cf6cbe9f76
A recent PR (#552) fixed this for all Scala / Java examples. We need to do it for python too. Note that this blocks on #799, which makes `bin/pyspark` go through Spark submit. With only the changes in this PR, the only way to run these examples is through Spark submit. Once #799 goes in, you can use `bin/pyspark` to run them too. For example, ``` bin/pyspark examples/src/main/python/pi.py 100 --master local-cluster[4,1,512] ``` Author: Andrew Or <andrewor14@gmail.com> Closes #802 from andrewor14/python-examples and squashes the following commits: cf50b9f [Andrew Or] De-indent python comments (minor) 50f80b1 [Andrew Or] Remove pyFiles from SparkContext construction c362f69 [Andrew Or] Update docs to use spark-submit for python applications 7072c6a [Andrew Or] Merge branch 'master' of github.com:apache/spark into python-examples 427a5f0 [Andrew Or] Update docs d32072c [Andrew Or] Remove <master> from examples + update usages
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| layout | title |
|---|---|
| global | Spark Overview |
Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Scala, Java, and Python that make parallel jobs easy to write, and an optimized engine that supports general computation graphs. It also supports a rich set of higher-level tools including Shark (Hive on Spark), MLlib for machine learning, GraphX for graph processing, and Spark Streaming.
Downloading
Get Spark by visiting the downloads page of the Apache Spark site. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page contains Spark packages for many popular HDFS versions. If you'd like to build Spark from scratch, visit the building with Maven page.
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). All you need to run it is
to have java to installed on your system PATH, or the JAVA_HOME environment variable
pointing to a Java installation.
For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_BINARY_VERSION}}. If you write applications in Scala, you will need to use a compatible Scala version (e.g. {{site.SCALA_BINARY_VERSION}}.X) -- newer major versions may not work. You can get the right version of Scala from scala-lang.org.
Running the Examples and Shell
Spark comes with several sample programs. Scala, Java and Python 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 modified versions 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 interface. To run Spark interactively in a Python interpreter, use
bin/pyspark. As in Spark shell, you can also pass in the --master option to configure your
master URL.
./bin/pyspark --master local[2]
Example applications are also provided in Python. For example,
./bin/spark-submit examples/src/main/python/pi.py 10
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:
- Amazon EC2: our EC2 scripts let you launch a cluster in about 5 minutes
- Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
- Apache Mesos
- Hadoop YARN
Where to Go from Here
Programming guides:
- Quick Start: a quick introduction to the Spark API; start here!
- Spark Programming Guide: an overview of Spark concepts, and details on the Scala API
- Java Programming Guide: using Spark from Java
- Python Programming Guide: using Spark from Python
- Spark Streaming: Spark's API for processing data streams
- Spark SQL: Support for running relational queries on Spark
- MLlib (Machine Learning): Spark's built-in machine learning library
- Bagel (Pregel on Spark): simple graph processing model
- GraphX (Graphs on Spark): Spark's new API for graphs
API Docs:
Deployment guides:
- Cluster Overview: overview of concepts and components when running on a cluster
- Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
- Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
- Mesos: deploy a private cluster using Apache Mesos
- YARN: deploy Spark on top of Hadoop NextGen (YARN)
Other documents:
- Configuration: customize Spark via its configuration system
- Tuning Guide: best practices to optimize performance and memory use
- Security: Spark security support
- Hardware Provisioning: recommendations for cluster hardware
- Job Scheduling: scheduling resources across and within Spark applications
- Building Spark with Maven: build Spark using the Maven system
- Contributing to Spark
External resources:
- Spark Homepage
- Shark: Apache Hive over Spark
- Mailing Lists: ask questions about Spark here
- AMP Camps: a series of training camps at UC Berkeley that featured talks and exercises about Spark, Shark, Spark Streaming, Mesos, and more. Videos, slides and exercises are available online for free.
- Code Examples: more are also available in the examples subfolder of Spark
- Paper Describing Spark
- Paper Describing Spark Streaming
Community
To get help using Spark or keep up with Spark development, sign up for the user mailing list.
If you're in the San Francisco Bay Area, there's a regular Spark meetup every few weeks. Come by to meet the developers and other users.
Finally, if you'd like to contribute code to Spark, read how to contribute.