Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/10809#discussion_r50036589
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/UnsafeProjectionBenchmark.scala
---
@@ -0,0 +1,127 @@
+/*
+ * 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
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.encoders.RowEncoder
+import org.apache.spark.sql.catalyst.expressions.UnsafeProjection
+import org.apache.spark.sql.types._
+import org.apache.spark.util.Benchmark
+
+/**
+ * Benchmark [[UnsafeProjection]] for flat schema(primitive-type fields).
+ */
+object UnsafeProjectionBenchmark {
+
+ def generateRows(schema: StructType, numRows: Int): Array[InternalRow] =
{
+ val generator = RandomDataGenerator.forType(schema, nullable =
false).get
+ val encoder = RowEncoder(schema)
+ (1 to numRows).map(_ =>
encoder.toRow(generator().asInstanceOf[Row]).copy()).toArray
+ }
+
+ def main(args: Array[String]) {
+ val iters = 1024 * 16
+ val numRows = 1024 * 16
+
+ val benchmark = new Benchmark("unsafe projection", iters * numRows)
+
+
+ val schema1 = new StructType().add("l", LongType, false)
+ val attrs1 = schema1.toAttributes
+ val rows1 = generateRows(schema1, numRows)
+ val projection1 = UnsafeProjection.create(attrs1, attrs1)
+
+ benchmark.addCase("single long") { _ =>
+ for (_ <- 1 to iters) {
+ var sum = 0L
+ var i = 0
+ while (i < numRows) {
+ sum += projection1(rows1(i)).getLong(0)
+ i += 1
+ }
+ }
+ }
+
+ val schema2 = new StructType().add("l", LongType, true)
+ val attrs2 = schema2.toAttributes
+ val rows2 = generateRows(schema2, numRows)
+ val projection2 = UnsafeProjection.create(attrs2, attrs2)
+
+ benchmark.addCase("single nullable long") { _ =>
+ for (_ <- 1 to iters) {
+ var sum = 0L
+ var i = 0
+ while (i < numRows) {
+ sum += projection2(rows2(i)).getLong(0)
+ i += 1
+ }
+ }
+ }
+
+
+ val schema3 = new StructType()
+ .add("boolean", BooleanType, false)
+ .add("byte", ByteType, false)
+ .add("short", ShortType, false)
+ .add("int", IntegerType, false)
+ .add("long", LongType, false)
+ .add("float", FloatType, false)
+ .add("double", DoubleType, false)
+ val attrs3 = schema3.toAttributes
+ val rows3 = generateRows(schema3, numRows)
+ val projection3 = UnsafeProjection.create(attrs3, attrs3)
+
+ benchmark.addCase("primitive types") { _ =>
+ for (_ <- 1 to iters) {
+ var sum = 0L
+ var i = 0
+ while (i < numRows) {
+ sum += projection3(rows3(i)).getLong(0)
+ i += 1
+ }
+ }
+ }
+
+
+ val schema4 = new StructType()
+ .add("boolean", BooleanType, true)
+ .add("byte", ByteType, true)
+ .add("short", ShortType, true)
+ .add("int", IntegerType, true)
+ .add("long", LongType, true)
+ .add("float", FloatType, true)
+ .add("double", DoubleType, true)
+ val attrs4 = schema4.toAttributes
+ val rows4 = generateRows(schema4, numRows)
+ val projection4 = UnsafeProjection.create(attrs4, attrs4)
+
+ benchmark.addCase("nullable primitive types") { _ =>
+ for (_ <- 1 to iters) {
+ var sum = 0L
+ var i = 0
+ while (i < numRows) {
+ sum += projection4(rows4(i)).getLong(0)
+ i += 1
+ }
+ }
+ }
+
+
+ benchmark.run()
--- End diff --
just put the result of new version? Actually I copy and paste this
benchmark to master and run, so that I can see how much this PR speeds up.
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