[SPARK-17115][SQL] decrease the threshold when split expressions

## What changes were proposed in this pull request?

In 2.0, we change the threshold of splitting expressions from 16K to 64K, which cause very bad performance on wide table, because the generated method can't be JIT compiled by default (above the limit of 8K bytecode).

This PR will decrease it to 1K, based on the benchmark results for a wide table with 400 columns of LongType.

It also fix a bug around splitting expression in whole-stage codegen (it should not split them).

## How was this patch tested?

Added benchmark suite.

Author: Davies Liu <[email protected]>

Closes #14692 from davies/split_exprs.

(cherry picked from commit 8d35a6f)
Signed-off-by: Wenchen Fan <[email protected]>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala

@@ -584,15 +584,18 @@ class CodegenContext {
    * @param expressions the codes to evaluate expressions.
    */
   def splitExpressions(row: String, expressions: Seq[String]): String = {
-    if (row == null) {
+    if (row == null || currentVars != null) {
       // Cannot split these expressions because they are not created from a row object.
       return expressions.mkString("\n")
     }
     val blocks = new ArrayBuffer[String]()
     val blockBuilder = new StringBuilder()
     for (code <- expressions) {
-      // We can't know how many byte code will be generated, so use the number of bytes as limit
-      if (blockBuilder.length > 64 * 1000) {
+      // We can't know how many bytecode will be generated, so use the length of source code
+      // as metric. A method should not go beyond 8K, otherwise it will not be JITted, should
+      // also not be too small, or it will have many function calls (for wide table), see the
+      // results in BenchmarkWideTable.
+      if (blockBuilder.length > 1024) {
         blocks.append(blockBuilder.toString())
         blockBuilder.clear()
       }

sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala

@@ -603,8 +603,6 @@ case class HashAggregateExec(
 
     // create grouping key
     ctx.currentVars = input
-    // make sure that the generated code will not be splitted as multiple functions
-    ctx.INPUT_ROW = null
     val unsafeRowKeyCode = GenerateUnsafeProjection.createCode(
       ctx, groupingExpressions.map(e => BindReferences.bindReference[Expression](e, child.output)))
     val vectorizedRowKeys = ctx.generateExpressions(

sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/BenchmarkWideTable.scala

@@ -0,0 +1,53 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.benchmark
+
+import org.apache.spark.util.Benchmark
+
+
+/**
+ * Benchmark to measure performance for wide table.
+ * To run this:
+ *  build/sbt "sql/test-only *benchmark.BenchmarkWideTable"
+ *
+ * Benchmarks in this file are skipped in normal builds.
+ */
+class BenchmarkWideTable extends BenchmarkBase {
+
+  ignore("project on wide table") {
+    val N = 1 << 20
+    val df = sparkSession.range(N)
+    val columns = (0 until 400).map{ i => s"id as id$i"}
+    val benchmark = new Benchmark("projection on wide table", N)
+    benchmark.addCase("wide table", numIters = 5) { iter =>
+      df.selectExpr(columns : _*).queryExecution.toRdd.count()
+    }
+    benchmark.run()
+
+    /**
+     * Here are some numbers with different split threshold:
+     *
+     *  Split threshold      methods       Rate(M/s)   Per Row(ns)
+     *  10                   400           0.4         2279
+     *  100                  200           0.6         1554
+     *  1k                   37            0.9         1116
+     *  8k                   5             0.5         2025
+     *  64k                  1             0.0        21649
+     */
+  }
+}