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apache / spark / refs/heads/branch-1.2
commit307f27e24e17afd92030194a3e6fec312fc19f4f[log] [tgz]
authorYuhao Yang <hhbyyh@gmail.com>Wed Nov 18 13:25:15 2015 -0800
committerXiangrui Meng <meng@databricks.com>Wed Nov 18 13:26:18 2015 -0800
treeb64eba9ca7a74188beebef8839387f351a536d19
parent4b6e24e25e69169fdb5aab7cdcba5b32d9789c93 [diff]
[SPARK-11813][MLLIB] Avoid serialization of vocab in Word2Vec

jira: https://issues.apache.org/jira/browse/SPARK-11813

I found the problem during training a large corpus. Avoid serialization of vocab in Word2Vec has 2 benefits.
1. Performance improvement for less serialization.
2. Increase the capacity of Word2Vec a lot.
Currently in the fit of word2vec, the closure mainly includes serialization of Word2Vec and 2 global table.
the main part of Word2vec is the vocab of size: vocab * 40 * 2 * 4 = 320 vocab
2 global table: vocab * vectorSize * 8. If vectorSize = 20, that's 160 vocab.

Their sum cannot exceed Int.max due to the restriction of ByteArrayOutputStream. In any case, avoiding serialization of vocab helps decrease the size of the closure serialization, especially when vectorSize is small, thus to allow larger vocabulary.

Actually there's another possible fix, make local copy of fields to avoid including Word2Vec in the closure. Let me know if that's preferred.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #9803 from hhbyyh/w2vVocab.

(cherry picked from commit e391abdf2cb6098a35347bd123b815ee9ac5b689)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
1 file changed
tree: b64eba9ca7a74188beebef8839387f351a536d19
  1. assembly/
  2. bagel/
  3. bin/
  4. conf/
  5. core/
  6. data/
  7. dev/
  8. docker/
  9. docs/
  10. ec2/
  11. examples/
  12. external/
  13. extras/
  14. graphx/
  15. mllib/
  16. network/
  17. project/
  18. python/
  19. repl/
  20. sbin/
  21. sbt/
  22. sql/
  23. streaming/
  24. tools/
  25. yarn/
  26. .gitattributes
  27. .gitignore
  28. .rat-excludes
  29. CHANGES.txt
  30. CONTRIBUTING.md
  31. LICENSE
  32. make-distribution.sh
  33. NOTICE
  34. pom.xml
  35. README.md
  36. scalastyle-config.xml
  37. tox.ini
README.md

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.