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### What changes were proposed in this pull request? This PR aims to use `mvn` instead of `sbt` in `dev/scalastyle` to recover GitHub Action. ### Why are the changes needed? As of now, Apache Spark sbt build is broken by the Maven Central repository policy. https://stackoverflow.com/questions/59764749/requests-to-http-repo1-maven-org-maven2-return-a-501-https-required-status-an > Effective January 15, 2020, The Central Maven Repository no longer supports insecure > communication over plain HTTP and requires that all requests to the repository are > encrypted over HTTPS. We can reproduce this locally by the following. ``` $ rm -rf ~/.m2/repository/org/apache/apache/18/ $ build/sbt clean ``` And, in GitHub Action, `lint-scala` is the only one which is using `sbt`. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? First of all, GitHub Action should be recovered. Also, manually, do the following. **Without Scalastyle violation** ``` $ dev/scalastyle OpenJDK 64-Bit Server VM warning: ignoring option MaxPermSize=384m; support was removed in 8.0 Using `mvn` from path: /usr/local/bin/mvn Scalastyle checks passed. ``` **With Scalastyle violation** ``` $ dev/scalastyle OpenJDK 64-Bit Server VM warning: ignoring option MaxPermSize=384m; support was removed in 8.0 Using `mvn` from path: /usr/local/bin/mvn Scalastyle checks failed at following occurrences: error file=/Users/dongjoon/PRS/SPARK-HTTP-501/core/src/main/scala/org/apache/spark/SparkConf.scala message=There should be no empty line separating imports in the same group. line=22 column=0 error file=/Users/dongjoon/PRS/SPARK-HTTP-501/core/src/test/scala/org/apache/spark/resource/ResourceProfileSuite.scala message=There should be no empty line separating imports in the same group. line=22 column=0 ``` Closes #27242 from dongjoon-hyun/SPARK-30534. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.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.