310cd8eef1
This PR proposes to migrate Coverage report from Jenkins to GitHub Actions by setting a dailly cron job. For some background, currently PySpark code coverage is being reported in this specific Jenkins job: https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/ Because of the security issue between [Codecov service](https://app.codecov.io/gh/) and Jenkins machines, we had to work around by manually hosting a coverage site via GitHub pages, see also https://spark-test.github.io/pyspark-coverage-site/ by spark-test account (which is shared to only subset of PMC members). Since we now run the build via GitHub Actions, we can leverage [Codecov plugin](https://github.com/codecov/codecov-action), and remove the workaround we used. Virtually no. Coverage site (UI) might change but the information it holds should be virtually the same. I manually tested: - Scheduled run: https://github.com/HyukjinKwon/spark/actions/runs/1082261484 - Coverage report:73f0291a7d/python/pyspark- Run against a PR: https://github.com/HyukjinKwon/spark/actions/runs/1082367175 Closes #33591 from HyukjinKwon/SPARK-36092. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org> (cherry picked from commitc0d1860f25) Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
110 lines
4.4 KiB
Markdown
110 lines
4.4 KiB
Markdown
# Apache Spark
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Spark is a unified analytics engine for large-scale data processing. It provides
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high-level APIs in Scala, Java, Python, and R, and an optimized engine that
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supports general computation graphs for data analysis. It also supports a
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rich set of higher-level tools including Spark SQL for SQL and DataFrames,
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MLlib for machine learning, GraphX for graph processing,
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and Structured Streaming for stream processing.
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<https://spark.apache.org/>
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[](https://github.com/apache/spark/actions/workflows/build_and_test.yml?query=branch%3Amaster)
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[](https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-3.2)
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[](https://ci.appveyor.com/project/ApacheSoftwareFoundation/spark)
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[](https://codecov.io/gh/apache/spark)
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## Online Documentation
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You can find the latest Spark documentation, including a programming
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guide, on the [project web page](https://spark.apache.org/documentation.html).
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This README file only contains basic setup instructions.
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## Building Spark
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Spark is built using [Apache Maven](https://maven.apache.org/).
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To build Spark and its example programs, run:
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./build/mvn -DskipTests clean package
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(You do not need to do this if you downloaded a pre-built package.)
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More detailed documentation is available from the project site, at
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["Building Spark"](https://spark.apache.org/docs/latest/building-spark.html).
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For general development tips, including info on developing Spark using an IDE, see ["Useful Developer Tools"](https://spark.apache.org/developer-tools.html).
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## Interactive Scala Shell
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The easiest way to start using Spark is through the Scala shell:
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./bin/spark-shell
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Try the following command, which should return 1,000,000,000:
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scala> spark.range(1000 * 1000 * 1000).count()
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## Interactive Python Shell
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Alternatively, if you prefer Python, you can use the Python shell:
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./bin/pyspark
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And run the following command, which should also return 1,000,000,000:
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>>> spark.range(1000 * 1000 * 1000).count()
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## Example Programs
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Spark also comes with several sample programs in the `examples` directory.
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To run one of them, use `./bin/run-example <class> [params]`. For example:
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./bin/run-example SparkPi
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will run the Pi example locally.
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You can set the MASTER environment variable when running examples to submit
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examples to a cluster. This can be a mesos:// or spark:// URL,
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"yarn" to run on YARN, and "local" to run
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locally with one thread, or "local[N]" to run locally with N threads. You
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can also use an abbreviated class name if the class is in the `examples`
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package. For instance:
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MASTER=spark://host:7077 ./bin/run-example SparkPi
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Many of the example programs print usage help if no params are given.
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## Running Tests
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Testing first requires [building Spark](#building-spark). Once Spark is built, tests
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can be run using:
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./dev/run-tests
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Please see the guidance on how to
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[run tests for a module, or individual tests](https://spark.apache.org/developer-tools.html#individual-tests).
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There is also a Kubernetes integration test, see resource-managers/kubernetes/integration-tests/README.md
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## A Note About Hadoop Versions
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Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
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storage systems. Because the protocols have changed in different versions of
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Hadoop, you must build Spark against the same version that your cluster runs.
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Please refer to the build documentation at
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["Specifying the Hadoop Version and Enabling YARN"](https://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version-and-enabling-yarn)
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for detailed guidance on building for a particular distribution of Hadoop, including
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building for particular Hive and Hive Thriftserver distributions.
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## Configuration
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Please refer to the [Configuration Guide](https://spark.apache.org/docs/latest/configuration.html)
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in the online documentation for an overview on how to configure Spark.
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## Contributing
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Please review the [Contribution to Spark guide](https://spark.apache.org/contributing.html)
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for information on how to get started contributing to the project.
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