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[SPARK-5493] [core] Add option to impersonate user.

Marcelo Vanzin authored 10 years ago

Hadoop has a feature that allows users to impersonate other users
when submitting applications or talking to HDFS, for example. These
impersonated users are referred generally as "proxy users".

Services such as Oozie or Hive use this feature to run applications
as the requesting user.

This change makes SparkSubmit accept a new command line option to
run the application as a proxy user. It also fixes the plumbing
of the user name through the UI (and a couple of other places) to
refer to the correct user running the application, which can be
different than `sys.props("user.name")` even without proxies (e.g.
when using kerberos).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #4405 from vanzin/SPARK-5493 and squashes the following commits:

df82427 [Marcelo Vanzin] Clarify the reason for the special exception handling.
05bfc08 [Marcelo Vanzin] Remove unneeded annotation.
4840de9 [Marcelo Vanzin] Review feedback.
8af06ff [Marcelo Vanzin] Fix usage string.
2e4fa8f [Marcelo Vanzin] Merge branch 'master' into SPARK-5493
b6c947d [Marcelo Vanzin] Merge branch 'master' into SPARK-5493
0540d38 [Marcelo Vanzin] [SPARK-5493] [core] Add option to impersonate user.

ed167e70

ed167e70 10 years ago

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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.

http://spark.apache.org/

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.