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Conflicts:
core/src/main/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManager.scala
project/SparkBuild.scala
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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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using Scala 2.10.3,
resolved maven-scala-plugin warning
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Conflicts:
core/src/main/scala/org/apache/spark/SparkContext.scala
project/SparkBuild.scala
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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
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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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log4j.properties to be put into hdfs.
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Conflicts:
project/SparkBuild.scala
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indexedrdd_graphx
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element.
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Standalone Scheduler fault tolerance using ZooKeeper
This patch implements full distributed fault tolerance for standalone scheduler Masters.
There is only one master Leader at a time, which is actively serving scheduling
requests. If this Leader crashes, another master will eventually be elected, reconstruct
the state from the first Master, and continue serving scheduling requests.
Leader election is performed using the ZooKeeper leader election pattern. We try to minimize
the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of
retries and session monitoring on top of the ZooKeeper client.
Master failover follows directly from the single-node Master recovery via the file
system (patch d5a96fe), save that the Master state is stored in ZooKeeper instead.
Configuration:
By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE).
By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url
to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled.
By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory
to an appropriate directory accessible by the Master, we will keep the behavior of from d5a96fe.
Additionally, places where a Master could be specificied by a spark:// url can now take
comma-delimited lists to specify backup masters. Note that this is only used for registration
of NEW Workers and application Clients. Once a Worker or Client has registered with the
Master Leader, it is "in the system" and will never need to register again.
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This patch implements full distributed fault tolerance for standalone scheduler Masters.
There is only one master Leader at a time, which is actively serving scheduling
requests. If this Leader crashes, another master will eventually be elected, reconstruct
the state from the first Master, and continue serving scheduling requests.
Leader election is performed using the ZooKeeper leader election pattern. We try to minimize
the use of ZooKeeper and the assumptions about ZooKeeper's behavior, so there is a layer of
retries and session monitoring on top of the ZooKeeper client.
Master failover follows directly from the single-node Master recovery via the file
system (patch 194ba4b8), save that the Master state is stored in ZooKeeper instead.
Configuration:
By default, no recovery mechanism is enabled (spark.deploy.recoveryMode = NONE).
By setting spark.deploy.recoveryMode to ZOOKEEPER and setting spark.deploy.zookeeper.url
to an appropriate ZooKeeper URL, ZooKeeper recovery mode is enabled.
By setting spark.deploy.recoveryMode to FILESYSTEM and setting spark.deploy.recoveryDirectory
to an appropriate directory accessible by the Master, we will keep the behavior of from 194ba4b8.
Additionally, places where a Master could be specificied by a spark:// url can now take
comma-delimited lists to specify backup masters. Note that this is only used for registration
of NEW Workers and application Clients. Once a Worker or Client has registered with the
Master Leader, it is "in the system" and will never need to register again.
Forthcoming:
Documentation, tests (! - only ad hoc testing has been performed so far)
I do not intend for this commit to be merged until tests are added, but this patch should
still be mostly reviewable until then.
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This uses a seperate Hadoop version for YARN artifact. This means when people link against
spark-yarn, things will resolve correctly.
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Adding Manen dependency for Ganglia
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This includes the following changes:
- The "assembly" package now builds in Maven by default, and creates an
assembly containing both hadoop-client and Spark, unlike the old
BigTop distribution assembly that skipped hadoop-client
- There is now a bigtop-dist package to build the old BigTop assembly
- The repl-bin package is no longer built by default since the scripts
don't reply on it; instead it can be enabled with -Prepl-bin
- Py4J is now included in the assembly/lib folder as a local Maven repo,
so that the Maven package can link to it
- run-example now adds the original Spark classpath as well because the
Maven examples assembly lists spark-core and such as provided
- The various Maven projects add a spark-yarn dependency correctly
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This reverts commit 1fb1b0992838c8cdd57eec45793e67a0490f1a52, reversing
changes made to c69c48947d5102c81a9425cb380d861c3903685c.
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Update build docs
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Conflicts:
core/src/main/scala/spark/PairRDDFunctions.scala
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