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Herman van Hovell authored
## What changes were proposed in this pull request?
The optimizer tries to remove redundant alias only projections from the query plan using the `RemoveAliasOnlyProject` rule. The current rule identifies removes such a project and rewrites the project's attributes in the **entire** tree. This causes problems when parts of the tree are duplicated (for instance a self join on a temporary view/CTE)  and the duplicated part contains the alias only project, in this case the rewrite will break the tree.

This PR fixes these problems by using a blacklist for attributes that are not to be moved, and by making sure that attribute remapping is only done for the parent tree, and not for unrelated parts of the query plan.

The current tree transformation infrastructure works very well if the transformation at hand requires little or a global contextual information. In this case we need to know both the attributes that were not to be moved, and we also needed to know which child attributes were modified. This cannot be done easily using the current infrastructure, and solutions typically involves transversing the query plan multiple times (which is super slow). I have moved around some code in `TreeNode`, `QueryPlan` and `LogicalPlan`to make this much more straightforward; this basically allows you to manually traverse the tree.

This PR subsumes the following PRs by windpiger:
Closes https://github.com/apache/spark/pull/16267
Closes https://github.com/apache/spark/pull/16255

## How was this patch tested?
I have added unit tests to `RemoveRedundantAliasAndProjectSuite` and I have added integration tests to the `SQLQueryTestSuite.union` and `SQLQueryTestSuite.cte` test cases.

Author: Herman van Hovell <hvanhovell@databricks.com>

Closes #16757 from hvanhovell/SPARK-18609.
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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. 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.

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.

## Contributing

Please review the Contribution to Spark guide for information on how to get started contributing to the project.