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[SPARK-17271][SQL] Planner adds un-necessary Sort even if child ordering is...

Tejas Patil authored 8 years ago

[SPARK-17271][SQL] Planner adds un-necessary Sort even if child ordering is semantically same as required ordering

## What changes were proposed in this pull request?

Jira : https://issues.apache.org/jira/browse/SPARK-17271

Planner is adding un-needed SORT operation due to bug in the way comparison for `SortOrder` is done at https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/EnsureRequirements.scala#L253
`SortOrder` needs to be compared semantically because `Expression` within two `SortOrder` can be "semantically equal" but not literally equal objects.

eg. In case of `sql("SELECT * FROM table1 a JOIN table2 b ON a.col1=b.col1")`

Expression in required SortOrder:
```
      AttributeReference(
        name = "col1",
        dataType = LongType,
        nullable = false
      ) (exprId = exprId,
        qualifier = Some("a")
      )
```

Expression in child SortOrder:
```
      AttributeReference(
        name = "col1",
        dataType = LongType,
        nullable = false
      ) (exprId = exprId)
```

Notice that the output column has a qualifier but the child attribute does not but the inherent expression is the same and hence in this case we can say that the child satisfies the required sort order.

This PR includes following changes:
- Added a `semanticEquals` method to `SortOrder` so that it can compare underlying child expressions semantically (and not using default Object.equals)
- Fixed `EnsureRequirements` to use semantic comparison of SortOrder

## How was this patch tested?

- Added a test case to `PlannerSuite`. Ran rest tests in `PlannerSuite`

Author: Tejas Patil <tejasp@fb.com>

Closes #14841 from tejasapatil/SPARK-17271_sort_order_equals_bug.

095862a3

095862a3 8 years ago

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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 and project wiki. 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 developing Spark using an IDE, see Eclipse and IntelliJ.

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.

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" 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.