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[SPARK-4772] Clear local copies of accumulators as soon as we're done with them

Nathan Kronenfeld authored 10 years ago

Accumulators keep thread-local copies of themselves. These copies were only cleared at the beginning of a task. This meant that (a) the memory they used was tied up until the next task ran on that thread, and (b) if a thread died, the memory it had used for accumulators was locked up forever on that worker.

This PR clears the thread-local copies of accumulators at the end of each task, in the tasks finally block, to make sure they are cleaned up between tasks. It also stores them in a ThreadLocal object, so that if, for some reason, the thread dies, any memory they are using at the time should be freed up.

Author: Nathan Kronenfeld <nkronenfeld@oculusinfo.com>

Closes #3570 from nkronenfeld/Accumulator-Improvements and squashes the following commits:

a581f3f [Nathan Kronenfeld] Change Accumulators to private[spark] instead of adding mima exclude to get around false positive in mima tests
b6c2180 [Nathan Kronenfeld] Include MiMa exclude as per build error instructions - this version incompatibility should be irrelevent, as it will only surface if a master is talking to a worker running a different version of spark.
537baad [Nathan Kronenfeld] Fuller refactoring as intended, incorporating JR's suggestions for ThreadLocal localAccums, and keeping clear(), but also calling it in tasks' finally block, rather than just at the beginning of the task.
39a82f2 [Nathan Kronenfeld] Clear local copies of accumulators as soon as we're done with them

94b377f9

94b377f9 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 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.