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apache / spark / 3d49bd496e8abfda816beea03269cd4094f2ec52
commit3d49bd496e8abfda816beea03269cd4094f2ec52[log] [tgz]
authorWenchen Fan <wenchen@databricks.com>Tue Apr 30 10:35:23 2019 +0800
committerWenchen Fan <wenchen@databricks.com>Tue Apr 30 10:35:52 2019 +0800
tree4ce42fb8801774bbe9a05a83a88a3dfc531e47a2
parentba9e12d55d043d6331df59a3829d40e41a9e2171 [diff]
[SPARK-24935][SQL][FOLLOWUP] support INIT -> UPDATE -> MERGE -> FINISH in Hive UDAF adapter

## What changes were proposed in this pull request?

This is a followup of https://github.com/apache/spark/pull/24144 . #24144 missed one case: when hash aggregate fallback to sort aggregate, the life cycle of UDAF is: INIT -> UPDATE -> MERGE -> FINISH.

However, not all Hive UDAF can support it. Hive UDAF knows the aggregation mode when creating the aggregation buffer, so that it can create different buffers for different inputs: the original data or the aggregation buffer. Please see an example in the [sketches library](https://github.com/DataSketches/sketches-hive/blob/7f9e76e9e03807277146291beb2c7bec40e8672b/src/main/java/com/yahoo/sketches/hive/cpc/DataToSketchUDAF.java#L107). The buffer for UPDATE may not support MERGE.

This PR updates the Hive UDAF adapter in Spark to support INIT -> UPDATE -> MERGE -> FINISH, by turning it to  INIT -> UPDATE -> FINISH + IINIT -> MERGE -> FINISH.

## How was this patch tested?

a new test case

Closes #24459 from cloud-fan/hive-udaf.

Authored-by: Wenchen Fan <wenchen@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 7432e7ded44cc0014590d229827546f5d8f93868)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2 files changed
tree: 4ce42fb8801774bbe9a05a83a88a3dfc531e47a2
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  11. examples/
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  18. mllib/
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  20. project/
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  25. sbin/
  26. sql/
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  32. CONTRIBUTING.md
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  37. pom.xml
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README.md

Apache Spark

Spark is a fast and general cluster computing system for Big Data. 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 Spark Streaming for stream processing.

http://spark.apache.org/

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 1000:

scala> sc.parallelize(1 to 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 1000:

>>> sc.parallelize(range(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.