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### What changes were proposed in this pull request? Add a new config, `spark.shuffle.service.name`, which allows for Spark applications to look for a YARN shuffle service which is defined at a name other than the default `spark_shuffle`. Add a new config, `spark.yarn.shuffle.service.metrics.namespace`, which allows for configuring the namespace used when emitting metrics from the shuffle service into the NodeManager's `metrics2` system. Add a new mechanism by which to override shuffle service configurations independently of the configurations in the NodeManager. When a resource `spark-shuffle-site.xml` is present on the classpath of the shuffle service, the configs present within it will be used to override the configs coming from `yarn-site.xml` (via the NodeManager). ### Why are the changes needed? There are two use cases which can benefit from these changes. One use case is to run multiple instances of the shuffle service side-by-side in the same NodeManager. This can be helpful, for example, when running a YARN cluster with a mixed workload of applications running multiple Spark versions, since a given version of the shuffle service is not always compatible with other versions of Spark (e.g. see SPARK-27780). With this PR, it is possible to run two shuffle services like `spark_shuffle` and `spark_shuffle_3.2.0`, one of which is "legacy" and one of which is for new applications. This is possible because YARN versions since 2.9.0 support the ability to run shuffle services within an isolated classloader (see YARN-4577), meaning multiple Spark versions can coexist. Besides this, the separation of shuffle service configs into `spark-shuffle-site.xml` can be useful for administrators who want to change and/or deploy Spark shuffle service configurations independently of the configurations for the NodeManager (e.g., perhaps they are owned by two different teams). ### Does this PR introduce _any_ user-facing change? Yes. There are two new configurations related to the external shuffle service, and a new mechanism which can optionally be used to configure the shuffle service. `docs/running-on-yarn.md` has been updated to provide user instructions; please see this guide for more details. ### How was this patch tested? In addition to the new unit tests added, I have deployed this to a live YARN cluster and successfully deployed two Spark shuffle services simultaneously, one running a modified version of Spark 2.3.0 (which supports some of the newer shuffle protocols) and one running Spark 3.1.1. Spark applications of both versions are able to communicate with their respective shuffle services without issue. Closes #31936 from xkrogen/xkrogen-SPARK-34828-shufflecompat-config-from-classpath. Authored-by: Erik Krogen <xkrogen@apache.org> Signed-off-by: Thomas Graves <tgraves@apache.org> |
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, 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 DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured 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:
./build/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".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
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 1,000,000,000:
scala> spark.range(1000 * 1000 * 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 1,000,000,000:
>>> spark.range(1000 * 1000 * 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" 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 tests for a module, or individual tests.
There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
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 and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Configuration
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Contributing
Please review the Contribution to Spark guide for information on how to get started contributing to the project.