Apache Spark - A unified analytics engine for large-scale data processing
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Josh Rosen 002c12384d [SPARK-7311] Introduce internal Serializer API for determining if serializers support object relocation
This patch extends the `Serializer` interface with a new `Private` API which allows serializers to indicate whether they support relocation of serialized objects in serializer stream output.

This relocatibilty property is described in more detail in `Serializer.scala`, but in a nutshell a serializer supports relocation if reordering the bytes of serialized objects in serialization stream output is equivalent to having re-ordered those elements prior to serializing them.  The optimized shuffle path introduced in #4450 and #5868 both rely on serializers having this property; this patch just centralizes the logic for determining whether a serializer has this property.  I also added tests and comments clarifying when this works for KryoSerializer.

This change allows the optimizations in #4450 to be applied for shuffles that use `SqlSerializer2`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #5924 from JoshRosen/SPARK-7311 and squashes the following commits:

50a68ca [Josh Rosen] Address minor nits
0a7ebd7 [Josh Rosen] Clarify reason why SqlSerializer2 supports this serializer
123b992 [Josh Rosen] Cleanup for submitting as standalone patch.
4aa61b2 [Josh Rosen] Add missing newline
2c1233a [Josh Rosen] Small refactoring of SerializerPropertiesSuite to enable test re-use:
0ba75e6 [Josh Rosen] Add tests for serializer relocation property.
450fa21 [Josh Rosen] Back out accidental log4j.properties change
86d4dcd [Josh Rosen] Flag that SparkSqlSerializer2 supports relocation
b9624ee [Josh Rosen] Expand serializer API and use new function to help control when new UnsafeShuffle path is used.
2015-05-06 10:52:55 -07:00
assembly [SPARK-7168] [BUILD] Update plugin versions in Maven build and centralize versions 2015-04-28 07:48:34 -04:00
bagel [SPARK-6758]block the right jetty package in log 2015-04-09 17:44:08 -04:00
bin Limit help option regex 2015-05-01 19:26:55 +01:00
build SPARK-5856: In Maven build script, launch Zinc with more memory 2015-02-17 10:10:01 -08:00
conf [SPARK-2691] [MESOS] Support for Mesos DockerInfo 2015-05-01 18:41:22 -07:00
core [SPARK-7311] Introduce internal Serializer API for determining if serializers support object relocation 2015-05-06 10:52:55 -07:00
data/mllib [SPARK-5939][MLLib] make FPGrowth example app take parameters 2015-02-23 08:47:28 -08:00
dev [MINOR] Fix python test typo? 2015-05-04 17:17:55 +01:00
docker [SPARK-2691] [MESOS] Support for Mesos DockerInfo 2015-05-01 18:41:22 -07:00
docs Revert "[SPARK-3454] separate json endpoints for data in the UI" 2015-05-05 19:27:30 -07:00
ec2 [SPARK-4897] [PySpark] Python 3 support 2015-04-16 16:20:57 -07:00
examples [SPARK-6612] [MLLIB] [PYSPARK] Python KMeans parity 2015-05-05 07:57:39 -07:00
external [SPARK-7113] [STREAMING] Support input information reporting for Direct Kafka stream 2015-05-05 02:01:06 -07:00
extras [SPARK-6440][CORE]Handle IPv6 addresses properly when constructing URI 2015-04-13 12:55:25 +01:00
graphx [SPARK-5854] personalized page rank 2015-05-01 11:55:43 -07:00
launcher [SPARK-7031] [THRIFTSERVER] let thrift server take SPARK_DAEMON_MEMORY and SPARK_DAEMON_JAVA_OPTS 2015-05-03 00:47:47 +01:00
mllib [SPARK-6940] [MLLIB] Add CrossValidator to Python ML pipeline API 2015-05-06 01:28:43 -07:00
network [SPARK-6229] Add SASL encryption to network library. 2015-05-01 19:01:46 -07:00
project [Build] Enable MiMa checks for SQL 2015-04-30 16:23:01 -07:00
python [SPARK-6940] [MLLIB] Add CrossValidator to Python ML pipeline API 2015-05-06 01:28:43 -07:00
R [SPARK-6841] [SPARKR] add support for mean, median, stdev etc. 2015-05-05 20:39:56 -07:00
repl [SPARK-7092] Update spark scala version to 2.11.6 2015-04-25 18:07:34 -04:00
sbin [SPARK-5338] [MESOS] Add cluster mode support for Mesos 2015-04-28 13:33:57 -07:00
sbt Adde LICENSE Header to build/mvn, build/sbt and sbt/sbt 2014-12-29 10:48:53 -08:00
sql [SPARK-7311] Introduce internal Serializer API for determining if serializers support object relocation 2015-05-06 10:52:55 -07:00
streaming [SPARK-6939] [STREAMING] [WEBUI] Add timeline and histogram graphs for streaming statistics 2015-05-05 12:52:16 -07:00
tools [SPARK-4550] In sort-based shuffle, store map outputs in serialized form 2015-04-30 23:14:14 -07:00
unsafe [MINOR] Minor update for document 2015-05-05 14:44:02 +01:00
yarn [SPARK-6653] [YARN] New config to specify port for sparkYarnAM actor system 2015-05-05 11:09:51 +01:00
.gitattributes [SPARK-3870] EOL character enforcement 2014-10-31 12:39:52 -07:00
.gitignore [SPARK-5654] Integrate SparkR 2015-04-08 22:45:40 -07:00
.rat-excludes Revert "[SPARK-3454] separate json endpoints for data in the UI" 2015-05-05 19:27:30 -07:00
CONTRIBUTING.md [SPARK-6889] [DOCS] CONTRIBUTING.md updates to accompany contribution doc updates 2015-04-21 22:34:31 -07:00
LICENSE [SPARK-6939] [STREAMING] [WEBUI] Add timeline and histogram graphs for streaming statistics 2015-05-05 12:52:16 -07:00
make-distribution.sh [SPARK-7302] [DOCS] SPARK building documentation still mentions building for yarn 0.23 2015-05-03 21:22:31 +01:00
NOTICE SPARK-1827. LICENSE and NOTICE files need a refresh to contain transitive dependency info 2014-05-14 09:38:33 -07:00
pom.xml Revert "[SPARK-3454] separate json endpoints for data in the UI" 2015-05-05 19:27:30 -07:00
README.md [docs] [SPARK-6306] Readme points to dead link 2015-03-12 15:01:33 +00:00
scalastyle-config.xml [SPARK-6428] Turn on explicit type checking for public methods. 2015-04-03 01:25:02 -07:00
tox.ini [SPARK-3073] [PySpark] use external sort in sortBy() and sortByKey() 2014-08-26 16:57:40 -07:00

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.