61a5cced04
This creates a new module `network/yarn` that depends on `network/shuffle` recently created in #3001. This PR introduces a custom Yarn auxiliary service that runs the external shuffle service. As of the changes here this shuffle service is required for using dynamic allocation with Spark. This is still WIP mainly because it doesn't handle security yet. I have tested this on a stable Yarn cluster. Author: Andrew Or <andrew@databricks.com> Closes #3082 from andrewor14/yarn-shuffle-service and squashes the following commits: ef3ddae [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service 0ee67a2 [Andrew Or] Minor wording suggestions 1c66046 [Andrew Or] Remove unused provided dependencies 0eb6233 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service 6489db5 [Andrew Or] Try catch at the right places 7b71d8f [Andrew Or] Add detailed java docs + reword a few comments d1124e4 [Andrew Or] Add security to shuffle service (INCOMPLETE) 5f8a96f [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service 9b6e058 [Andrew Or] Address various feedback f48b20c [Andrew Or] Fix tests again f39daa6 [Andrew Or] Do not make network-yarn an assembly module 761f58a [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-service 15a5b37 [Andrew Or] Fix build for Hadoop 1.x baff916 [Andrew Or] Fix tests 5bf9b7e [Andrew Or] Address a few minor comments 5b419b8 [Andrew Or] Add missing license header 804e7ff [Andrew Or] Include the Yarn shuffle service jar in the distribution cd076a4 [Andrew Or] Require external shuffle service for dynamic allocation ea764e0 [Andrew Or] Connect to Yarn shuffle service only if it's enabled 1bf5109 [Andrew Or] Use the shuffle service port specified through hadoop config b4b1f0c [Andrew Or] 4 tabs -> 2 tabs 43dcb96 [Andrew Or] First cut integration of shuffle service with Yarn aux service b54a0c4 [Andrew Or] Initial skeleton for Yarn shuffle service |
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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. 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.