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In Spark 1.0.0+, calling `stop()` on a StreamingContext that has not been started is a no-op which has no side-effects. This allows users to call `stop()` on a fresh StreamingContext followed by `start()`. I believe that this almost always indicates an error and is not behavior that we should support. Since we don't allow `start() stop() start()` then I don't think it makes sense to allow `stop() start()`. The current behavior can lead to resource leaks when StreamingContext constructs its own SparkContext: if I call `stop(stopSparkContext=True)`, then I expect StreamingContext's underlying SparkContext to be stopped irrespective of whether the StreamingContext has been started. This is useful when writing unit test fixtures. Prior discussions: - https://github.com/apache/spark/pull/3053#discussion-diff-19710333R490 - https://github.com/apache/spark/pull/3121#issuecomment-61927353 Author: Josh Rosen <joshrosen@databricks.com> Closes #3160 from JoshRosen/SPARK-4301 and squashes the following commits: dbcc929 [Josh Rosen] Address more review comments bdbe5da [Josh Rosen] Stop SparkContext after stopping scheduler, not before. 03e9c40 [Josh Rosen] Always stop SparkContext, even if stop(false) has already been called. 832a7f4 [Josh Rosen] Address review comment 5142517 [Josh Rosen] Add tests; improve Scaladoc. 813e471 [Josh Rosen] Revert workaround added in https://github.com/apache/spark/pull/3053/files#diff-e144dbee130ed84f9465853ddce65f8eR49 5558e70 [Josh Rosen] StreamingContext.stop() should stop SparkContext even if StreamingContext has not been started yet. |
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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.
Online Documentation
You can find the latest Spark documentation, including a programming guide, on the project web page. 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 with Maven".
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.