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apache / spark / 90376424779f6792aa04fb745e50db561eb93346
commit90376424779f6792aa04fb745e50db561eb93346[log] [tgz]
authoraokolnychyi <aokolnychyi@apple.com>Wed Apr 05 22:10:50 2023 -0700
committerGengliang Wang <gengliang@apache.org>Wed Apr 05 22:10:57 2023 -0700
tree13950a47a434bebcf32f744dbd9497ee47ed604c
parentf2900f875e684b960525a55f2abf19bde1cf649c [diff]
[SPARK-43041][SQL] Restore constructors of exceptions for compatibility in connector API

### What changes were proposed in this pull request?

This PR adds back old constructors for exceptions used in the public connector API based on Spark 3.3.

### Why are the changes needed?

These changes are needed to avoid breaking connectors when consuming Spark 3.4.

Here is a list of exceptions used in the connector API (`org.apache.spark.sql.connector`):

```
NoSuchNamespaceException
NoSuchTableException
NoSuchViewException
NoSuchPartitionException
NoSuchPartitionsException (not referenced by public Catalog API but I assume it may be related to the exception above, which is referenced)
NoSuchFunctionException
NoSuchIndexException

NamespaceAlreadyExistsException
TableAlreadyExistsException
ViewAlreadyExistsException
PartitionAlreadyExistsException (not referenced by public Catalog API but I assume it may be related to the exception below, which is referenced)
PartitionsAlreadyExistException
IndexAlreadyExistsException
```

### Does this PR introduce _any_ user-facing change?

Adds back previously released constructors.

### How was this patch tested?

Existing tests.

Closes #40679 from aokolnychyi/spark-43041.

Authored-by: aokolnychyi <aokolnychyi@apple.com>
Signed-off-by: Gengliang Wang <gengliang@apache.org>
(cherry picked from commit 6546988ead06af8de33108ad0eb3f25af839eadc)
Signed-off-by: Gengliang Wang <gengliang@apache.org>
2 files changed
tree: 13950a47a434bebcf32f744dbd9497ee47ed604c
  1. .github/
  2. assembly/
  3. bin/
  4. binder/
  5. build/
  6. common/
  7. conf/
  8. connector/
  9. core/
  10. data/
  11. dev/
  12. docs/
  13. examples/
  14. graphx/
  15. hadoop-cloud/
  16. launcher/
  17. licenses/
  18. licenses-binary/
  19. mllib/
  20. mllib-local/
  21. project/
  22. python/
  23. R/
  24. repl/
  25. resource-managers/
  26. sbin/
  27. sql/
  28. streaming/
  29. tools/
  30. .asf.yaml
  31. .gitattributes
  32. .gitignore
  33. appveyor.yml
  34. CONTRIBUTING.md
  35. LICENSE
  36. LICENSE-binary
  37. NOTICE
  38. NOTICE-binary
  39. pom.xml
  40. README.md
  41. scalastyle-config.xml
README.md

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, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

GitHub Actions Build AppVeyor Build PySpark Coverage PyPI Downloads

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.)

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.