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/*
* 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.benchmark
import scala.concurrent.duration._
import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFPercentileApprox
import org.apache.spark.sql.Column
import org.apache.spark.sql.catalyst.expressions.{ExpressionInfo, Literal}
import org.apache.spark.sql.catalyst.expressions.aggregate.ApproximatePercentile
import org.apache.spark.sql.hive.HiveSessionCatalog
import org.apache.spark.sql.hive.execution.TestingTypedCount
import org.apache.spark.sql.hive.test.TestHiveSingleton
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.LongType
import org.apache.spark.util.Benchmark
class ObjectHashAggregateExecBenchmark extends BenchmarkBase with TestHiveSingleton {
ignore("Hive UDAF vs Spark AF") {
val N = 2 << 15
val benchmark = new Benchmark(
name = "hive udaf vs spark af",
valuesPerIteration = N,
minNumIters = 5,
warmupTime = 5.seconds,
minTime = 10.seconds,
outputPerIteration = true
)
registerHiveFunction("hive_percentile_approx", classOf[GenericUDAFPercentileApprox])
sparkSession.range(N).createOrReplaceTempView("t")
benchmark.addCase("hive udaf w/o group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "false")
sparkSession.sql("SELECT hive_percentile_approx(id, 0.5) FROM t").collect()
}
benchmark.addCase("spark af w/o group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
sparkSession.sql("SELECT percentile_approx(id, 0.5) FROM t").collect()
}
benchmark.addCase("hive udaf w/ group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "false")
sparkSession.sql(
s"SELECT hive_percentile_approx(id, 0.5) FROM t GROUP BY CAST(id / ${N / 4} AS BIGINT)"
).collect()
}
benchmark.addCase("spark af w/ group by w/o fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
sparkSession.sql(
s"SELECT percentile_approx(id, 0.5) FROM t GROUP BY CAST(id / ${N / 4} AS BIGINT)"
).collect()
}
benchmark.addCase("spark af w/ group by w/ fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
sparkSession.conf.set(SQLConf.OBJECT_AGG_SORT_BASED_FALLBACK_THRESHOLD.key, "2")
sparkSession.sql(
s"SELECT percentile_approx(id, 0.5) FROM t GROUP BY CAST(id / ${N / 4} AS BIGINT)"
).collect()
}
benchmark.run()
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
hive udaf vs spark af: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hive udaf w/o group by 5326 / 5408 0.0 81264.2 1.0X
spark af w/o group by 93 / 111 0.7 1415.6 57.4X
hive udaf w/ group by 3804 / 3946 0.0 58050.1 1.4X
spark af w/ group by w/o fallback 71 / 90 0.9 1085.7 74.8X
spark af w/ group by w/ fallback 98 / 111 0.7 1501.6 54.1X
*/
}
ignore("ObjectHashAggregateExec vs SortAggregateExec - typed_count") {
val N: Long = 1024 * 1024 * 100
val benchmark = new Benchmark(
name = "object agg v.s. sort agg",
valuesPerIteration = N,
minNumIters = 1,
warmupTime = 10.seconds,
minTime = 45.seconds,
outputPerIteration = true
)
import sparkSession.implicits._
def typed_count(column: Column): Column =
Column(TestingTypedCount(column.expr).toAggregateExpression())
val df = sparkSession.range(N)
benchmark.addCase("sort agg w/ group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "false")
df.groupBy($"id" < (N / 2)).agg(typed_count($"id")).collect()
}
benchmark.addCase("object agg w/ group by w/o fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
df.groupBy($"id" < (N / 2)).agg(typed_count($"id")).collect()
}
benchmark.addCase("object agg w/ group by w/ fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
sparkSession.conf.set(SQLConf.OBJECT_AGG_SORT_BASED_FALLBACK_THRESHOLD.key, "2")
df.groupBy($"id" < (N / 2)).agg(typed_count($"id")).collect()
}
benchmark.addCase("sort agg w/o group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "false")
df.select(typed_count($"id")).collect()
}
benchmark.addCase("object agg w/o group by w/o fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
df.select(typed_count($"id")).collect()
}
benchmark.run()
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
object agg v.s. sort agg: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
sort agg w/ group by 31251 / 31908 3.4 298.0 1.0X
object agg w/ group by w/o fallback 6903 / 7141 15.2 65.8 4.5X
object agg w/ group by w/ fallback 20945 / 21613 5.0 199.7 1.5X
sort agg w/o group by 4734 / 5463 22.1 45.2 6.6X
object agg w/o group by w/o fallback 4310 / 4529 24.3 41.1 7.3X
*/
}
ignore("ObjectHashAggregateExec vs SortAggregateExec - percentile_approx") {
val N = 2 << 20
val benchmark = new Benchmark(
name = "object agg v.s. sort agg",
valuesPerIteration = N,
minNumIters = 5,
warmupTime = 15.seconds,
minTime = 45.seconds,
outputPerIteration = true
)
import sparkSession.implicits._
val df = sparkSession.range(N).coalesce(1)
benchmark.addCase("sort agg w/ group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "false")
df.groupBy($"id" / (N / 4) cast LongType).agg(percentile_approx($"id", 0.5)).collect()
}
benchmark.addCase("object agg w/ group by w/o fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
df.groupBy($"id" / (N / 4) cast LongType).agg(percentile_approx($"id", 0.5)).collect()
}
benchmark.addCase("object agg w/ group by w/ fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
sparkSession.conf.set(SQLConf.OBJECT_AGG_SORT_BASED_FALLBACK_THRESHOLD.key, "2")
df.groupBy($"id" / (N / 4) cast LongType).agg(percentile_approx($"id", 0.5)).collect()
}
benchmark.addCase("sort agg w/o group by") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "false")
df.select(percentile_approx($"id", 0.5)).collect()
}
benchmark.addCase("object agg w/o group by w/o fallback") { _ =>
sparkSession.conf.set(SQLConf.USE_OBJECT_HASH_AGG.key, "true")
df.select(percentile_approx($"id", 0.5)).collect()
}
benchmark.run()
/*
Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.10.5
Intel(R) Core(TM) i7-4960HQ CPU @ 2.60GHz
object agg v.s. sort agg: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
sort agg w/ group by 3418 / 3530 0.6 1630.0 1.0X
object agg w/ group by w/o fallback 3210 / 3314 0.7 1530.7 1.1X
object agg w/ group by w/ fallback 3419 / 3511 0.6 1630.1 1.0X
sort agg w/o group by 4336 / 4499 0.5 2067.3 0.8X
object agg w/o group by w/o fallback 4271 / 4372 0.5 2036.7 0.8X
*/
}
private def registerHiveFunction(functionName: String, clazz: Class[_]): Unit = {
val sessionCatalog = sparkSession.sessionState.catalog.asInstanceOf[HiveSessionCatalog]
val builder = sessionCatalog.makeFunctionBuilder(functionName, clazz.getName)
val info = new ExpressionInfo(clazz.getName, functionName)
sessionCatalog.createTempFunction(functionName, info, builder, ignoreIfExists = false)
}
private def percentile_approx(
column: Column, percentage: Double, isDistinct: Boolean = false): Column = {
val approxPercentile = new ApproximatePercentile(column.expr, Literal(percentage))
Column(approxPercentile.toAggregateExpression(isDistinct))
}
}
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