[SPARK-32421][SQL] Add code-gen for shuffled hash join

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

Adding codegen for shuffled hash join. Shuffled hash join codegen is very similar to broadcast hash join codegen. So most of code change is to refactor existing codegen in `BroadcastHashJoinExec` to `HashJoin`.

Example codegen for query in [`JoinBenchmark`](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/JoinBenchmark.scala#L153):

```
  def shuffleHashJoin(): Unit = {
    val N: Long = 4 << 20
    withSQLConf(
      SQLConf.SHUFFLE_PARTITIONS.key -> "2",
      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "10000000",
      SQLConf.PREFER_SORTMERGEJOIN.key -> "false") {
      codegenBenchmark("shuffle hash join", N) {
        val df1 = spark.range(N).selectExpr(s"id as k1")
        val df2 = spark.range(N / 3).selectExpr(s"id * 3 as k2")
        val df = df1.join(df2, col("k1") === col("k2"))
        assert(df.queryExecution.sparkPlan.find(_.isInstanceOf[ShuffledHashJoinExec]).isDefined)
        df.noop()
      }
    }
  }
```

Shuffled hash join codegen:

```
== Subtree 3 / 3 (maxMethodCodeSize:113; maxConstantPoolSize:126(0.19% used); numInnerClasses:0) ==
*(3) ShuffledHashJoin [k1#2L], [k2#6L], Inner, BuildRight
:- *(1) Project [id#0L AS k1#2L]
:  +- *(1) Range (0, 4194304, step=1, splits=1)
+- *(2) Project [(id#4L * 3) AS k2#6L]
   +- *(2) Range (0, 1398101, step=1, splits=1)

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIteratorForCodegenStage3(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=3
/* 006 */ final class GeneratedIteratorForCodegenStage3 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */   private Object[] references;
/* 008 */   private scala.collection.Iterator[] inputs;
/* 009 */   private scala.collection.Iterator inputadapter_input_0;
/* 010 */   private org.apache.spark.sql.execution.joins.HashedRelation shj_relation_0;
/* 011 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] shj_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[1];
/* 012 */
/* 013 */   public GeneratedIteratorForCodegenStage3(Object[] references) {
/* 014 */     this.references = references;
/* 015 */   }
/* 016 */
/* 017 */   public void init(int index, scala.collection.Iterator[] inputs) {
/* 018 */     partitionIndex = index;
/* 019 */     this.inputs = inputs;
/* 020 */     inputadapter_input_0 = inputs[0];
/* 021 */     shj_relation_0 = ((org.apache.spark.sql.execution.joins.ShuffledHashJoinExec) references[0] /* plan */).buildHashedRelation(inputs[1]);
/* 022 */     shj_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(2, 0);
/* 023 */
/* 024 */   }
/* 025 */
/* 026 */   private void shj_doConsume_0(InternalRow inputadapter_row_0, long shj_expr_0_0) throws java.io.IOException {
/* 027 */     // generate join key for stream side
/* 028 */
/* 029 */     // find matches from HashRelation
/* 030 */     scala.collection.Iterator shj_matches_0 = false ?
/* 031 */     null : (scala.collection.Iterator)shj_relation_0.get(shj_expr_0_0);
/* 032 */     if (shj_matches_0 != null) {
/* 033 */       while (shj_matches_0.hasNext()) {
/* 034 */         UnsafeRow shj_matched_0 = (UnsafeRow) shj_matches_0.next();
/* 035 */         {
/* 036 */           ((org.apache.spark.sql.execution.metric.SQLMetric) references[1] /* numOutputRows */).add(1);
/* 037 */
/* 038 */           long shj_value_1 = shj_matched_0.getLong(0);
/* 039 */           shj_mutableStateArray_0[0].reset();
/* 040 */
/* 041 */           shj_mutableStateArray_0[0].write(0, shj_expr_0_0);
/* 042 */
/* 043 */           shj_mutableStateArray_0[0].write(1, shj_value_1);
/* 044 */           append((shj_mutableStateArray_0[0].getRow()).copy());
/* 045 */
/* 046 */         }
/* 047 */       }
/* 048 */     }
/* 049 */
/* 050 */   }
/* 051 */
/* 052 */   protected void processNext() throws java.io.IOException {
/* 053 */     while ( inputadapter_input_0.hasNext()) {
/* 054 */       InternalRow inputadapter_row_0 = (InternalRow) inputadapter_input_0.next();
/* 055 */
/* 056 */       long inputadapter_value_0 = inputadapter_row_0.getLong(0);
/* 057 */
/* 058 */       shj_doConsume_0(inputadapter_row_0, inputadapter_value_0);
/* 059 */       if (shouldStop()) return;
/* 060 */     }
/* 061 */   }
/* 062 */
/* 063 */ }
```

Broadcast hash join codegen for the same query (for reference here):

```
== Subtree 2 / 2 (maxMethodCodeSize:280; maxConstantPoolSize:218(0.33% used); numInnerClasses:0) ==
*(2) BroadcastHashJoin [k1#2L], [k2#6L], Inner, BuildRight, false
:- *(2) Project [id#0L AS k1#2L]
:  +- *(2) Range (0, 4194304, step=1, splits=1)
+- BroadcastExchange HashedRelationBroadcastMode(List(input[0, bigint, false]),false), [id=#22]
   +- *(1) Project [(id#4L * 3) AS k2#6L]
      +- *(1) Range (0, 1398101, step=1, splits=1)

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIteratorForCodegenStage2(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=2
/* 006 */ final class GeneratedIteratorForCodegenStage2 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */   private Object[] references;
/* 008 */   private scala.collection.Iterator[] inputs;
/* 009 */   private boolean range_initRange_0;
/* 010 */   private long range_nextIndex_0;
/* 011 */   private TaskContext range_taskContext_0;
/* 012 */   private InputMetrics range_inputMetrics_0;
/* 013 */   private long range_batchEnd_0;
/* 014 */   private long range_numElementsTodo_0;
/* 015 */   private org.apache.spark.sql.execution.joins.LongHashedRelation bhj_relation_0;
/* 016 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] range_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[4];
/* 017 */
/* 018 */   public GeneratedIteratorForCodegenStage2(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 */
/* 026 */     range_taskContext_0 = TaskContext.get();
/* 027 */     range_inputMetrics_0 = range_taskContext_0.taskMetrics().inputMetrics();
/* 028 */     range_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 029 */     range_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 030 */     range_mutableStateArray_0[2] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 031 */
/* 032 */     bhj_relation_0 = ((org.apache.spark.sql.execution.joins.LongHashedRelation) ((org.apache.spark.broadcast.TorrentBroadcast) references[1] /* broadcast */).value()).asReadOnlyCopy();
/* 033 */     incPeakExecutionMemory(bhj_relation_0.estimatedSize());
/* 034 */
/* 035 */     range_mutableStateArray_0[3] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(2, 0);
/* 036 */
/* 037 */   }
/* 038 */
/* 039 */   private void initRange(int idx) {
/* 040 */     java.math.BigInteger index = java.math.BigInteger.valueOf(idx);
/* 041 */     java.math.BigInteger numSlice = java.math.BigInteger.valueOf(1L);
/* 042 */     java.math.BigInteger numElement = java.math.BigInteger.valueOf(4194304L);
/* 043 */     java.math.BigInteger step = java.math.BigInteger.valueOf(1L);
/* 044 */     java.math.BigInteger start = java.math.BigInteger.valueOf(0L);
/* 045 */     long partitionEnd;
/* 046 */
/* 047 */     java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start);
/* 048 */     if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 049 */       range_nextIndex_0 = Long.MAX_VALUE;
/* 050 */     } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 051 */       range_nextIndex_0 = Long.MIN_VALUE;
/* 052 */     } else {
/* 053 */       range_nextIndex_0 = st.longValue();
/* 054 */     }
/* 055 */     range_batchEnd_0 = range_nextIndex_0;
/* 056 */
/* 057 */     java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice)
/* 058 */     .multiply(step).add(start);
/* 059 */     if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 060 */       partitionEnd = Long.MAX_VALUE;
/* 061 */     } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 062 */       partitionEnd = Long.MIN_VALUE;
/* 063 */     } else {
/* 064 */       partitionEnd = end.longValue();
/* 065 */     }
/* 066 */
/* 067 */     java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract(
/* 068 */       java.math.BigInteger.valueOf(range_nextIndex_0));
/* 069 */     range_numElementsTodo_0  = startToEnd.divide(step).longValue();
/* 070 */     if (range_numElementsTodo_0 < 0) {
/* 071 */       range_numElementsTodo_0 = 0;
/* 072 */     } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) {
/* 073 */       range_numElementsTodo_0++;
/* 074 */     }
/* 075 */   }
/* 076 */
/* 077 */   private void bhj_doConsume_0(long bhj_expr_0_0) throws java.io.IOException {
/* 078 */     // generate join key for stream side
/* 079 */
/* 080 */     // find matches from HashedRelation
/* 081 */     UnsafeRow bhj_matched_0 = false ? null: (UnsafeRow)bhj_relation_0.getValue(bhj_expr_0_0);
/* 082 */     if (bhj_matched_0 != null) {
/* 083 */       {
/* 084 */         ((org.apache.spark.sql.execution.metric.SQLMetric) references[2] /* numOutputRows */).add(1);
/* 085 */
/* 086 */         long bhj_value_2 = bhj_matched_0.getLong(0);
/* 087 */         range_mutableStateArray_0[3].reset();
/* 088 */
/* 089 */         range_mutableStateArray_0[3].write(0, bhj_expr_0_0);
/* 090 */
/* 091 */         range_mutableStateArray_0[3].write(1, bhj_value_2);
/* 092 */         append((range_mutableStateArray_0[3].getRow()));
/* 093 */
/* 094 */       }
/* 095 */     }
/* 096 */
/* 097 */   }
/* 098 */
/* 099 */   protected void processNext() throws java.io.IOException {
/* 100 */     // initialize Range
/* 101 */     if (!range_initRange_0) {
/* 102 */       range_initRange_0 = true;
/* 103 */       initRange(partitionIndex);
/* 104 */     }
/* 105 */
/* 106 */     while (true) {
/* 107 */       if (range_nextIndex_0 == range_batchEnd_0) {
/* 108 */         long range_nextBatchTodo_0;
/* 109 */         if (range_numElementsTodo_0 > 1000L) {
/* 110 */           range_nextBatchTodo_0 = 1000L;
/* 111 */           range_numElementsTodo_0 -= 1000L;
/* 112 */         } else {
/* 113 */           range_nextBatchTodo_0 = range_numElementsTodo_0;
/* 114 */           range_numElementsTodo_0 = 0;
/* 115 */           if (range_nextBatchTodo_0 == 0) break;
/* 116 */         }
/* 117 */         range_batchEnd_0 += range_nextBatchTodo_0 * 1L;
/* 118 */       }
/* 119 */
/* 120 */       int range_localEnd_0 = (int)((range_batchEnd_0 - range_nextIndex_0) / 1L);
/* 121 */       for (int range_localIdx_0 = 0; range_localIdx_0 < range_localEnd_0; range_localIdx_0++) {
/* 122 */         long range_value_0 = ((long)range_localIdx_0 * 1L) + range_nextIndex_0;
/* 123 */
/* 124 */         bhj_doConsume_0(range_value_0);
/* 125 */
/* 126 */         if (shouldStop()) {
/* 127 */           range_nextIndex_0 = range_value_0 + 1L;
/* 128 */           ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(range_localIdx_0 + 1);
/* 129 */           range_inputMetrics_0.incRecordsRead(range_localIdx_0 + 1);
/* 130 */           return;
/* 131 */         }
/* 132 */
/* 133 */       }
/* 134 */       range_nextIndex_0 = range_batchEnd_0;
/* 135 */       ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(range_localEnd_0);
/* 136 */       range_inputMetrics_0.incRecordsRead(range_localEnd_0);
/* 137 */       range_taskContext_0.killTaskIfInterrupted();
/* 138 */     }
/* 139 */   }
/* 140 */
/* 141 */ }
```

### Why are the changes needed?

Codegen shuffled hash join can help save CPU cost. We added shuffled hash join codegen internally in our fork, and seeing obvious improvement in benchmark compared to current non-codegen code path.

Test example query in [`JoinBenchmark`](https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/JoinBenchmark.scala#L153), seeing 30% wall clock time improvement compared to existing non-codegen code path:

Enable shuffled hash join code-gen:

```
Running benchmark: shuffle hash join
  Running case: shuffle hash join wholestage off
  Stopped after 2 iterations, 1358 ms
  Running case: shuffle hash join wholestage on
  Stopped after 5 iterations, 2323 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 wholestage off                    649            679          43          6.5         154.7       1.0X
shuffle hash join wholestage on                     436            465          45          9.6         103.9       1.5X
```

Disable shuffled hash join codegen:

```
Running benchmark: shuffle hash join
  Running case: shuffle hash join wholestage off
  Stopped after 2 iterations, 1345 ms
  Running case: shuffle hash join wholestage on
  Stopped after 5 iterations, 2967 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 wholestage off                    646            673          37          6.5         154.1       1.0X
shuffle hash join wholestage on                     549            594          47          7.6         130.9       1.2X
```

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

No.

### How was this patch tested?

Added unit test in `WholeStageCodegenSuite`.

Closes apache#29277 from c21/codegen.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

Author: c21

sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala

@@ -34,7 +34,7 @@ import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.catalyst.rules.Rule
 import org.apache.spark.sql.execution.aggregate.HashAggregateExec
 import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec
-import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, SortMergeJoinExec}
+import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, ShuffledHashJoinExec, SortMergeJoinExec}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.types._
@@ -50,6 +50,7 @@ trait CodegenSupport extends SparkPlan {
   private def variablePrefix: String = this match {
     case _: HashAggregateExec => "agg"
     case _: BroadcastHashJoinExec => "bhj"
+    case _: ShuffledHashJoinExec => "shj"
     case _: SortMergeJoinExec => "smj"
     case _: RDDScanExec => "rdd"
     case _: DataSourceScanExec => "scan"
@@ -903,6 +904,10 @@ case class CollapseCodegenStages(
         // The children of SortMergeJoin should do codegen separately.
         j.withNewChildren(j.children.map(
           child => InputAdapter(insertWholeStageCodegen(child))))
+      case j: ShuffledHashJoinExec =>
+        // The children of ShuffledHashJoin should do codegen separately.
+        j.withNewChildren(j.children.map(
+          child => InputAdapter(insertWholeStageCodegen(child))))
       case p => p.withNewChildren(p.children.map(insertInputAdapter))
     }
   }