Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/12087#discussion_r58577527
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala ---
@@ -0,0 +1,79 @@
+/*
+ * 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.SparkContext
+import org.apache.spark.api.java.function.MapFunction
+import org.apache.spark.util.Benchmark
+
+/**
+ * Benchmark for Dataset typed operations.
+ */
+object DatasetBenchmark {
+
+ case class Data(i: Int, s: String)
+
+ def main(args: Array[String]): Unit = {
+ val sparkContext = new SparkContext("local[*]", "benchmark")
+ val sqlContext = new SQLContext(sparkContext)
+
+ import sqlContext.implicits._
+
+ val numRows = 10000000
+ val ds = sqlContext.range(numRows).map(l => Data(l.toInt, l.toString))
+ ds.cache()
+ ds.collect() // make sure data are cached
+
+ val benchmark = new Benchmark("Dataset.map", numRows)
+
+ val scalaFunc = (d: Data) => Data(d.i + 1, d.s)
+ benchmark.addCase("scala function") { iter =>
+ var res = ds
+ var i = 0
+ while (i < 10) {
+ res = res.map(scalaFunc)
+ i += 1
+ }
+ res.queryExecution.toRdd.count()
+ }
+
+ val javaFunc = new MapFunction[Data, Data] {
+ override def call(d: Data): Data = Data(d.i + 1, d.s)
+ }
+ val enc = implicitly[Encoder[Data]]
+ benchmark.addCase("java function") { iter =>
+ var res = ds
+ var i = 0
+ while (i < 10) {
+ res = res.map(javaFunc, enc)
+ i += 1
+ }
+ res.queryExecution.toRdd.count()
--- End diff --
This is doing the expensive conversion to external rows. Could you try to
run the existing benchmark which avoids this and also compares against RDDs?
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