| Commit message (Collapse) | Author | Age | Files | Lines |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
(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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
https://spark-project.atlassian.net/browse/SPARK-1090
spark-shell should print help information about parameters and should allow user to configure exe memory
there is no document about hot to set --cores/-c in spark-shell
and also
users should be able to set executor memory through command line options
In this PR I also check the format of the options passed by the user
Author: CodingCat <zhunansjtu@gmail.com>
Closes #599 from CodingCat/spark_shell_improve and squashes the following commits:
de5aa38 [CodingCat] add parameter to set driver memory
915cbf8 [CodingCat] improvement on spark_shell (help information, configure memory)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
Fixed wrong path to compute-classpath.cmd
compute-classpath.cmd is in bin, not in sbin directory
Author: Stevo Slavić <sslavic@gmail.com>
== Merge branch commits ==
commit 23deca32b69e9429b33ad31d35b7e1bfc9459f59
Author: Stevo Slavić <sslavic@gmail.com>
Date: Tue Feb 4 15:01:47 2014 +0100
Fixed wrong path to compute-classpath.cmd
compute-classpath.cmd is in bin, not in sbin directory
|
|\
| |
| |
| |
| |
| |
| |
| |
| |
| | |
Made run-example respect SPARK_JAVA_OPTS and SPARK_MEM.
bin/run-example scripts was not passing Java properties set through the SPARK_JAVA_OPTS to the example. This is important for examples like Twitter** as the Twitter authentication information must be set through java properties. Hence added the same JAVA_OPTS code in run-example as it is in bin/spark-class script.
Also added SPARK_MEM, in case someone wants to run the example with different amounts of memory. This can be removed if it is not tune with the intended semantics of the run-example scripts.
@matei Please check this soon I want this to go in 0.9-rc4
|
| | |
|
| | |
|
|\ \
| |/
|/|
| |
| |
| |
| | |
SPARK-1028 : fix "set MASTER automatically fails" bug.
spark-shell intends to set MASTER automatically if we do not provide the option when we start the shell , but there's a problem.
The condition is "if [[ "x" != "x$SPARK_MASTER_IP" && "y" != "y$SPARK_MASTER_PORT" ]];" we sure will set SPARK_MASTER_IP explicitly, the SPARK_MASTER_PORT option, however, we probably do not set just using spark default port 7077. So if we do not set SPARK_MASTER_PORT, the condition will never be true. We should just use default port if users do not set port explicitly I think.
|
| | |
|
| |
| |
| |
| | |
spark-shell intends to set MASTER automatically if we do not provide the option when we start the shell , but there's a problem.
The condition is "if [[ "x" != "x$SPARK_MASTER_IP" && "y" != "y$SPARK_MASTER_PORT" ]];" we sure will set SPARK_MASTER_IP explicitly, the SPARK_MASTER_PORT option, however, we probably do not set just using spark default port 7077. So if we do not set SPARK_MASTER_PORT, the condition will never be true. We should just use default port if users do not set port explicitly I think.
|
|/
|
|
| |
JIRA SPARK-1029:https://spark-project.atlassian.net/browse/SPARK-1029
|
|\ |
|
| |\
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
Simplify and fix pyspark script.
This patch removes compatibility for IPython < 1.0 but fixes the launch
script and makes it much simpler.
I tested this using the three commands in the PySpark documentation page:
1. IPYTHON=1 ./pyspark
2. IPYTHON_OPTS="notebook" ./pyspark
3. IPYTHON_OPTS="notebook --pylab inline" ./pyspark
There are two changes:
- We rely on PYTHONSTARTUP env var to start PySpark
- Removed the quotes around $IPYTHON_OPTS... having quotes
gloms them together as a single argument passed to `exec` which
seemed to cause ipython to fail (it instead expects them as
multiple arguments).
|
| | | |
|
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
This patch removes compatibility for IPython < 1.0 but fixes the launch
script and makes it much simpler.
I tested this using the three commands in the PySpark documentation page:
1. IPYTHON=1 ./pyspark
2. IPYTHON_OPTS="notebook" ./pyspark
3. IPYTHON_OPTS="notebook --pylab inline" ./pyspark
There are two changes:
- We rely on PYTHONSTARTUP env var to start PySpark
- Removed the quotes around $IPYTHON_OPTS... having quotes
gloms them together as a single argument passed to `exec` which
seemed to cause ipython to fail (it instead expects them as
multiple arguments).
|
| | | |
|
| | | |
|
|\| |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
Conflicts:
README.md
core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala
core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala
core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala
pom.xml
project/SparkBuild.scala
repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
|
| |\ \
| | |/
| |/|
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
Refactored the streaming project to separate external libraries like Twitter, Kafka, Flume, etc.
At a high level, these are the following changes.
1. All the external code was put in `SPARK_HOME/external/` as separate SBT projects and Maven modules. Their artifact names are `spark-streaming-twitter`, `spark-streaming-kafka`, etc. Both SparkBuild.scala and pom.xml files have been updated. References to external libraries and repositories have been removed from the settings of root and streaming projects/modules.
2. To avail the external functionality (say, creating a Twitter stream), the developer has to `import org.apache.spark.streaming.twitter._` . For Scala API, the developer has to call `TwitterUtils.createStream(streamingContext, ...)`. For the Java API, the developer has to call `TwitterUtils.createStream(javaStreamingContext, ...)`.
3. Each external project has its own scala and java unit tests. Note the unit tests of each external library use classes of the streaming unit tests (`TestSuiteBase`, `LocalJavaStreamingContext`, etc.). To enable this code sharing among test classes, `dependsOn(streaming % "compile->compile,test->test")` was used in the SparkBuild.scala . In the streaming/pom.xml, an additional `maven-jar-plugin` was necessary to capture this dependency (see comment inside the pom.xml for more information).
4. Jars of the external projects have been added to examples project but not to the assembly project.
5. In some files, imports have been rearrange to conform to the Spark coding guidelines.
|
| | | |
|
| | | |
|
| |/ |
|
| |\
| | |
| | |
| | |
| | |
| | | |
Conflicts:
core/src/test/scala/org/apache/spark/DriverSuite.scala
docs/python-programming-guide.md
|
| | | |
|
| | | |
|
| | | |
|
| |/|
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | | |
spark-915-segregate-scripts
Conflicts:
bin/spark-shell
core/pom.xml
core/src/main/scala/org/apache/spark/SparkContext.scala
core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala
core/src/test/scala/org/apache/spark/DriverSuite.scala
python/run-tests
sbin/compute-classpath.sh
sbin/spark-class
sbin/stop-slaves.sh
|
| | | |
|
| | | |
|
| | |
| | |
| | |
| | |
| | |
| | | |
later if needed
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
|
| | |
| | |
| | |
| | |
| | |
| | | |
instead of SPARK_MEM, user should add application jars to SPARK_CLASSPATH
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
|
| | |
| | |
| | |
| | | |
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
|
| | |
| | |
| | |
| | | |
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
|
| | |
| | |
| | |
| | | |
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
|
| | |
| | |
| | |
| | | |
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
|
| |\ \ |
|
| | | | |
|
| |\ \ \
| | | |/
| | |/| |
|
| |\ \ \
| | | | |
| | | | |
| | | | |
| | | | |
| | | | |
| | | | |
| | | | | |
Conflicts:
core/src/main/scala/spark/Utils.scala
core/src/test/scala/spark/ui/UISuite.scala
project/SparkBuild.scala
run
|
| |\ \ \ \
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | |
| | | | | | |
Conflicts:
README.md
core/pom.xml
core/src/main/scala/spark/deploy/JsonProtocol.scala
core/src/main/scala/spark/deploy/LocalSparkCluster.scala
core/src/main/scala/spark/deploy/master/Master.scala
core/src/main/scala/spark/deploy/master/MasterWebUI.scala
core/src/main/scala/spark/deploy/worker/Worker.scala
core/src/main/scala/spark/deploy/worker/WorkerWebUI.scala
core/src/main/scala/spark/storage/BlockManagerUI.scala
core/src/main/scala/spark/util/AkkaUtils.scala
pom.xml
project/SparkBuild.scala
streaming/src/main/scala/spark/streaming/receivers/ActorReceiver.scala
|
| | | | | | |
|
| | | | | | |
|
|\ \ \ \ \ \
| | |_|_|_|/
| |/| | | |
| | | | | |
| | | | | | |
Conflicts:
project/SparkBuild.scala
|
| |\ \ \ \ \
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | |
| | | | | | | |
Add SBT target to assemble dependencies
This pull request is an attempt to address the long assembly build times during development. Instead of rebuilding the assembly jar for every Spark change, this pull request adds a new SBT target `spark` that packages all the Spark modules and builds an assembly of the dependencies.
So the work flow that should work now would be something like
```
./sbt/sbt spark # Doing this once should suffice
## Make changes
./sbt/sbt compile
./sbt/sbt test or ./spark-shell
```
|
| | | | | | | |
|
| | |\ \ \ \ \
| | | | | | | |
| | | | | | | |
| | | | | | | | |
sbt-assembly-deps
|
| | | |_|_|_|/
| | |/| | | | |
|
|\| | | | | |
| | | | | | |
| | | | | | |
| | | | | | | |
indexedrdd_graphx
|
| | | | | | | |
|
| | | | | | | |
|
| | | | | | | |
|