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### What changes were proposed in this pull request?

Add support for full outer join inside shuffled hash join. Currently if the query is a full outer join, we only use sort merge join as the physical operator. However it can be CPU and IO intensive in case input table is large for sort merge join. Shuffled hash join on the other hand saves the sort CPU and IO compared to sort merge join, especially when table is large.

This PR implements the full outer join as followed:
* Process rows from stream side by looking up hash relation, and mark the matched rows from build side by:
  * for joining with unique key, a `BitSet` is used to record matched rows from build side (`key index` to represent each row)
  * for joining with non-unique key, a `HashSet[Long]` is  used to record matched rows from build side (`key index` + `value index` to represent each row).
`key index` is defined as the index into key addressing array `longArray` in `BytesToBytesMap`.
`value index` is defined as the iterator index of values for same key.

* Process rows from build side by iterating hash relation, and filter out rows from build side being looked up already (done in `ShuffledHashJoinExec.fullOuterJoin`)

For context, this PR was originally implemented as followed (up to commit e332276):
1. Construct hash relation from build side, with extra boolean value at the end of row to track look up information (done in `ShuffledHashJoinExec.buildHashedRelation` and `UnsafeHashedRelation.apply`).
2. Process rows from stream side by looking up hash relation, and mark the matched rows from build side be looked up (done in `ShuffledHashJoinExec.fullOuterJoin`).
3. Process rows from build side by iterating hash relation, and filter out rows from build side being looked up already (done in `ShuffledHashJoinExec.fullOuterJoin`).

See discussion of pros and cons between these two approaches [here](#29342 (comment)), [here](#29342 (comment)) and [here](#29342 (comment)).

TODO: codegen for full outer shuffled hash join can be implemented in another followup PR.

### Why are the changes needed?

As implementation in this PR, full outer shuffled hash join will have overhead to iterate build side twice (once for building hash map, and another for outputting non-matching rows), and iterate stream side once. However, full outer sort merge join needs to iterate both sides twice, and sort the large table can be more CPU and IO intensive. So full outer shuffled hash join can be more efficient than sort merge join when stream side is much more larger than build side.

For example query below, full outer SHJ saved 30% wall clock time compared to full outer SMJ.

```
def shuffleHashJoin(): Unit = {
    val N: Long = 4 << 22
    withSQLConf(
      SQLConf.SHUFFLE_PARTITIONS.key -> "2",
      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "20000000") {
      codegenBenchmark("shuffle hash join", N) {
        val df1 = spark.range(N).selectExpr(s"cast(id as string) as k1")
        val df2 = spark.range(N / 10).selectExpr(s"cast(id * 10 as string) as k2")
        val df = df1.join(df2, col("k1") === col("k2"), "full_outer")
        df.noop()
    }
  }
}
```

```
Running benchmark: shuffle hash join
  Running case: shuffle hash join off
  Stopped after 2 iterations, 16602 ms
  Running case: shuffle hash join on
  Stopped after 5 iterations, 31911 ms

Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Mac OS X 10.15.4
Intel(R) Core(TM) i9-9980HK CPU  2.40GHz
shuffle hash join:                        Best Time(ms)   Avg Time(ms)   Stdev(ms)    Rate(M/s)   Per Row(ns)   Relative
------------------------------------------------------------------------------------------------------------------------
shuffle hash join off                              7900           8301         567          2.1         470.9       1.0X
shuffle hash join on                               6250           6382          95          2.7         372.5       1.3X
```

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Added unit test in `JoinSuite.scala`, `AbstractBytesToBytesMapSuite.java` and `HashedRelationSuite.scala`.

Closes #29342 from c21/full-outer-shj.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
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README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. 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 Structured Streaming for stream processing.

https://spark.apache.org/

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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.)

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 1,000,000,000:

scala> spark.range(1000 * 1000 * 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 1,000,000,000:

>>> spark.range(1000 * 1000 * 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.

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