| commit | b7e95bad882482168b7dd301fcfa3daf80477a7a | [log] [tgz] |
|---|---|---|
| author | Josh Rosen <joshrosen@databricks.com> | Sat Jun 04 09:12:42 2022 +0300 |
| committer | Max Gekk <max.gekk@gmail.com> | Sat Jun 04 09:12:53 2022 +0300 |
| tree | d0c38fafc006c3a83801f7e689dbaa332071da98 | |
| parent | b2046c282a5be8ade421db61b583a6738f0e9ed6 [diff] |
[SPARK-39259][SQL][FOLLOWUP] Fix source and binary incompatibilities in transformDownWithSubqueries ### What changes were proposed in this pull request? This is a followup to #36654. That PR modified the existing `QueryPlan.transformDownWithSubqueries` to add additional arguments for tree pattern pruning. In this PR, I roll back the change to that method's signature and instead add a new `transformDownWithSubqueriesAndPruning` method. ### Why are the changes needed? The original change breaks binary and source compatibility in Catalyst. Technically speaking, Catalyst APIs are considered internal to Spark and are subject to change between minor releases (see [source](https://github.com/apache/spark/blob/bb51add5c79558df863d37965603387d40cc4387/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/package.scala#L20-L24)), but I think it's nice to try to avoid API breakage when possible. While trying to compile some custom Catalyst code, I ran into issues when trying to call the `transformDownWithSubqueries` method without supplying a tree pattern filter condition. If I do `transformDownWithSubqueries() { f} ` then I get a compilation error. I think this is due to the first parameter group containing all default parameters. My PR's solution of adding a new `transformDownWithSubqueriesAndPruning` method solves this problem. It's also more consistent with the naming convention used for other pruning-enabled tree transformation methods. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Existing tests. Closes #36765 from JoshRosen/SPARK-39259-binary-compatibility-followup. Authored-by: Josh Rosen <joshrosen@databricks.com> Signed-off-by: Max Gekk <max.gekk@gmail.com> (cherry picked from commit eda6c4b9987f0515cb0aae4686c8a0ae0a3987d4) Signed-off-by: Max Gekk <max.gekk@gmail.com>
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 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.
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.