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SPARK-3883: SSL support for Akka connections and Jetty based file servers. This story introduced the following changes: - Introduced SSLOptions object which holds the SSL configuration and can build the appropriate configuration for Akka or Jetty. SSLOptions can be created by parsing SparkConf entries at a specified namespace. - SSLOptions is created and kept by SecurityManager - All Akka actor address creation snippets based on interpolated strings were replaced by a dedicated methods from AkkaUtils. Those methods select the proper Akka protocol - whether akka.tcp or akka.ssl.tcp - Added tests cases for AkkaUtils, FileServer, SSLOptions and SecurityManager - Added a way to use node local SSL configuration by executors and driver in standalone mode. It can be done by specifying spark.ssl.useNodeLocalConf in SparkConf. - Made CoarseGrainedExecutorBackend not overwrite the settings which are executor startup configuration - they are passed anyway from Worker Refer to https://github.com/apache/spark/pull/3571 for discussion and details Author: Jacek Lewandowski <lewandowski.jacek@gmail.com> Author: Jacek Lewandowski <jacek.lewandowski@datastax.com> Closes #3571 from jacek-lewandowski/SPARK-3883-master and squashes the following commits: 9ef4ed1 [Jacek Lewandowski] Merge pull request #2 from jacek-lewandowski/SPARK-3883-docs2 fb31b49 [Jacek Lewandowski] SPARK-3883: Added SSL setup documentation 2532668 [Jacek Lewandowski] SPARK-3883: Refactored AkkaUtils.protocol method to not use Try 90a8762 [Jacek Lewandowski] SPARK-3883: Refactored methods to resolve Akka address and made it possible to easily configure multiple communication layers for SSL 72b2541 [Jacek Lewandowski] SPARK-3883: A reference to the fallback SSLOptions can be provided when constructing SSLOptions 93050f4 [Jacek Lewandowski] SPARK-3883: SSL support for HttpServer and Akka |
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data/mllib | ||
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examples | ||
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tox.ini |
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".
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.