Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/20265#discussion_r161981031
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/FilterPushdownBenchmark.scala ---
@@ -0,0 +1,243 @@
+/*
+ * 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 java.io.File
+
+import scala.util.{Random, Try}
+
+import org.apache.spark.SparkConf
+import org.apache.spark.sql.functions.monotonically_increasing_id
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.util.{Benchmark, Utils}
+
+
+/**
+ * Benchmark to measure read performance with Filter pushdown.
+ */
+object FilterPushdownBenchmark {
+ val conf = new SparkConf()
+ conf.set("orc.compression", "snappy")
+ conf.set("spark.sql.parquet.compression.codec", "snappy")
+
+ private val spark = SparkSession.builder()
+ .master("local[1]")
+ .appName("FilterPushdownBenchmark")
+ .config(conf)
+ .getOrCreate()
+
+ def withTempPath(f: File => Unit): Unit = {
+ val path = Utils.createTempDir()
+ path.delete()
+ try f(path) finally Utils.deleteRecursively(path)
+ }
+
+ def withTempTable(tableNames: String*)(f: => Unit): Unit = {
+ try f finally tableNames.foreach(spark.catalog.dropTempView)
+ }
+
+ def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
+ val (keys, values) = pairs.unzip
+ val currentValues = keys.map(key => Try(spark.conf.get(key)).toOption)
+ (keys, values).zipped.foreach(spark.conf.set)
+ try f finally {
+ keys.zip(currentValues).foreach {
+ case (key, Some(value)) => spark.conf.set(key, value)
+ case (key, None) => spark.conf.unset(key)
+ }
+ }
+ }
+
+ private def prepareTable(dir: File, numRows: Int, width: Int): Unit = {
+ import spark.implicits._
+ val selectExpr = (1 to width).map(i => s"CAST(value AS STRING) c$i")
+ val df = spark.range(numRows).map(_ =>
Random.nextLong).selectExpr(selectExpr: _*)
+ .withColumn("id", monotonically_increasing_id())
+
+ val dirORC = dir.getCanonicalPath + "/orc"
+ val dirParquet = dir.getCanonicalPath + "/parquet"
+
+ df.write.mode("overwrite").orc(dirORC)
+ df.write.mode("overwrite").parquet(dirParquet)
+
+ spark.read.orc(dirORC).createOrReplaceTempView("orcTable")
+ spark.read.parquet(dirParquet).createOrReplaceTempView("parquetTable")
+ }
+
+ def filterPushDownBenchmark(
+ values: Int,
+ title: String,
+ whereExpr: String,
+ selectExpr: String = "*"): Unit = {
+ val benchmark = new Benchmark(title, values, minNumIters = 5)
+
+ Seq(false, true).foreach { pushDownEnabled =>
+ val name = s"Parquet Vectorized ${if (pushDownEnabled) s"(Pushdown)"
else ""}"
+ benchmark.addCase(name) { _ =>
+ withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key ->
s"$pushDownEnabled") {
+ spark.sql(s"SELECT $selectExpr FROM parquetTable WHERE
$whereExpr").collect()
+ }
+ }
+ }
+
+ Seq(false, true).foreach { pushDownEnabled =>
+ val name = s"Native ORC Vectorized ${if (pushDownEnabled)
s"(Pushdown)" else ""}"
+ benchmark.addCase(name) { _ =>
+ withSQLConf(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key ->
s"$pushDownEnabled") {
+ spark.sql(s"SELECT $selectExpr FROM orcTable WHERE
$whereExpr").collect()
+ }
+ }
+ }
+
+ /*
+ Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.2
+ Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz
+
+ Select 0 row (id IS NULL): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7906 / 7955 2.0
502.6 1.0X
+ Parquet Vectorized (Pushdown) 56 / 60 281.1
3.6 141.3X
+ Native ORC Vectorized 5655 / 5700 2.8
359.5 1.4X
+ Native ORC Vectorized (Pushdown) 68 / 71 233.0
4.3 117.1X
+
+ Select 0 row (7864320 < id < 7864320): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7891 / 7922 2.0
501.7 1.0X
+ Parquet Vectorized (Pushdown) 746 / 769 21.1
47.5 10.6X
+ Native ORC Vectorized 5645 / 5686 2.8
358.9 1.4X
+ Native ORC Vectorized (Pushdown) 82 / 84 192.9
5.2 96.8X
+
+ Select 1 row (id = 7864320): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7963 / 8069 2.0
506.3 1.0X
+ Parquet Vectorized (Pushdown) 752 / 778 20.9
47.8 10.6X
+ Native ORC Vectorized 5726 / 5789 2.7
364.1 1.4X
+ Native ORC Vectorized (Pushdown) 78 / 81 201.4
5.0 102.0X
+
+ Select 1 row (id <=> 7864320): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7983 / 8015 2.0
507.5 1.0X
+ Parquet Vectorized (Pushdown) 753 / 774 20.9
47.9 10.6X
+ Native ORC Vectorized 5772 / 5814 2.7
367.0 1.4X
+ Native ORC Vectorized (Pushdown) 76 / 78 207.3
4.8 105.2X
+
+ Select 1 row (7864320 <= id <= 7864320):Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7929 / 7999 2.0
504.1 1.0X
+ Parquet Vectorized (Pushdown) 747 / 770 21.1
47.5 10.6X
+ Native ORC Vectorized 5756 / 5810 2.7
366.0 1.4X
+ Native ORC Vectorized (Pushdown) 76 / 79 206.4
4.8 104.0X
+
+ Select 1 row (7864319 < id < 7864321): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7968 / 8027 2.0
506.6 1.0X
+ Parquet Vectorized (Pushdown) 750 / 771 21.0
47.7 10.6X
+ Native ORC Vectorized 5776 / 5811 2.7
367.2 1.4X
+ Native ORC Vectorized (Pushdown) 75 / 78 208.5
4.8 105.6X
+
+ Select 10% rows (id < 1572864): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 8156 / 8257 1.9
518.5 1.0X
+ Parquet Vectorized (Pushdown) 1620 / 1684 9.7
103.0 5.0X
+ Native ORC Vectorized 5951 / 5990 2.6
378.3 1.4X
+ Native ORC Vectorized (Pushdown) 803 / 810 19.6
51.0 10.2X
+
+ Select 50% rows (id < 7864320): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 8690 / 8717 1.8
552.5 1.0X
+ Parquet Vectorized (Pushdown) 5067 / 5099 3.1
322.2 1.7X
+ Native ORC Vectorized 6530 / 6552 2.4
415.1 1.3X
+ Native ORC Vectorized (Pushdown) 3630 / 3670 4.3
230.8 2.4X
+
+ Select 90% rows (id < 14155776): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 9241 / 9293 1.7
587.5 1.0X
+ Parquet Vectorized (Pushdown) 8474 / 8505 1.9
538.8 1.1X
+ Native ORC Vectorized 7080 / 7107 2.2
450.1 1.3X
+ Native ORC Vectorized (Pushdown) 6507 / 6552 2.4
413.7 1.4X
+
+ Select all rows (id IS NOT NULL): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 9317 / 9366 1.7
592.4 1.0X
+ Parquet Vectorized (Pushdown) 9316 / 9367 1.7
592.3 1.0X
+ Native ORC Vectorized 7148 / 7210 2.2
454.5 1.3X
+ Native ORC Vectorized (Pushdown) 7092 / 7152 2.2
450.9 1.3X
+
+ Select all rows (id > -1): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 9307 / 9353 1.7
591.7 1.0X
+ Parquet Vectorized (Pushdown) 9303 / 9340 1.7
591.5 1.0X
+ Native ORC Vectorized 7192 / 7249 2.2
457.2 1.3X
+ Native ORC Vectorized (Pushdown) 7182 / 7216 2.2
456.6 1.3X
+
+ Select all rows (id != -1): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 9145 / 9328 1.7
581.4 1.0X
+ Parquet Vectorized (Pushdown) 9320 / 9368 1.7
592.5 1.0X
+ Native ORC Vectorized 7202 / 7230 2.2
457.9 1.3X
+ Native ORC Vectorized (Pushdown) 7170 / 7206 2.2
455.9 1.3X
+ */
+ benchmark.run()
+ }
+
+ def main(args: Array[String]): Unit = {
+ val numRows = 1024 * 1024 * 15
+ val width = 5
+ val mid = numRows / 2
+
+ withTempPath { dir =>
+ withTempTable("orcTable", "patquetTable") {
+ prepareTable(dir, numRows, width)
+
+ Seq("id IS NULL", s"$mid < id AND id < $mid").foreach { whereExpr
=>
+ val title = s"Select 0 row ($whereExpr)".replace("id AND id",
"id")
+ filterPushDownBenchmark(numRows, title, whereExpr)
+ }
+
+ Seq(
+ s"id = $mid",
+ s"id <=> $mid",
+ s"$mid <= id AND id <= $mid",
+ s"${mid - 1} < id AND id < ${mid + 1}"
+ ).foreach { whereExpr =>
+ val title = s"Select 1 row ($whereExpr)".replace("id AND id",
"id")
+ filterPushDownBenchmark(numRows, title, whereExpr)
+ }
+
+ val selectExpr = (1 to width).map(i =>
s"LENGTH(c$i)").mkString("SUM(", "+", ")")
--- End diff --
I see. I'll update and rerun.
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