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### What changes were proposed in this pull request? JIRA: https://issues.apache.org/jira/browse/SPARK-28762 TL;DR: Automatically read the `Main-Class` from a JAR's manifest even if the JAR isn't in the local file system (i.e. in S3 or HDFS). ### Why are the changes needed? When deploying a fat JAR (e.g. using `sbt-assembly`) to S3/HDFS, users might choose to include the main class for the JAR in its manifest. This change allows the user to `spark-submit` the JAR without having to specify the main class again via the `--class` argument. ### Does this PR introduce any user-facing change? Yes. Previously, if the primary resource is a JAR and isn't located in the local file system, it will fail with the error: ``` $ spark-submit s3a://nonexistent.jar Exception in thread "main" org.apache.spark.SparkException: Cannot load main class from JAR s3a://nonexistent.jar with URI s3a. Please specify a class through --class. ... ``` With this PR, the main class will be read from the manifest, assuming the classpath contains the appropriate JAR to read the file system. ### How was this patch tested? Added some tests in `core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala`. Closes #25910 from igozali/SPARK-28762. Authored-by: Ivan Gozali <gozaliivan@gmail.com> Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com> |
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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.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". 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.