spark-instrumented-optimizer/bin/spark-class2.cmd

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@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 SCALA_VERSION=2.10
rem Figure out where the Spark framework is installed
set FWDIR=%~dp0..\
rem Export this as SPARK_HOME
set SPARK_HOME=%FWDIR%
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 Test that an argument was given
if not "x%1"=="x" goto arg_given
echo Usage: spark-class ^<class^> [^<args^>]
goto exit
:arg_given
SPARK-929: Fully deprecate usage of SPARK_MEM (Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615) This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables: SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public. SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property. SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory. Other memory considerations: - The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY. - run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class). This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however. Author: Aaron Davidson <aaron@databricks.com> Closes #99 from aarondav/sparkmem and squashes the following commits: 9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
2014-03-09 14:08:39 -04:00
if not "x%SPARK_MEM%"=="x" (
echo Warning: SPARK_MEM is deprecated, please use a more specific config option
echo e.g., spark.executor.memory or SPARK_DRIVER_MEMORY.
)
rem Use SPARK_MEM or 512m as the default memory, to be overridden by specific options
set OUR_JAVA_MEM=%SPARK_MEM%
if "x%OUR_JAVA_MEM%"=="x" set OUR_JAVA_MEM=512m
set SPARK_DAEMON_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% -Dspark.akka.logLifecycleEvents=true
[SPARK-1276] Add a HistoryServer to render persisted UI The new feature of event logging, introduced in #42, allows the user to persist the details of his/her Spark application to storage, and later replay these events to reconstruct an after-the-fact SparkUI. Currently, however, a persisted UI can only be rendered through the standalone Master. This greatly limits the use case of this new feature as many people also run Spark on Yarn / Mesos. This PR introduces a new entity called the HistoryServer, which, given a log directory, keeps track of all completed applications independently of a Spark Master. Unlike Master, the HistoryServer needs not be running while the application is still running. It is relatively light-weight in that it only maintains static information of applications and performs no scheduling. To quickly test it out, generate event logs with ```spark.eventLog.enabled=true``` and run ```sbin/start-history-server.sh <log-dir-path>```. Your HistoryServer awaits on port 18080. Comments and feedback are most welcome. --- A few other changes introduced in this PR include refactoring the WebUI interface, which is beginning to have a lot of duplicate code now that we have added more functionality to it. Two new SparkListenerEvents have been introduced (SparkListenerApplicationStart/End) to keep track of application name and start/finish times. This PR also clarifies the semantics of the ReplayListenerBus introduced in #42. A potential TODO in the future (not part of this PR) is to render live applications in addition to just completed applications. This is useful when applications fail, a condition that our current HistoryServer does not handle unless the user manually signals application completion (by creating the APPLICATION_COMPLETION file). Handling live applications becomes significantly more challenging, however, because it is now necessary to render the same SparkUI multiple times. To avoid reading the entire log every time, which is inefficient, we must handle reading the log from where we previously left off, but this becomes fairly complicated because we must deal with the arbitrary behavior of each input stream. Author: Andrew Or <andrewor14@gmail.com> Closes #204 from andrewor14/master and squashes the following commits: 7b7234c [Andrew Or] Finished -> Completed b158d98 [Andrew Or] Address Patrick's comments 69d1b41 [Andrew Or] Do not block on posting SparkListenerApplicationEnd 19d5dd0 [Andrew Or] Merge github.com:apache/spark f7f5bf0 [Andrew Or] Make history server's web UI port a Spark configuration 2dfb494 [Andrew Or] Decouple checking for application completion from replaying d02dbaa [Andrew Or] Expose Spark version and include it in event logs 2282300 [Andrew Or] Add documentation for the HistoryServer 567474a [Andrew Or] Merge github.com:apache/spark 6edf052 [Andrew Or] Merge github.com:apache/spark 19e1fb4 [Andrew Or] Address Thomas' comments 248cb3d [Andrew Or] Limit number of live applications + add configurability a3598de [Andrew Or] Do not close file system with ReplayBus + fix bind address bc46fc8 [Andrew Or] Merge github.com:apache/spark e2f4ff9 [Andrew Or] Merge github.com:apache/spark 050419e [Andrew Or] Merge github.com:apache/spark 81b568b [Andrew Or] Fix strange error messages... 0670743 [Andrew Or] Decouple page rendering from loading files from disk 1b2f391 [Andrew Or] Minor changes a9eae7e [Andrew Or] Merge branch 'master' of github.com:apache/spark d5154da [Andrew Or] Styling and comments 5dbfbb4 [Andrew Or] Merge branch 'master' of github.com:apache/spark 60bc6d5 [Andrew Or] First complete implementation of HistoryServer (only for finished apps) 7584418 [Andrew Or] Report application start/end times to HistoryServer 8aac163 [Andrew Or] Add basic application table c086bd5 [Andrew Or] Add HistoryServer and scripts ++ Refactor WebUI interface
2014-04-10 13:39:34 -04:00
rem Add java opts and memory settings for master, worker, history server, executors, and repl.
rem Master, Worker and HistoryServer use SPARK_DAEMON_JAVA_OPTS (and specific opts) + SPARK_DAEMON_MEMORY.
SPARK-929: Fully deprecate usage of SPARK_MEM (Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615) This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables: SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public. SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property. SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory. Other memory considerations: - The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY. - run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class). This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however. Author: Aaron Davidson <aaron@databricks.com> Closes #99 from aarondav/sparkmem and squashes the following commits: 9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
2014-03-09 14:08:39 -04:00
if "%1"=="org.apache.spark.deploy.master.Master" (
set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_MASTER_OPTS%
if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY%
) else if "%1"=="org.apache.spark.deploy.worker.Worker" (
set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_WORKER_OPTS%
if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY%
[SPARK-1276] Add a HistoryServer to render persisted UI The new feature of event logging, introduced in #42, allows the user to persist the details of his/her Spark application to storage, and later replay these events to reconstruct an after-the-fact SparkUI. Currently, however, a persisted UI can only be rendered through the standalone Master. This greatly limits the use case of this new feature as many people also run Spark on Yarn / Mesos. This PR introduces a new entity called the HistoryServer, which, given a log directory, keeps track of all completed applications independently of a Spark Master. Unlike Master, the HistoryServer needs not be running while the application is still running. It is relatively light-weight in that it only maintains static information of applications and performs no scheduling. To quickly test it out, generate event logs with ```spark.eventLog.enabled=true``` and run ```sbin/start-history-server.sh <log-dir-path>```. Your HistoryServer awaits on port 18080. Comments and feedback are most welcome. --- A few other changes introduced in this PR include refactoring the WebUI interface, which is beginning to have a lot of duplicate code now that we have added more functionality to it. Two new SparkListenerEvents have been introduced (SparkListenerApplicationStart/End) to keep track of application name and start/finish times. This PR also clarifies the semantics of the ReplayListenerBus introduced in #42. A potential TODO in the future (not part of this PR) is to render live applications in addition to just completed applications. This is useful when applications fail, a condition that our current HistoryServer does not handle unless the user manually signals application completion (by creating the APPLICATION_COMPLETION file). Handling live applications becomes significantly more challenging, however, because it is now necessary to render the same SparkUI multiple times. To avoid reading the entire log every time, which is inefficient, we must handle reading the log from where we previously left off, but this becomes fairly complicated because we must deal with the arbitrary behavior of each input stream. Author: Andrew Or <andrewor14@gmail.com> Closes #204 from andrewor14/master and squashes the following commits: 7b7234c [Andrew Or] Finished -> Completed b158d98 [Andrew Or] Address Patrick's comments 69d1b41 [Andrew Or] Do not block on posting SparkListenerApplicationEnd 19d5dd0 [Andrew Or] Merge github.com:apache/spark f7f5bf0 [Andrew Or] Make history server's web UI port a Spark configuration 2dfb494 [Andrew Or] Decouple checking for application completion from replaying d02dbaa [Andrew Or] Expose Spark version and include it in event logs 2282300 [Andrew Or] Add documentation for the HistoryServer 567474a [Andrew Or] Merge github.com:apache/spark 6edf052 [Andrew Or] Merge github.com:apache/spark 19e1fb4 [Andrew Or] Address Thomas' comments 248cb3d [Andrew Or] Limit number of live applications + add configurability a3598de [Andrew Or] Do not close file system with ReplayBus + fix bind address bc46fc8 [Andrew Or] Merge github.com:apache/spark e2f4ff9 [Andrew Or] Merge github.com:apache/spark 050419e [Andrew Or] Merge github.com:apache/spark 81b568b [Andrew Or] Fix strange error messages... 0670743 [Andrew Or] Decouple page rendering from loading files from disk 1b2f391 [Andrew Or] Minor changes a9eae7e [Andrew Or] Merge branch 'master' of github.com:apache/spark d5154da [Andrew Or] Styling and comments 5dbfbb4 [Andrew Or] Merge branch 'master' of github.com:apache/spark 60bc6d5 [Andrew Or] First complete implementation of HistoryServer (only for finished apps) 7584418 [Andrew Or] Report application start/end times to HistoryServer 8aac163 [Andrew Or] Add basic application table c086bd5 [Andrew Or] Add HistoryServer and scripts ++ Refactor WebUI interface
2014-04-10 13:39:34 -04:00
) else if "%1"=="org.apache.spark.deploy.history.HistoryServer" (
set OUR_JAVA_OPTS=%SPARK_DAEMON_JAVA_OPTS% %SPARK_HISTORY_OPTS%
if not "x%SPARK_DAEMON_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DAEMON_MEMORY%
SPARK-929: Fully deprecate usage of SPARK_MEM (Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615) This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables: SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public. SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property. SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory. Other memory considerations: - The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY. - run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class). This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however. Author: Aaron Davidson <aaron@databricks.com> Closes #99 from aarondav/sparkmem and squashes the following commits: 9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
2014-03-09 14:08:39 -04:00
rem Executors use SPARK_JAVA_OPTS + SPARK_EXECUTOR_MEMORY.
) else if "%1"=="org.apache.spark.executor.CoarseGrainedExecutorBackend" (
set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_EXECUTOR_OPTS%
if not "x%SPARK_EXECUTOR_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_EXECUTOR_MEMORY%
) else if "%1"=="org.apache.spark.executor.MesosExecutorBackend" (
set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_EXECUTOR_OPTS%
if not "x%SPARK_EXECUTOR_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_EXECUTOR_MEMORY%
rem All drivers use SPARK_JAVA_OPTS + SPARK_DRIVER_MEMORY. The repl also uses SPARK_REPL_OPTS.
) else if "%1"=="org.apache.spark.repl.Main" (
set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS% %SPARK_REPL_OPTS%
if not "x%SPARK_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DRIVER_MEMORY%
) else (
set OUR_JAVA_OPTS=%SPARK_JAVA_OPTS%
if not "x%SPARK_DRIVER_MEMORY%"=="x" set OUR_JAVA_MEM=%SPARK_DRIVER_MEMORY%
)
rem Set JAVA_OPTS to be able to load native libraries and to set heap size
SPARK-929: Fully deprecate usage of SPARK_MEM (Continued from old repo, prior discussion at https://github.com/apache/incubator-spark/pull/615) This patch cements our deprecation of the SPARK_MEM environment variable by replacing it with three more specialized variables: SPARK_DAEMON_MEMORY, SPARK_EXECUTOR_MEMORY, and SPARK_DRIVER_MEMORY The creation of the latter two variables means that we can safely set driver/job memory without accidentally setting the executor memory. Neither is public. SPARK_EXECUTOR_MEMORY is only used by the Mesos scheduler (and set within SparkContext). The proper way of configuring executor memory is through the "spark.executor.memory" property. SPARK_DRIVER_MEMORY is the new way of specifying the amount of memory run by jobs launched by spark-class, without possibly affecting executor memory. Other memory considerations: - The repl's memory can be set through the "--drivermem" command-line option, which really just sets SPARK_DRIVER_MEMORY. - run-example doesn't use spark-class, so the only way to modify examples' memory is actually an unusual use of SPARK_JAVA_OPTS (which is normally overriden in all cases by spark-class). This patch also fixes a lurking bug where spark-shell misused spark-class (the first argument is supposed to be the main class name, not java options), as well as a bug in the Windows spark-class2.cmd. I have not yet tested this patch on either Windows or Mesos, however. Author: Aaron Davidson <aaron@databricks.com> Closes #99 from aarondav/sparkmem and squashes the following commits: 9df4c68 [Aaron Davidson] SPARK-929: Fully deprecate usage of SPARK_MEM
2014-03-09 14:08:39 -04:00
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!
rem Test whether the user has built Spark
if exist "%FWDIR%RELEASE" goto skip_build_test
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" (
echo Failed to find Spark assembly JAR.
echo You need to build Spark with sbt\sbt assembly before running this program.
goto exit
)
:skip_build_test
set TOOLS_DIR=%FWDIR%tools
set SPARK_TOOLS_JAR=
for %%d in ("%TOOLS_DIR%\target\scala-%SCALA_VERSION%\spark-tools*assembly*.jar") do (
set SPARK_TOOLS_JAR=%%d
)
rem Compute classpath using external script
set DONT_PRINT_CLASSPATH=1
call "%FWDIR%bin\compute-classpath.cmd"
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set DONT_PRINT_CLASSPATH=0
2013-12-18 12:16:04 -05:00
set CLASSPATH=%CLASSPATH%;%SPARK_TOOLS_JAR%
rem Figure out where java is.
set RUNNER=java
if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java
"%RUNNER%" -cp "%CLASSPATH%" %JAVA_OPTS% %*
:exit