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[SPARK-6752][Streaming] Allow StreamingContext to be recreated from checkpoint...

Tathagata Das authored 10 years ago

[SPARK-6752][Streaming] Allow StreamingContext to be recreated from checkpoint and existing SparkContext

Currently if you want to create a StreamingContext from checkpoint information, the system will create a new SparkContext. This prevent StreamingContext to be recreated from checkpoints in managed environments where SparkContext is precreated.

The solution in this PR: Introduce the following methods on StreamingContext
1. `new StreamingContext(checkpointDirectory, sparkContext)`
   Recreate StreamingContext from checkpoint using the provided SparkContext
2. `StreamingContext.getOrCreate(checkpointDirectory, sparkContext, createFunction: SparkContext => StreamingContext)`
   If checkpoint file exists, then recreate StreamingContext using the provided SparkContext (that is, call 1.), else create StreamingContext using the provided createFunction

TODO: the corresponding Java and Python API has to be added as well.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #5428 from tdas/SPARK-6752 and squashes the following commits:

94db63c [Tathagata Das] Fix long line.
524f519 [Tathagata Das] Many changes based on PR comments.
eabd092 [Tathagata Das] Added Function0, Java API and unit tests for StreamingContext.getOrCreate
36a7823 [Tathagata Das] Minor changes.
204814e [Tathagata Das] Added StreamingContext.getOrCreate with existing SparkContext

534f2a43

534f2a43 10 years ago

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Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, and Python, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page and project wiki. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".

Interactive Scala Shell

The easiest way to start using Spark is through the Scala shell:

./bin/spark-shell

Try the following command, which should return 1000:

scala> sc.parallelize(1 to 1000).count()

Interactive Python Shell

Alternatively, if you prefer Python, you can use the Python shell:

./bin/pyspark

And run the following command, which should also return 1000:

>>> sc.parallelize(range(1000)).count()

Example Programs

Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example <class> [params]. For example:

./bin/run-example SparkPi

will run the Pi example locally.

You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be a mesos:// or spark:// URL, "yarn-cluster" or "yarn-client" to run on YARN, and "local" to run locally with one thread, or "local[N]" to run locally with N threads. You can also use an abbreviated class name if the class is in the examples package. For instance:

MASTER=spark://host:7077 ./bin/run-example SparkPi

Many of the example programs print usage help if no params are given.

Running Tests

Testing first requires building Spark. Once Spark is built, tests can be run using:

./dev/run-tests

Please see the guidance on how to run all automated tests.

A Note About Hadoop Versions

Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.

Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.

Configuration

Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.