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[SPARK-16461][SQL] Support partition batch pruning with `<=>` predicate in InMemoryTableScanExec

hyukjinkwon authored 8 years ago

## What changes were proposed in this pull request?

It seems `EqualNullSafe` filter was missed for batch pruneing partitions in cached tables.

It seems supporting this improves the performance roughly 5 times faster.

Running the codes below:

```scala
test("Null-safe equal comparison") {
  val N = 20000000
  val df = spark.range(N).repartition(20)
  val benchmark = new Benchmark("Null-safe equal comparison", N)
  df.createOrReplaceTempView("t")
  spark.catalog.cacheTable("t")
  sql("select id from t where id <=> 1").collect()

  benchmark.addCase("Null-safe equal comparison", 10) { _ =>
    sql("select id from t where id <=> 1").collect()
  }
  benchmark.run()
}
```

produces the results below:

**Before:**

```
Running benchmark: Null-safe equal comparison
  Running case: Null-safe equal comparison
  Stopped after 10 iterations, 2098 ms

Java HotSpot(TM) 64-Bit Server VM 1.8.0_45-b14 on Mac OS X 10.11.5
Intel(R) Core(TM) i7-4850HQ CPU  2.30GHz

Null-safe equal comparison:              Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------
Null-safe equal comparison                     204 /  210         98.1          10.2       1.0X
```

**After:**

```
Running benchmark: Null-safe equal comparison
  Running case: Null-safe equal comparison
  Stopped after 10 iterations, 478 ms

Java HotSpot(TM) 64-Bit Server VM 1.8.0_45-b14 on Mac OS X 10.11.5
Intel(R) Core(TM) i7-4850HQ CPU  2.30GHz

Null-safe equal comparison:              Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------
Null-safe equal comparison                      42 /   48        474.1           2.1       1.0X
```

## How was this patch tested?

Unit tests in `PartitionBatchPruningSuite`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14117 from HyukjinKwon/SPARK-16461.

d314677c

d314677c 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.