Github user marmbrus commented on a diff in the pull request:
https://github.com/apache/spark/pull/12087#discussion_r58582999
--- 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 --
Okay... its harder than I thought to run it on master :(
At least base the benchmark on these tests:
https://github.com/databricks/spark-sql-perf/blob/master/src/main/scala/com/databricks/spark/sql/perf/DatasetPerformance.scala
(back to back maps, compare with RDDs)
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
