Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/19943#discussion_r159221957
  
    --- Diff: 
sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala ---
    @@ -0,0 +1,357 @@
    +/*
    + * 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.hive.orc
    +
    +import java.io.File
    +
    +import scala.util.Random
    +
    +import org.apache.spark.SparkConf
    +import org.apache.spark.sql.{DataFrame, SparkSession}
    +import org.apache.spark.sql.internal.SQLConf
    +import org.apache.spark.sql.types._
    +import org.apache.spark.util.{Benchmark, Utils}
    +
    +
    +/**
    + * Benchmark to measure ORC read performance.
    + *
    + * This is in `sql/hive` module in order to compare `sql/core` and 
`sql/hive` ORC data sources.
    + */
    +// scalastyle:off line.size.limit
    +object OrcReadBenchmark {
    +  val conf = new SparkConf()
    +  conf.set("orc.compression", "snappy")
    +
    +  private val spark = SparkSession.builder()
    +    .master("local[1]")
    +    .appName("OrcReadBenchmark")
    +    .config(conf)
    +    .getOrCreate()
    +
    +  // Set default configs. Individual cases will change them if necessary.
    +  spark.conf.set(SQLConf.ORC_FILTER_PUSHDOWN_ENABLED.key, "true")
    +
    +  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)
    +  }
    +
    +  private val NATIVE_ORC_FORMAT = 
"org.apache.spark.sql.execution.datasources.orc.OrcFileFormat"
    +  private val HIVE_ORC_FORMAT = 
"org.apache.spark.sql.hive.orc.OrcFileFormat"
    +
    +  private def prepareTable(dir: File, df: DataFrame, partition: 
Option[String] = None): Unit = {
    +    val dirORC = dir.getCanonicalPath
    +
    +    if (partition.isDefined) {
    +      df.write.partitionBy(partition.get).orc(dirORC)
    +    } else {
    +      df.write.orc(dirORC)
    +    }
    +
    +    
spark.read.format(NATIVE_ORC_FORMAT).load(dirORC).createOrReplaceTempView("nativeOrcTable")
    +    
spark.read.format(HIVE_ORC_FORMAT).load(dirORC).createOrReplaceTempView("hiveOrcTable")
    +  }
    +
    +  def numericScanBenchmark(values: Int, dataType: DataType): Unit = {
    +    val sqlBenchmark = new Benchmark(s"SQL Single ${dataType.sql} Column 
Scan", values)
    +
    +    withTempPath { dir =>
    +      withTempTable("t1", "nativeOrcTable", "hiveOrcTable") {
    +        import spark.implicits._
    +        spark.range(values).map(_ => 
Random.nextLong).createOrReplaceTempView("t1")
    +
    +        prepareTable(dir, spark.sql(s"SELECT CAST(value as 
${dataType.sql}) id FROM t1"))
    +
    +        sqlBenchmark.addCase("Native ORC") { _ =>
    +          spark.sql("SELECT sum(id) FROM nativeOrcTable").collect()
    +        }
    +
    +        sqlBenchmark.addCase("Hive built-in ORC") { _ =>
    +          spark.sql("SELECT sum(id) FROM hiveOrcTable").collect()
    +        }
    +
    +        /*
    +        Java HotSpot(TM) 64-Bit Server VM 1.8.0_152-b16 on Mac OS X 10.13.1
    +        Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz
    +
    +        SQL Single TINYINT Column Scan:          Best/Avg Time(ms)    
Rate(M/s)   Per Row(ns)   Relative
    +        
------------------------------------------------------------------------------------------------
    +        Native ORC                                     132 /  138        
119.4           8.4       1.0X
    --- End diff --
    
    can we compare `native ORC`, `vectorized native ORC` and `Hive built-in 
ORC`?


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