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apache / spark / e26db0153ac25ae1b1759eb780da70dbd598b0d4
commite26db0153ac25ae1b1759eb780da70dbd598b0d4[log] [tgz]
authorYuming Wang <yumwang@ebay.com>Thu Jun 09 16:43:31 2022 +0300
committerMax Gekk <max.gekk@gmail.com>Thu Jun 09 16:43:44 2022 +0300
tree89a82af14ea305e24ffa3c48037be982ea8b1f06
parentd622f3133c1acd3f00aba0108838fdba826cef9b [diff]
[SPARK-39226][DOCS][FOLLOWUP] Update the migration guide after fixing the precision of the return type of round-like functions

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

Update the migration guide after fixing the precision of the return type of round-like functions.

How to reproduce this issue:
```sql
-- Spark 3.2
CREATE TABLE t1(CURNCY_AMT DECIMAL(18,6)) using parquet;
CREATE VIEW v1 AS SELECT BROUND(CURNCY_AMT, 6) AS CURNCY_AMT FROM t1;
```
```sql
-- Spark 3.3
 SELECT * FROM v1;
org.apache.spark.sql.AnalysisException: [CANNOT_UP_CAST_DATATYPE] Cannot up cast CURNCY_AMT from "DECIMAL(19,6)" to "DECIMAL(18,6)".
```

### Why are the changes needed?

Update the migration guide.

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

No.

### How was this patch tested?

N/A

Closes #36821 from wangyum/SPARK-39226.

Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Max Gekk <max.gekk@gmail.com>
(cherry picked from commit 105379406a371624569ac820e30d45fee3f017fc)
Signed-off-by: Max Gekk <max.gekk@gmail.com>
1 file changed
tree: 89a82af14ea305e24ffa3c48037be982ea8b1f06
  1. .github/
  2. .idea/
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  4. bin/
  5. binder/
  6. build/
  7. common/
  8. conf/
  9. core/
  10. data/
  11. dev/
  12. docs/
  13. examples/
  14. external/
  15. graphx/
  16. hadoop-cloud/
  17. launcher/
  18. licenses/
  19. licenses-binary/
  20. mllib/
  21. mllib-local/
  22. project/
  23. python/
  24. R/
  25. repl/
  26. resource-managers/
  27. sbin/
  28. sql/
  29. streaming/
  30. tools/
  31. .asf.yaml
  32. .gitattributes
  33. .gitignore
  34. appveyor.yml
  35. CONTRIBUTING.md
  36. LICENSE
  37. LICENSE-binary
  38. NOTICE
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  40. pom.xml
  41. README.md
  42. 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 Action Build AppVeyor Build PySpark Coverage

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.