| commit | da5aa706147c6a462a8470f8fdf2c3cd17fede01 | [log] [tgz] |
|---|---|---|
| author | Kent Yao <yao@apache.org> | Sat Nov 04 01:10:30 2023 +0800 |
| committer | Kent Yao <yao@apache.org> | Sat Nov 04 01:10:30 2023 +0800 |
| tree | 8d20f8f2810d1d4ada6e00d8ba35bf0f67eabe42 | |
| parent | ec875c521feed18f72200a8f87a2be5d9e3ccf96 [diff] |
[SPARK-45779][INFRA] Add a check step for jira issue ticket to be resolved ### What changes were proposed in this pull request? This PR moves the step for printing the summary of the Jira issue and adds a confirmation step for committers before assigning it to a user. Also, we show the final status of the Jira issue after resolution. ### Why are the changes needed? The JIRA ID mentioned in the PR title can sometimes be incorrect due to carelessness. Double-checking the ID is necessary to ensure that it is accurate. It is now too late for the committers to modify the ID once they have seen the ticket summary. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? Tested with SPARK-45776. Note that the message below may seem noisy because I declined the merge script at first. ``` Would you like to update an associated JIRA? (y/n): y Enter a JIRA id[SPARK-45776]: === JIRA SPARK-45776 === summary Remove the defensive null check added in SPARK-39553. assignee None status Open url https://issues.apache.org/jira/browse/SPARK-45776 Check if the JIRA information is as expected(y/n): n Enter the revised JIRA ID again or leave blank to skip[]: SPARK-45776 === JIRA SPARK-45776 === summary Remove the defensive null check added in SPARK-39553. assignee None status Open url https://issues.apache.org/jira/browse/SPARK-45776 Check if the JIRA information is as expected(y/n): y JIRA is unassigned, choose assignee [0] Yang Jie (Reporter) Enter number of user, or userid, to assign to (blank to leave unassigned):0 Enter comma-separated fix version(s) [4.0.0]: === JIRA SPARK-45776 === summary Remove the defensive null check added in SPARK-39553. assignee Yang Jie status RESOLVED url https://issues.apache.org/jira/browse/SPARK-45776 Successfully resolved SPARK-45776 with fixVersions=['4.0.0']! Enter a JIRA id[SPARK-39553]: ``` ### Was this patch authored or co-authored using generative AI tooling? no Closes #43648 from yaooqinn/SPARK-45779. Authored-by: Kent Yao <yao@apache.org> Signed-off-by: Kent Yao <yao@apache.org>
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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
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”.
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()
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()
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 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.
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
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.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.
