| commit | 11ee9d191e26a41a44ff0ca8730a129934942ee7 | [log] [tgz] |
|---|---|---|
| author | Yuhao Yang <hhbyyh@gmail.com> | Wed Nov 18 13:25:15 2015 -0800 |
| committer | Xiangrui Meng <meng@databricks.com> | Wed Nov 18 13:26:39 2015 -0800 |
| tree | 41087c8d7e2ba8fdc0fdbc7d2882cf6d4f4ba92a | |
| parent | 19835ec1fae13a7251f0c66fe010c228da92c456 [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>
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.
You can find the latest Spark documentation, including a programming guide, on the project webpage at http://spark.apache.org/documentation.html. This README file only contains basic setup instructions.
Spark is built on Scala 2.10. To build Spark and its example programs, run:
./sbt/sbt assembly
(You do not need to do this if you downloaded a pre-built package.)
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()
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()
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.
Testing first requires building Spark. Once Spark is built, tests can be run using:
./dev/run-tests
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. You can change the version by setting -Dhadoop.version when building Spark.
For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop versions without YARN, use:
# Apache Hadoop 1.2.1 $ sbt/sbt -Dhadoop.version=1.2.1 assembly # Cloudera CDH 4.2.0 with MapReduce v1 $ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly
For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions with YARN, also set -Pyarn:
# Apache Hadoop 2.0.5-alpha $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly # Cloudera CDH 4.2.0 with MapReduce v2 $ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly # Apache Hadoop 2.2.X and newer $ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly
When developing a Spark application, specify the Hadoop version by adding the “hadoop-client” artifact to your project‘s dependencies. For example, if you’re using Hadoop 1.2.1 and build your application using SBT, add this entry to libraryDependencies:
"org.apache.hadoop" % "hadoop-client" % "1.2.1"
If your project is built with Maven, add this to your POM file's <dependencies> section:
<dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>1.2.1</version> </dependency>
Spark SQL supports Thrift JDBC server and CLI. See sql-programming-guide.md for more information about using the JDBC server and CLI. You can use those features by setting -Phive when building Spark as follows.
$ sbt/sbt -Phive assembly
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Contributions via GitHub pull requests are gladly accepted from their original author. Along with any pull requests, please state that the contribution is your original work and that you license the work to the project under the project‘s open source license. Whether or not you state this explicitly, by submitting any copyrighted material via pull request, email, or other means you agree to license the material under the project’s open source license and warrant that you have the legal authority to do so.