158ad0bba9
This patch adds the ability to register lambda functions written in Python, Java or Scala as UDFs for use in SQL or HiveQL. Scala: ```scala registerFunction("strLenScala", (_: String).length) sql("SELECT strLenScala('test')") ``` Python: ```python sqlCtx.registerFunction("strLenPython", lambda x: len(x), IntegerType()) sqlCtx.sql("SELECT strLenPython('test')") ``` Java: ```java sqlContext.registerFunction("stringLengthJava", new UDF1<String, Integer>() { Override public Integer call(String str) throws Exception { return str.length(); } }, DataType.IntegerType); sqlContext.sql("SELECT stringLengthJava('test')"); ``` Author: Michael Armbrust <michael@databricks.com> Closes #1063 from marmbrus/udfs and squashes the following commits: 9eda0fe [Michael Armbrust] newline 747c05e [Michael Armbrust] Add some scala UDF tests. d92727d [Michael Armbrust] Merge remote-tracking branch 'apache/master' into udfs 005d684 [Michael Armbrust] Fix naming and formatting. d14dac8 [Michael Armbrust] Fix last line of autogened java files. 8135c48 [Michael Armbrust] Move UDF unit tests to pyspark. 40b0ffd [Michael Armbrust] Merge remote-tracking branch 'apache/master' into udfs 6a36890 [Michael Armbrust] Switch logging so that SQLContext can be serializable. 7a83101 [Michael Armbrust] Drop toString 795fd15 [Michael Armbrust] Try to avoid capturing SQLContext. e54fb45 [Michael Armbrust] Docs and tests. 437cbe3 [Michael Armbrust] Update use of dataTypes, fix some python tests, address review comments. 01517d6 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udfs 8e6c932 [Michael Armbrust] WIP 3f96a52 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into udfs 6237c8d [Michael Armbrust] WIP 2766f0b [Michael Armbrust] Move udfs support to SQL from hive. Add support for Java UDFs. 0f7d50c [Michael Armbrust] Draft of native Spark SQL UDFs for Scala and Python. |
||
---|---|---|
assembly | ||
bagel | ||
bin | ||
conf | ||
core | ||
data/mllib | ||
dev | ||
docker | ||
docs | ||
ec2 | ||
examples | ||
external | ||
extras | ||
graphx | ||
mllib | ||
project | ||
python | ||
repl | ||
sbin | ||
sbt | ||
sql | ||
streaming | ||
tools | ||
yarn | ||
.gitignore | ||
.rat-excludes | ||
.travis.yml | ||
LICENSE | ||
make-distribution.sh | ||
NOTICE | ||
pom.xml | ||
README.md | ||
scalastyle-config.xml | ||
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>
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.