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Reverts to 04d83fc37f9eef89c20331c85291a0a169f75e6d:examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala.
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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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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.
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for creating XYZ streams.
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Conflicts:
examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java
streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala
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package. Also fixed packages of Flume and MQTT tests.
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their own self-contained scala API, java API, scala unit tests and java unit tests. Updated examples to use the external projects.
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Suggested small changes to Java code for slightly more standard style, encapsulation and in some cases performance
Sorry if this is too abrupt or not a welcome set of changes, but thought I'd see if I could contribute a little. I'm a Java developer and just getting seriously into Spark. So I thought I'd suggest a number of small changes to the couple Java parts of the code to make it a little tighter, more standard and even a bit faster.
Feel free to take all, some or none of this. Happy to explain any of it.
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encapsulation and in some cases performance
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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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- 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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Conflicts:
core/src/main/scala/org/apache/spark/util/collection/PrimitiveVector.scala
streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
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scala-2.10
Conflicts:
core/src/main/scala/org/apache/spark/deploy/client/Client.scala
core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala
core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.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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Since they rely on println to display results, we need to first collect
those results to the driver to have them actually display locally.
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Also remove unused imports as I found them along the way.
Remove return statements when returning value in the Scala code.
Passing compile and tests.
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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
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Conflicts:
project/SparkBuild.scala
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MQTT Adapter for Spark Streaming
MQTT is a machine-to-machine (M2M)/Internet of Things connectivity protocol.
It was designed as an extremely lightweight publish/subscribe messaging transport. You may read more about it here http://mqtt.org/
Message Queue Telemetry Transport (MQTT) is an open message protocol for M2M communications. It enables the transfer of telemetry-style data in the form of messages from devices like sensors and actuators, to mobile phones, embedded systems on vehicles, or laptops and full scale computers.
The protocol was invented by Andy Stanford-Clark of IBM, and Arlen Nipper of Cirrus Link Solutions
This protocol enables a publish/subscribe messaging model in an extremely lightweight way. It is useful for connections with remote locations where line of code and network bandwidth is a constraint.
MQTT is one of the widely used protocol for 'Internet of Things'. This protocol is getting much attraction as anything and everything is getting connected to internet and they all produce data. Researchers and companies predict some 25 billion devices will be connected to the internet by 2015.
Plugin/Support for MQTT is available in popular MQs like RabbitMQ, ActiveMQ etc.
Support for MQTT in Spark will help people with Internet of Things (IoT) projects to use Spark Streaming for their real time data processing needs (from sensors and other embedded devices etc).
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Upgrade Kafka 0.7.2 to Kafka 0.8.0-beta1 for Spark Streaming
Conflicts:
streaming/pom.xml
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BroadcastTest2 example added for testing broadcasts.
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indexedrdd_graphx
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- Examples assembly included a log4j.properties which clobbered Spark's
- Example had an error where some classes weren't serializable
- Did some other clean-up in this example
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