| Commit message (Collapse) | Author | Age | Files | Lines |
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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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instead of SPARK_MEM, user should add application jars to SPARK_CLASSPATH
Signed-off-by: shane-huang <shengsheng.huang@intel.com>
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Signed-off-by: shane-huang <shengsheng.huang@intel.com>
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Signed-off-by: shane-huang <shengsheng.huang@intel.com>
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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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Conflicts:
core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
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Bug fixes for file input stream and checkpointing
- Fixed bugs in the file input stream that led the stream to fail due to transient HDFS errors (listing files when a background thread it deleting fails caused errors, etc.)
- Updated Spark's CheckpointRDD and Streaming's CheckpointWriter to use SparkContext.hadoopConfiguration, to allow checkpoints to be written to any HDFS compatible store requiring special configuration.
- Changed the API of SparkContext.setCheckpointDir() - eliminated the unnecessary 'useExisting' parameter. Now SparkContext will always create a unique subdirectory within the user specified checkpoint directory. This is to ensure that previous checkpoint files are not accidentally overwritten.
- Fixed bug where setting checkpoint directory as a relative local path caused the checkpointing to fail.
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Reynold's comments on PR 289.
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tests so we don't get the test spark.conf on the classpath.
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Conflicts:
core/src/main/scala/org/apache/spark/SparkContext.scala
core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
core/src/main/scala/org/apache/spark/scheduler/local/LocalScheduler.scala
core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala
core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala
core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala
streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala
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Python bindings for mllib
This pull request contains Python bindings for the regression, clustering, classification, and recommendation tools in mllib.
For each 'train' frontend exposed, there is a Scala stub in PythonMLLibAPI.scala and a Python stub in mllib.py. The Python stub serialises the input RDD and any vector/matrix arguments into a mutually-understood format and calls the Scala stub. The Scala stub deserialises the RDD and the vector/matrix arguments, calls the appropriate 'train' function, serialises the resulting model, and returns the serialised model.
ALSModel is slightly different since a MatrixFactorizationModel has RDDs inside. The Scala stub returns a handle to a Scala MatrixFactorizationModel; prediction is done by calling the Scala predict method.
I have tested these bindings on an x86_64 machine running Linux. There is a risk that these bindings may fail on some choose-your-own-endian platform if Python's endian differs from java.nio.ByteBuffer's idea of the native byte order.
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pyspark.mllib.
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type and length checking errors until we've got at least one working stub that we're all happy with.
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The test in context.py created two different instances of the
SparkContext class by copying "globals", so that some tests can have a
global "sc" object and others can try initializing their own contexts.
This led to two JVM gateways being created since SparkConf also looked
at pyspark.context.SparkContext to get the JVM.
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Add collectPartition to JavaRDD interface.
This interface is useful for implementing `take` from other language frontends where the data is serialized. Also remove `takePartition` from PythonRDD and use `collectPartition` in rdd.py.
Thanks @concretevitamin for the original change and tests.
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Change the implementation to use runJob instead of PartitionPruningRDD.
Also update the unit tests and the python take implementation
to use the new interface.
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Also remove takePartition from PythonRDD and use collectPartition in rdd.py.
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Conflicts:
core/pom.xml
core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
pom.xml
project/SparkBuild.scala
streaming/pom.xml
yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala
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Fixes SPARK-970.
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Conflicts:
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithContextRDD.scala
core/src/main/scala/org/apache/spark/rdd/RDD.scala
python/pyspark/rdd.py
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