Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/20265#discussion_r161973650
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/FilterPushdownBenchmark.scala ---
@@ -0,0 +1,230 @@
+/*
+ * 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, expr: 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 * FROM parquetTable WHERE $expr").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 * FROM orcTable WHERE $expr").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 2091 / 2258 0.5
1993.9 1.0X
+ Parquet Vectorized (Pushdown) 41 / 44 25.6
39.0 51.1X
+ Native ORC Vectorized 1625 / 1648 0.6
1549.6 1.3X
+ Native ORC Vectorized (Pushdown) 45 / 47 23.5
42.5 46.9X
+
+ Select 0 row (524288 < id < 524288): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 2202 / 2294 0.5
2099.7 1.0X
+ Parquet Vectorized (Pushdown) 734 / 844 1.4
699.9 3.0X
+ Native ORC Vectorized 1632 / 1659 0.6
1556.0 1.3X
+ Native ORC Vectorized (Pushdown) 94 / 98 11.2
89.6 23.4X
+
+ Select 1 row (id = 524288): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 2113 / 2160 0.5
2015.3 1.0X
+ Parquet Vectorized (Pushdown) 711 / 790 1.5
677.7 3.0X
+ Native ORC Vectorized 1612 / 1657 0.7
1537.2 1.3X
+ Native ORC Vectorized (Pushdown) 92 / 95 11.4
87.7 23.0X
+
+ Select 1 row (id <=> 524288): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 2105 / 2149 0.5
2007.9 1.0X
+ Parquet Vectorized (Pushdown) 712 / 794 1.5
679.2 3.0X
+ Native ORC Vectorized 1619 / 1655 0.6
1543.7 1.3X
+ Native ORC Vectorized (Pushdown) 90 / 93 11.6
85.9 23.4X
+
+ Select 1 row (524288 <= id <= 524288): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 2081 / 2120 0.5
1984.8 1.0X
+ Parquet Vectorized (Pushdown) 700 / 793 1.5
667.5 3.0X
+ Native ORC Vectorized 1618 / 1653 0.6
1542.7 1.3X
+ Native ORC Vectorized (Pushdown) 91 / 94 11.5
86.6 22.9X
+
+ Select 1 row (524287 < id < 524289): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 2094 / 2127 0.5
1997.3 1.0X
+ Parquet Vectorized (Pushdown) 714 / 792 1.5
680.8 2.9X
+ Native ORC Vectorized 1621 / 1644 0.6
1546.3 1.3X
+ Native ORC Vectorized (Pushdown) 90 / 94 11.6
86.1 23.2X
+
+ Select 10% rows (id < 104857): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 2498 / 2591 0.4
2381.9 1.0X
+ Parquet Vectorized (Pushdown) 1047 / 1082 1.0
998.2 2.4X
+ Native ORC Vectorized 1986 / 2119 0.5
1893.8 1.3X
+ Native ORC Vectorized (Pushdown) 552 / 582 1.9
526.1 4.5X
+
+ Select 50% rows (id < 524288): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 4321 / 5021 0.2
4121.3 1.0X
+ Parquet Vectorized (Pushdown) 3967 / 4183 0.3
3783.6 1.1X
+ Native ORC Vectorized 4107 / 4565 0.3
3916.9 1.1X
+ Native ORC Vectorized (Pushdown) 2983 / 3861 0.4
2844.5 1.4X
+
+ Select 90% rows (id < 943718): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 6815 / 7287 0.2
6499.0 1.0X
+ Parquet Vectorized (Pushdown) 6891 / 7220 0.2
6571.5 1.0X
+ Native ORC Vectorized 7337 / 7565 0.1
6997.1 0.9X
+ Native ORC Vectorized (Pushdown) 7274 / 7523 0.1
6936.6 0.9X
+
+ Select all rows (id IS NOT NULL): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7321 / 7380 0.1
6981.5 1.0X
+ Parquet Vectorized (Pushdown) 7352 / 7398 0.1
7011.2 1.0X
+ Native ORC Vectorized 7386 / 7660 0.1
7043.9 1.0X
+ Native ORC Vectorized (Pushdown) 7629 / 7705 0.1
7275.9 1.0X
+
+ Select all rows (id > -1): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7125 / 7384 0.1
6795.2 1.0X
+ Parquet Vectorized (Pushdown) 7334 / 7390 0.1
6994.3 1.0X
+ Native ORC Vectorized 7517 / 7642 0.1
7168.7 0.9X
+ Native ORC Vectorized (Pushdown) 7323 / 7601 0.1
6983.7 1.0X
+
+ Select all rows (id != -1): Best/Avg Time(ms) Rate(M/s)
Per Row(ns) Relative
+
-----------------------------------------------------------------------------------------------
+ Parquet Vectorized 7281 / 7850 0.1
6944.0 1.0X
+ Parquet Vectorized (Pushdown) 7311 / 7939 0.1
6972.7 1.0X
+ Native ORC Vectorized 7530 / 7748 0.1
7181.4 1.0X
+ Native ORC Vectorized (Pushdown) 7309 / 7667 0.1
6970.2 1.0X
+ */
+ benchmark.run()
+ }
+
+ def main(args: Array[String]): Unit = {
+ val numRows = 1024 * 1024
+ val width = 20
--- End diff --
No problem. I'll set to 5.
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