c7032290a3
DecisionTree improvements: (1) TreePoint representation to avoid binning multiple times (2) Bug fix: isSampleValid indexed bins incorrectly for unordered categorical features (3) Timing for DecisionTree internals Details: (1) TreePoint representation to avoid binning multiple times [https://issues.apache.org/jira/browse/SPARK-3022] Added private[tree] TreePoint class for representing binned feature values. The input RDD of LabeledPoint is converted to the TreePoint representation initially and then cached. This avoids the previous problem of re-computing bins multiple times. (2) Bug fix: isSampleValid indexed bins incorrectly for unordered categorical features [https://issues.apache.org/jira/browse/SPARK-3041] isSampleValid used to treat unordered categorical features incorrectly: It treated the bins as if indexed by featured values, rather than by subsets of values/categories. * exhibited for unordered features (multi-class classification with categorical features of low arity) * Fix: Index bins correctly for unordered categorical features. (3) Timing for DecisionTree internals Added tree/impl/TimeTracker.scala class which is private[tree] for now, for timing key parts of DT code. Prints timing info via logDebug. CC: mengxr manishamde chouqin Very similar update, with one bug fix. Many apologies for the conflicting update, but I hope that a few more optimizations I have on the way (which depend on this update) will prove valuable to you: SPARK-3042 and SPARK-3043 Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #1950 from jkbradley/dt-opt1 and squashes the following commits: 5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint 6b5651e [Joseph K. Bradley] Updates based on code review. 1 major change: persisting to memory + disk, not just memory. 2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs. d036089 [Joseph K. Bradley] Print timing info to logDebug. e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private 8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree 3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging) f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing 511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing a95bc22 [Joseph K. Bradley] timing for DecisionTree internals |
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conf | ||
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docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
mllib | ||
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sbt | ||
sql | ||
streaming | ||
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README.md | ||
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tox.ini |
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.
Online Documentation
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.
Building Spark
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.)
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:
./sbt/sbt test
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.
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>
A Note About Thrift JDBC server and CLI for Spark SQL
Spark SQL supports Thrift JDBC server and CLI.
See sql-programming-guide.md for more information about those features.
You can use those features by setting -Phive-thriftserver
when building Spark as follows.
$ sbt/sbt -Phive-thriftserver assembly
Configuration
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.
Contributing to 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.