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Latest commit 28ab0ec Dec 27, 2016 Yuexin Zhang committed with srowen [SPARK-19006][DOCS] mention spark.kryoserializer.buffer.max must be l… 
…ess than 2048m in doc

## What changes were proposed in this pull request?

On configuration doc page:https://spark.apache.org/docs/latest/configuration.html
We mentioned spark.kryoserializer.buffer.max : Maximum allowable size of Kryo serialization buffer. This must be larger than any object you attempt to serialize. Increase this if you get a "buffer limit exceeded" exception inside Kryo.
from source code, it has hard coded upper limit :
```
val maxBufferSizeMb = conf.getSizeAsMb("spark.kryoserializer.buffer.max", "64m").toInt
if (maxBufferSizeMb >= ByteUnit.GiB.toMiB(2))
{ throw new IllegalArgumentException("spark.kryoserializer.buffer.max must be less than " + s"2048 mb, got: + $maxBufferSizeMb mb.") }
```
We should mention "this value must be less than 2048 mb" on the configuration doc page as well.

## How was this patch tested?

None. Since it's minor doc change.

Author: Yuexin Zhang <[email protected]>

Closes #16412 from cnZach/SPARK-19006.
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.github [SPARK-18073][DOCS][WIP] Migrate wiki to spark.apache.org web site Nov 23, 2016
R [SPARK-18958][SPARKR] R API toJSON on DataFrame Dec 23, 2016
assembly [SPARK-18695] Bump master branch version to 2.2.0-SNAPSHOT Dec 3, 2016
bin [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed Nov 16, 2016
build [SPARK-18638][BUILD] Upgrade sbt, Zinc, and Maven plugins Dec 3, 2016
common [SPARK-18963] o.a.s.unsafe.types.UTF8StringSuite.writeToOutputStreamI… Dec 23, 2016
conf [SPARK-11653][DEPLOY] Allow spark-daemon.sh to run in the foreground Oct 20, 2016
core [SPARK-17755][CORE] Use workerRef to send RegisterWorkerResponse to a… Dec 26, 2016
data [SPARK-16421][EXAMPLES][ML] Improve ML Example Outputs Aug 5, 2016
dev [BUILD] make-distribution should find JAVA_HOME for non-RHEL systems Dec 22, 2016
docs [SPARK-19006][DOCS] mention spark.kryoserializer.buffer.max must be l… Dec 27, 2016
examples [SPARK-18325][SPARKR][ML] SparkR ML wrappers example code and user guide Dec 8, 2016
external [SPARK-17807][CORE] split test-tags into test-JAR Dec 22, 2016
graphx [SPARK-17807][CORE] split test-tags into test-JAR Dec 22, 2016
launcher [SPARK-17807][CORE] split test-tags into test-JAR Dec 22, 2016
licenses [MINOR][BUILD] Add modernizr MIT license; specify "2014 and onwards" … Jun 4, 2016
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project [SPARK-18537][WEB UI] Add a REST api to serve spark streaming informa… Dec 22, 2016
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scalastyle-config.xml [SPARK-13747][CORE] Fix potential ThreadLocal leaks in RPC when using… Dec 13, 2016

README.md

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, 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 DataFrames, 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. This README file only contains basic setup instructions.

Building Spark

Spark is built using Apache Maven. To build Spark and its example programs, run:

build/mvn -DskipTests clean package

(You do not need to do this if you downloaded a pre-built package.)

You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".

For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".

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" 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 tests for a module, or individual 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.

Configuration

Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.

 Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.