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### What changes were proposed in this pull request? Refactor the logic for constructing the user classpath from `yarn.ApplicationMaster` into `yarn.Client` so that it can be leveraged on the executor side as well, instead of having the driver construct it and pass it to the executor via command-line arguments. A new method, `getUserClassPath`, is added to `CoarseGrainedExecutorBackend` which defaults to `Nil` (consistent with the existing behavior where non-YARN resource managers do not configure the user classpath). `YarnCoarseGrainedExecutorBackend` overrides this to construct the user classpath from the existing `APP_JAR` and `SECONDARY_JARS` configs. ### Why are the changes needed? User-provided JARs are made available to executors using a custom classloader, so they do not appear on the standard Java classpath. Instead, they are passed as a list to the executor which then creates a classloader out of the URLs. Currently in the case of YARN, this list of JARs is crafted by the Driver (in `ExecutorRunnable`), which then passes the information to the executors (`CoarseGrainedExecutorBackend`) by specifying each JAR on the executor command line as `--user-class-path /path/to/myjar.jar`. This can cause extremely long argument lists when there are many JARs, which can cause the OS argument length to be exceeded, typically manifesting as the error message: > /bin/bash: Argument list too long A [Google search](https://www.google.com/search?q=spark%20%22%2Fbin%2Fbash%3A%20argument%20list%20too%20long%22&oq=spark%20%22%2Fbin%2Fbash%3A%20argument%20list%20too%20long%22) indicates that this is not a theoretical problem and afflicts real users, including ours. Passing this list using the configurations instead resolves this issue. ### Does this PR introduce _any_ user-facing change? No, except for fixing the bug, allowing for larger JAR lists to be passed successfully. Configuration of JARs is identical to before. ### How was this patch tested? New unit tests were added in `YarnClusterSuite`. Also, we have been running a similar fix internally for 4 months with great success. Closes #32810 from xkrogen/xkrogen-SPARK-35672-classpath-scalable. 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.