b004150adb
Important updates to the streaming programming guide - Make the fault-tolerance properties easier to understand, with information about write ahead logs - Update the information about deploying the spark streaming app with information about Driver HA - Update Receiver guide to discuss reliable vs unreliable receivers. Author: Tathagata Das <tathagata.das1565@gmail.com> Author: Josh Rosen <joshrosen@databricks.com> Author: Josh Rosen <rosenville@gmail.com> Closes #3653 from tdas/streaming-doc-update-1.2 and squashes the following commits: f53154a [Tathagata Das] Addressed Josh's comments. ce299e4 [Tathagata Das] Minor update. ca19078 [Tathagata Das] Minor change f746951 [Tathagata Das] Mentioned performance problem with WAL 7787209 [Tathagata Das] Merge branch 'streaming-doc-update-1.2' of github.com:tdas/spark into streaming-doc-update-1.2 2184729 [Tathagata Das] Updated Kafka and Flume guides with reliability information. 2f3178c [Tathagata Das] Added more information about writing reliable receivers in the custom receiver guide. 91aa5aa [Tathagata Das] Improved API Docs menu 5707581 [Tathagata Das] Added Pythn API badge b9c8c24 [Tathagata Das] Merge pull request #26 from JoshRosen/streaming-programming-guide b8c8382 [Josh Rosen] minor fixes a4ef126 [Josh Rosen] Restructure parts of the fault-tolerance section to read a bit nicer when skipping over the headings 65f66cd [Josh Rosen] Fix broken link to fault-tolerance semantics section. f015397 [Josh Rosen] Minor grammar / pluralization fixes. 3019f3a [Josh Rosen] Fix minor Markdown formatting issues aa8bb87 [Tathagata Das] Small update. 195852c [Tathagata Das] Updated based on Josh's comments, updated receiver reliability and deploying section, and also updated configuration. 17b99fb [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into streaming-doc-update-1.2 a0217c0 [Tathagata Das] Changed Deploying menu layout 67fcffc [Tathagata Das] Added cluster mode + supervise example to submitting application guide. e45453b [Tathagata Das] Update streaming guide, added deploying section. 192c7a7 [Tathagata Das] Added more info about Python API, and rewrote the checkpointing section. |
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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.
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 with Maven".
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.