Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21296#discussion_r187499797
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVBenchmarks.scala
---
@@ -0,0 +1,92 @@
+/*
+ * 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.datasources.csv
+
+import java.io.File
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.{Column, Row, SparkSession}
+import org.apache.spark.sql.functions.lit
+import org.apache.spark.sql.types._
+import org.apache.spark.util.{Benchmark, Utils}
+
+/**
+ * Benchmark to measure CSV read/write performance.
+ * To run this:
+ * spark-submit --class <this class> --jars <spark sql test jar>
+ */
+object CSVBenchmarks {
+ val conf = new SparkConf()
+
+ val spark = SparkSession.builder
+ .master("local[1]")
+ .appName("benchmark-csv-datasource")
+ .config(conf)
+ .getOrCreate()
+ import spark.implicits._
+
+ def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ def multiColumnsBenchmark(rowsNum: Int): Unit = {
+ val colsNum = 1000
+ val benchmark = new Benchmark(s"Wide rows with $colsNum columns",
rowsNum)
+
+ withTempPath { path =>
+ val fields = Seq.tabulate(colsNum)(i => StructField(s"col$i",
IntegerType))
+ val schema = StructType(fields)
+ val values = (0 until colsNum).map(i => i.toString).mkString(",")
+ val columnNames = schema.fieldNames
+
+ spark.range(rowsNum)
+ .select(Seq.tabulate(colsNum)(i => lit(i).as(s"col$i")): _*)
+ .write.option("header", true)
+ .csv(path.getAbsolutePath)
+
+ val ds = spark.read.schema(schema).csv(path.getAbsolutePath)
+
+ benchmark.addCase(s"Select $colsNum columns", 3) { _ =>
+ ds.select("*").filter((row: Row) => true).count()
+ }
+ val cols100 = columnNames.take(100).map(Column(_))
+ benchmark.addCase(s"Select 100 columns", 3) { _ =>
+ ds.select(cols100: _*).filter((row: Row) => true).count()
+ }
+ benchmark.addCase(s"Select one column", 3) { _ =>
+ ds.select($"col1").filter((row: Row) => true).count()
+ }
+
+ /*
+ Intel(R) Core(TM) i7-7920HQ CPU @ 3.10GHz
+
+ Wide rows with 1000 columns: Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
--------------------------------------------------------------------------------------------
+ Select 1000 columns 76910 / 78065 0.0
76909.8 1.0X
+ Select 100 columns 28625 / 32884 0.0
28625.1 2.7X
+ Select one column 22498 / 22669 0.0
22497.8 3.4X
--- End diff --
BTW, I think we are already doing the column pruning by avoiding casting
cost which is relatively expensive comparing to the parsing logic.
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