cce0048c78
### What changes were proposed in this pull request?
As title. This PR is to add code-gen support for LEFT ANTI sort merge join. The main change is to extract `loadStreamed` in `SortMergeJoinExec.doProduce()`. That is to set all columns values for streamed row, when the streamed row has no output row.
Example query:
```
val df1 = spark.range(10).select($"id".as("k1"))
val df2 = spark.range(4).select($"id".as("k2"))
df1.join(df2.hint("SHUFFLE_MERGE"), $"k1" === $"k2", "left_anti")
```
Example generated code:
```
== Subtree 5 / 5 (maxMethodCodeSize:296; maxConstantPoolSize:156(0.24% used); numInnerClasses:0) ==
*(5) Project [id#0L AS k1#2L]
+- *(5) SortMergeJoin [id#0L], [k2#6L], LeftAnti
:- *(2) Sort [id#0L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#0L, 5), ENSURE_REQUIREMENTS, [id=#27]
: +- *(1) Range (0, 10, step=1, splits=2)
+- *(4) Sort [k2#6L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(k2#6L, 5), ENSURE_REQUIREMENTS, [id=#33]
+- *(3) Project [id#4L AS k2#6L]
+- *(3) Range (0, 4, step=1, splits=2)
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage5(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=5
/* 006 */ final class GeneratedIteratorForCodegenStage5 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */ private Object[] references;
/* 008 */ private scala.collection.Iterator[] inputs;
/* 009 */ private scala.collection.Iterator smj_streamedInput_0;
/* 010 */ private scala.collection.Iterator smj_bufferedInput_0;
/* 011 */ private InternalRow smj_streamedRow_0;
/* 012 */ private InternalRow smj_bufferedRow_0;
/* 013 */ private long smj_value_2;
/* 014 */ private org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray smj_matches_0;
/* 015 */ private long smj_value_3;
/* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] smj_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2];
/* 017 */
/* 018 */ public GeneratedIteratorForCodegenStage5(Object[] references) {
/* 019 */ this.references = references;
/* 020 */ }
/* 021 */
/* 022 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 023 */ partitionIndex = index;
/* 024 */ this.inputs = inputs;
/* 025 */ smj_streamedInput_0 = inputs[0];
/* 026 */ smj_bufferedInput_0 = inputs[1];
/* 027 */
/* 028 */ smj_matches_0 = new org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray(1, 2147483647);
/* 029 */ smj_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 030 */ smj_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 031 */
/* 032 */ }
/* 033 */
/* 034 */ private boolean findNextJoinRows(
/* 035 */ scala.collection.Iterator streamedIter,
/* 036 */ scala.collection.Iterator bufferedIter) {
/* 037 */ smj_streamedRow_0 = null;
/* 038 */ int comp = 0;
/* 039 */ while (smj_streamedRow_0 == null) {
/* 040 */ if (!streamedIter.hasNext()) return false;
/* 041 */ smj_streamedRow_0 = (InternalRow) streamedIter.next();
/* 042 */ long smj_value_0 = smj_streamedRow_0.getLong(0);
/* 043 */ if (false) {
/* 044 */ if (!smj_matches_0.isEmpty()) {
/* 045 */ smj_matches_0.clear();
/* 046 */ }
/* 047 */ return false;
/* 048 */
/* 049 */ }
/* 050 */ if (!smj_matches_0.isEmpty()) {
/* 051 */ comp = 0;
/* 052 */ if (comp == 0) {
/* 053 */ comp = (smj_value_0 > smj_value_3 ? 1 : smj_value_0 < smj_value_3 ? -1 : 0);
/* 054 */ }
/* 055 */
/* 056 */ if (comp == 0) {
/* 057 */ return true;
/* 058 */ }
/* 059 */ smj_matches_0.clear();
/* 060 */ }
/* 061 */
/* 062 */ do {
/* 063 */ if (smj_bufferedRow_0 == null) {
/* 064 */ if (!bufferedIter.hasNext()) {
/* 065 */ smj_value_3 = smj_value_0;
/* 066 */ return !smj_matches_0.isEmpty();
/* 067 */ }
/* 068 */ smj_bufferedRow_0 = (InternalRow) bufferedIter.next();
/* 069 */ long smj_value_1 = smj_bufferedRow_0.getLong(0);
/* 070 */ if (false) {
/* 071 */ smj_bufferedRow_0 = null;
/* 072 */ continue;
/* 073 */ }
/* 074 */ smj_value_2 = smj_value_1;
/* 075 */ }
/* 076 */
/* 077 */ comp = 0;
/* 078 */ if (comp == 0) {
/* 079 */ comp = (smj_value_0 > smj_value_2 ? 1 : smj_value_0 < smj_value_2 ? -1 : 0);
/* 080 */ }
/* 081 */
/* 082 */ if (comp > 0) {
/* 083 */ smj_bufferedRow_0 = null;
/* 084 */ } else if (comp < 0) {
/* 085 */ if (!smj_matches_0.isEmpty()) {
/* 086 */ smj_value_3 = smj_value_0;
/* 087 */ return true;
/* 088 */ } else {
/* 089 */ return false;
/* 090 */ }
/* 091 */ } else {
/* 092 */ if (smj_matches_0.isEmpty()) {
/* 093 */ smj_matches_0.add((UnsafeRow) smj_bufferedRow_0);
/* 094 */ }
/* 095 */
/* 096 */ smj_bufferedRow_0 = null;
/* 097 */ }
/* 098 */ } while (smj_streamedRow_0 != null);
/* 099 */ }
/* 100 */ return false; // unreachable
/* 101 */ }
/* 102 */
/* 103 */ protected void processNext() throws java.io.IOException {
/* 104 */ while (smj_streamedInput_0.hasNext()) {
/* 105 */ findNextJoinRows(smj_streamedInput_0, smj_bufferedInput_0);
/* 106 */
/* 107 */ long smj_value_4 = -1L;
/* 108 */ smj_value_4 = smj_streamedRow_0.getLong(0);
/* 109 */ scala.collection.Iterator<UnsafeRow> smj_iterator_0 = smj_matches_0.generateIterator();
/* 110 */
/* 111 */ boolean wholestagecodegen_hasOutputRow_0 = false;
/* 112 */
/* 113 */ while (!wholestagecodegen_hasOutputRow_0 && smj_iterator_0.hasNext()) {
/* 114 */ InternalRow smj_bufferedRow_1 = (InternalRow) smj_iterator_0.next();
/* 115 */
/* 116 */ wholestagecodegen_hasOutputRow_0 = true;
/* 117 */ }
/* 118 */
/* 119 */ if (!wholestagecodegen_hasOutputRow_0) {
/* 120 */ // load all values of streamed row, because the values not in join condition are not
/* 121 */ // loaded yet.
/* 122 */
/* 123 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 124 */
/* 125 */ // common sub-expressions
/* 126 */
/* 127 */ smj_mutableStateArray_0[1].reset();
/* 128 */
/* 129 */ smj_mutableStateArray_0[1].write(0, smj_value_4);
/* 130 */ append((smj_mutableStateArray_0[1].getRow()).copy());
/* 131 */
/* 132 */ }
/* 133 */ if (shouldStop()) return;
/* 134 */ }
/* 135 */ ((org.apache.spark.sql.execution.joins.SortMergeJoinExec) references[1] /* plan */).cleanupResources();
/* 136 */ }
/* 137 */
/* 138 */ }
```
### Why are the changes needed?
Improve the query CPU performance.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Added unit test in `WholeStageCodegenSuite.scala`, and existed unit test in `ExistenceJoinSuite.scala`.
Closes #32547 from c21/smj-left-anti.
Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org>
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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.
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.