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[SPARK-4937][SQL] Normalizes conjunctions and disjunctions to eliminate common predicates

Cheng Lian authored 10 years ago

This PR is a simplified version of several filter optimization rules introduced in #3778 authored by scwf. Newly introduced optimizations include:

1. `a && a` => `a`
2. `a || a` => `a`
3. `(a || b || c || ...) && (a || b || d || ...)` => `a && b && (c || d || ...)`

The 3rd rule is particularly useful for optimizing the following query, which is planned into a cartesian product

```sql
SELECT *
  FROM t1, t2
 WHERE (t1.key = t2.key AND t1.value > 10)
    OR (t1.key = t2.key AND t2.value < 20)
```

to the following one, which is planned into an equi-join:

```sql
SELECT *
  FROM t1, t2
 WHERE t1.key = t2.key
   AND (t1.value > 10 OR t2.value < 20)
```

The example above is quite artificial, but common predicates are likely to appear in real life complex queries (like the one mentioned in #3778).

A difference between this PR and #3778 is that these optimizations are not limited to `Filter`, but are generalized to all logical plan nodes. Thanks to scwf for bringing up these optimizations, and chenghao-intel for the generalization suggestion.

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Author: Cheng Lian <lian@databricks.com>

Closes #3784 from liancheng/normalize-filters and squashes the following commits:

caca560 [Cheng Lian] Moves filter normalization into BooleanSimplification rule
4ab3a58 [Cheng Lian] Fixes test failure, adds more tests
5d54349 [Cheng Lian] Fixes typo in comment
2abbf8e [Cheng Lian] Forgot our sacred Apache licence header...
cf95639 [Cheng Lian] Adds an optimization rule for filter normalization

61a99f6a

61a99f6a 10 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, and Python, 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 structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

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Building Spark

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mvn -DskipTests clean package

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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()

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./bin/run-example SparkPi

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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 all automated 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. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.

Configuration

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