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[SPARK-6030] [CORE] Using simulated field layout method to compute class shellSize

Ye Xianjin authored 9 years ago

SizeEstimator gives wrong result for Integer on 64bit JVM with UseCompressedOops on, this pr fixes that. For more details, please refer [SPARK-6030](https://issues.apache.org/jira/browse/SPARK-6030)
sryza, I noticed there is a pr to expose SizeEstimator, maybe that should be waited by this pr get merged if we confirm this problem.
And shivaram would you mind to review this pr since you contribute related code. Also cc to srowen and mateiz

Author: Ye Xianjin <advancedxy@gmail.com>

Closes #4783 from advancedxy/SPARK-6030 and squashes the following commits:

c4dcb41 [Ye Xianjin] Add super.beforeEach in the beforeEach method to make the trait stackable.. Remove useless leading whitespace.
3f80640 [Ye Xianjin] The size of Integer class changes from 24 to 16 on a 64-bit JVM with -UseCompressedOops flag on after the fix. I don't how 100000 was originally calculated, It looks like 100000 is the magic number which makes sure spilling. Because of the size change, It fails because there is no spilling at all. Change the number to a slightly larger number fixes that.
e849d2d [Ye Xianjin] Merge two shellSize assignments into one. Add some explanation to alignSizeUp method.
85a0b51 [Ye Xianjin] Fix typos and update wording in comments. Using alignSizeUp to compute alignSize.
d27eb77 [Ye Xianjin] Add some detailed comments in the code. Add some test cases. It's very difficult to design test cases as the final object alignment will hide a lot of filed layout details if we just considering the whole size.
842aed1 [Ye Xianjin] primitiveSize(cls) can just return Int. Use a simplified class field layout method to calculate class instance size. Will add more documents and test cases. Add a new alignSizeUp function which uses bitwise operators to speedup.
62e8ab4 [Ye Xianjin] Don't alignSize for objects' shellSize, alignSize when added to state.size. Add some primitive wrapper objects size tests.

bfcd528d

bfcd528d 9 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.