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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
import java.util.Properties
import scala.util.Random
import org.apache.spark._
import org.apache.spark.memory.{TaskMemoryManager, TestMemoryManager}
import org.apache.spark.sql.{RandomDataGenerator, Row}
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, UnsafeProjection, UnsafeRow}
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types._
/**
* Test suite for [[UnsafeKVExternalSorter]], with randomly generated test data.
*/
class UnsafeKVExternalSorterSuite extends SparkFunSuite with SharedSQLContext {
private val keyTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
private val valueTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
testKVSorter(new StructType, new StructType, spill = true)
testKVSorter(new StructType().add("c1", IntegerType), new StructType, spill = true)
testKVSorter(new StructType, new StructType().add("c1", IntegerType), spill = true)
private val rand = new Random(42)
for (i <- 0 until 6) {
val keySchema = RandomDataGenerator.randomSchema(rand, rand.nextInt(10) + 1, keyTypes)
val valueSchema = RandomDataGenerator.randomSchema(rand, rand.nextInt(10) + 1, valueTypes)
testKVSorter(keySchema, valueSchema, spill = i > 3)
}
/**
* Create a test case using randomly generated data for the given key and value schema.
*
* The approach works as follows:
*
* - Create input by randomly generating data based on the given schema
* - Run [[UnsafeKVExternalSorter]] on the generated data
* - Collect the output from the sorter, and make sure the keys are sorted in ascending order
* - Sort the input by both key and value, and sort the sorter output also by both key and value.
* Compare the sorted input and sorted output together to make sure all the key/values match.
*
* If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
*/
private def testKVSorter(keySchema: StructType, valueSchema: StructType, spill: Boolean): Unit = {
// Create the data converters
val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
val vExternalConverter = CatalystTypeConverters.createToCatalystConverter(valueSchema)
val kConverter = UnsafeProjection.create(keySchema)
val vConverter = UnsafeProjection.create(valueSchema)
val keyDataGen = RandomDataGenerator.forType(keySchema, nullable = false).get
val valueDataGen = RandomDataGenerator.forType(valueSchema, nullable = false).get
val inputData = Seq.fill(1024) {
val k = kConverter(kExternalConverter.apply(keyDataGen.apply()).asInstanceOf[InternalRow])
val v = vConverter(vExternalConverter.apply(valueDataGen.apply()).asInstanceOf[InternalRow])
(k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
}
val keySchemaStr = keySchema.map(_.dataType.simpleString).mkString("[", ",", "]")
val valueSchemaStr = valueSchema.map(_.dataType.simpleString).mkString("[", ",", "]")
test(s"kv sorting key schema $keySchemaStr and value schema $valueSchemaStr") {
testKVSorter(
keySchema,
valueSchema,
inputData,
pageSize = 16 * 1024 * 1024,
spill
)
}
}
/**
* Create a test case using the given input data for the given key and value schema.
*
* The approach works as follows:
*
* - Create input by randomly generating data based on the given schema
* - Run [[UnsafeKVExternalSorter]] on the input data
* - Collect the output from the sorter, and make sure the keys are sorted in ascending order
* - Sort the input by both key and value, and sort the sorter output also by both key and value.
* Compare the sorted input and sorted output together to make sure all the key/values match.
*
* If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
*/
private def testKVSorter(
keySchema: StructType,
valueSchema: StructType,
inputData: Seq[(InternalRow, InternalRow)],
pageSize: Long,
spill: Boolean): Unit = {
val memoryManager =
new TestMemoryManager(new SparkConf().set("spark.memory.offHeap.enabled", "false"))
val taskMemMgr = new TaskMemoryManager(memoryManager, 0)
TaskContext.setTaskContext(new TaskContextImpl(
stageId = 0,
partitionId = 0,
taskAttemptId = 98456,
attemptNumber = 0,
taskMemoryManager = taskMemMgr,
localProperties = new Properties,
metricsSystem = null))
val sorter = new UnsafeKVExternalSorter(
keySchema, valueSchema, SparkEnv.get.blockManager, SparkEnv.get.serializerManager, pageSize)
// Insert the keys and values into the sorter
inputData.foreach { case (k, v) =>
sorter.insertKV(k.asInstanceOf[UnsafeRow], v.asInstanceOf[UnsafeRow])
// 1% chance we will spill
if (rand.nextDouble() < 0.01 && spill) {
memoryManager.markExecutionAsOutOfMemoryOnce()
sorter.closeCurrentPage()
}
}
// Collect the sorted output
val out = new scala.collection.mutable.ArrayBuffer[(InternalRow, InternalRow)]
val iter = sorter.sortedIterator()
while (iter.next()) {
out += Tuple2(iter.getKey.copy(), iter.getValue.copy())
}
sorter.cleanupResources()
val keyOrdering = InterpretedOrdering.forSchema(keySchema.map(_.dataType))
val valueOrdering = InterpretedOrdering.forSchema(valueSchema.map(_.dataType))
val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
keyOrdering.compare(x._1, y._1) match {
case 0 => valueOrdering.compare(x._2, y._2)
case cmp => cmp
}
}
}
// Testing to make sure output from the sorter is sorted by key
var prevK: InternalRow = null
out.zipWithIndex.foreach { case ((k, v), i) =>
if (prevK != null) {
assert(keyOrdering.compare(prevK, k) <= 0,
s"""
|key is not in sorted order:
|previous key: $prevK
|current key : $k
""".stripMargin)
}
prevK = k
}
// Testing to make sure the key/value in output matches input
assert(out.sorted(kvOrdering) === inputData.sorted(kvOrdering))
// Make sure there is no memory leak
assert(0 === taskMemMgr.cleanUpAllAllocatedMemory)
TaskContext.unset()
}
test("kv sorting with records that exceed page size") {
val pageSize = 128
val schema = StructType(StructField("b", BinaryType) :: Nil)
val externalConverter = CatalystTypeConverters.createToCatalystConverter(schema)
val converter = UnsafeProjection.create(schema)
val rand = new Random()
val inputData = Seq.fill(1024) {
val kBytes = new Array[Byte](rand.nextInt(pageSize))
val vBytes = new Array[Byte](rand.nextInt(pageSize))
rand.nextBytes(kBytes)
rand.nextBytes(vBytes)
val k = converter(externalConverter.apply(Row(kBytes)).asInstanceOf[InternalRow])
val v = converter(externalConverter.apply(Row(vBytes)).asInstanceOf[InternalRow])
(k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
}
testKVSorter(
schema,
schema,
inputData,
pageSize,
spill = true
)
}
}
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