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spark-instrumented-optimizer/docs/index.md
Joseph K. Bradley 5ffd5d3838 [SPARK-14817][ML][MLLIB][DOC] Made DataFrame-based API primary in MLlib guide 
## What changes were proposed in this pull request?

Made DataFrame-based API primary
* Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html
* mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html
* ml-guide.html includes a "maintenance mode" announcement about the RDD-based API
  * **Reviewers: please check this carefully**
* (minor) Titles for DF API no longer include "- spark.ml" suffix.  Titles for RDD API have "- RDD-based API" suffix
* Moved migration guide to ml-guide from mllib-guide
  * Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides
  * **Reviewers**: I did not change any of the content of the migration guides.

Reorganized DataFrame-based guide:
* ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc.
* Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html
  * **Reviewers**: I did not change the content of these guides, except some intro text.
* Sidebar remains the same, but with pipeline and tuning sections added

Other:
* ml-classification-regression.html: Moved text about linear methods to new section in page

## How was this patch tested?

Generated docs locally

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #14213 from jkbradley/ml-guide-2.0.
2016-07-15 13:38:23 -07:00

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layout displayTitle title description
global Spark Overview Overview Apache Spark SPARK_VERSION_SHORT documentation homepage

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. 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.

Downloading

Get Spark from the downloads page of the project website. This documentation is for Spark version {{site.SPARK_VERSION}}. Spark uses Hadoop's client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions. Users can also download a "Hadoop free" binary and run Spark with any Hadoop version by augmenting Spark's classpath.

If you'd like to build Spark from source, visit Building Spark.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It's easy to run locally on one machine --- all you need is to have java installed on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.

Spark runs on Java 7+, Python 2.6+/3.4+ and R 3.1+. For the Scala API, Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. You will need to use a compatible Scala version ({{site.SCALA_BINARY_VERSION}}.x).

Running the Examples and Shell

Spark comes with several sample programs. Scala, Java, Python and R examples are in the examples/src/main directory. To run one of the Java or Scala sample programs, use bin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, this invokes the more general spark-submit script for launching applications). For example,

./bin/run-example SparkPi 10

You can also run Spark interactively through a modified version of the Scala shell. This is a great way to learn the framework.

./bin/spark-shell --master local[2]

The --master option specifies the master URL for a distributed cluster, or local to run locally with one thread, or local[N] to run locally with N threads. You should start by using local for testing. For a full list of options, run Spark shell with the --help option.

Spark also provides a Python API. To run Spark interactively in a Python interpreter, use bin/pyspark:

./bin/pyspark --master local[2]

Example applications are also provided in Python. For example,

./bin/spark-submit examples/src/main/python/pi.py 10

Spark also provides an experimental R API since 1.4 (only DataFrames APIs included). To run Spark interactively in a R interpreter, use bin/sparkR:

./bin/sparkR --master local[2]

Example applications are also provided in R. For example,

./bin/spark-submit examples/src/main/r/dataframe.R

Launching on a Cluster

The Spark cluster mode overview explains the key concepts in running on a cluster. Spark can run both by itself, or over several existing cluster managers. It currently provides several options for deployment:

Where to Go from Here

Programming Guides:

API Docs:

Deployment Guides:

Other Documents:

External Resources: