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The changes were to support a custom banner in spark-shell for use by
graphx-shell, but once GraphX is merged into Spark, a separate shell
will be unnecessary.
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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
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Added ‘-i’ command line option to Spark REPL
We had to create a new implementation of both scala.tools.nsc.CompilerCommand and scala.tools.nsc.Settings, because using scala.tools.nsc.GenericRunnerSettings would bring in other options (-howtorun, -save and -execute) which don’t make sense in Spark.
Any new Spark specific command line option could now be added to org.apache.spark.repl.SparkRunnerSettings class.
Since the behavior of loading a script from the command line should be the same as loading it using the “:load” command inside the shell, the script should be loaded when the SparkContext is available, that’s why we had to move the call to ‘loadfiles(settings)’ _after_ the call to postInitialization(). This still doesn’t work if ‘isAsync = true’.
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We had to create a new implementation of both scala.tools.nsc.CompilerCommand and scala.tools.nsc.Settings, because using scala.tools.nsc.GenericRunnerSettings would bring in other options (-howtorun, -save and -execute) which don’t make sense in Spark.
Any new Spark specific command line option could now be added to org.apache.spark.repl.SparkRunnerSettings class.
Since the behavior of loading a script from the command line should be the same as loading it using the “:load” command inside the shell, the script should be loaded when the SparkContext is available, that’s why we had to move the call to ‘loadfiles(settings)’ _after_ the call to postInitialization(). This still doesn’t work if ‘isAsync = true’.
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Conflicts:
core/src/test/scala/org/apache/spark/DriverSuite.scala
docs/python-programming-guide.md
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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
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Also replaced SparkConf.getOrElse with just a "get" that takes a default
value, and added getInt, getLong, etc to make code that uses this
simpler later on.
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- Got rid of global SparkContext.globalConf
- Pass SparkConf to serializers and compression codecs
- Made SparkConf public instead of private[spark]
- Improved API of SparkContext and SparkConf
- Switched executor environment vars to be passed through SparkConf
- Fixed some places that were still using system properties
- Fixed some tests, though others are still failing
This still fails several tests in core, repl and streaming, likely due
to properties not being set or cleared correctly (some of the tests run
fine in isolation).
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build/dep-resolution atleast a bit faster.
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I've diff'd this patch against my own -- since they were both created
independently, this means that two sets of eyes have gone over all the
merge conflicts that were created, so I'm feeling significantly more
confident in the resulting PR.
@rxin has looked at the changes to the repl and is resoundingly
confident that they are correct.
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Conflicts:
bagel/pom.xml
core/pom.xml
core/src/test/scala/org/apache/spark/ui/UISuite.scala
examples/pom.xml
mllib/pom.xml
pom.xml
project/SparkBuild.scala
repl/pom.xml
streaming/pom.xml
tools/pom.xml
In scala 2.10, a shorter representation is used for naming artifacts
so changed to shorter scala version for artifacts and made it a property in pom.
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Conflicts:
core/src/main/scala/org/apache/spark/ui/jobs/JobProgressUI.scala
docs/_config.yml
project/SparkBuild.scala
repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
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Conflicts:
core/src/main/scala/org/apache/spark/SparkContext.scala
project/SparkBuild.scala
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with old.
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Conflicts:
core/pom.xml
core/src/main/scala/spark/MapOutputTracker.scala
core/src/main/scala/spark/RDD.scala
core/src/main/scala/spark/RDDCheckpointData.scala
core/src/main/scala/spark/SparkContext.scala
core/src/main/scala/spark/Utils.scala
core/src/main/scala/spark/api/python/PythonRDD.scala
core/src/main/scala/spark/deploy/client/Client.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/rdd/BlockRDD.scala
core/src/main/scala/spark/rdd/ZippedRDD.scala
core/src/main/scala/spark/scheduler/cluster/StandaloneSchedulerBackend.scala
core/src/main/scala/spark/storage/BlockManager.scala
core/src/main/scala/spark/storage/BlockManagerMaster.scala
core/src/main/scala/spark/storage/BlockManagerMasterActor.scala
core/src/main/scala/spark/storage/BlockManagerUI.scala
core/src/main/scala/spark/util/AkkaUtils.scala
core/src/test/scala/spark/SizeEstimatorSuite.scala
pom.xml
project/SparkBuild.scala
repl/src/main/scala/spark/repl/SparkILoop.scala
repl/src/test/scala/spark/repl/ReplSuite.scala
streaming/src/main/scala/spark/streaming/StreamingContext.scala
streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala
streaming/src/main/scala/spark/streaming/dstream/KafkaInputDStream.scala
streaming/src/main/scala/spark/streaming/util/MasterFailureTest.scala
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Fixed maven build without netty fix
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status messages
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spark-repl to the actual execution thread.
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the spark shell but with GraphX packages automatically imported
and with Kryo serialization enabled for GraphX types.
In addition the graphx-shell has a nifty new logo.
To make these changes minimally invasive in the SparkILoop.scala
I added some additional environment variables:
SPARK_BANNER_TEXT: If set this string is displayed instead
of the spark logo
SPARK_SHELL_INIT_BLOCK: if set this expression is evaluated in the
spark shell after the spark context is created.
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