMapFunction; import org.apache.spark.api.java.function.Function2; import org.apache.spark.api.java.function.PairFunction; @@ -48,25 +49,23 @@ * To run this on your local machine, you need to first run a Netcat server * `$ nc -lk 9999` * and then run the example - * `$ ./run org.apache.spark.examples.streaming.JavaCustomReceiver local[2] localhost 9999` + * `$ bin/run-example org.apache.spark.examples.streaming.JavaCustomReceiver localhost 9999` */ public class JavaCustomReceiver extends Receiver { private static final Pattern SPACE = Pattern.compile(" "); public static void main(String[] args) { - if (args.length < 3) { - System.err.println("Usage: JavaNetworkWordCount \n" + - "In local mode, should be 'local[n]' with n > 1"); + if (args.length < 2) { + System.err.println("Usage: JavaNetworkWordCount "); System.exit(1); } StreamingExamples.setStreamingLogLevels(); // Create the context with a 1 second batch size - JavaStreamingContext ssc = new JavaStreamingContext(args[0], "JavaNetworkWordCount", - new Duration(1000), System.getenv("SPARK_HOME"), - JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class)); + SparkConf sparkConf = new SparkConf().setAppName("JavaCustomReceiver"); + JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000)); // Create a input stream with the custom receiver on target ip:port and count the // words in input stream of \n delimited text (eg. generated by 'nc') diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java index 400b68c2215b3..da56637fe891a 100644 --- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java @@ -33,10 +33,12 @@ * Your Flume AvroSink should be pointed to this address. * * Usage: JavaFlumeEventCount - * * is the host the Flume receiver will be started on - a receiver * creates a server and listens for flume events. * is the port the Flume receiver will listen on. + * + * To run this example: + * `$ bin/run-example org.apache.spark.examples.streaming.JavaFlumeEventCount ` */ public final class JavaFlumeEventCount { private JavaFlumeEventCount() { @@ -56,7 +58,7 @@ public static void main(String[] args) { Duration batchInterval = new Duration(2000); SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount"); JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, batchInterval); - JavaReceiverInputDStream flumeStream = FlumeUtils.createStream(ssc, "localhost", port); + JavaReceiverInputDStream flumeStream = FlumeUtils.createStream(ssc, host, port); flumeStream.count(); diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java index 6a74cc50d19ed..16ae9a3319ee2 100644 --- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java @@ -40,15 +40,15 @@ /** * Consumes messages from one or more topics in Kafka and does wordcount. + * * Usage: JavaKafkaWordCount * is a list of one or more zookeeper servers that make quorum * is the name of kafka consumer group * is a list of one or more kafka topics to consume from * is the number of threads the kafka consumer should use * - * Example: - * `./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \ + * To run this example: + * `$ bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \ * zoo03 my-consumer-group topic1,topic2 1` */ diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java index e5cbd39f437c2..45bcedebb4117 100644 --- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java +++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java @@ -24,7 +24,7 @@ import org.apache.spark.api.java.function.FlatMapFunction; import org.apache.spark.api.java.function.Function2; import org.apache.spark.api.java.function.PairFunction; -import org.apache.spark.examples.streaming.StreamingExamples; +import org.apache.spark.api.java.StorageLevels; import org.apache.spark.streaming.Duration; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; @@ -41,8 +41,7 @@ * To run this on your local machine, you need to first run a Netcat server * `$ nc -lk 9999` * and then run the example - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999` + * `$ bin/run-example org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999` */ public final class JavaNetworkWordCount { private static final Pattern SPACE = Pattern.compile(" "); @@ -54,13 +53,17 @@ public static void main(String[] args) { } StreamingExamples.setStreamingLogLevels(); - SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount"); + // Create the context with a 1 second batch size + SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount"); JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000)); // Create a JavaReceiverInputDStream on target ip:port and count the // words in input stream of \n delimited text (eg. generated by 'nc') - JavaReceiverInputDStream lines = ssc.socketTextStream(args[0], Integer.parseInt(args[1])); + // Note that no duplication in storage level only for running locally. + // Replication necessary in distributed scenario for fault tolerance. + JavaReceiverInputDStream lines = ssc.socketTextStream( + args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER); JavaDStream words = lines.flatMap(new FlatMapFunction() { @Override public Iterable call(String x) { diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py index 01552dc1d449e..f0b46cd28b7aa 100755 --- a/examples/src/main/python/als.py +++ b/examples/src/main/python/als.py @@ -46,15 +46,15 @@ def update(i, vec, mat, ratings): return np.linalg.solve(XtX, Xty) if __name__ == "__main__": - if len(sys.argv) < 2: - print >> sys.stderr, "Usage: als " - exit(-1) - sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)]) - M = int(sys.argv[2]) if len(sys.argv) > 2 else 100 - U = int(sys.argv[3]) if len(sys.argv) > 3 else 500 - F = int(sys.argv[4]) if len(sys.argv) > 4 else 10 - ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5 - slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2 + """ + Usage: als [M] [U] [F] [iterations] [slices]" + """ + sc = SparkContext(appName="PythonALS") + M = int(sys.argv[1]) if len(sys.argv) > 1 else 100 + U = int(sys.argv[2]) if len(sys.argv) > 2 else 500 + F = int(sys.argv[3]) if len(sys.argv) > 3 else 10 + ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5 + slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2 print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \ (M, U, F, ITERATIONS, slices) diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index e3596488faf9e..fc16586c28a46 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -45,14 +45,14 @@ def closestPoint(p, centers): if __name__ == "__main__": - if len(sys.argv) < 5: - print >> sys.stderr, "Usage: kmeans " + if len(sys.argv) != 4: + print >> sys.stderr, "Usage: kmeans " exit(-1) - sc = SparkContext(sys.argv[1], "PythonKMeans") - lines = sc.textFile(sys.argv[2]) + sc = SparkContext(appName="PythonKMeans") + lines = sc.textFile(sys.argv[1]) data = lines.map(parseVector).cache() - K = int(sys.argv[3]) - convergeDist = float(sys.argv[4]) + K = int(sys.argv[2]) + convergeDist = float(sys.argv[3]) kPoints = data.takeSample(False, K, 1) tempDist = 1.0 diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py index fe5373cf799b1..0f22d0b32319e 100755 --- a/examples/src/main/python/logistic_regression.py +++ b/examples/src/main/python/logistic_regression.py @@ -47,12 +47,12 @@ def readPointBatch(iterator): return [matrix] if __name__ == "__main__": - if len(sys.argv) != 4: - print >> sys.stderr, "Usage: logistic_regression " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: logistic_regression " exit(-1) - sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)]) - points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache() - iterations = int(sys.argv[3]) + sc = SparkContext(appName="PythonLR") + points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache() + iterations = int(sys.argv[2]) # Initialize w to a random value w = 2 * np.random.ranf(size=D) - 1 diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py index dec82ff34fbac..b308132c9aeeb 100755 --- a/examples/src/main/python/mllib/kmeans.py +++ b/examples/src/main/python/mllib/kmeans.py @@ -33,12 +33,12 @@ def parseVector(line): if __name__ == "__main__": - if len(sys.argv) < 4: - print >> sys.stderr, "Usage: kmeans " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: kmeans " exit(-1) - sc = SparkContext(sys.argv[1], "KMeans") - lines = sc.textFile(sys.argv[2]) + sc = SparkContext(appName="KMeans") + lines = sc.textFile(sys.argv[1]) data = lines.map(parseVector) - k = int(sys.argv[3]) + k = int(sys.argv[2]) model = KMeans.train(data, k) print "Final centers: " + str(model.clusterCenters) diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py index 8631051d00ff2..6e0f7a4ee5a81 100755 --- a/examples/src/main/python/mllib/logistic_regression.py +++ b/examples/src/main/python/mllib/logistic_regression.py @@ -39,12 +39,12 @@ def parsePoint(line): if __name__ == "__main__": - if len(sys.argv) != 4: - print >> sys.stderr, "Usage: logistic_regression " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: logistic_regression " exit(-1) - sc = SparkContext(sys.argv[1], "PythonLR") - points = sc.textFile(sys.argv[2]).map(parsePoint) - iterations = int(sys.argv[3]) + sc = SparkContext(appName="PythonLR") + points = sc.textFile(sys.argv[1]).map(parsePoint) + iterations = int(sys.argv[2]) model = LogisticRegressionWithSGD.train(points, iterations) print "Final weights: " + str(model.weights) print "Final intercept: " + str(model.intercept) diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py index cd774cf3a319f..d350fa46fa49a 100755 --- a/examples/src/main/python/pagerank.py +++ b/examples/src/main/python/pagerank.py @@ -36,19 +36,19 @@ def parseNeighbors(urls): if __name__ == "__main__": - if len(sys.argv) < 3: - print >> sys.stderr, "Usage: pagerank " + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: pagerank " exit(-1) # Initialize the spark context. - sc = SparkContext(sys.argv[1], "PythonPageRank") + sc = SparkContext(appName="PythonPageRank") # Loads in input file. It should be in format of: # URL neighbor URL # URL neighbor URL # URL neighbor URL # ... - lines = sc.textFile(sys.argv[2], 1) + lines = sc.textFile(sys.argv[1], 1) # Loads all URLs from input file and initialize their neighbors. links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache() @@ -57,7 +57,7 @@ def parseNeighbors(urls): ranks = links.map(lambda (url, neighbors): (url, 1.0)) # Calculates and updates URL ranks continuously using PageRank algorithm. - for iteration in xrange(int(sys.argv[3])): + for iteration in xrange(int(sys.argv[2])): # Calculates URL contributions to the rank of other URLs. contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)): computeContribs(urls, rank)) diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py index ab0645fc2f326..234720b55fa49 100755 --- a/examples/src/main/python/pi.py +++ b/examples/src/main/python/pi.py @@ -23,11 +23,11 @@ if __name__ == "__main__": - if len(sys.argv) == 1: - print >> sys.stderr, "Usage: pi []" - exit(-1) - sc = SparkContext(sys.argv[1], "PythonPi") - slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 + """ + Usage: pi [slices] + """ + sc = SparkContext(appName="PythonPi") + slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2 n = 100000 * slices def f(_): x = random() * 2 - 1 diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py index 5de20a6d98f43..4913ee926aa03 100755 --- a/examples/src/main/python/sort.py +++ b/examples/src/main/python/sort.py @@ -21,11 +21,11 @@ if __name__ == "__main__": - if len(sys.argv) < 3: - print >> sys.stderr, "Usage: sort " + if len(sys.argv) != 2: + print >> sys.stderr, "Usage: sort " exit(-1) - sc = SparkContext(sys.argv[1], "PythonSort") - lines = sc.textFile(sys.argv[2], 1) + sc = SparkContext(appName="PythonSort") + lines = sc.textFile(sys.argv[1], 1) sortedCount = lines.flatMap(lambda x: x.split(' ')) \ .map(lambda x: (int(x), 1)) \ .sortByKey(lambda x: x) diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py index 744cce6651607..8698369b13d84 100755 --- a/examples/src/main/python/transitive_closure.py +++ b/examples/src/main/python/transitive_closure.py @@ -36,11 +36,11 @@ def generateGraph(): if __name__ == "__main__": - if len(sys.argv) == 1: - print >> sys.stderr, "Usage: transitive_closure []" - exit(-1) - sc = SparkContext(sys.argv[1], "PythonTransitiveClosure") - slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2 + """ + Usage: transitive_closure [slices] + """ + sc = SparkContext(appName="PythonTransitiveClosure") + slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2 tc = sc.parallelize(generateGraph(), slices).cache() # Linear transitive closure: each round grows paths by one edge, diff --git a/examples/src/main/python/wordcount.py b/examples/src/main/python/wordcount.py index b9139b9d76520..dcc095fdd0ed9 100755 --- a/examples/src/main/python/wordcount.py +++ b/examples/src/main/python/wordcount.py @@ -22,11 +22,11 @@ if __name__ == "__main__": - if len(sys.argv) < 3: - print >> sys.stderr, "Usage: wordcount " + if len(sys.argv) != 2: + print >> sys.stderr, "Usage: wordcount " exit(-1) - sc = SparkContext(sys.argv[1], "PythonWordCount") - lines = sc.textFile(sys.argv[2], 1) + sc = SparkContext(appName="PythonWordCount") + lines = sc.textFile(sys.argv[1], 1) counts = lines.flatMap(lambda x: x.split(' ')) \ .map(lambda x: (x, 1)) \ .reduceByKey(add) diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala index 4001908c98015..56b02b65d8724 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala @@ -29,8 +29,9 @@ import org.apache.spark.mllib.optimization.{SquaredL2Updater, L1Updater} /** * An example app for binary classification. Run with * {{{ - * ./bin/run-example org.apache.spark.examples.mllib.BinaryClassification + * bin/run-example org.apache.spark.examples.mllib.BinaryClassification * }}} + * A synthetic dataset is located at `data/mllib/sample_binary_classification_data.txt`. * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object BinaryClassification { @@ -81,6 +82,15 @@ object BinaryClassification { .required() .text("input paths to labeled examples in LIBSVM format") .action((x, c) => c.copy(input = x)) + note( + """ + |For example, the following command runs this app on a synthetic dataset: + | + | bin/spark-submit --class org.apache.spark.examples.mllib.BinaryClassification \ + | examples/target/scala-*/spark-examples-*.jar \ + | --algorithm LR --regType L2 --regParam 1.0 \ + | data/mllib/sample_binary_classification_data.txt + """.stripMargin) } parser.parse(args, defaultParams).map { params => diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala index 658d370f8656e..4811bb70e4b28 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LinearRegression.scala @@ -28,8 +28,9 @@ import org.apache.spark.mllib.optimization.{SimpleUpdater, SquaredL2Updater, L1U /** * An example app for linear regression. Run with * {{{ - * ./bin/run-example org.apache.spark.examples.mllib.LinearRegression + * bin/run-example org.apache.spark.examples.mllib.LinearRegression * }}} + * A synthetic dataset can be found at `data/mllib/sample_linear_regression_data.txt`. * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object LinearRegression extends App { @@ -68,6 +69,14 @@ object LinearRegression extends App { .required() .text("input paths to labeled examples in LIBSVM format") .action((x, c) => c.copy(input = x)) + note( + """ + |For example, the following command runs this app on a synthetic dataset: + | + | bin/spark-submit --class org.apache.spark.examples.mllib.LinearRegression \ + | examples/target/scala-*/spark-examples-*.jar \ + | data/mllib/sample_linear_regression_data.txt + """.stripMargin) } parser.parse(args, defaultParams).map { params => diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala index 0e4447e0de24f..6eb41e7ba36fb 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala @@ -29,6 +29,12 @@ import org.apache.spark.serializer.{KryoSerializer, KryoRegistrator} /** * An example app for ALS on MovieLens data (http://grouplens.org/datasets/movielens/). + * Run with + * {{{ + * bin/run-example org.apache.spark.examples.mllib.MovieLensALS + * }}} + * A synthetic dataset in MovieLens format can be found at `data/mllib/sample_movielens_data.txt`. + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object MovieLensALS { @@ -70,6 +76,15 @@ object MovieLensALS { .required() .text("input paths to a MovieLens dataset of ratings") .action((x, c) => c.copy(input = x)) + note( + """ + |For example, the following command runs this app on a synthetic dataset: + | + | bin/spark-submit --class org.apache.spark.examples.mllib.MovieLensALS \ + | examples/target/scala-*/spark-examples-*.jar \ + | --rank 5 --numIterations 20 --lambda 1.0 --kryo \ + | data/mllib/sample_movielens_data.txt + """.stripMargin) } parser.parse(args, defaultParams).map { params => diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala index e29e16a9c1b17..b433082dce1a2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala @@ -130,11 +130,9 @@ object FeederActor { * and describe the AkkaSystem that Spark Sample feeder is running on. * * To run this example locally, you may run Feeder Actor as - * `./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999` + * `$ bin/run-example org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999` * and then run the example - * `./bin/spark-submit examples.jar --class org.apache.spark.examples.streaming.ActorWordCount \ - * 127.0.1.1 9999` + * `$ bin/run-example org.apache.spark.examples.streaming.ActorWordCount 127.0.1.1 9999` */ object ActorWordCount { def main(args: Array[String]) { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala index e317e2d36ae43..6bb659fbd8be8 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala @@ -20,7 +20,7 @@ package org.apache.spark.examples.streaming import java.io.{InputStreamReader, BufferedReader, InputStream} import java.net.Socket -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Seconds, StreamingContext} import org.apache.spark.streaming.StreamingContext._ @@ -30,32 +30,27 @@ import org.apache.spark.streaming.receiver.Receiver * Custom Receiver that receives data over a socket. Received bytes is interpreted as * text and \n delimited lines are considered as records. They are then counted and printed. * - * Usage: CustomReceiver - * is the Spark master URL. In local mode, should be 'local[n]' with n > 1. - * and of the TCP server that Spark Streaming would connect to receive data. - * * To run this on your local machine, you need to first run a Netcat server * `$ nc -lk 9999` * and then run the example - * `$ ./run org.apache.spark.examples.streaming.CustomReceiver local[2] localhost 9999` + * `$ bin/run-example org.apache.spark.examples.streaming.CustomReceiver localhost 9999` */ object CustomReceiver { def main(args: Array[String]) { - if (args.length < 3) { - System.err.println("Usage: NetworkWordCount \n" + - "In local mode, should be 'local[n]' with n > 1") + if (args.length < 2) { + System.err.println("Usage: CustomReceiver ") System.exit(1) } StreamingExamples.setStreamingLogLevels() // Create the context with a 1 second batch size - val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1), - System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq) + val sparkConf = new SparkConf().setAppName("CustomReceiver") + val ssc = new StreamingContext(sparkConf, Seconds(1)) // Create a input stream with the custom receiver on target ip:port and count the // words in input stream of \n delimited text (eg. generated by 'nc') - val lines = ssc.receiverStream(new CustomReceiver(args(1), args(2).toInt)) + val lines = ssc.receiverStream(new CustomReceiver(args(0), args(1).toInt)) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.print() diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala index 38362edac27f8..20e7df7c45b1b 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala @@ -31,14 +31,16 @@ import org.apache.spark.util.IntParam * Your Flume AvroSink should be pointed to this address. * * Usage: FlumeEventCount - * * is the host the Flume receiver will be started on - a receiver * creates a server and listens for flume events. * is the port the Flume receiver will listen on. + * + * To run this example: + * `$ bin/run-example org.apache.spark.examples.streaming.FlumeEventCount ` */ object FlumeEventCount { def main(args: Array[String]) { - if (args.length != 3) { + if (args.length < 2) { System.err.println( "Usage: FlumeEventCount ") System.exit(1) @@ -49,8 +51,9 @@ object FlumeEventCount { val Array(host, IntParam(port)) = args val batchInterval = Milliseconds(2000) - val sparkConf = new SparkConf().setAppName("FlumeEventCount") + // Create the context and set the batch size + val sparkConf = new SparkConf().setAppName("FlumeEventCount") val ssc = new StreamingContext(sparkConf, batchInterval) // Create a flume stream diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala index 55ac48cfb6d10..6c24bc3ad09e0 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala @@ -27,8 +27,9 @@ import org.apache.spark.streaming.StreamingContext._ * is the directory that Spark Streaming will use to find and read new text files. * * To run this on your local machine on directory `localdir`, run this example - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.HdfsWordCount localdir` + * $ bin/run-example \ + * org.apache.spark.examples.streaming.HdfsWordCount localdir + * * Then create a text file in `localdir` and the words in the file will get counted. */ object HdfsWordCount { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala index 3af806981f37a..566ba6f911e02 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala @@ -35,9 +35,9 @@ import org.apache.spark.SparkConf * is the number of threads the kafka consumer should use * * Example: - * `./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 \ - * my-consumer-group topic1,topic2 1` + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.KafkaWordCount zoo01,zoo02,zoo03 \ + * my-consumer-group topic1,topic2 1` */ object KafkaWordCount { def main(args: Array[String]) { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala index 3a10daa9ab84a..e4283e04a1b11 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala @@ -75,14 +75,14 @@ object MQTTPublisher { * Example Java code for Mqtt Publisher and Subscriber can be found here * https://bitbucket.org/mkjinesh/mqttclient * Usage: MQTTWordCount -\ * and describe where Mqtt publisher is running. + * and describe where Mqtt publisher is running. * * To run this example locally, you may run publisher as - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo` + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo` * and run the example as - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.MQTTWordCount tcp://localhost:1883 foo` + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.MQTTWordCount tcp://localhost:1883 foo` */ object MQTTWordCount { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala index ad7a199b2c0ab..ae0a08c6cdb1a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala @@ -23,7 +23,7 @@ import org.apache.spark.streaming.StreamingContext._ import org.apache.spark.storage.StorageLevel /** - * Counts words in text encoded with UTF8 received from the network every second. + * Counts words in UTF8 encoded, '\n' delimited text received from the network every second. * * Usage: NetworkWordCount * and describe the TCP server that Spark Streaming would connect to receive data. @@ -31,8 +31,7 @@ import org.apache.spark.storage.StorageLevel * To run this on your local machine, you need to first run a Netcat server * `$ nc -lk 9999` * and then run the example - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.NetworkWordCount localhost 9999` + * `$ bin/run-example org.apache.spark.examples.streaming.NetworkWordCount localhost 9999` */ object NetworkWordCount { def main(args: Array[String]) { @@ -42,13 +41,16 @@ object NetworkWordCount { } StreamingExamples.setStreamingLogLevels() - val sparkConf = new SparkConf().setAppName("NetworkWordCount"); + // Create the context with a 1 second batch size + val sparkConf = new SparkConf().setAppName("NetworkWordCount") val ssc = new StreamingContext(sparkConf, Seconds(1)) - // Create a NetworkInputDStream on target ip:port and count the + // Create a socket stream on target ip:port and count the // words in input stream of \n delimited text (eg. generated by 'nc') - val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER) + // Note that no duplication in storage level only for running locally. + // Replication necessary in distributed scenario for fault tolerance. + val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_AND_DISK_SER) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.print() diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala index ace785d9fe4c5..6af3a0f33efc2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala @@ -46,8 +46,7 @@ import org.apache.spark.util.IntParam * * and run the example as * - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.RecoverableNetworkWordCount \ + * `$ ./bin/run-example org.apache.spark.examples.streaming.RecoverableNetworkWordCount \ * localhost 9999 ~/checkpoint/ ~/out` * * If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create @@ -57,7 +56,7 @@ import org.apache.spark.util.IntParam * * To run this example in a local standalone cluster with automatic driver recovery, * - * `$ ./spark-class org.apache.spark.deploy.Client -s launch \ + * `$ bin/spark-class org.apache.spark.deploy.Client -s launch \ * \ * org.apache.spark.examples.streaming.RecoverableNetworkWordCount \ * localhost 9999 ~/checkpoint ~/out` @@ -81,7 +80,7 @@ object RecoverableNetworkWordCount { // Create the context with a 1 second batch size val ssc = new StreamingContext(sparkConf, Seconds(1)) - // Create a NetworkInputDStream on target ip:port and count the + // Create a socket stream on target ip:port and count the // words in input stream of \n delimited text (eg. generated by 'nc') val lines = ssc.socketTextStream(ip, port) val words = lines.flatMap(_.split(" ")) diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala index 5e1415f3cc536..daa1ced63c701 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala @@ -31,8 +31,8 @@ import org.apache.spark.streaming.StreamingContext._ * To run this on your local machine, you need to first run a Netcat server * `$ nc -lk 9999` * and then run the example - * `$ ./bin/spark-submit examples.jar - * --class org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999` + * `$ bin/run-example + * org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999` */ object StatefulNetworkWordCount { def main(args: Array[String]) { @@ -51,7 +51,7 @@ object StatefulNetworkWordCount { Some(currentCount + previousCount) } - val sparkConf = new SparkConf().setAppName("NetworkWordCumulativeCountUpdateStateByKey") + val sparkConf = new SparkConf().setAppName("StatefulNetworkWordCount") // Create the context with a 1 second batch size val ssc = new StreamingContext(sparkConf, Seconds(1)) ssc.checkpoint(".") diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala index 1ddff22cb8a42..f55d23ab3924b 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala @@ -28,13 +28,29 @@ import org.apache.spark.SparkConf * stream. The stream is instantiated with credentials and optionally filters supplied by the * command line arguments. * + * Run this on your local machine as + * */ object TwitterPopularTags { def main(args: Array[String]) { + if (args.length < 4) { + System.err.println("Usage: TwitterPopularTags " + + " []") + System.exit(1) + } StreamingExamples.setStreamingLogLevels() - val filters = args + val Array(consumerKey, consumerSecret, accessToken, accessTokenSecret) = args.take(4) + val filters = args.takeRight(args.length - 4) + + // Set the system properties so that Twitter4j library used by twitter stream + // can use them to generat OAuth credentials + System.setProperty("twitter4j.oauth.consumerKey", consumerKey) + System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret) + System.setProperty("twitter4j.oauth.accessToken", accessToken) + System.setProperty("twitter4j.oauth.accessTokenSecret", accessTokenSecret) + val sparkConf = new SparkConf().setAppName("TwitterPopularTags") val ssc = new StreamingContext(sparkConf, Seconds(2)) val stream = TwitterUtils.createStream(ssc, None, filters) @@ -52,13 +68,13 @@ object TwitterPopularTags { // Print popular hashtags topCounts60.foreachRDD(rdd => { - val topList = rdd.take(5) + val topList = rdd.take(10) println("\nPopular topics in last 60 seconds (%s total):".format(rdd.count())) topList.foreach{case (count, tag) => println("%s (%s tweets)".format(tag, count))} }) topCounts10.foreachRDD(rdd => { - val topList = rdd.take(5) + val topList = rdd.take(10) println("\nPopular topics in last 10 seconds (%s total):".format(rdd.count())) topList.foreach{case (count, tag) => println("%s (%s tweets)".format(tag, count))} }) diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala index 7ade3f1018ee8..79905af381a12 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala @@ -68,11 +68,11 @@ object SimpleZeroMQPublisher { * and describe where zeroMq publisher is running. * * To run this example locally, you may run publisher as - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar` + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar` * and run the example as - * `$ ./bin/spark-submit examples.jar \ - * --class org.apache.spark.examples.streaming.ZeroMQWordCount tcp://127.0.1.1:1234 foo` + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.ZeroMQWordCount tcp://127.0.1.1:1234 foo` */ // scalastyle:on object ZeroMQWordCount { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala index 97e0cb92078dc..8402491b62671 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala @@ -40,11 +40,13 @@ object PageView extends Serializable { /** Generates streaming events to simulate page views on a website. * * This should be used in tandem with PageViewStream.scala. Example: - * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10 - * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444 * - * When running this, you may want to set the root logging level to ERROR in - * conf/log4j.properties to reduce the verbosity of the output. + * To run the generator + * `$ bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10` + * To process the generated stream + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444` + * */ // scalastyle:on object PageViewGenerator { diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala index d30ceffbe29cb..d9b886eff77cc 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewStream.scala @@ -26,8 +26,11 @@ import org.apache.spark.examples.streaming.StreamingExamples * operators available in Spark streaming. * * This should be used in tandem with PageViewStream.scala. Example: - * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10 - * $ ./bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444 + * To run the generator + * `$ bin/run-example org.apache.spark.examples.streaming.clickstream.PageViewGenerator 44444 10` + * To process the generated stream + * `$ bin/run-example \ + * org.apache.spark.examples.streaming.clickstream.PageViewStream errorRatePerZipCode localhost 44444` */ // scalastyle:on object PageViewStream { diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java new file mode 100644 index 0000000000000..d31aa5f5c096c --- /dev/null +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Spark streaming receiver for Flume. + */ +package org.apache.spark.streaming.flume; \ No newline at end of file diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala new file mode 100644 index 0000000000000..9bfab68c4b8b7 --- /dev/null +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Spark streaming receiver for Flume. + */ +package object flume diff --git a/external/flume/src/test/resources/log4j.properties b/external/flume/src/test/resources/log4j.properties index d1bd73a8430e1..45d2ec676df66 100644 --- a/external/flume/src/test/resources/log4j.properties +++ b/external/flume/src/test/resources/log4j.properties @@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=external/flume/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java new file mode 100644 index 0000000000000..947bae115a620 --- /dev/null +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Kafka receiver for spark streaming. + */ +package org.apache.spark.streaming.kafka; \ No newline at end of file diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala new file mode 100644 index 0000000000000..47c5187f8751f --- /dev/null +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Kafka receiver for spark streaming, + */ +package object kafka diff --git a/external/kafka/src/test/resources/log4j.properties b/external/kafka/src/test/resources/log4j.properties index 38910d113050a..45d2ec676df66 100644 --- a/external/kafka/src/test/resources/log4j.properties +++ b/external/kafka/src/test/resources/log4j.properties @@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=external/kafka/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java new file mode 100644 index 0000000000000..728e0d8663d01 --- /dev/null +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * MQTT receiver for Spark Streaming. + */ +package org.apache.spark.streaming.mqtt; \ No newline at end of file diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala new file mode 100644 index 0000000000000..63d0d138183a9 --- /dev/null +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * MQTT receiver for Spark Streaming. + */ +package object mqtt diff --git a/external/mqtt/src/test/resources/log4j.properties b/external/mqtt/src/test/resources/log4j.properties index d0462c7336df5..45d2ec676df66 100644 --- a/external/mqtt/src/test/resources/log4j.properties +++ b/external/mqtt/src/test/resources/log4j.properties @@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=external/mqtt/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala index 7bca1407116fa..5ea2e5549d7df 100644 --- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala @@ -63,36 +63,48 @@ class TwitterReceiver( storageLevel: StorageLevel ) extends Receiver[Status](storageLevel) with Logging { - var twitterStream: TwitterStream = _ + private var twitterStream: TwitterStream = _ def onStart() { - twitterStream = new TwitterStreamFactory().getInstance(twitterAuth) - twitterStream.addListener(new StatusListener { - def onStatus(status: Status) = { - store(status) - } - // Unimplemented - def onDeletionNotice(statusDeletionNotice: StatusDeletionNotice) {} - def onTrackLimitationNotice(i: Int) {} - def onScrubGeo(l: Long, l1: Long) {} - def onStallWarning(stallWarning: StallWarning) {} - def onException(e: Exception) { - restart("Error receiving tweets", e) - } - }) + try { + val newTwitterStream = new TwitterStreamFactory().getInstance(twitterAuth) + newTwitterStream.addListener(new StatusListener { + def onStatus(status: Status) = { + store(status) + } + // Unimplemented + def onDeletionNotice(statusDeletionNotice: StatusDeletionNotice) {} + def onTrackLimitationNotice(i: Int) {} + def onScrubGeo(l: Long, l1: Long) {} + def onStallWarning(stallWarning: StallWarning) {} + def onException(e: Exception) { + restart("Error receiving tweets", e) + } + }) - val query = new FilterQuery - if (filters.size > 0) { - query.track(filters.toArray) - twitterStream.filter(query) - } else { - twitterStream.sample() + val query = new FilterQuery + if (filters.size > 0) { + query.track(filters.toArray) + newTwitterStream.filter(query) + } else { + newTwitterStream.sample() + } + setTwitterStream(newTwitterStream) + logInfo("Twitter receiver started") + } catch { + case e: Exception => restart("Error starting Twitter stream", e) } - logInfo("Twitter receiver started") } def onStop() { - twitterStream.shutdown() + setTwitterStream(null) logInfo("Twitter receiver stopped") } + + private def setTwitterStream(newTwitterStream: TwitterStream) = synchronized { + if (twitterStream != null) { + twitterStream.shutdown() + } + twitterStream = newTwitterStream + } } diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java new file mode 100644 index 0000000000000..258c0950a0aa7 --- /dev/null +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Twitter feed receiver for spark streaming. + */ +package org.apache.spark.streaming.twitter; \ No newline at end of file diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala new file mode 100644 index 0000000000000..580e37fa8f814 --- /dev/null +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Twitter feed receiver for spark streaming. + */ +package object twitter diff --git a/external/twitter/src/test/resources/log4j.properties b/external/twitter/src/test/resources/log4j.properties index c918335fcdc70..45d2ec676df66 100644 --- a/external/twitter/src/test/resources/log4j.properties +++ b/external/twitter/src/test/resources/log4j.properties @@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=external/twitter/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java new file mode 100644 index 0000000000000..587c524e2120f --- /dev/null +++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Zeromq receiver for spark streaming. + */ +package org.apache.spark.streaming.zeromq; \ No newline at end of file diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala new file mode 100644 index 0000000000000..65e6e57f2c05d --- /dev/null +++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Zeromq receiver for spark streaming. + */ +package object zeromq diff --git a/external/zeromq/src/test/resources/log4j.properties b/external/zeromq/src/test/resources/log4j.properties index 304683dd0bac3..45d2ec676df66 100644 --- a/external/zeromq/src/test/resources/log4j.properties +++ b/external/zeromq/src/test/resources/log4j.properties @@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=external/zeromq/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java index feabca6733484..84d3b6f243c72 100644 --- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java +++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java @@ -39,6 +39,7 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.*; +import org.apache.spark.util.Utils; /** * Most of these tests replicate org.apache.spark.JavaAPISuite using java 8 @@ -249,6 +250,7 @@ public void mapPartitions() { @Test public void sequenceFile() { File tempDir = Files.createTempDir(); + tempDir.deleteOnExit(); String outputDir = new File(tempDir, "output").getAbsolutePath(); List> pairs = Arrays.asList( new Tuple2(1, "a"), @@ -265,6 +267,7 @@ public void sequenceFile() { JavaPairRDD readRDD = sc.sequenceFile(outputDir, IntWritable.class, Text.class) .mapToPair(pair -> new Tuple2(pair._1().get(), pair._2().toString())); Assert.assertEquals(pairs, readRDD.collect()); + Utils.deleteRecursively(tempDir); } @Test diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala b/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala index 580faa0866789..7e842ec4cc82f 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Edge.scala @@ -56,7 +56,13 @@ case class Edge[@specialized(Char, Int, Boolean, Byte, Long, Float, Double) ED] object Edge { private[graphx] def lexicographicOrdering[ED] = new Ordering[Edge[ED]] { - override def compare(a: Edge[ED], b: Edge[ED]): Int = - (if (a.srcId != b.srcId) a.srcId - b.srcId else a.dstId - b.dstId).toInt + override def compare(a: Edge[ED], b: Edge[ED]): Int = { + if (a.srcId == b.srcId) { + if (a.dstId == b.dstId) 0 + else if (a.dstId < b.dstId) -1 + else 1 + } else if (a.srcId < b.srcId) -1 + else 1 + } } } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java new file mode 100644 index 0000000000000..e987ee8560ff7 --- /dev/null +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Various analytics functions for graphs. + */ +package org.apache.spark.graphx.lib; diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala new file mode 100644 index 0000000000000..7cfd617a32a34 --- /dev/null +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/package.scala @@ -0,0 +1,23 @@ +/* + * 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.graphx + +/** + * Various analytics functions for graphs. + */ +package object lib diff --git a/graphx/src/main/scala/org/apache/spark/graphx/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/package-info.java new file mode 100644 index 0000000000000..f659cc518ebd3 --- /dev/null +++ b/graphx/src/main/scala/org/apache/spark/graphx/package-info.java @@ -0,0 +1,22 @@ +/* + * 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. + */ + +/** + * ALPHA COMPONENT + * GraphX is a graph processing framework built on top of Spark. + */ +package org.apache.spark.graphx; \ No newline at end of file diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java b/graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java new file mode 100644 index 0000000000000..90cd1d46db177 --- /dev/null +++ b/graphx/src/main/scala/org/apache/spark/graphx/util/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Collections of utilities used by graphx. + */ +package org.apache.spark.graphx.util; \ No newline at end of file diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/package.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/package.scala new file mode 100644 index 0000000000000..2303541d2e427 --- /dev/null +++ b/graphx/src/main/scala/org/apache/spark/graphx/util/package.scala @@ -0,0 +1,23 @@ +/* + * 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.graphx + +/** + * Collections of utilities used by graphx. + */ +package object util diff --git a/graphx/src/test/resources/log4j.properties b/graphx/src/test/resources/log4j.properties index 85e57f0c4b504..26b73a1b39744 100644 --- a/graphx/src/test/resources/log4j.properties +++ b/graphx/src/test/resources/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=graphx/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala new file mode 100644 index 0000000000000..5a2c73b414279 --- /dev/null +++ b/graphx/src/test/scala/org/apache/spark/graphx/EdgeSuite.scala @@ -0,0 +1,39 @@ +/* + * 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.graphx + +import org.scalatest.FunSuite + +class EdgeSuite extends FunSuite { + test ("compare") { + // decending order + val testEdges: Array[Edge[Int]] = Array( + Edge(0x7FEDCBA987654321L, -0x7FEDCBA987654321L, 1), + Edge(0x2345L, 0x1234L, 1), + Edge(0x1234L, 0x5678L, 1), + Edge(0x1234L, 0x2345L, 1), + Edge(-0x7FEDCBA987654321L, 0x7FEDCBA987654321L, 1) + ) + // to ascending order + val sortedEdges = testEdges.sorted(Edge.lexicographicOrdering[Int]) + + for (i <- 0 until testEdges.length) { + assert(sortedEdges(i) == testEdges(testEdges.length - i - 1)) + } + } +} diff --git a/make-distribution.sh b/make-distribution.sh index 1cc2844703fbb..1c89027d68bed 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -171,17 +171,31 @@ echo "Spark $VERSION built for Hadoop $SPARK_HADOOP_VERSION" > "$DISTDIR/RELEASE cp $FWDIR/assembly/target/scala*/*assembly*hadoop*.jar "$DISTDIR/lib/" cp $FWDIR/examples/target/scala*/spark-examples*.jar "$DISTDIR/lib/" +# Copy example sources (needed for python and SQL) +mkdir -p "$DISTDIR/examples/src/main" +cp -r $FWDIR/examples/src/main "$DISTDIR/examples/src/" + if [ "$SPARK_HIVE" == "true" ]; then cp $FWDIR/lib_managed/jars/datanucleus*.jar "$DISTDIR/lib/" fi +# Copy license and ASF files +cp "$FWDIR/LICENSE" "$DISTDIR" +cp "$FWDIR/NOTICE" "$DISTDIR" + +if [ -e $FWDIR/CHANGES.txt ]; then + cp "$FWDIR/CHANGES.txt" "$DISTDIR" +fi + # Copy other things mkdir "$DISTDIR"/conf cp "$FWDIR"/conf/*.template "$DISTDIR"/conf cp "$FWDIR"/conf/slaves "$DISTDIR"/conf +cp "$FWDIR/README.md" "$DISTDIR" cp -r "$FWDIR/bin" "$DISTDIR" cp -r "$FWDIR/python" "$DISTDIR" cp -r "$FWDIR/sbin" "$DISTDIR" +cp -r "$FWDIR/ec2" "$DISTDIR" # Download and copy in tachyon, if requested if [ "$SPARK_TACHYON" == "true" ]; then diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala new file mode 100644 index 0000000000000..87bdc8558aaf5 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/package.scala @@ -0,0 +1,25 @@ +/* + * 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.mllib.api + +/** + * Internal support for MLLib Python API. + * + * @see [[org.apache.spark.mllib.api.python.PythonMLLibAPI]] + */ +package object python diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala index 6332301e30cbd..b7a1d90d24d72 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala @@ -17,9 +17,10 @@ package org.apache.spark.mllib.classification +import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector import org.apache.spark.rdd.RDD -import org.apache.spark.annotation.Experimental /** * :: Experimental :: @@ -43,4 +44,12 @@ trait ClassificationModel extends Serializable { * @return predicted category from the trained model */ def predict(testData: Vector): Double + + /** + * Predict values for examples stored in a JavaRDD. + * @param testData JavaRDD representing data points to be predicted + * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction + */ + def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] = + predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]] } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index e05213536e64a..316ecd713b715 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -65,7 +65,7 @@ class SVMModel private[mllib] ( intercept: Double) = { val margin = weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept threshold match { - case Some(t) => if (margin < 0) 0.0 else 1.0 + case Some(t) => if (margin < t) 0.0 else 1.0 case None => margin } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index ce14b06241932..fba21aefaaacd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -17,6 +17,7 @@ package org.apache.spark.mllib.clustering +import org.apache.spark.api.java.JavaRDD import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ import org.apache.spark.mllib.linalg.Vector @@ -40,6 +41,10 @@ class KMeansModel private[mllib] (val clusterCenters: Array[Vector]) extends Ser points.map(p => KMeans.findClosest(centersWithNorm, new BreezeVectorWithNorm(p))._1) } + /** Maps given points to their cluster indices. */ + def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] = + predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]] + /** * Return the K-means cost (sum of squared distances of points to their nearest center) for this * model on the given data. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index b10857fe7c8a1..07dfadf2f7869 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -440,7 +440,7 @@ object RowMatrix { val n = v.size v match { case dv: DenseVector => - blas.dspr("U", n, 1.0, dv.values, 1, U) + blas.dspr("U", n, alpha, dv.values, 1, U) case sv: SparseVector => val indices = sv.indices val values = sv.values diff --git a/mllib/src/main/scala/org/apache/spark/mllib/package-info.java b/mllib/src/main/scala/org/apache/spark/mllib/package-info.java new file mode 100644 index 0000000000000..4991bc9e972c0 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Spark's machine learning library. + */ +package org.apache.spark.mllib; \ No newline at end of file diff --git a/mllib/src/main/scala/org/apache/spark/mllib/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/package.scala new file mode 100644 index 0000000000000..5c2b2160c030e --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/package.scala @@ -0,0 +1,23 @@ +/* + * 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 + +/** + * Spark's machine learning library. + */ +package object mllib diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala index b27e158b43f9a..64b02f7a6e7a9 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala @@ -17,9 +17,10 @@ package org.apache.spark.mllib.regression +import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD import org.apache.spark.rdd.RDD import org.apache.spark.mllib.linalg.Vector -import org.apache.spark.annotation.Experimental @Experimental trait RegressionModel extends Serializable { @@ -38,4 +39,12 @@ trait RegressionModel extends Serializable { * @return Double prediction from the trained model */ def predict(testData: Vector): Double + + /** + * Predict values for examples stored in a JavaRDD. + * @param testData JavaRDD representing data points to be predicted + * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction + */ + def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] = + predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]] } diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java index c80b1134ed1b2..743a43a139c0c 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java @@ -19,6 +19,8 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; import org.junit.After; @@ -87,4 +89,18 @@ public void runUsingStaticMethods() { int numAccurate2 = validatePrediction(POINTS, model2); Assert.assertEquals(POINTS.size(), numAccurate2); } + + @Test + public void testPredictJavaRDD() { + JavaRDD examples = sc.parallelize(POINTS, 2).cache(); + NaiveBayesModel model = NaiveBayes.train(examples.rdd()); + JavaRDD vectors = examples.map(new Function() { + @Override + public Vector call(LabeledPoint v) throws Exception { + return v.features(); + }}); + JavaRDD predictions = model.predict(vectors); + // Should be able to get the first prediction. + predictions.first(); + } } diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java index 49a614bd90cab..0c916ca378034 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java @@ -88,4 +88,18 @@ public void runKMeansUsingConstructor() { .run(data.rdd()); assertEquals(expectedCenter, model.clusterCenters()[0]); } + + @Test + public void testPredictJavaRDD() { + List points = Lists.newArrayList( + Vectors.dense(1.0, 2.0, 6.0), + Vectors.dense(1.0, 3.0, 0.0), + Vectors.dense(1.0, 4.0, 6.0) + ); + JavaRDD data = sc.parallelize(points, 2); + KMeansModel model = new KMeans().setK(1).setMaxIterations(5).run(data.rdd()); + JavaRDD predictions = model.predict(data); + // Should be able to get the first prediction. + predictions.first(); + } } diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java index 7151e553512b3..6dc6877691036 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaLinearRegressionSuite.java @@ -25,8 +25,10 @@ import org.junit.Before; import org.junit.Test; +import org.apache.spark.api.java.function.Function; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.util.LinearDataGenerator; public class JavaLinearRegressionSuite implements Serializable { @@ -92,4 +94,23 @@ public void runLinearRegressionUsingStaticMethods() { Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0); } + @Test + public void testPredictJavaRDD() { + int nPoints = 100; + double A = 0.0; + double[] weights = {10, 10}; + JavaRDD testRDD = sc.parallelize( + LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache(); + LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD(); + LinearRegressionModel model = linSGDImpl.run(testRDD.rdd()); + JavaRDD vectors = testRDD.map(new Function() { + @Override + public Vector call(LabeledPoint v) throws Exception { + return v.features(); + } + }); + JavaRDD predictions = model.predict(vectors); + // Should be able to get the first prediction. + predictions.first(); + } } diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties index 4265ba6e5de33..ddfc4ac6b23ed 100644 --- a/mllib/src/test/resources/log4j.properties +++ b/mllib/src/test/resources/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=mllib/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala index 77d6f04b32320..886c71dde3af7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala @@ -69,6 +69,43 @@ class SVMSuite extends FunSuite with LocalSparkContext { assert(numOffPredictions < input.length / 5) } + test("SVM with threshold") { + val nPoints = 10000 + + // NOTE: Intercept should be small for generating equal 0s and 1s + val A = 0.01 + val B = -1.5 + val C = 1.0 + + val testData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 42) + + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val svm = new SVMWithSGD().setIntercept(true) + svm.optimizer.setStepSize(1.0).setRegParam(1.0).setNumIterations(100) + + val model = svm.run(testRDD) + + val validationData = SVMSuite.generateSVMInput(A, Array[Double](B, C), nPoints, 17) + val validationRDD = sc.parallelize(validationData, 2) + + // Test prediction on RDD. + + var predictions = model.predict(validationRDD.map(_.features)).collect() + assert(predictions.count(_ == 0.0) != predictions.length) + + // High threshold makes all the predictions 0.0 + model.setThreshold(10000.0) + predictions = model.predict(validationRDD.map(_.features)).collect() + assert(predictions.count(_ == 0.0) == predictions.length) + + // Low threshold makes all the predictions 1.0 + model.setThreshold(-10000.0) + predictions = model.predict(validationRDD.map(_.features)).collect() + assert(predictions.count(_ == 1.0) == predictions.length) + } + test("SVM using local random SGD") { val nPoints = 10000 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala index 6af1b502eb4dd..820eca9b1bf65 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala @@ -43,7 +43,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { // Add an extra variable consisting of all 1.0's for the intercept. val testData = GradientDescentSuite.generateGDInput(A, B, nPoints, 42) val data = testData.map { case LabeledPoint(label, features) => - label -> Vectors.dense(1.0, features.toArray: _*) + label -> Vectors.dense(1.0 +: features.toArray) } lazy val dataRDD = sc.parallelize(data, 2).cache() @@ -55,7 +55,7 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with ShouldMatchers { test("LBFGS loss should be decreasing and match the result of Gradient Descent.") { val regParam = 0 - val initialWeightsWithIntercept = Vectors.dense(1.0, initialWeights: _*) + val initialWeightsWithIntercept = Vectors.dense(1.0 +: initialWeights.toArray) val convergenceTol = 1e-12 val maxNumIterations = 10 diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala index 3f64baf6fe41f..3d05fb68988c8 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/MLUtilsSuite.scala @@ -32,6 +32,7 @@ import com.google.common.io.Files import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.MLUtils._ +import org.apache.spark.util.Utils class MLUtilsSuite extends FunSuite with LocalSparkContext { @@ -67,6 +68,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext { |-1 2:4.0 4:5.0 6:6.0 """.stripMargin val tempDir = Files.createTempDir() + tempDir.deleteOnExit() val file = new File(tempDir.getPath, "part-00000") Files.write(lines, file, Charsets.US_ASCII) val path = tempDir.toURI.toString @@ -90,7 +92,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext { assert(multiclassPoints(1).label === -1.0) assert(multiclassPoints(2).label === -1.0) - deleteQuietly(tempDir) + Utils.deleteRecursively(tempDir) } test("saveAsLibSVMFile") { @@ -107,7 +109,7 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext { .toSet val expected = Set("1.1 1:1.23 3:4.56", "0.0 1:1.01 2:2.02 3:3.03") assert(lines === expected) - deleteQuietly(tempDir) + Utils.deleteRecursively(tempDir) } test("appendBias") { @@ -158,16 +160,5 @@ class MLUtilsSuite extends FunSuite with LocalSparkContext { } } - /** Delete a file/directory quietly. */ - def deleteQuietly(f: File) { - if (f.isDirectory) { - f.listFiles().foreach(deleteQuietly) - } - try { - f.delete() - } catch { - case _: Throwable => - } - } } diff --git a/pom.xml b/pom.xml index c4e1c6be52a1b..86264d1132ec4 100644 --- a/pom.xml +++ b/pom.xml @@ -113,7 +113,8 @@ 2.10.4 2.10 - 0.13.0 + 0.18.1 + shaded-protobuf org.spark-project.akka 2.2.3-shaded-protobuf 1.7.5 @@ -122,13 +123,14 @@ 2.4.1 ${hadoop.version} 0.94.6 + 3.4.5 0.12.0 1.4.3 1.2.3 8.1.14.v20131031 0.3.6 3.0.0 - 1.7.4 + 1.7.6 0.7.1 64m @@ -193,6 +195,17 @@ false + + mapr-repo + MapR Repository + http://repository.mapr.com/maven + + true + + + false + + @@ -349,6 +362,13 @@ org.apache.mesos mesos ${mesos.version} + ${mesos.classifier} + + + com.google.protobuf + protobuf-java + + commons-net @@ -482,24 +502,30 @@ commons-logging commons-logging + + org.mortbay.jetty + servlet-api-2.5 + + + junit + junit + org.apache.avro avro ${avro.version} - - - io.netty - netty - - org.apache.avro avro-ipc ${avro.version} + + io.netty + netty + org.mortbay.jetty jetty @@ -527,6 +553,22 @@ io.netty netty + + org.mortbay.jetty + jetty + + + org.mortbay.jetty + jetty-util + + + org.mortbay.jetty + servlet-api + + + org.apache.velocity + velocity + @@ -559,6 +601,10 @@ org.jboss.netty netty + + commons-logging + commons-logging + @@ -582,6 +628,10 @@ javax.servlet servlet-api + + commons-logging + commons-logging + @@ -601,6 +651,14 @@ org.jboss.netty netty + + javax.servlet + servlet-api + + + commons-logging + commons-logging + @@ -624,6 +682,10 @@ javax.servlet servlet-api + + commons-logging + commons-logging + @@ -788,6 +850,21 @@ + + org.apache.maven.plugins + maven-clean-plugin + 2.5 + + + + work + + + checkpoint + + + + @@ -922,6 +999,19 @@ + + mapr + + false + + + 1.0.3-mapr-3.0.3 + 2.3.0-mapr-4.0.0-beta + 0.94.17-mapr-1403 + 3.4.5-mapr-1401 + + + hadoop-provided @@ -967,7 +1057,7 @@ org.apache.zookeeper zookeeper - 3.4.5 + ${zookeeper.version} provided diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala index fafc9b36a77d3..e147be7ddaa61 100644 --- a/project/MimaBuild.scala +++ b/project/MimaBuild.scala @@ -30,7 +30,7 @@ object MimaBuild { // Read package-private excludes from file val excludeFilePath = (base.getAbsolutePath + "/.mima-excludes") - val excludeFile = file(excludeFilePath) + val excludeFile = file(excludeFilePath) val packagePrivateList: Seq[String] = if (!excludeFile.exists()) { Seq() @@ -41,10 +41,10 @@ object MimaBuild { // Exclude a single class and its corresponding object def excludeClass(className: String) = { Seq( - excludePackage(className), + excludePackage(className), ProblemFilters.exclude[MissingClassProblem](className), ProblemFilters.exclude[MissingTypesProblem](className), - excludePackage(className + "$"), + excludePackage(className + "$"), ProblemFilters.exclude[MissingClassProblem](className + "$"), ProblemFilters.exclude[MissingTypesProblem](className + "$") ) @@ -78,6 +78,8 @@ object MimaBuild { excludeSparkClass("graphx.VertexRDD") ++ excludeSparkClass("graphx.impl.GraphImpl") ++ excludeSparkClass("graphx.impl.RoutingTable") ++ + excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++ + excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++ excludeSparkClass("mllib.recommendation.MFDataGenerator") ++ excludeSparkClass("mllib.optimization.SquaredGradient") ++ excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++ diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 12791e490ae2b..b9d92340ff75b 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -183,6 +183,7 @@ object SparkBuild extends Build { javaOptions in Test += "-Dspark.testing=1", javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true", javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark").map { case (k,v) => s"-D$k=$v" }.toSeq, + javaOptions in Test ++= "-Xmx3g -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g".split(" ").toSeq, javaOptions += "-Xmx3g", // Show full stack trace and duration in test cases. testOptions in Test += Tests.Argument("-oDF"), @@ -317,6 +318,7 @@ object SparkBuild extends Build { val excludeFastutil = ExclusionRule(organization = "it.unimi.dsi") val excludeJruby = ExclusionRule(organization = "org.jruby") val excludeThrift = ExclusionRule(organization = "org.apache.thrift") + val excludeServletApi = ExclusionRule(organization = "javax.servlet", artifact = "servlet-api") def sparkPreviousArtifact(id: String, organization: String = "org.apache.spark", version: String = "0.9.0-incubating", crossVersion: String = "2.10"): Option[sbt.ModuleID] = { @@ -343,7 +345,7 @@ object SparkBuild extends Build { "org.spark-project.akka" %% "akka-testkit" % akkaVersion % "test", "org.json4s" %% "json4s-jackson" % "3.2.6" excludeAll(excludeScalap), "colt" % "colt" % "1.2.0", - "org.apache.mesos" % "mesos" % "0.13.0", + "org.apache.mesos" % "mesos" % "0.18.1" classifier("shaded-protobuf") exclude("com.google.protobuf", "protobuf-java"), "commons-net" % "commons-net" % "2.2", "net.java.dev.jets3t" % "jets3t" % jets3tVersion excludeAll(excludeCommonsLogging), "org.apache.derby" % "derby" % "10.4.2.0" % "test", @@ -489,9 +491,9 @@ object SparkBuild extends Build { name := "spark-hive", javaOptions += "-XX:MaxPermSize=1g", libraryDependencies ++= Seq( - "org.apache.hive" % "hive-metastore" % hiveVersion, - "org.apache.hive" % "hive-exec" % hiveVersion, - "org.apache.hive" % "hive-serde" % hiveVersion + "org.spark-project.hive" % "hive-metastore" % hiveVersion, + "org.spark-project.hive" % "hive-exec" % hiveVersion excludeAll(excludeCommonsLogging), + "org.spark-project.hive" % "hive-serde" % hiveVersion ), // Multiple queries rely on the TestHive singleton. See comments there for more details. parallelExecution in Test := false, @@ -563,10 +565,10 @@ object SparkBuild extends Build { libraryDependencies ++= Seq( // Exclude rule required for all ? "org.apache.hadoop" % hadoopClient % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm), - "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm), - "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm), - "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm), - "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm) + "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging), + "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging), + "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging), + "org.apache.hadoop" % "hadoop-yarn-server-web-proxy" % hadoopVersion excludeAll(excludeJBossNetty, excludeAsm, excludeOldAsm, excludeCommonsLogging, excludeServletApi) ) ) diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py index 49b68d57ab02c..8eff4a242a529 100644 --- a/python/pyspark/conf.py +++ b/python/pyspark/conf.py @@ -33,7 +33,7 @@ >>> sc.sparkHome == None True ->>> conf = SparkConf() +>>> conf = SparkConf(loadDefaults=False) >>> conf.setSparkHome("/path") >>> conf.get("spark.home") diff --git a/python/pyspark/context.py b/python/pyspark/context.py index cac133d0fcf6c..c9ff82d23b3cf 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -211,6 +211,13 @@ def defaultParallelism(self): """ return self._jsc.sc().defaultParallelism() + @property + def defaultMinPartitions(self): + """ + Default min number of partitions for Hadoop RDDs when not given by user + """ + return self._jsc.sc().defaultMinPartitions() + def __del__(self): self.stop() @@ -264,7 +271,7 @@ def textFile(self, name, minPartitions=None): return RDD(self._jsc.textFile(name, minPartitions), self, UTF8Deserializer()) - def wholeTextFiles(self, path): + def wholeTextFiles(self, path, minPartitions=None): """ Read a directory of text files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system @@ -300,7 +307,8 @@ def wholeTextFiles(self, path): >>> sorted(textFiles.collect()) [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')] """ - return RDD(self._jsc.wholeTextFiles(path), self, + minPartitions = minPartitions or self.defaultMinPartitions + return RDD(self._jsc.wholeTextFiles(path, minPartitions), self, PairDeserializer(UTF8Deserializer(), UTF8Deserializer())) def _checkpointFile(self, name, input_deserializer): diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 3d0936fdca911..91ae8263f66b8 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -18,12 +18,12 @@ import os import sys import signal +import shlex import platform from subprocess import Popen, PIPE from threading import Thread from py4j.java_gateway import java_import, JavaGateway, GatewayClient - def launch_gateway(): SPARK_HOME = os.environ["SPARK_HOME"] @@ -34,9 +34,11 @@ def launch_gateway(): # Launch the Py4j gateway using Spark's run command so that we pick up the # proper classpath and settings from spark-env.sh on_windows = platform.system() == "Windows" - script = "./bin/spark-class.cmd" if on_windows else "./bin/spark-class" - command = [os.path.join(SPARK_HOME, script), "py4j.GatewayServer", - "--die-on-broken-pipe", "0"] + script = "./bin/spark-submit.cmd" if on_windows else "./bin/spark-submit" + submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS") + submit_args = submit_args if submit_args is not None else "" + submit_args = shlex.split(submit_args) + command = [os.path.join(SPARK_HOME, script), "pyspark-shell"] + submit_args if not on_windows: # Don't send ctrl-c / SIGINT to the Java gateway: def preexec_func(): diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 4f74824ba4cf2..07578b8d937fc 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -1152,6 +1152,10 @@ def groupByKey(self, numPartitions=None): Group the values for each key in the RDD into a single sequence. Hash-partitions the resulting RDD with into numPartitions partitions. + Note: If you are grouping in order to perform an aggregation (such as a + sum or average) over each key, using reduceByKey will provide much better + performance. + >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect())) [('a', [1, 1]), ('b', [1])] diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index d172d588bfbd8..ebd714db7a918 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -40,7 +40,7 @@ if os.environ.get("SPARK_EXECUTOR_URI"): SparkContext.setSystemProperty("spark.executor.uri", os.environ["SPARK_EXECUTOR_URI"]) -sc = SparkContext(os.environ.get("MASTER", "local[*]"), "PySparkShell", pyFiles=add_files) +sc = SparkContext(appName="PySparkShell", pyFiles=add_files) print("""Welcome to ____ __ diff --git a/python/pyspark/sql.py b/python/pyspark/sql.py index 6789d7002b3b7..bbe69e7d8f89b 100644 --- a/python/pyspark/sql.py +++ b/python/pyspark/sql.py @@ -28,7 +28,7 @@ class SQLContext: register L{SchemaRDD}s as tables, execute sql over tables, cache tables, and read parquet files. """ - def __init__(self, sparkContext): + def __init__(self, sparkContext, sqlContext = None): """ Create a new SQLContext. @@ -58,10 +58,13 @@ def __init__(self, sparkContext): self._jvm = self._sc._jvm self._pythonToJavaMap = self._jvm.PythonRDD.pythonToJavaMap + if sqlContext: + self._scala_SQLContext = sqlContext + @property def _ssql_ctx(self): """ - Accessor for the JVM SparkSQL context. Subclasses can overrite this property to provide + Accessor for the JVM SparkSQL context. Subclasses can override this property to provide their own JVM Contexts. """ if not hasattr(self, '_scala_SQLContext'): diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 296da740687ec..5f34362ccd973 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -962,11 +962,10 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, private def getMaster(): String = { val master = this.master match { case Some(m) => m - case None => { + case None => val envMaster = sys.env.get("MASTER") val propMaster = sys.props.get("spark.master") - envMaster.orElse(propMaster).getOrElse("local[*]") - } + propMaster.orElse(envMaster).getOrElse("local[*]") } master } @@ -993,7 +992,13 @@ object SparkILoop { implicit def loopToInterpreter(repl: SparkILoop): SparkIMain = repl.intp private def echo(msg: String) = Console println msg - def getAddedJars: Array[String] = Option(System.getenv("ADD_JARS")).map(_.split(',')).getOrElse(new Array[String](0)) + def getAddedJars: Array[String] = { + val envJars = sys.env.get("ADD_JARS") + val propJars = sys.props.get("spark.jars").flatMap { p => + if (p == "") None else Some(p) + } + propJars.orElse(envJars).map(_.split(",")).getOrElse(Array.empty) + } // Designed primarily for use by test code: take a String with a // bunch of code, and prints out a transcript of what it would look diff --git a/repl/src/test/resources/log4j.properties b/repl/src/test/resources/log4j.properties index a6d33e69d21f7..9c4896e49698c 100644 --- a/repl/src/test/resources/log4j.properties +++ b/repl/src/test/resources/log4j.properties @@ -19,7 +19,7 @@ log4j.rootCategory=INFO, file log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=repl/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala index 336df988a1b7f..c0af7ceb6d3ef 100644 --- a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala +++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.repl import java.io.File -import java.net.URLClassLoader +import java.net.{URL, URLClassLoader} import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite @@ -26,21 +26,35 @@ import org.scalatest.FunSuite import com.google.common.io.Files import org.apache.spark.TestUtils +import org.apache.spark.util.Utils class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { val childClassNames = List("ReplFakeClass1", "ReplFakeClass2") val parentClassNames = List("ReplFakeClass1", "ReplFakeClass2", "ReplFakeClass3") - val tempDir1 = Files.createTempDir() - val tempDir2 = Files.createTempDir() - val url1 = "file://" + tempDir1 - val urls2 = List(tempDir2.toURI.toURL).toArray + var tempDir1: File = _ + var tempDir2: File = _ + var url1: String = _ + var urls2: Array[URL] = _ override def beforeAll() { + super.beforeAll() + tempDir1 = Files.createTempDir() + tempDir1.deleteOnExit() + tempDir2 = Files.createTempDir() + tempDir2.deleteOnExit() + url1 = "file://" + tempDir1 + urls2 = List(tempDir2.toURI.toURL).toArray childClassNames.foreach(TestUtils.createCompiledClass(_, tempDir1, "1")) parentClassNames.foreach(TestUtils.createCompiledClass(_, tempDir2, "2")) } + override def afterAll() { + super.afterAll() + Utils.deleteRecursively(tempDir1) + Utils.deleteRecursively(tempDir2) + } + test("child first") { val parentLoader = new URLClassLoader(urls2, null) val classLoader = new ExecutorClassLoader(url1, parentLoader, true) diff --git a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala index 566d96e16ed83..95460aa205331 100644 --- a/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala +++ b/repl/src/test/scala/org/apache/spark/repl/ReplSuite.scala @@ -26,6 +26,7 @@ import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.spark.SparkContext import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.util.Utils class ReplSuite extends FunSuite { @@ -178,6 +179,7 @@ class ReplSuite extends FunSuite { test("interacting with files") { val tempDir = Files.createTempDir() + tempDir.deleteOnExit() val out = new FileWriter(tempDir + "/input") out.write("Hello world!\n") out.write("What's up?\n") @@ -196,6 +198,7 @@ class ReplSuite extends FunSuite { assertContains("res0: Long = 3", output) assertContains("res1: Long = 3", output) assertContains("res2: Long = 3", output) + Utils.deleteRecursively(tempDir) } test("local-cluster mode") { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 792ef6cee6f5d..196695a0a188f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -41,6 +41,9 @@ object ScalaReflection { /** Returns a catalyst DataType for the given Scala Type using reflection. */ def schemaFor(tpe: `Type`): DataType = tpe match { + case t if t <:< typeOf[Option[_]] => + val TypeRef(_, _, Seq(optType)) = t + schemaFor(optType) case t if t <:< typeOf[Product] => val params = t.member("": TermName).asMethod.paramss StructType( @@ -59,9 +62,6 @@ object ScalaReflection { case t if t <:< typeOf[String] => StringType case t if t <:< typeOf[Timestamp] => TimestampType case t if t <:< typeOf[BigDecimal] => DecimalType - case t if t <:< typeOf[Option[_]] => - val TypeRef(_, _, Seq(optType)) = t - schemaFor(optType) case t if t <:< typeOf[java.lang.Integer] => IntegerType case t if t <:< typeOf[java.lang.Long] => LongType case t if t <:< typeOf[java.lang.Double] => DoubleType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index b3a3a1ef1b5eb..f2b9b2c1a3ad5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -93,6 +93,7 @@ class SqlParser extends StandardTokenParsers with PackratParsers { protected val AND = Keyword("AND") protected val AS = Keyword("AS") protected val ASC = Keyword("ASC") + protected val APPROXIMATE = Keyword("APPROXIMATE") protected val AVG = Keyword("AVG") protected val BY = Keyword("BY") protected val CAST = Keyword("CAST") @@ -318,6 +319,12 @@ class SqlParser extends StandardTokenParsers with PackratParsers { COUNT ~> "(" ~ "*" <~ ")" ^^ { case _ => Count(Literal(1)) } | COUNT ~> "(" ~ expression <~ ")" ^^ { case dist ~ exp => Count(exp) } | COUNT ~> "(" ~> DISTINCT ~> expression <~ ")" ^^ { case exp => CountDistinct(exp :: Nil) } | + APPROXIMATE ~> COUNT ~> "(" ~> DISTINCT ~> expression <~ ")" ^^ { + case exp => ApproxCountDistinct(exp) + } | + APPROXIMATE ~> "(" ~> floatLit ~ ")" ~ COUNT ~ "(" ~ DISTINCT ~ expression <~ ")" ^^ { + case s ~ _ ~ _ ~ _ ~ _ ~ e => ApproxCountDistinct(e, s.toDouble) + } | FIRST ~> "(" ~> expression <~ ")" ^^ { case exp => First(exp) } | AVG ~> "(" ~> expression <~ ")" ^^ { case exp => Average(exp) } | MIN ~> "(" ~> expression <~ ")" ^^ { case exp => Min(exp) } | diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 78d3a1d8096af..3cf163f9a9a75 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -31,26 +31,29 @@ import org.apache.spark.sql.catalyst.types._ * A collection of implicit conversions that create a DSL for constructing catalyst data structures. * * {{{ - * scala> import catalyst.dsl._ + * scala> import org.apache.spark.sql.catalyst.dsl.expressions._ * * // Standard operators are added to expressions. + * scala> import org.apache.spark.sql.catalyst.expressions.Literal * scala> Literal(1) + Literal(1) - * res1: catalyst.expressions.Add = (1 + 1) + * res0: org.apache.spark.sql.catalyst.expressions.Add = (1 + 1) * * // There is a conversion from 'symbols to unresolved attributes. * scala> 'a.attr - * res2: catalyst.analysis.UnresolvedAttribute = 'a + * res1: org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute = 'a * * // These unresolved attributes can be used to create more complicated expressions. * scala> 'a === 'b - * res3: catalyst.expressions.Equals = ('a = 'b) + * res2: org.apache.spark.sql.catalyst.expressions.Equals = ('a = 'b) * * // SQL verbs can be used to construct logical query plans. - * scala> TestRelation('key.int, 'value.string).where('key === 1).select('value).analyze - * res4: catalyst.plans.logical.LogicalPlan = - * Project {value#1} - * Filter (key#0 = 1) - * TestRelation {key#0,value#1} + * scala> import org.apache.spark.sql.catalyst.plans.logical._ + * scala> import org.apache.spark.sql.catalyst.dsl.plans._ + * scala> LocalRelation('key.int, 'value.string).where('key === 1).select('value).analyze + * res3: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan = + * Project [value#3] + * Filter (key#2 = 1) + * LocalRelation [key#2,value#3], [] * }}} */ package object dsl { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 7777d372903e2..5dbaaa3b0ce35 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql.catalyst.expressions +import com.clearspring.analytics.stream.cardinality.HyperLogLog + import org.apache.spark.sql.catalyst.types._ import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.errors.TreeNodeException @@ -146,7 +148,6 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr override def eval(input: Row): Any = currentMax } - case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references override def nullable = false @@ -166,10 +167,47 @@ case class CountDistinct(expressions: Seq[Expression]) extends AggregateExpressi override def references = expressions.flatMap(_.references).toSet override def nullable = false override def dataType = IntegerType - override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")}})" + override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})" override def newInstance() = new CountDistinctFunction(expressions, this) } +case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double) + extends AggregateExpression with trees.UnaryNode[Expression] { + override def references = child.references + override def nullable = false + override def dataType = child.dataType + override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" + override def newInstance() = new ApproxCountDistinctPartitionFunction(child, this, relativeSD) +} + +case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double) + extends AggregateExpression with trees.UnaryNode[Expression] { + override def references = child.references + override def nullable = false + override def dataType = IntegerType + override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" + override def newInstance() = new ApproxCountDistinctMergeFunction(child, this, relativeSD) +} + +case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05) + extends PartialAggregate with trees.UnaryNode[Expression] { + override def references = child.references + override def nullable = false + override def dataType = IntegerType + override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" + + override def asPartial: SplitEvaluation = { + val partialCount = + Alias(ApproxCountDistinctPartition(child, relativeSD), "PartialApproxCountDistinct")() + + SplitEvaluation( + ApproxCountDistinctMerge(partialCount.toAttribute, relativeSD), + partialCount :: Nil) + } + + override def newInstance() = new CountDistinctFunction(child :: Nil, this) +} + case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { override def references = child.references override def nullable = false @@ -269,6 +307,42 @@ case class CountFunction(expr: Expression, base: AggregateExpression) extends Ag override def eval(input: Row): Any = count } +case class ApproxCountDistinctPartitionFunction( + expr: Expression, + base: AggregateExpression, + relativeSD: Double) + extends AggregateFunction { + def this() = this(null, null, 0) // Required for serialization. + + private val hyperLogLog = new HyperLogLog(relativeSD) + + override def update(input: Row): Unit = { + val evaluatedExpr = expr.eval(input) + if (evaluatedExpr != null) { + hyperLogLog.offer(evaluatedExpr) + } + } + + override def eval(input: Row): Any = hyperLogLog +} + +case class ApproxCountDistinctMergeFunction( + expr: Expression, + base: AggregateExpression, + relativeSD: Double) + extends AggregateFunction { + def this() = this(null, null, 0) // Required for serialization. + + private val hyperLogLog = new HyperLogLog(relativeSD) + + override def update(input: Row): Unit = { + val evaluatedExpr = expr.eval(input) + hyperLogLog.addAll(evaluatedExpr.asInstanceOf[HyperLogLog]) + } + + override def eval(input: Row): Any = hyperLogLog.cardinality() +} + case class SumFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { def this() = this(null, null) // Required for serialization. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index 195ca2eb3d589..b6aeae92f8bec 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -74,7 +74,7 @@ case class GetField(child: Expression, fieldName: String) extends UnaryExpressio type EvaluatedType = Any def dataType = field.dataType - override def nullable = field.nullable + override def nullable = child.nullable || field.nullable override def foldable = child.foldable protected def structType = child.dataType match { 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 6ee479939d25c..d111578530506 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 @@ -98,13 +98,19 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate { override def eval(input: Row): Any = { val l = left.eval(input) - val r = right.eval(input) - if (l == false || r == false) { - false - } else if (l == null || r == null ) { - null + if (l == false) { + false } else { - true + val r = right.eval(input) + if (r == false) { + false + } else { + if (l != null && r != null) { + true + } else { + null + } + } } } } @@ -114,13 +120,19 @@ case class Or(left: Expression, right: Expression) extends BinaryPredicate { override def eval(input: Row): Any = { val l = left.eval(input) - val r = right.eval(input) - if (l == true || r == true) { + if (l == true) { true - } else if (l == null || r == null) { - null } else { - false + val r = right.eval(input) + if (r == true) { + true + } else { + if (l != null && r != null) { + false + } else { + null + } + } } } } @@ -133,8 +145,12 @@ case class Equals(left: Expression, right: Expression) extends BinaryComparison def symbol = "=" override def eval(input: Row): Any = { val l = left.eval(input) - val r = right.eval(input) - if (l == null || r == null) null else l == r + if (l == null) { + null + } else { + val r = right.eval(input) + if (r == null) null else l == r + } } } @@ -162,7 +178,7 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi extends Expression { def children = predicate :: trueValue :: falseValue :: Nil - def nullable = trueValue.nullable || falseValue.nullable + override def nullable = trueValue.nullable || falseValue.nullable def references = children.flatMap(_.references).toSet override lazy val resolved = childrenResolved && trueValue.dataType == falseValue.dataType def dataType = { @@ -175,8 +191,9 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi } type EvaluatedType = Any + override def eval(input: Row): Any = { - if (predicate.eval(input).asInstanceOf[Boolean]) { + if (true == predicate.eval(input)) { trueValue.eval(input) } else { falseValue.eval(input) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 3037d45cc6e35..406ffd6801e98 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -25,13 +25,13 @@ import org.apache.spark.sql.catalyst.types._ object Optimizer extends RuleExecutor[LogicalPlan] { val batches = - Batch("ConstantFolding", Once, + Batch("ConstantFolding", FixedPoint(100), NullPropagation, ConstantFolding, BooleanSimplification, SimplifyFilters, SimplifyCasts) :: - Batch("Filter Pushdown", Once, + Batch("Filter Pushdown", FixedPoint(100), CombineFilters, PushPredicateThroughProject, PushPredicateThroughInnerJoin, @@ -49,17 +49,19 @@ object Optimizer extends RuleExecutor[LogicalPlan] { */ object ColumnPruning extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { + // Eliminate attributes that are not needed to calculate the specified aggregates. case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty => - // Project away references that are not needed to calculate the required aggregates. a.copy(child = Project(a.references.toSeq, child)) + // Eliminate unneeded attributes from either side of a Join. case Project(projectList, Join(left, right, joinType, condition)) => // Collect the list of off references required either above or to evaluate the condition. val allReferences: Set[Attribute] = projectList.flatMap(_.references).toSet ++ condition.map(_.references).getOrElse(Set.empty) - /** Applies a projection when the child is producing unnecessary attributes */ + + /** Applies a projection only when the child is producing unnecessary attributes */ def prunedChild(c: LogicalPlan) = - if ((allReferences.filter(c.outputSet.contains) -- c.outputSet).nonEmpty) { + if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) { Project(allReferences.filter(c.outputSet.contains).toSeq, c) } else { c @@ -67,6 +69,7 @@ object ColumnPruning extends Rule[LogicalPlan] { Project(projectList, Join(prunedChild(left), prunedChild(right), joinType, condition)) + // Combine adjacent Projects. case Project(projectList1, Project(projectList2, child)) => // Create a map of Aliases to their values from the child projection. // e.g., 'SELECT ... FROM (SELECT a + b AS c, d ...)' produces Map(c -> Alias(a + b, c)). @@ -83,6 +86,9 @@ object ColumnPruning extends Rule[LogicalPlan] { }).asInstanceOf[Seq[NamedExpression]] Project(substitutedProjection, child) + + // Eliminate no-op Projects + case Project(projectList, child) if(child.output == projectList) => child } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala index 0e3a8a6bd30a8..4544b32958c7e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala @@ -129,11 +129,12 @@ object HashFilteredJoin extends Logging with PredicateHelper { // as join keys. def splitPredicates(allPredicates: Seq[Expression], join: Join): Option[ReturnType] = { val Join(left, right, joinType, _) = join - val (joinPredicates, otherPredicates) = allPredicates.partition { - case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) || - (canEvaluate(l, right) && canEvaluate(r, left)) => true - case _ => false - } + val (joinPredicates, otherPredicates) = + allPredicates.flatMap(splitConjunctivePredicates).partition { + case Equals(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) || + (canEvaluate(l, right) && canEvaluate(r, left)) => true + case _ => false + } val joinKeys = joinPredicates.map { case Equals(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java b/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java new file mode 100644 index 0000000000000..53603614518f5 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Allows the execution of relational queries, including those expressed in SQL using Spark. + */ +package org.apache.spark.sql; \ No newline at end of file diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 344d8a304fc11..1132a30b42767 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -364,6 +364,19 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(GetField(BoundReference(2, AttributeReference("c", typeS)()), "a"), "aa", row) checkEvaluation(GetField(Literal(null, typeS), "a"), null, row) + + val typeS_notNullable = StructType( + StructField("a", StringType, nullable = false) + :: StructField("b", StringType, nullable = false) :: Nil + ) + + assert(GetField(BoundReference(2, + AttributeReference("c", typeS)()), "a").nullable === true) + assert(GetField(BoundReference(2, + AttributeReference("c", typeS_notNullable, nullable = false)()), "a").nullable === false) + + assert(GetField(Literal(null, typeS), "a").nullable === true) + assert(GetField(Literal(null, typeS_notNullable), "a").nullable === true) } test("arithmetic") { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala index 57facbe10fc96..6f7d431b9a819 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/java/JavaSQLContext.scala @@ -33,9 +33,9 @@ import org.apache.spark.util.Utils /** * The entry point for executing Spark SQL queries from a Java program. */ -class JavaSQLContext(sparkContext: JavaSparkContext) { +class JavaSQLContext(val sqlContext: SQLContext) { - val sqlContext = new SQLContext(sparkContext.sc) + def this(sparkContext: JavaSparkContext) = this(new SQLContext(sparkContext.sc)) /** * Executes a query expressed in SQL, returning the result as a JavaSchemaRDD diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index 1c6e29b3cdee9..34b355e906695 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -21,8 +21,10 @@ import java.nio.ByteBuffer import scala.reflect.ClassTag +import com.clearspring.analytics.stream.cardinality.HyperLogLog import com.esotericsoftware.kryo.io.{Input, Output} import com.esotericsoftware.kryo.{Serializer, Kryo} +import com.twitter.chill.AllScalaRegistrar import org.apache.spark.{SparkEnv, SparkConf} import org.apache.spark.serializer.KryoSerializer @@ -34,20 +36,14 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co val kryo = new Kryo() kryo.setRegistrationRequired(false) kryo.register(classOf[MutablePair[_, _]]) - kryo.register(classOf[Array[Any]]) - // This is kinda hacky... - kryo.register(classOf[scala.collection.immutable.Map$Map1], new MapSerializer) - kryo.register(classOf[scala.collection.immutable.Map$Map2], new MapSerializer) - kryo.register(classOf[scala.collection.immutable.Map$Map3], new MapSerializer) - kryo.register(classOf[scala.collection.immutable.Map$Map4], new MapSerializer) - kryo.register(classOf[scala.collection.immutable.Map[_,_]], new MapSerializer) - kryo.register(classOf[scala.collection.Map[_,_]], new MapSerializer) kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow]) kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericMutableRow]) - kryo.register(classOf[scala.collection.mutable.ArrayBuffer[_]]) + kryo.register(classOf[com.clearspring.analytics.stream.cardinality.HyperLogLog], + new HyperLogLogSerializer) kryo.register(classOf[scala.math.BigDecimal], new BigDecimalSerializer) kryo.setReferences(false) kryo.setClassLoader(Utils.getSparkClassLoader) + new AllScalaRegistrar().apply(kryo) kryo } } @@ -81,19 +77,16 @@ private[sql] class BigDecimalSerializer extends Serializer[BigDecimal] { } } -/** - * Maps do not have a no arg constructor and so cannot be serialized by default. So, we serialize - * them as `Array[(k,v)]`. - */ -private[sql] class MapSerializer extends Serializer[Map[_,_]] { - def write(kryo: Kryo, output: Output, map: Map[_,_]) { - kryo.writeObject(output, map.flatMap(e => Seq(e._1, e._2)).toArray) +private[sql] class HyperLogLogSerializer extends Serializer[HyperLogLog] { + def write(kryo: Kryo, output: Output, hyperLogLog: HyperLogLog) { + val bytes = hyperLogLog.getBytes() + output.writeInt(bytes.length) + output.writeBytes(bytes) } - def read(kryo: Kryo, input: Input, tpe: Class[Map[_,_]]): Map[_,_] = { - kryo.readObject(input, classOf[Array[Any]]) - .sliding(2,2) - .map { case Array(k,v) => (k,v) } - .toMap + def read(kryo: Kryo, input: Input, tpe: Class[HyperLogLog]): HyperLogLog = { + val length = input.readInt() + val bytes = input.readBytes(length) + HyperLogLog.Builder.build(bytes) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index f763106da4e0e..394a59700dbaf 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -140,12 +140,35 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { InsertIntoParquetTable(relation, planLater(child), overwrite=true)(sparkContext) :: Nil case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) => InsertIntoParquetTable(table, planLater(child), overwrite)(sparkContext) :: Nil - case PhysicalOperation(projectList, filters, relation: ParquetRelation) => - // TODO: Should be pushing down filters as well. + case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => { + val remainingFilters = + if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) { + filters.filter { + // Note: filters cannot be pushed down to Parquet if they contain more complex + // expressions than simple "Attribute cmp Literal" comparisons. Here we remove + // all filters that have been pushed down. Note that a predicate such as + // "(A AND B) OR C" can result in "A OR C" being pushed down. + filter => + val recordFilter = ParquetFilters.createFilter(filter) + if (!recordFilter.isDefined) { + // First case: the pushdown did not result in any record filter. + true + } else { + // Second case: a record filter was created; here we are conservative in + // the sense that even if "A" was pushed and we check for "A AND B" we + // still want to keep "A AND B" in the higher-level filter, not just "B". + !ParquetFilters.findExpression(recordFilter.get, filter).isDefined + } + } + } else { + filters + } pruneFilterProject( projectList, - filters, - ParquetTableScan(_, relation, None)(sparkContext)) :: Nil + remainingFilters, + ParquetTableScan(_, relation, filters)(sparkContext)) :: Nil + } + case _ => Nil } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala new file mode 100644 index 0000000000000..052b0a9196717 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala @@ -0,0 +1,436 @@ +/* + * 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.parquet + +import org.apache.hadoop.conf.Configuration + +import parquet.filter._ +import parquet.filter.ColumnPredicates._ +import parquet.column.ColumnReader + +import com.google.common.io.BaseEncoding + +import org.apache.spark.sql.catalyst.types._ +import org.apache.spark.sql.catalyst.expressions.{Predicate => CatalystPredicate} +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.execution.SparkSqlSerializer + +object ParquetFilters { + val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter" + // set this to false if pushdown should be disabled + val PARQUET_FILTER_PUSHDOWN_ENABLED = "spark.sql.hints.parquetFilterPushdown" + + def createRecordFilter(filterExpressions: Seq[Expression]): UnboundRecordFilter = { + val filters: Seq[CatalystFilter] = filterExpressions.collect { + case (expression: Expression) if createFilter(expression).isDefined => + createFilter(expression).get + } + if (filters.length > 0) filters.reduce(AndRecordFilter.and) else null + } + + def createFilter(expression: Expression): Option[CatalystFilter] = { + def createEqualityFilter( + name: String, + literal: Literal, + predicate: CatalystPredicate) = literal.dataType match { + case BooleanType => + ComparisonFilter.createBooleanFilter(name, literal.value.asInstanceOf[Boolean], predicate) + case IntegerType => + ComparisonFilter.createIntFilter( + name, + (x: Int) => x == literal.value.asInstanceOf[Int], + predicate) + case LongType => + ComparisonFilter.createLongFilter( + name, + (x: Long) => x == literal.value.asInstanceOf[Long], + predicate) + case DoubleType => + ComparisonFilter.createDoubleFilter( + name, + (x: Double) => x == literal.value.asInstanceOf[Double], + predicate) + case FloatType => + ComparisonFilter.createFloatFilter( + name, + (x: Float) => x == literal.value.asInstanceOf[Float], + predicate) + case StringType => + ComparisonFilter.createStringFilter(name, literal.value.asInstanceOf[String], predicate) + } + def createLessThanFilter( + name: String, + literal: Literal, + predicate: CatalystPredicate) = literal.dataType match { + case IntegerType => + ComparisonFilter.createIntFilter( + name, + (x: Int) => x < literal.value.asInstanceOf[Int], + predicate) + case LongType => + ComparisonFilter.createLongFilter( + name, + (x: Long) => x < literal.value.asInstanceOf[Long], + predicate) + case DoubleType => + ComparisonFilter.createDoubleFilter( + name, + (x: Double) => x < literal.value.asInstanceOf[Double], + predicate) + case FloatType => + ComparisonFilter.createFloatFilter( + name, + (x: Float) => x < literal.value.asInstanceOf[Float], + predicate) + } + def createLessThanOrEqualFilter( + name: String, + literal: Literal, + predicate: CatalystPredicate) = literal.dataType match { + case IntegerType => + ComparisonFilter.createIntFilter( + name, + (x: Int) => x <= literal.value.asInstanceOf[Int], + predicate) + case LongType => + ComparisonFilter.createLongFilter( + name, + (x: Long) => x <= literal.value.asInstanceOf[Long], + predicate) + case DoubleType => + ComparisonFilter.createDoubleFilter( + name, + (x: Double) => x <= literal.value.asInstanceOf[Double], + predicate) + case FloatType => + ComparisonFilter.createFloatFilter( + name, + (x: Float) => x <= literal.value.asInstanceOf[Float], + predicate) + } + // TODO: combine these two types somehow? + def createGreaterThanFilter( + name: String, + literal: Literal, + predicate: CatalystPredicate) = literal.dataType match { + case IntegerType => + ComparisonFilter.createIntFilter( + name, + (x: Int) => x > literal.value.asInstanceOf[Int], + predicate) + case LongType => + ComparisonFilter.createLongFilter( + name, + (x: Long) => x > literal.value.asInstanceOf[Long], + predicate) + case DoubleType => + ComparisonFilter.createDoubleFilter( + name, + (x: Double) => x > literal.value.asInstanceOf[Double], + predicate) + case FloatType => + ComparisonFilter.createFloatFilter( + name, + (x: Float) => x > literal.value.asInstanceOf[Float], + predicate) + } + def createGreaterThanOrEqualFilter( + name: String, + literal: Literal, + predicate: CatalystPredicate) = literal.dataType match { + case IntegerType => + ComparisonFilter.createIntFilter( + name, (x: Int) => x >= literal.value.asInstanceOf[Int], + predicate) + case LongType => + ComparisonFilter.createLongFilter( + name, + (x: Long) => x >= literal.value.asInstanceOf[Long], + predicate) + case DoubleType => + ComparisonFilter.createDoubleFilter( + name, + (x: Double) => x >= literal.value.asInstanceOf[Double], + predicate) + case FloatType => + ComparisonFilter.createFloatFilter( + name, + (x: Float) => x >= literal.value.asInstanceOf[Float], + predicate) + } + + /** + * TODO: we currently only filter on non-nullable (Parquet REQUIRED) attributes until + * https://github.com/Parquet/parquet-mr/issues/371 + * has been resolved. + */ + expression match { + case p @ Or(left: Expression, right: Expression) + if createFilter(left).isDefined && createFilter(right).isDefined => { + // If either side of this Or-predicate is empty then this means + // it contains a more complex comparison than between attribute and literal + // (e.g., it contained a CAST). The only safe thing to do is then to disregard + // this disjunction, which could be contained in a conjunction. If it stands + // alone then it is also safe to drop it, since a Null return value of this + // function is interpreted as having no filters at all. + val leftFilter = createFilter(left).get + val rightFilter = createFilter(right).get + Some(new OrFilter(leftFilter, rightFilter)) + } + case p @ And(left: Expression, right: Expression) => { + // This treats nested conjunctions; since either side of the conjunction + // may contain more complex filter expressions we may actually generate + // strictly weaker filter predicates in the process. + val leftFilter = createFilter(left) + val rightFilter = createFilter(right) + (leftFilter, rightFilter) match { + case (None, Some(filter)) => Some(filter) + case (Some(filter), None) => Some(filter) + case (_, _) => + Some(new AndFilter(leftFilter.get, rightFilter.get)) + } + } + case p @ Equals(left: Literal, right: NamedExpression) if !right.nullable => + Some(createEqualityFilter(right.name, left, p)) + case p @ Equals(left: NamedExpression, right: Literal) if !left.nullable => + Some(createEqualityFilter(left.name, right, p)) + case p @ LessThan(left: Literal, right: NamedExpression) if !right.nullable => + Some(createLessThanFilter(right.name, left, p)) + case p @ LessThan(left: NamedExpression, right: Literal) if !left.nullable => + Some(createLessThanFilter(left.name, right, p)) + case p @ LessThanOrEqual(left: Literal, right: NamedExpression) if !right.nullable => + Some(createLessThanOrEqualFilter(right.name, left, p)) + case p @ LessThanOrEqual(left: NamedExpression, right: Literal) if !left.nullable => + Some(createLessThanOrEqualFilter(left.name, right, p)) + case p @ GreaterThan(left: Literal, right: NamedExpression) if !right.nullable => + Some(createGreaterThanFilter(right.name, left, p)) + case p @ GreaterThan(left: NamedExpression, right: Literal) if !left.nullable => + Some(createGreaterThanFilter(left.name, right, p)) + case p @ GreaterThanOrEqual(left: Literal, right: NamedExpression) if !right.nullable => + Some(createGreaterThanOrEqualFilter(right.name, left, p)) + case p @ GreaterThanOrEqual(left: NamedExpression, right: Literal) if !left.nullable => + Some(createGreaterThanOrEqualFilter(left.name, right, p)) + case _ => None + } + } + + /** + * Note: Inside the Hadoop API we only have access to `Configuration`, not to + * [[org.apache.spark.SparkContext]], so we cannot use broadcasts to convey + * the actual filter predicate. + */ + def serializeFilterExpressions(filters: Seq[Expression], conf: Configuration): Unit = { + if (filters.length > 0) { + val serialized: Array[Byte] = SparkSqlSerializer.serialize(filters) + val encoded: String = BaseEncoding.base64().encode(serialized) + conf.set(PARQUET_FILTER_DATA, encoded) + } + } + + /** + * Note: Inside the Hadoop API we only have access to `Configuration`, not to + * [[org.apache.spark.SparkContext]], so we cannot use broadcasts to convey + * the actual filter predicate. + */ + def deserializeFilterExpressions(conf: Configuration): Seq[Expression] = { + val data = conf.get(PARQUET_FILTER_DATA) + if (data != null) { + val decoded: Array[Byte] = BaseEncoding.base64().decode(data) + SparkSqlSerializer.deserialize(decoded) + } else { + Seq() + } + } + + /** + * Try to find the given expression in the tree of filters in order to + * determine whether it is safe to remove it from the higher level filters. Note + * that strictly speaking we could stop the search whenever an expression is found + * that contains this expression as subexpression (e.g., when searching for "a" + * and "(a or c)" is found) but we don't care about optimizations here since the + * filter tree is assumed to be small. + * + * @param filter The [[org.apache.spark.sql.parquet.CatalystFilter]] to expand + * and search + * @param expression The expression to look for + * @return An optional [[org.apache.spark.sql.parquet.CatalystFilter]] that + * contains the expression. + */ + def findExpression( + filter: CatalystFilter, + expression: Expression): Option[CatalystFilter] = filter match { + case f @ OrFilter(_, leftFilter, rightFilter, _) => + if (f.predicate == expression) { + Some(f) + } else { + val left = findExpression(leftFilter, expression) + if (left.isDefined) left else findExpression(rightFilter, expression) + } + case f @ AndFilter(_, leftFilter, rightFilter, _) => + if (f.predicate == expression) { + Some(f) + } else { + val left = findExpression(leftFilter, expression) + if (left.isDefined) left else findExpression(rightFilter, expression) + } + case f @ ComparisonFilter(_, _, predicate) => + if (predicate == expression) Some(f) else None + case _ => None + } +} + +abstract private[parquet] class CatalystFilter( + @transient val predicate: CatalystPredicate) extends UnboundRecordFilter + +private[parquet] case class ComparisonFilter( + val columnName: String, + private var filter: UnboundRecordFilter, + @transient override val predicate: CatalystPredicate) + extends CatalystFilter(predicate) { + override def bind(readers: java.lang.Iterable[ColumnReader]): RecordFilter = { + filter.bind(readers) + } +} + +private[parquet] case class OrFilter( + private var filter: UnboundRecordFilter, + @transient val left: CatalystFilter, + @transient val right: CatalystFilter, + @transient override val predicate: Or) + extends CatalystFilter(predicate) { + def this(l: CatalystFilter, r: CatalystFilter) = + this( + OrRecordFilter.or(l, r), + l, + r, + Or(l.predicate, r.predicate)) + + override def bind(readers: java.lang.Iterable[ColumnReader]): RecordFilter = { + filter.bind(readers) + } +} + +private[parquet] case class AndFilter( + private var filter: UnboundRecordFilter, + @transient val left: CatalystFilter, + @transient val right: CatalystFilter, + @transient override val predicate: And) + extends CatalystFilter(predicate) { + def this(l: CatalystFilter, r: CatalystFilter) = + this( + AndRecordFilter.and(l, r), + l, + r, + And(l.predicate, r.predicate)) + + override def bind(readers: java.lang.Iterable[ColumnReader]): RecordFilter = { + filter.bind(readers) + } +} + +private[parquet] object ComparisonFilter { + def createBooleanFilter( + columnName: String, + value: Boolean, + predicate: CatalystPredicate): CatalystFilter = + new ComparisonFilter( + columnName, + ColumnRecordFilter.column( + columnName, + ColumnPredicates.applyFunctionToBoolean( + new BooleanPredicateFunction { + def functionToApply(input: Boolean): Boolean = input == value + } + )), + predicate) + + def createStringFilter( + columnName: String, + value: String, + predicate: CatalystPredicate): CatalystFilter = + new ComparisonFilter( + columnName, + ColumnRecordFilter.column( + columnName, + ColumnPredicates.applyFunctionToString ( + new ColumnPredicates.PredicateFunction[String] { + def functionToApply(input: String): Boolean = input == value + } + )), + predicate) + + def createIntFilter( + columnName: String, + func: Int => Boolean, + predicate: CatalystPredicate): CatalystFilter = + new ComparisonFilter( + columnName, + ColumnRecordFilter.column( + columnName, + ColumnPredicates.applyFunctionToInteger( + new IntegerPredicateFunction { + def functionToApply(input: Int) = func(input) + } + )), + predicate) + + def createLongFilter( + columnName: String, + func: Long => Boolean, + predicate: CatalystPredicate): CatalystFilter = + new ComparisonFilter( + columnName, + ColumnRecordFilter.column( + columnName, + ColumnPredicates.applyFunctionToLong( + new LongPredicateFunction { + def functionToApply(input: Long) = func(input) + } + )), + predicate) + + def createDoubleFilter( + columnName: String, + func: Double => Boolean, + predicate: CatalystPredicate): CatalystFilter = + new ComparisonFilter( + columnName, + ColumnRecordFilter.column( + columnName, + ColumnPredicates.applyFunctionToDouble( + new DoublePredicateFunction { + def functionToApply(input: Double) = func(input) + } + )), + predicate) + + def createFloatFilter( + columnName: String, + func: Float => Boolean, + predicate: CatalystPredicate): CatalystFilter = + new ComparisonFilter( + columnName, + ColumnRecordFilter.column( + columnName, + ColumnPredicates.applyFunctionToFloat( + new FloatPredicateFunction { + def functionToApply(input: Float) = func(input) + } + )), + predicate) +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index f825ca3c028ef..65ba1246fbf9a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -27,26 +27,27 @@ import org.apache.hadoop.mapreduce._ import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat, FileOutputCommitter} -import parquet.hadoop.{ParquetInputFormat, ParquetOutputFormat} +import parquet.hadoop.{ParquetRecordReader, ParquetInputFormat, ParquetOutputFormat} +import parquet.hadoop.api.ReadSupport import parquet.hadoop.util.ContextUtil import parquet.io.InvalidRecordException import parquet.schema.MessageType -import org.apache.spark.{SerializableWritable, SparkContext, TaskContext} +import org.apache.spark.{Logging, SerializableWritable, SparkContext, TaskContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row} import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode} /** * Parquet table scan operator. Imports the file that backs the given - * [[ParquetRelation]] as a RDD[Row]. + * [[org.apache.spark.sql.parquet.ParquetRelation]] as a ``RDD[Row]``. */ case class ParquetTableScan( // note: output cannot be transient, see // https://issues.apache.org/jira/browse/SPARK-1367 output: Seq[Attribute], relation: ParquetRelation, - columnPruningPred: Option[Expression])( + columnPruningPred: Seq[Expression])( @transient val sc: SparkContext) extends LeafNode { @@ -62,18 +63,30 @@ case class ParquetTableScan( for (path <- fileList if !path.getName.startsWith("_")) { NewFileInputFormat.addInputPath(job, path) } + + // Store Parquet schema in `Configuration` conf.set( RowReadSupport.PARQUET_ROW_REQUESTED_SCHEMA, ParquetTypesConverter.convertFromAttributes(output).toString) - // TODO: think about adding record filters - /* Comments regarding record filters: it would be nice to push down as much filtering - to Parquet as possible. However, currently it seems we cannot pass enough information - to materialize an (arbitrary) Catalyst [[Predicate]] inside Parquet's - ``FilteredRecordReader`` (via Configuration, for example). Simple - filter-rows-by-column-values however should be supported. - */ - sc.newAPIHadoopRDD(conf, classOf[ParquetInputFormat[Row]], classOf[Void], classOf[Row]) - .map(_._2) + + // Store record filtering predicate in `Configuration` + // Note 1: the input format ignores all predicates that cannot be expressed + // as simple column predicate filters in Parquet. Here we just record + // the whole pruning predicate. + // Note 2: you can disable filter predicate pushdown by setting + // "spark.sql.hints.parquetFilterPushdown" to false inside SparkConf. + if (columnPruningPred.length > 0 && + sc.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) { + ParquetFilters.serializeFilterExpressions(columnPruningPred, conf) + } + + sc.newAPIHadoopRDD( + conf, + classOf[org.apache.spark.sql.parquet.FilteringParquetRowInputFormat], + classOf[Void], + classOf[Row]) + .map(_._2) + .filter(_ != null) // Parquet's record filters may produce null values } override def otherCopyArgs = sc :: Nil @@ -184,10 +197,19 @@ case class InsertIntoParquetTable( override def otherCopyArgs = sc :: Nil - // based on ``saveAsNewAPIHadoopFile`` in [[PairRDDFunctions]] - // TODO: Maybe PairRDDFunctions should use Product2 instead of Tuple2? - // .. then we could use the default one and could use [[MutablePair]] - // instead of ``Tuple2`` + /** + * Stores the given Row RDD as a Hadoop file. + * + * Note: We cannot use ``saveAsNewAPIHadoopFile`` from [[org.apache.spark.rdd.PairRDDFunctions]] + * together with [[org.apache.spark.util.MutablePair]] because ``PairRDDFunctions`` uses + * ``Tuple2`` and not ``Product2``. Also, we want to allow appending files to an existing + * directory and need to determine which was the largest written file index before starting to + * write. + * + * @param rdd The [[org.apache.spark.rdd.RDD]] to writer + * @param path The directory to write to. + * @param conf A [[org.apache.hadoop.conf.Configuration]]. + */ private def saveAsHadoopFile( rdd: RDD[Row], path: String, @@ -244,8 +266,10 @@ case class InsertIntoParquetTable( } } -// TODO: this will be able to append to directories it created itself, not necessarily -// to imported ones +/** + * TODO: this will be able to append to directories it created itself, not necessarily + * to imported ones. + */ private[parquet] class AppendingParquetOutputFormat(offset: Int) extends parquet.hadoop.ParquetOutputFormat[Row] { // override to accept existing directories as valid output directory @@ -262,6 +286,30 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) } } +/** + * We extend ParquetInputFormat in order to have more control over which + * RecordFilter we want to use. + */ +private[parquet] class FilteringParquetRowInputFormat + extends parquet.hadoop.ParquetInputFormat[Row] with Logging { + override def createRecordReader( + inputSplit: InputSplit, + taskAttemptContext: TaskAttemptContext): RecordReader[Void, Row] = { + val readSupport: ReadSupport[Row] = new RowReadSupport() + + val filterExpressions = + ParquetFilters.deserializeFilterExpressions(ContextUtil.getConfiguration(taskAttemptContext)) + if (filterExpressions.length > 0) { + logInfo(s"Pushing down predicates for RecordFilter: ${filterExpressions.mkString(", ")}") + new ParquetRecordReader[Row]( + readSupport, + ParquetFilters.createRecordFilter(filterExpressions)) + } else { + new ParquetRecordReader[Row](readSupport) + } + } +} + private[parquet] object FileSystemHelper { def listFiles(pathStr: String, conf: Configuration): Seq[Path] = { val origPath = new Path(pathStr) @@ -278,7 +326,9 @@ private[parquet] object FileSystemHelper { fs.listStatus(path).map(_.getPath) } - // finds the maximum taskid in the output file names at the given path + /** + * Finds the maximum taskid in the output file names at the given path. + */ def findMaxTaskId(pathStr: String, conf: Configuration): Int = { val files = FileSystemHelper.listFiles(pathStr, conf) // filename pattern is part-r-.parquet diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala index f37976f7313c1..46c7172985642 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTestData.scala @@ -19,15 +19,34 @@ package org.apache.spark.sql.parquet import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.hadoop.mapreduce.Job +import parquet.example.data.{GroupWriter, Group} +import parquet.example.data.simple.SimpleGroup import parquet.hadoop.ParquetWriter -import parquet.hadoop.util.ContextUtil +import parquet.hadoop.api.WriteSupport +import parquet.hadoop.api.WriteSupport.WriteContext +import parquet.io.api.RecordConsumer import parquet.schema.{MessageType, MessageTypeParser} -import org.apache.spark.sql.catalyst.expressions.GenericRow import org.apache.spark.util.Utils +// Write support class for nested groups: ParquetWriter initializes GroupWriteSupport +// with an empty configuration (it is after all not intended to be used in this way?) +// and members are private so we need to make our own in order to pass the schema +// to the writer. +private class TestGroupWriteSupport(schema: MessageType) extends WriteSupport[Group] { + var groupWriter: GroupWriter = null + override def prepareForWrite(recordConsumer: RecordConsumer): Unit = { + groupWriter = new GroupWriter(recordConsumer, schema) + } + override def init(configuration: Configuration): WriteContext = { + new WriteContext(schema, new java.util.HashMap[String, String]()) + } + override def write(record: Group) { + groupWriter.write(record) + } +} + private[sql] object ParquetTestData { val testSchema = @@ -43,7 +62,7 @@ private[sql] object ParquetTestData { // field names for test assertion error messages val testSchemaFieldNames = Seq( "myboolean:Boolean", - "mtint:Int", + "myint:Int", "mystring:String", "mylong:Long", "myfloat:Float", @@ -58,6 +77,18 @@ private[sql] object ParquetTestData { |} """.stripMargin + val testFilterSchema = + """ + |message myrecord { + |required boolean myboolean; + |required int32 myint; + |required binary mystring; + |required int64 mylong; + |required float myfloat; + |required double mydouble; + |} + """.stripMargin + // field names for test assertion error messages val subTestSchemaFieldNames = Seq( "myboolean:Boolean", @@ -65,36 +96,57 @@ private[sql] object ParquetTestData { ) val testDir = Utils.createTempDir() + val testFilterDir = Utils.createTempDir() lazy val testData = new ParquetRelation(testDir.toURI.toString) def writeFile() = { testDir.delete val path: Path = new Path(new Path(testDir.toURI), new Path("part-r-0.parquet")) - val job = new Job() - val configuration: Configuration = ContextUtil.getConfiguration(job) val schema: MessageType = MessageTypeParser.parseMessageType(testSchema) + val writeSupport = new TestGroupWriteSupport(schema) + val writer = new ParquetWriter[Group](path, writeSupport) - val writeSupport = new RowWriteSupport() - writeSupport.setSchema(schema, configuration) - val writer = new ParquetWriter(path, writeSupport) for(i <- 0 until 15) { - val data = new Array[Any](6) + val record = new SimpleGroup(schema) if (i % 3 == 0) { - data.update(0, true) + record.add(0, true) } else { - data.update(0, false) + record.add(0, false) } if (i % 5 == 0) { - data.update(1, 5) + record.add(1, 5) + } + record.add(2, "abc") + record.add(3, i.toLong << 33) + record.add(4, 2.5F) + record.add(5, 4.5D) + writer.write(record) + } + writer.close() + } + + def writeFilterFile(records: Int = 200) = { + // for microbenchmark use: records = 300000000 + testFilterDir.delete + val path: Path = new Path(new Path(testFilterDir.toURI), new Path("part-r-0.parquet")) + val schema: MessageType = MessageTypeParser.parseMessageType(testFilterSchema) + val writeSupport = new TestGroupWriteSupport(schema) + val writer = new ParquetWriter[Group](path, writeSupport) + + for(i <- 0 to records) { + val record = new SimpleGroup(schema) + if (i % 4 == 0) { + record.add(0, true) } else { - data.update(1, null) // optional + record.add(0, false) } - data.update(2, "abc") - data.update(3, i.toLong << 33) - data.update(4, 2.5F) - data.update(5, 4.5D) - writer.write(new GenericRow(data.toArray)) + record.add(1, i) + record.add(2, i.toString) + record.add(3, i.toLong) + record.add(4, i.toFloat + 0.5f) + record.add(5, i.toDouble + 0.5d) + writer.write(record) } writer.close() } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala index 92a707ea57504..f43e98d614094 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DslQuerySuite.scala @@ -69,12 +69,36 @@ class DslQuerySuite extends QueryTest { checkAnswer( testData2.orderBy('a.desc, 'b.asc), Seq((3,1), (3,2), (2,1), (2,2), (1,1), (1,2))) + + checkAnswer( + arrayData.orderBy(GetItem('data, 0).asc), + arrayData.collect().sortBy(_.data(0)).toSeq) + + checkAnswer( + arrayData.orderBy(GetItem('data, 0).desc), + arrayData.collect().sortBy(_.data(0)).reverse.toSeq) + + checkAnswer( + mapData.orderBy(GetItem('data, 1).asc), + mapData.collect().sortBy(_.data(1)).toSeq) + + checkAnswer( + mapData.orderBy(GetItem('data, 1).desc), + mapData.collect().sortBy(_.data(1)).reverse.toSeq) } test("limit") { checkAnswer( testData.limit(10), testData.take(10).toSeq) + + checkAnswer( + arrayData.limit(1), + arrayData.take(1).toSeq) + + checkAnswer( + mapData.limit(1), + mapData.take(1).toSeq) } test("average") { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala index 73d87963b3a0d..4f0b85f26254b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/InsertIntoSuite.scala @@ -29,6 +29,7 @@ class InsertIntoSuite extends QueryTest { test("insertInto() created parquet file") { val testFilePath = File.createTempFile("sparkSql", "pqt") testFilePath.delete() + testFilePath.deleteOnExit() val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath) testFile.registerAsTable("createAndInsertTest") @@ -76,11 +77,14 @@ class InsertIntoSuite extends QueryTest { sql("SELECT * FROM createAndInsertTest"), testData.collect().toSeq ) + + testFilePath.delete() } test("INSERT INTO parquet table") { val testFilePath = File.createTempFile("sparkSql", "pqt") testFilePath.delete() + testFilePath.deleteOnExit() val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath) testFile.registerAsTable("createAndInsertSQLTest") @@ -126,23 +130,31 @@ class InsertIntoSuite extends QueryTest { sql("SELECT * FROM createAndInsertSQLTest"), testData.collect().toSeq ) + + testFilePath.delete() } test("Double create fails when allowExisting = false") { val testFilePath = File.createTempFile("sparkSql", "pqt") testFilePath.delete() + testFilePath.deleteOnExit() val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath) intercept[RuntimeException] { createParquetFile[TestData](testFilePath.getCanonicalPath, allowExisting = false) } + + testFilePath.delete() } test("Double create does not fail when allowExisting = true") { val testFilePath = File.createTempFile("sparkSql", "pqt") testFilePath.delete() + testFilePath.deleteOnExit() val testFile = createParquetFile[TestData](testFilePath.getCanonicalPath) createParquetFile[TestData](testFilePath.getCanonicalPath, allowExisting = true) + + testFilePath.delete() } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index e966d89c30cf5..189dccd5253e5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -85,6 +85,36 @@ class SQLQuerySuite extends QueryTest { checkAnswer( sql("SELECT * FROM testData2 ORDER BY a DESC, b ASC"), Seq((3,1), (3,2), (2,1), (2,2), (1,1), (1,2))) + + checkAnswer( + sql("SELECT * FROM arrayData ORDER BY data[0] ASC"), + arrayData.collect().sortBy(_.data(0)).toSeq) + + checkAnswer( + sql("SELECT * FROM arrayData ORDER BY data[0] DESC"), + arrayData.collect().sortBy(_.data(0)).reverse.toSeq) + + checkAnswer( + sql("SELECT * FROM mapData ORDER BY data[1] ASC"), + mapData.collect().sortBy(_.data(1)).toSeq) + + checkAnswer( + sql("SELECT * FROM mapData ORDER BY data[1] DESC"), + mapData.collect().sortBy(_.data(1)).reverse.toSeq) + } + + test("limit") { + checkAnswer( + sql("SELECT * FROM testData LIMIT 10"), + testData.take(10).toSeq) + + checkAnswer( + sql("SELECT * FROM arrayData LIMIT 1"), + arrayData.collect().take(1).toSeq) + + checkAnswer( + sql("SELECT * FROM mapData LIMIT 1"), + mapData.collect().take(1).toSeq) } test("average") { @@ -96,8 +126,25 @@ class SQLQuerySuite extends QueryTest { test("count") { checkAnswer( sql("SELECT COUNT(*) FROM testData2"), - testData2.count() - ) + testData2.count()) + } + + test("count distinct") { + checkAnswer( + sql("SELECT COUNT(DISTINCT b) FROM testData2"), + 2) + } + + test("approximate count distinct") { + checkAnswer( + sql("SELECT APPROXIMATE COUNT(DISTINCT a) FROM testData2"), + 3) + } + + test("approximate count distinct with user provided standard deviation") { + checkAnswer( + sql("SELECT APPROXIMATE(0.04) COUNT(DISTINCT a) FROM testData2"), + 3) } // No support for primitive nulls yet. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala index aa71e274f7f4b..1aca3872524d7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala @@ -74,6 +74,16 @@ object TestData { ArrayData(Seq(2,3,4), Seq(Seq(2,3,4))) :: Nil) arrayData.registerAsTable("arrayData") + case class MapData(data: Map[Int, String]) + val mapData = + TestSQLContext.sparkContext.parallelize( + MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) :: + MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) :: + MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) :: + MapData(Map(1 -> "a4", 2 -> "b4")) :: + MapData(Map(1 -> "a5")) :: Nil) + mapData.registerAsTable("mapData") + case class StringData(s: String) val repeatedData = TestSQLContext.sparkContext.parallelize(List.fill(2)(StringData("test"))) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala index e24c74a7a5572..c563d63627544 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala @@ -21,7 +21,7 @@ import org.scalatest.FunSuite import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical +import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.execution import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.test.TestSQLContext.planner._ @@ -57,4 +57,21 @@ class PlannerSuite extends FunSuite { val planned = PartialAggregation(query) assert(planned.isEmpty) } + + test("equi-join is hash-join") { + val x = testData2.as('x) + val y = testData2.as('y) + val join = x.join(y, Inner, Some("x.a".attr === "y.a".attr)).queryExecution.analyzed + val planned = planner.HashJoin(join) + assert(planned.size === 1) + } + + test("multiple-key equi-join is hash-join") { + val x = testData2.as('x) + val y = testData2.as('y) + val join = x.join(y, Inner, + Some("x.a".attr === "y.a".attr && "x.b".attr === "y.b".attr)).queryExecution.analyzed + val planned = planner.HashJoin(join) + assert(planned.size === 1) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala index d9c9b9a076ab9..65f4c17aeee3a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala @@ -17,31 +17,45 @@ package org.apache.spark.sql.parquet -import java.io.File - import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.apache.hadoop.fs.{Path, FileSystem} import org.apache.hadoop.mapreduce.Job import parquet.hadoop.ParquetFileWriter -import parquet.schema.MessageTypeParser import parquet.hadoop.util.ContextUtil +import parquet.schema.MessageTypeParser import org.apache.spark.sql._ import org.apache.spark.sql.catalyst.util.getTempFilePath -import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.TestData +import org.apache.spark.sql.SchemaRDD +import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.catalyst.expressions.Equals +import org.apache.spark.sql.catalyst.types.IntegerType import org.apache.spark.util.Utils -import org.apache.spark.sql.catalyst.types.{StringType, IntegerType, DataType} -import org.apache.spark.sql.{parquet, SchemaRDD} // Implicits import org.apache.spark.sql.test.TestSQLContext._ case class TestRDDEntry(key: Int, value: String) +case class NullReflectData( + intField: java.lang.Integer, + longField: java.lang.Long, + floatField: java.lang.Float, + doubleField: java.lang.Double, + booleanField: java.lang.Boolean) + +case class OptionalReflectData( + intField: Option[Int], + longField: Option[Long], + floatField: Option[Float], + doubleField: Option[Double], + booleanField: Option[Boolean]) + class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll { import TestData._ TestData // Load test data tables. @@ -50,12 +64,16 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll { override def beforeAll() { ParquetTestData.writeFile() + ParquetTestData.writeFilterFile() testRDD = parquetFile(ParquetTestData.testDir.toString) testRDD.registerAsTable("testsource") + parquetFile(ParquetTestData.testFilterDir.toString) + .registerAsTable("testfiltersource") } override def afterAll() { Utils.deleteRecursively(ParquetTestData.testDir) + Utils.deleteRecursively(ParquetTestData.testFilterDir) // here we should also unregister the table?? } @@ -106,7 +124,7 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll { val scanner = new ParquetTableScan( ParquetTestData.testData.output, ParquetTestData.testData, - None)(TestSQLContext.sparkContext) + Seq())(TestSQLContext.sparkContext) val projected = scanner.pruneColumns(ParquetTypesConverter .convertToAttributes(MessageTypeParser .parseMessageType(ParquetTestData.subTestSchema))) @@ -182,7 +200,6 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll { assert(true) } - test("insert (appending) to same table via Scala API") { sql("INSERT INTO testsource SELECT * FROM testsource").collect() val double_rdd = sql("SELECT * FROM testsource").collect() @@ -195,5 +212,151 @@ class ParquetQuerySuite extends QueryTest with FunSuite with BeforeAndAfterAll { Utils.deleteRecursively(ParquetTestData.testDir) ParquetTestData.writeFile() } + + test("save and load case class RDD with nulls as parquet") { + val data = NullReflectData(null, null, null, null, null) + val rdd = sparkContext.parallelize(data :: Nil) + + val file = getTempFilePath("parquet") + val path = file.toString + rdd.saveAsParquetFile(path) + val readFile = parquetFile(path) + + val rdd_saved = readFile.collect() + assert(rdd_saved(0) === Seq.fill(5)(null)) + Utils.deleteRecursively(file) + assert(true) + } + + test("save and load case class RDD with Nones as parquet") { + val data = OptionalReflectData(null, null, null, null, null) + val rdd = sparkContext.parallelize(data :: Nil) + + val file = getTempFilePath("parquet") + val path = file.toString + rdd.saveAsParquetFile(path) + val readFile = parquetFile(path) + + val rdd_saved = readFile.collect() + assert(rdd_saved(0) === Seq.fill(5)(null)) + Utils.deleteRecursively(file) + assert(true) + } + + test("create RecordFilter for simple predicates") { + val attribute1 = new AttributeReference("first", IntegerType, false)() + val predicate1 = new Equals(attribute1, new Literal(1, IntegerType)) + val filter1 = ParquetFilters.createFilter(predicate1) + assert(filter1.isDefined) + assert(filter1.get.predicate == predicate1, "predicates do not match") + assert(filter1.get.isInstanceOf[ComparisonFilter]) + val cmpFilter1 = filter1.get.asInstanceOf[ComparisonFilter] + assert(cmpFilter1.columnName == "first", "column name incorrect") + + val predicate2 = new LessThan(attribute1, new Literal(4, IntegerType)) + val filter2 = ParquetFilters.createFilter(predicate2) + assert(filter2.isDefined) + assert(filter2.get.predicate == predicate2, "predicates do not match") + assert(filter2.get.isInstanceOf[ComparisonFilter]) + val cmpFilter2 = filter2.get.asInstanceOf[ComparisonFilter] + assert(cmpFilter2.columnName == "first", "column name incorrect") + + val predicate3 = new And(predicate1, predicate2) + val filter3 = ParquetFilters.createFilter(predicate3) + assert(filter3.isDefined) + assert(filter3.get.predicate == predicate3, "predicates do not match") + assert(filter3.get.isInstanceOf[AndFilter]) + + val predicate4 = new Or(predicate1, predicate2) + val filter4 = ParquetFilters.createFilter(predicate4) + assert(filter4.isDefined) + assert(filter4.get.predicate == predicate4, "predicates do not match") + assert(filter4.get.isInstanceOf[OrFilter]) + + val attribute2 = new AttributeReference("second", IntegerType, false)() + val predicate5 = new GreaterThan(attribute1, attribute2) + val badfilter = ParquetFilters.createFilter(predicate5) + assert(badfilter.isDefined === false) + } + + test("test filter by predicate pushdown") { + for(myval <- Seq("myint", "mylong", "mydouble", "myfloat")) { + println(s"testing field $myval") + val query1 = sql(s"SELECT * FROM testfiltersource WHERE $myval < 150 AND $myval >= 100") + assert( + query1.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan], + "Top operator should be ParquetTableScan after pushdown") + val result1 = query1.collect() + assert(result1.size === 50) + assert(result1(0)(1) === 100) + assert(result1(49)(1) === 149) + val query2 = sql(s"SELECT * FROM testfiltersource WHERE $myval > 150 AND $myval <= 200") + assert( + query2.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan], + "Top operator should be ParquetTableScan after pushdown") + val result2 = query2.collect() + assert(result2.size === 50) + if (myval == "myint" || myval == "mylong") { + assert(result2(0)(1) === 151) + assert(result2(49)(1) === 200) + } else { + assert(result2(0)(1) === 150) + assert(result2(49)(1) === 199) + } + } + for(myval <- Seq("myint", "mylong")) { + val query3 = sql(s"SELECT * FROM testfiltersource WHERE $myval > 190 OR $myval < 10") + assert( + query3.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan], + "Top operator should be ParquetTableScan after pushdown") + val result3 = query3.collect() + assert(result3.size === 20) + assert(result3(0)(1) === 0) + assert(result3(9)(1) === 9) + assert(result3(10)(1) === 191) + assert(result3(19)(1) === 200) + } + for(myval <- Seq("mydouble", "myfloat")) { + val result4 = + if (myval == "mydouble") { + val query4 = sql(s"SELECT * FROM testfiltersource WHERE $myval > 190.5 OR $myval < 10.0") + assert( + query4.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan], + "Top operator should be ParquetTableScan after pushdown") + query4.collect() + } else { + // CASTs are problematic. Here myfloat will be casted to a double and it seems there is + // currently no way to specify float constants in SqlParser? + sql(s"SELECT * FROM testfiltersource WHERE $myval > 190.5 OR $myval < 10").collect() + } + assert(result4.size === 20) + assert(result4(0)(1) === 0) + assert(result4(9)(1) === 9) + assert(result4(10)(1) === 191) + assert(result4(19)(1) === 200) + } + val query5 = sql(s"SELECT * FROM testfiltersource WHERE myboolean = true AND myint < 40") + assert( + query5.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan], + "Top operator should be ParquetTableScan after pushdown") + val booleanResult = query5.collect() + assert(booleanResult.size === 10) + for(i <- 0 until 10) { + if (!booleanResult(i).getBoolean(0)) { + fail(s"Boolean value in result row $i not true") + } + if (booleanResult(i).getInt(1) != i * 4) { + fail(s"Int value in result row $i should be ${4*i}") + } + } + val query6 = sql("SELECT * FROM testfiltersource WHERE mystring = \"100\"") + assert( + query6.queryExecution.executedPlan(0)(0).isInstanceOf[ParquetTableScan], + "Top operator should be ParquetTableScan after pushdown") + val stringResult = query6.collect() + assert(stringResult.size === 1) + assert(stringResult(0).getString(2) == "100", "stringvalue incorrect") + assert(stringResult(0).getInt(1) === 100) + } } diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 889d249146b8c..9254b70e64a08 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -44,23 +44,44 @@ ${project.version} - org.apache.hive + org.spark-project.hive hive-metastore ${hive.version} - org.apache.hive + org.spark-project.hive hive-exec ${hive.version} + + + commons-logging + commons-logging + + org.codehaus.jackson jackson-mapper-asl - org.apache.hive + org.spark-project.hive hive-serde ${hive.version} + + + commons-logging + commons-logging + + + commons-logging + commons-logging-api + + + + + + org.apache.avro + avro org.scalatest diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index ba837a274c51c..636c4f7b93190 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -26,6 +26,7 @@ import org.apache.hadoop.hive.ql.plan.TableDesc import org.apache.hadoop.hive.ql.session.SessionState import org.apache.hadoop.hive.serde2.Deserializer +import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.Logging import org.apache.spark.sql.catalyst.analysis.{EliminateAnalysisOperators, Catalog} import org.apache.spark.sql.catalyst.expressions._ @@ -172,12 +173,18 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with override def unregisterAllTables() = {} } -private[hive] object HiveMetastoreTypes extends RegexParsers { +/** + * :: DeveloperApi :: + * Provides conversions between Spark SQL data types and Hive Metastore types. + */ +@DeveloperApi +object HiveMetastoreTypes extends RegexParsers { protected lazy val primitiveType: Parser[DataType] = "string" ^^^ StringType | "float" ^^^ FloatType | "int" ^^^ IntegerType | - "tinyint" ^^^ ShortType | + "tinyint" ^^^ ByteType | + "smallint" ^^^ ShortType | "double" ^^^ DoubleType | "bigint" ^^^ LongType | "binary" ^^^ BinaryType | @@ -221,7 +228,8 @@ private[hive] object HiveMetastoreTypes extends RegexParsers { case StringType => "string" case FloatType => "float" case IntegerType => "int" - case ShortType =>"tinyint" + case ByteType => "tinyint" + case ShortType => "smallint" case DoubleType => "double" case LongType => "bigint" case BinaryType => "binary" diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 1f688fe1117fe..93b9057a23816 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -233,6 +233,11 @@ private[hive] object HiveQl { } } catch { case e: Exception => throw new ParseException(sql, e) + case e: NotImplementedError => sys.error( + s""" + |Unsupported language features in query: $sql + |${dumpTree(getAst(sql))} + """.stripMargin) } } @@ -865,6 +870,17 @@ private[hive] object HiveQl { IsNull(nodeToExpr(child)) case Token("TOK_FUNCTION", Token("IN", Nil) :: value :: list) => In(nodeToExpr(value), list.map(nodeToExpr)) + case Token("TOK_FUNCTION", + Token("between", Nil) :: + Token("KW_FALSE", Nil) :: + target :: + minValue :: + maxValue :: Nil) => + + val targetExpression = nodeToExpr(target) + And( + GreaterThanOrEqual(targetExpression, nodeToExpr(minValue)), + LessThanOrEqual(targetExpression, nodeToExpr(maxValue))) /* Boolean Logic */ case Token(AND(), left :: right:: Nil) => And(nodeToExpr(left), nodeToExpr(right)) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala index 3ad66a3d7f45f..fa7d010459c63 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala @@ -99,6 +99,7 @@ class TestHiveContext(sc: SparkContext) extends LocalHiveContext(sc) { val hiveFilesTemp = File.createTempFile("catalystHiveFiles", "") hiveFilesTemp.delete() hiveFilesTemp.mkdir() + hiveFilesTemp.deleteOnExit() val inRepoTests = if (System.getProperty("user.dir").endsWith("sql" + File.separator + "hive")) { new File("src" + File.separator + "test" + File.separator + "resources" + File.separator) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index d50e2c65b7b36..572902042337f 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -248,17 +248,31 @@ private[hive] case class HiveGenericUdf(name: String, children: Seq[Expression]) isUDFDeterministic && children.foldLeft(true)((prev, n) => prev && n.foldable) } + protected lazy val deferedObjects = Array.fill[DeferredObject](children.length)({ + new DeferredObjectAdapter + }) + + // Adapter from Catalyst ExpressionResult to Hive DeferredObject + class DeferredObjectAdapter extends DeferredObject { + private var func: () => Any = _ + def set(func: () => Any) { + this.func = func + } + override def prepare(i: Int) = {} + override def get(): AnyRef = wrap(func()) + } + val dataType: DataType = inspectorToDataType(returnInspector) override def eval(input: Row): Any = { returnInspector // Make sure initialized. - val args = children.map { v => - new DeferredObject { - override def prepare(i: Int) = {} - override def get(): AnyRef = wrap(v.eval(input)) - } - }.toArray - unwrap(function.evaluate(args)) + var i = 0 + while (i < children.length) { + val idx = i + deferedObjects(i).asInstanceOf[DeferredObjectAdapter].set(() => {children(idx).eval(input)}) + i += 1 + } + unwrap(function.evaluate(deferedObjects)) } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java new file mode 100644 index 0000000000000..8b29fa7d1a8f7 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package-info.java @@ -0,0 +1,18 @@ +/* + * 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; \ No newline at end of file diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala new file mode 100644 index 0000000000000..a6c8ed4f7e866 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/package.scala @@ -0,0 +1,20 @@ +/* + * 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 + +package object hive diff --git a/sql/hive/src/test/resources/golden/Column pruning: filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be b/sql/hive/src/test/resources/golden/Column pruning - filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be rename to sql/hive/src/test/resources/golden/Column pruning - filter alias in-lining - query test-0-cb0737d268260da971487a8ac5e3d8be diff --git a/sql/hive/src/test/resources/golden/Column pruning: non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35 b/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35 similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35 rename to sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-c89dcddb8539bae211ea28cd1e695e35 diff --git a/sql/hive/src/test/resources/golden/Column pruning: projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa b/sql/hive/src/test/resources/golden/Column pruning - projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa rename to sql/hive/src/test/resources/golden/Column pruning - projects alias substituting - query test-0-9589ac54d6de575083765ee64051ebaa diff --git a/sql/hive/src/test/resources/golden/Column pruning: simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494 b/sql/hive/src/test/resources/golden/Column pruning - simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494 similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494 rename to sql/hive/src/test/resources/golden/Column pruning - simple top project without aliases - query test-0-d6d2726928df920ab99078015b8ff494 diff --git a/sql/hive/src/test/resources/golden/Column pruning: with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477 b/sql/hive/src/test/resources/golden/Column pruning - with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477 similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477 rename to sql/hive/src/test/resources/golden/Column pruning - with multiple projects - query test-0-cfe78c68302bd143d989c383c3b0477 diff --git a/sql/hive/src/test/resources/golden/Column pruning: with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee b/sql/hive/src/test/resources/golden/Column pruning - with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee rename to sql/hive/src/test/resources/golden/Column pruning - with non-partitioned table - query test-0-358525951e893ed9686c46bf1f7447ee diff --git a/sql/hive/src/test/resources/golden/Column pruning: with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91 b/sql/hive/src/test/resources/golden/Column pruning - with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91 similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91 rename to sql/hive/src/test/resources/golden/Column pruning - with partitioned table - query test-0-d9d50146537344742300ce7b52c18c91 diff --git a/sql/hive/src/test/resources/golden/Column pruning: without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049 b/sql/hive/src/test/resources/golden/Column pruning - without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049 similarity index 100% rename from sql/hive/src/test/resources/golden/Column pruning: without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049 rename to sql/hive/src/test/resources/golden/Column pruning - without filters - query test-0-c25cdafe7d85c42a9d9cd559d88a6049 diff --git a/sql/hive/src/test/resources/golden/Partition pruning: all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7 b/sql/hive/src/test/resources/golden/Partition pruning - all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7 similarity index 100% rename from sql/hive/src/test/resources/golden/Partition pruning: all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7 rename to sql/hive/src/test/resources/golden/Partition pruning - all partitions pruned - query test-0-63ab78355dbc6fa0d7619c8a35b0f7a7 diff --git a/sql/hive/src/test/resources/golden/Partition pruning: left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3 b/sql/hive/src/test/resources/golden/Partition pruning - left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3 similarity index 100% rename from sql/hive/src/test/resources/golden/Partition pruning: left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3 rename to sql/hive/src/test/resources/golden/Partition pruning - left only 1 partition - query test-0-3adc3a7f76b2abd059904ba81a595db3 diff --git a/sql/hive/src/test/resources/golden/Partiton pruning: non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344 b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344 similarity index 100% rename from sql/hive/src/test/resources/golden/Partiton pruning: non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344 rename to sql/hive/src/test/resources/golden/Partition pruning - non-partitioned table - query test-0-681332efaff7b12e3ca12d9f021cd344 diff --git a/sql/hive/src/test/resources/golden/Partition pruning: non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb similarity index 100% rename from sql/hive/src/test/resources/golden/Partition pruning: non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb rename to sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-3893ec7a86062215d9f817f1495a69bb diff --git a/sql/hive/src/test/resources/golden/Partition pruning: pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10 b/sql/hive/src/test/resources/golden/Partition pruning - pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10 similarity index 100% rename from sql/hive/src/test/resources/golden/Partition pruning: pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10 rename to sql/hive/src/test/resources/golden/Partition pruning - pruning with both column key and partition key - query test-0-75148567b91227053f728d72f7dc7c10 diff --git a/sql/hive/src/test/resources/golden/Partition pruning: with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190 b/sql/hive/src/test/resources/golden/Partition pruning - with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190 similarity index 100% rename from sql/hive/src/test/resources/golden/Partition pruning: with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190 rename to sql/hive/src/test/resources/golden/Partition pruning - with filter on int partition key - query test-0-4e4d2651e1e6e3c224242078d0201190 diff --git a/sql/hive/src/test/resources/golden/Partition pruning: with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5 b/sql/hive/src/test/resources/golden/Partition pruning - with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5 similarity index 100% rename from sql/hive/src/test/resources/golden/Partition pruning: with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5 rename to sql/hive/src/test/resources/golden/Partition pruning - with filter on string partition key - query test-0-b6e59464383591f02408f8765ac6a5d5 diff --git a/sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321 b/sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321 new file mode 100644 index 0000000000000..dcd1d8643e3cb --- /dev/null +++ b/sql/hive/src/test/resources/golden/between-0-df3cf89fcf2ef64199a582fae14a3321 @@ -0,0 +1 @@ +2 val_2 diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index d83732b51e9c2..9031abf733cd4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -169,7 +169,10 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "archive_corrupt", // No support for case sensitivity is resolution using hive properties atm. - "case_sensitivity" + "case_sensitivity", + + // Flaky test, Hive sometimes returns different set of 10 rows. + "lateral_view_outer" ) /** @@ -476,7 +479,6 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "join_view", "lateral_view", "lateral_view_cp", - "lateral_view_outer", "lateral_view_ppd", "lineage1", "literal_double", diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index 87a92d83383ab..1a2b2f89182ae 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -24,6 +24,10 @@ import org.apache.spark.sql.hive.test.TestHive._ */ class HiveQuerySuite extends HiveComparisonTest { + createQueryTest("between", + "SELECT * FROM src WHERE key between 1 and 2" + ) + test("Query expressed in SQL") { assert(sql("SELECT 1").collect() === Array(Seq(1))) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala index 25eca397462f7..34434449a0d77 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala @@ -28,7 +28,7 @@ import scala.collection.JavaConversions._ class PruningSuite extends HiveComparisonTest { // Column pruning tests - createPruningTest("Column pruning: with partitioned table", + createPruningTest("Column pruning - with partitioned table", "SELECT key FROM srcpart WHERE ds = '2008-04-08' LIMIT 3", Seq("key"), Seq("key"), @@ -36,43 +36,43 @@ class PruningSuite extends HiveComparisonTest { Seq("2008-04-08", "11"), Seq("2008-04-08", "12"))) - createPruningTest("Column pruning: with non-partitioned table", + createPruningTest("Column pruning - with non-partitioned table", "SELECT key FROM src WHERE key > 10 LIMIT 3", Seq("key"), Seq("key"), Seq.empty) - createPruningTest("Column pruning: with multiple projects", + createPruningTest("Column pruning - with multiple projects", "SELECT c1 FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3", Seq("c1"), Seq("key"), Seq.empty) - createPruningTest("Column pruning: projects alias substituting", + createPruningTest("Column pruning - projects alias substituting", "SELECT c1 AS c2 FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3", Seq("c2"), Seq("key"), Seq.empty) - createPruningTest("Column pruning: filter alias in-lining", + createPruningTest("Column pruning - filter alias in-lining", "SELECT c1 FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 WHERE c1 < 100 LIMIT 3", Seq("c1"), Seq("key"), Seq.empty) - createPruningTest("Column pruning: without filters", + createPruningTest("Column pruning - without filters", "SELECT c1 FROM (SELECT key AS c1 FROM src) t1 LIMIT 3", Seq("c1"), Seq("key"), Seq.empty) - createPruningTest("Column pruning: simple top project without aliases", + createPruningTest("Column pruning - simple top project without aliases", "SELECT key FROM (SELECT key FROM src WHERE key > 10) t1 WHERE key < 100 LIMIT 3", Seq("key"), Seq("key"), Seq.empty) - createPruningTest("Column pruning: non-trivial top project with aliases", + createPruningTest("Column pruning - non-trivial top project with aliases", "SELECT c1 * 2 AS double FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3", Seq("double"), Seq("key"), @@ -80,19 +80,19 @@ class PruningSuite extends HiveComparisonTest { // Partition pruning tests - createPruningTest("Partition pruning: non-partitioned, non-trivial project", + createPruningTest("Partition pruning - non-partitioned, non-trivial project", "SELECT key * 2 AS double FROM src WHERE value IS NOT NULL", Seq("double"), Seq("key", "value"), Seq.empty) - createPruningTest("Partiton pruning: non-partitioned table", + createPruningTest("Partition pruning - non-partitioned table", "SELECT value FROM src WHERE key IS NOT NULL", Seq("value"), Seq("value", "key"), Seq.empty) - createPruningTest("Partition pruning: with filter on string partition key", + createPruningTest("Partition pruning - with filter on string partition key", "SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08'", Seq("value", "hr"), Seq("value", "hr"), @@ -100,7 +100,7 @@ class PruningSuite extends HiveComparisonTest { Seq("2008-04-08", "11"), Seq("2008-04-08", "12"))) - createPruningTest("Partition pruning: with filter on int partition key", + createPruningTest("Partition pruning - with filter on int partition key", "SELECT value, hr FROM srcpart1 WHERE hr < 12", Seq("value", "hr"), Seq("value", "hr"), @@ -108,20 +108,20 @@ class PruningSuite extends HiveComparisonTest { Seq("2008-04-08", "11"), Seq("2008-04-09", "11"))) - createPruningTest("Partition pruning: left only 1 partition", + createPruningTest("Partition pruning - left only 1 partition", "SELECT value, hr FROM srcpart1 WHERE ds = '2008-04-08' AND hr < 12", Seq("value", "hr"), Seq("value", "hr"), Seq( Seq("2008-04-08", "11"))) - createPruningTest("Partition pruning: all partitions pruned", + createPruningTest("Partition pruning - all partitions pruned", "SELECT value, hr FROM srcpart1 WHERE ds = '2014-01-27' AND hr = 11", Seq("value", "hr"), Seq("value", "hr"), Seq.empty) - createPruningTest("Partition pruning: pruning with both column key and partition key", + createPruningTest("Partition pruning - pruning with both column key and partition key", "SELECT value, hr FROM srcpart1 WHERE value IS NOT NULL AND hr < 12", Seq("value", "hr"), Seq("value", "hr"), diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 75a3e9334e6d5..18605cac7006c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -142,12 +142,12 @@ class JavaStreamingContext(val ssc: StreamingContext) { */ def this(path: String, hadoopConf: Configuration) = this(new StreamingContext(path, hadoopConf)) - @deprecated("use sparkContext", "0.9.0") - val sc: JavaSparkContext = sparkContext - /** The underlying SparkContext */ val sparkContext = new JavaSparkContext(ssc.sc) + @deprecated("use sparkContext", "0.9.0") + val sc: JavaSparkContext = sparkContext + /** * Create an input stream from network source hostname:port. Data is received using * a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java new file mode 100644 index 0000000000000..d43d949d76bb3 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Java APIs for spark streaming. + */ +package org.apache.spark.streaming.api.java; \ No newline at end of file diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala new file mode 100644 index 0000000000000..2479670f2a5bb --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/package.scala @@ -0,0 +1,23 @@ +/* + * 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.api + +/** + * Spark streaming's Java API. + */ +package object java diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java new file mode 100644 index 0000000000000..05ca2ddffd3c0 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package-info.java @@ -0,0 +1,21 @@ +/* + * 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. + */ + +/** + * Various implementations of DStreams. + */ +package org.apache.spark.streaming.dstream; \ No newline at end of file diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala new file mode 100644 index 0000000000000..0179b92503e5d --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/package.scala @@ -0,0 +1,24 @@ +/* + * 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 + +/** + * Various implementations of DStream's. + * @see [[org.apache.spark.streaming.dstream.DStream]] + */ +package object dstream diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala index c48a38590e060..b3ed302db6a38 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/MasterFailureTest.scala @@ -21,6 +21,7 @@ import org.apache.spark.Logging import org.apache.spark.rdd.RDD import org.apache.spark.streaming._ import org.apache.spark.streaming.dstream.{DStream, ForEachDStream} +import org.apache.spark.util.Utils import StreamingContext._ import scala.util.Random @@ -380,6 +381,7 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long) override def run() { val localTestDir = Files.createTempDir() + localTestDir.deleteOnExit() var fs = testDir.getFileSystem(new Configuration()) val maxTries = 3 try { @@ -421,6 +423,7 @@ class FileGeneratingThread(input: Seq[String], testDir: Path, interval: Long) case e: Exception => logWarning("File generating in killing thread", e) } finally { fs.close() + Utils.deleteRecursively(localTestDir) } } } diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index f9bfb9b7444cc..4efeb8dfbe1ad 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -17,7 +17,6 @@ package org.apache.spark.streaming; -import org.apache.spark.streaming.api.java.*; import scala.Tuple2; import org.junit.Assert; @@ -37,6 +36,8 @@ import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.*; import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.api.java.*; +import org.apache.spark.util.Utils; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; @@ -54,6 +55,10 @@ public void equalIterable(Iterable a, Iterable b) { equalIterator(a.iterator(), b.iterator()); } + @Test + public void testInitialization() { + Assert.assertNotNull(ssc.sc()); + } @SuppressWarnings("unchecked") @Test @@ -1606,6 +1611,7 @@ public void testCheckpointMasterRecovery() throws InterruptedException { Arrays.asList(8,7)); File tempDir = Files.createTempDir(); + tempDir.deleteOnExit(); ssc.checkpoint(tempDir.getAbsolutePath()); JavaDStream stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1); @@ -1627,6 +1633,7 @@ public Integer call(String s) throws Exception { // will be re-processed after recovery List> finalResult = JavaCheckpointTestUtils.runStreams(ssc, 2, 3); assertOrderInvariantEquals(expectedFinal, finalResult.subList(1, 3)); + Utils.deleteRecursively(tempDir); } diff --git a/streaming/src/test/resources/log4j.properties b/streaming/src/test/resources/log4j.properties index 063529a9cbc67..45d2ec676df66 100644 --- a/streaming/src/test/resources/log4j.properties +++ b/streaming/src/test/resources/log4j.properties @@ -20,7 +20,7 @@ log4j.rootCategory=INFO, file # log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file=org.apache.log4j.FileAppender log4j.appender.file.append=false -log4j.appender.file.file=streaming/target/unit-tests.log +log4j.appender.file.file=target/unit-tests.log log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 25739956cb889..d20a7b728c741 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -232,6 +232,7 @@ class CheckpointSuite extends TestSuiteBase { test("recovery with file input stream") { // Set up the streaming context and input streams val testDir = Files.createTempDir() + testDir.deleteOnExit() var ssc = new StreamingContext(master, framework, Seconds(1)) ssc.checkpoint(checkpointDir) val fileStream = ssc.textFileStream(testDir.toString) @@ -326,6 +327,7 @@ class CheckpointSuite extends TestSuiteBase { ) // To ensure that all the inputs were received correctly assert(expectedOutput.last === output.last) + Utils.deleteRecursively(testDir) } 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 3fa254065cc44..cd0aa4d0dce70 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -98,6 +98,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { // Set up the streaming context and input streams val testDir = Files.createTempDir() + testDir.deleteOnExit() val ssc = new StreamingContext(conf, batchDuration) val fileStream = ssc.textFileStream(testDir.toString) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] diff --git a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala index 27a518ccda459..aeb3f0062df3b 100644 --- a/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala +++ b/yarn/common/src/main/scala/org/apache/spark/deploy/yarn/ClientBase.scala @@ -44,7 +44,7 @@ import org.apache.spark.{Logging, SparkConf, SparkContext} * Client submits an application to the YARN ResourceManager. * * Depending on the deployment mode this will launch one of two application master classes: - * 1. In standalone mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]] + * 1. In cluster mode, it will launch an [[org.apache.spark.deploy.yarn.ApplicationMaster]] * which launches a driver program inside of the cluster. * 2. In client mode, it will launch an [[org.apache.spark.deploy.yarn.ExecutorLauncher]] to * request executors on behalf of a driver running outside of the cluster. @@ -220,10 +220,11 @@ trait ClientBase extends Logging { } } + var cachedSecondaryJarLinks = ListBuffer.empty[String] val fileLists = List( (args.addJars, LocalResourceType.FILE, true), (args.files, LocalResourceType.FILE, false), (args.archives, LocalResourceType.ARCHIVE, false) ) - fileLists.foreach { case (flist, resType, appMasterOnly) => + fileLists.foreach { case (flist, resType, addToClasspath) => if (flist != null && !flist.isEmpty()) { flist.split(',').foreach { case file: String => val localURI = new URI(file.trim()) @@ -232,11 +233,15 @@ trait ClientBase extends Logging { val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) distCacheMgr.addResource(fs, conf, destPath, localResources, resType, - linkname, statCache, appMasterOnly) + linkname, statCache) + if (addToClasspath) { + cachedSecondaryJarLinks += linkname + } } } } } + sparkConf.set(ClientBase.CONF_SPARK_YARN_SECONDARY_JARS, cachedSecondaryJarLinks.mkString(",")) UserGroupInformation.getCurrentUser().addCredentials(credentials) localResources @@ -374,11 +379,12 @@ trait ClientBase extends Logging { } object ClientBase { - val SPARK_JAR: String = "spark.jar" - val APP_JAR: String = "app.jar" + val SPARK_JAR: String = "__spark__.jar" + val APP_JAR: String = "__app__.jar" val LOG4J_PROP: String = "log4j.properties" val LOG4J_CONF_ENV_KEY: String = "SPARK_LOG4J_CONF" val LOCAL_SCHEME = "local" + val CONF_SPARK_YARN_SECONDARY_JARS = "spark.yarn.secondary.jars" def getSparkJar = sys.env.get("SPARK_JAR").getOrElse(SparkContext.jarOfClass(this.getClass).head) @@ -479,66 +485,25 @@ object ClientBase { extraClassPath.foreach(addClasspathEntry) - addClasspathEntry(Environment.PWD.$()) + val cachedSecondaryJarLinks = + sparkConf.getOption(CONF_SPARK_YARN_SECONDARY_JARS).getOrElse("").split(",") // Normally the users app.jar is last in case conflicts with spark jars if (sparkConf.get("spark.yarn.user.classpath.first", "false").toBoolean) { addPwdClasspathEntry(APP_JAR) + cachedSecondaryJarLinks.foreach(addPwdClasspathEntry) addPwdClasspathEntry(SPARK_JAR) ClientBase.populateHadoopClasspath(conf, env) } else { addPwdClasspathEntry(SPARK_JAR) ClientBase.populateHadoopClasspath(conf, env) addPwdClasspathEntry(APP_JAR) + cachedSecondaryJarLinks.foreach(addPwdClasspathEntry) } + // Append all class files and jar files under the working directory to the classpath. + addClasspathEntry(Environment.PWD.$()) addPwdClasspathEntry("*") } - /** - * Adds the user jars which have local: URIs (or alternate names, such as APP_JAR) explicitly - * to the classpath. - */ - private def addUserClasspath(args: ClientArguments, env: HashMap[String, String]) = { - if (args != null) { - addClasspathEntry(args.userJar, APP_JAR, env) - } - - if (args != null && args.addJars != null) { - args.addJars.split(",").foreach { case file: String => - addClasspathEntry(file, null, env) - } - } - } - - /** - * Adds the given path to the classpath, handling "local:" URIs correctly. - * - * If an alternate name for the file is given, and it's not a "local:" file, the alternate - * name will be added to the classpath (relative to the job's work directory). - * - * If not a "local:" file and no alternate name, the environment is not modified. - * - * @param path Path to add to classpath (optional). - * @param fileName Alternate name for the file (optional). - * @param env Map holding the environment variables. - */ - private def addClasspathEntry(path: String, fileName: String, - env: HashMap[String, String]) : Unit = { - if (path != null) { - scala.util.control.Exception.ignoring(classOf[URISyntaxException]) { - val localPath = getLocalPath(path) - if (localPath != null) { - YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, localPath, - File.pathSeparator) - return - } - } - } - if (fileName != null) { - YarnSparkHadoopUtil.addToEnvironment(env, Environment.CLASSPATH.name, - Environment.PWD.$() + Path.SEPARATOR + fileName, File.pathSeparator); - } - } - /** * Returns the local path if the URI is a "local:" URI, or null otherwise. */ diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 2924189077b7d..e01ed5a57d697 100644 --- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -52,7 +52,7 @@ private[spark] class YarnClientSchedulerBackend( val argsArrayBuf = new ArrayBuffer[String]() argsArrayBuf += ( "--class", "notused", - "--jar", null, + "--jar", null, // The primary jar will be added dynamically in SparkContext. "--args", hostport, "--am-class", classOf[ExecutorLauncher].getName ) @@ -113,7 +113,7 @@ private[spark] class YarnClientSchedulerBackend( override def stop() { super.stop() client.stop() - logInfo("Stoped") + logInfo("Stopped") } }