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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.rdd
import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
import scala.collection.JavaConverters._
import scala.collection.mutable.{ArrayBuffer, HashMap}
import scala.reflect.ClassTag
import com.esotericsoftware.kryo.KryoException
import org.apache.spark._
import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
import org.apache.spark.rdd.RDDSuiteUtils._
import org.apache.spark.util.Utils
class RDDSuite extends SparkFunSuite with SharedSparkContext {
test("basic operations") {
val nums = sc.makeRDD(Array(1, 2, 3, 4), 2)
assert(nums.getNumPartitions === 2)
assert(nums.collect().toList === List(1, 2, 3, 4))
assert(nums.toLocalIterator.toList === List(1, 2, 3, 4))
val dups = sc.makeRDD(Array(1, 1, 2, 2, 3, 3, 4, 4), 2)
assert(dups.distinct().count() === 4)
assert(dups.distinct().count === 4) // Can distinct and count be called without parentheses?
assert(dups.distinct().collect === dups.distinct().collect)
assert(dups.distinct(2).collect === dups.distinct().collect)
assert(nums.reduce(_ + _) === 10)
assert(nums.fold(0)(_ + _) === 10)
assert(nums.map(_.toString).collect().toList === List("1", "2", "3", "4"))
assert(nums.filter(_ > 2).collect().toList === List(3, 4))
assert(nums.flatMap(x => 1 to x).collect().toList === List(1, 1, 2, 1, 2, 3, 1, 2, 3, 4))
assert(nums.union(nums).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4))
assert(nums.glom().map(_.toList).collect().toList === List(List(1, 2), List(3, 4)))
assert(nums.collect({ case i if i >= 3 => i.toString }).collect().toList === List("3", "4"))
assert(nums.keyBy(_.toString).collect().toList === List(("1", 1), ("2", 2), ("3", 3), ("4", 4)))
assert(!nums.isEmpty())
assert(nums.max() === 4)
assert(nums.min() === 1)
val partitionSums = nums.mapPartitions(iter => Iterator(iter.reduceLeft(_ + _)))
assert(partitionSums.collect().toList === List(3, 7))
val partitionSumsWithSplit = nums.mapPartitionsWithIndex {
case(split, iter) => Iterator((split, iter.reduceLeft(_ + _)))
}
assert(partitionSumsWithSplit.collect().toList === List((0, 3), (1, 7)))
val partitionSumsWithIndex = nums.mapPartitionsWithIndex {
case(split, iter) => Iterator((split, iter.reduceLeft(_ + _)))
}
assert(partitionSumsWithIndex.collect().toList === List((0, 3), (1, 7)))
intercept[UnsupportedOperationException] {
nums.filter(_ > 5).reduce(_ + _)
}
}
test("serialization") {
val empty = new EmptyRDD[Int](sc)
val serial = Utils.serialize(empty)
val deserial: EmptyRDD[Int] = Utils.deserialize(serial)
assert(!deserial.toString().isEmpty())
}
test("countApproxDistinct") {
def error(est: Long, size: Long): Double = math.abs(est - size) / size.toDouble
val size = 1000
val uniformDistro = for (i <- 1 to 5000) yield i % size
val simpleRdd = sc.makeRDD(uniformDistro, 10)
assert(error(simpleRdd.countApproxDistinct(8, 0), size) < 0.2)
assert(error(simpleRdd.countApproxDistinct(12, 0), size) < 0.1)
assert(error(simpleRdd.countApproxDistinct(0.02), size) < 0.1)
assert(error(simpleRdd.countApproxDistinct(0.5), size) < 0.22)
}
test("SparkContext.union") {
val nums = sc.makeRDD(Array(1, 2, 3, 4), 2)
assert(sc.union(nums).collect().toList === List(1, 2, 3, 4))
assert(sc.union(nums, nums).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4))
assert(sc.union(Seq(nums)).collect().toList === List(1, 2, 3, 4))
assert(sc.union(Seq(nums, nums)).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4))
}
test("SparkContext.union creates UnionRDD if at least one RDD has no partitioner") {
val rddWithPartitioner = sc.parallelize(Seq(1 -> true)).partitionBy(new HashPartitioner(1))
val rddWithNoPartitioner = sc.parallelize(Seq(2 -> true))
val unionRdd = sc.union(rddWithNoPartitioner, rddWithPartitioner)
assert(unionRdd.isInstanceOf[UnionRDD[_]])
}
test("SparkContext.union creates PartitionAwareUnionRDD if all RDDs have partitioners") {
val rddWithPartitioner = sc.parallelize(Seq(1 -> true)).partitionBy(new HashPartitioner(1))
val unionRdd = sc.union(rddWithPartitioner, rddWithPartitioner)
assert(unionRdd.isInstanceOf[PartitionerAwareUnionRDD[_]])
}
test("PartitionAwareUnionRDD raises exception if at least one RDD has no partitioner") {
val rddWithPartitioner = sc.parallelize(Seq(1 -> true)).partitionBy(new HashPartitioner(1))
val rddWithNoPartitioner = sc.parallelize(Seq(2 -> true))
intercept[IllegalArgumentException] {
new PartitionerAwareUnionRDD(sc, Seq(rddWithNoPartitioner, rddWithPartitioner))
}
}
test("partitioner aware union") {
def makeRDDWithPartitioner(seq: Seq[Int]): RDD[Int] = {
sc.makeRDD(seq, 1)
.map(x => (x, null))
.partitionBy(new HashPartitioner(2))
.mapPartitions(_.map(_._1), true)
}
val nums1 = makeRDDWithPartitioner(1 to 4)
val nums2 = makeRDDWithPartitioner(5 to 8)
assert(nums1.partitioner == nums2.partitioner)
assert(new PartitionerAwareUnionRDD(sc, Seq(nums1)).collect().toSet === Set(1, 2, 3, 4))
val union = new PartitionerAwareUnionRDD(sc, Seq(nums1, nums2))
assert(union.collect().toSet === Set(1, 2, 3, 4, 5, 6, 7, 8))
val nums1Parts = nums1.collectPartitions()
val nums2Parts = nums2.collectPartitions()
val unionParts = union.collectPartitions()
assert(nums1Parts.length === 2)
assert(nums2Parts.length === 2)
assert(unionParts.length === 2)
assert((nums1Parts(0) ++ nums2Parts(0)).toList === unionParts(0).toList)
assert((nums1Parts(1) ++ nums2Parts(1)).toList === unionParts(1).toList)
assert(union.partitioner === nums1.partitioner)
}
test("UnionRDD partition serialized size should be small") {
val largeVariable = new Array[Byte](1000 * 1000)
val rdd1 = sc.parallelize(1 to 10, 2).map(i => largeVariable.length)
val rdd2 = sc.parallelize(1 to 10, 3)
val ser = SparkEnv.get.closureSerializer.newInstance()
val union = rdd1.union(rdd2)
// The UnionRDD itself should be large, but each individual partition should be small.
assert(ser.serialize(union).limit() > 2000)
assert(ser.serialize(union.partitions.head).limit() < 2000)
}
test("aggregate") {
val pairs = sc.makeRDD(Array(("a", 1), ("b", 2), ("a", 2), ("c", 5), ("a", 3)))
type StringMap = HashMap[String, Int]
val emptyMap = new StringMap {
override def default(key: String): Int = 0
}
val mergeElement: (StringMap, (String, Int)) => StringMap = (map, pair) => {
map(pair._1) += pair._2
map
}
val mergeMaps: (StringMap, StringMap) => StringMap = (map1, map2) => {
for ((key, value) <- map2) {
map1(key) += value
}
map1
}
val result = pairs.aggregate(emptyMap)(mergeElement, mergeMaps)
assert(result.toSet === Set(("a", 6), ("b", 2), ("c", 5)))
}
test("treeAggregate") {
val rdd = sc.makeRDD(-1000 until 1000, 10)
def seqOp: (Long, Int) => Long = (c: Long, x: Int) => c + x
def combOp: (Long, Long) => Long = (c1: Long, c2: Long) => c1 + c2
for (depth <- 1 until 10) {
val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth)
assert(sum === -1000L)
}
}
test("treeReduce") {
val rdd = sc.makeRDD(-1000 until 1000, 10)
for (depth <- 1 until 10) {
val sum = rdd.treeReduce(_ + _, depth)
assert(sum === -1000)
}
}
test("basic caching") {
val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache()
assert(rdd.collect().toList === List(1, 2, 3, 4))
assert(rdd.collect().toList === List(1, 2, 3, 4))
assert(rdd.collect().toList === List(1, 2, 3, 4))
}
test("caching with failures") {
val onlySplit = new Partition { override def index: Int = 0 }
var shouldFail = true
val rdd = new RDD[Int](sc, Nil) {
override def getPartitions: Array[Partition] = Array(onlySplit)
override val getDependencies = List[Dependency[_]]()
override def compute(split: Partition, context: TaskContext): Iterator[Int] = {
throw new Exception("injected failure")
}
}.cache()
val thrown = intercept[Exception]{
rdd.collect()
}
assert(thrown.getMessage.contains("injected failure"))
}
test("empty RDD") {
val empty = new EmptyRDD[Int](sc)
assert(empty.count === 0)
assert(empty.collect().size === 0)
val thrown = intercept[UnsupportedOperationException]{
empty.reduce(_ + _)
}
assert(thrown.getMessage.contains("empty"))
val emptyKv = new EmptyRDD[(Int, Int)](sc)
val rdd = sc.parallelize(1 to 2, 2).map(x => (x, x))
assert(rdd.join(emptyKv).collect().size === 0)
assert(rdd.rightOuterJoin(emptyKv).collect().size === 0)
assert(rdd.leftOuterJoin(emptyKv).collect().size === 2)
assert(rdd.fullOuterJoin(emptyKv).collect().size === 2)
assert(rdd.cogroup(emptyKv).collect().size === 2)
assert(rdd.union(emptyKv).collect().size === 2)
}
test("repartitioned RDDs") {
val data = sc.parallelize(1 to 1000, 10)
// Coalesce partitions
val repartitioned1 = data.repartition(2)
assert(repartitioned1.partitions.size == 2)
val partitions1 = repartitioned1.glom().collect()
assert(partitions1(0).length > 0)
assert(partitions1(1).length > 0)
assert(repartitioned1.collect().toSet === (1 to 1000).toSet)
// Split partitions
val repartitioned2 = data.repartition(20)
assert(repartitioned2.partitions.size == 20)
val partitions2 = repartitioned2.glom().collect()
assert(partitions2(0).length > 0)
assert(partitions2(19).length > 0)
assert(repartitioned2.collect().toSet === (1 to 1000).toSet)
}
test("repartitioned RDDs perform load balancing") {
// Coalesce partitions
val input = Array.fill(1000)(1)
val initialPartitions = 10
val data = sc.parallelize(input, initialPartitions)
val repartitioned1 = data.repartition(2)
assert(repartitioned1.partitions.size == 2)
val partitions1 = repartitioned1.glom().collect()
// some noise in balancing is allowed due to randomization
assert(math.abs(partitions1(0).length - 500) < initialPartitions)
assert(math.abs(partitions1(1).length - 500) < initialPartitions)
assert(repartitioned1.collect() === input)
def testSplitPartitions(input: Seq[Int], initialPartitions: Int, finalPartitions: Int) {
val data = sc.parallelize(input, initialPartitions)
val repartitioned = data.repartition(finalPartitions)
assert(repartitioned.partitions.size === finalPartitions)
val partitions = repartitioned.glom().collect()
// assert all elements are present
assert(repartitioned.collect().sortWith(_ > _).toSeq === input.toSeq.sortWith(_ > _).toSeq)
// assert no bucket is overloaded
for (partition <- partitions) {
val avg = input.size / finalPartitions
val maxPossible = avg + initialPartitions
assert(partition.length <= maxPossible)
}
}
testSplitPartitions(Array.fill(100)(1), 10, 20)
testSplitPartitions(Array.fill(10000)(1) ++ Array.fill(10000)(2), 20, 100)
}
test("coalesced RDDs") {
val data = sc.parallelize(1 to 10, 10)
val coalesced1 = data.coalesce(2)
assert(coalesced1.collect().toList === (1 to 10).toList)
assert(coalesced1.glom().collect().map(_.toList).toList ===
List(List(1, 2, 3, 4, 5), List(6, 7, 8, 9, 10)))
// Check that the narrow dependency is also specified correctly
assert(coalesced1.dependencies.head.asInstanceOf[NarrowDependency[_]].getParents(0).toList ===
List(0, 1, 2, 3, 4))
assert(coalesced1.dependencies.head.asInstanceOf[NarrowDependency[_]].getParents(1).toList ===
List(5, 6, 7, 8, 9))
val coalesced2 = data.coalesce(3)
assert(coalesced2.collect().toList === (1 to 10).toList)
assert(coalesced2.glom().collect().map(_.toList).toList ===
List(List(1, 2, 3), List(4, 5, 6), List(7, 8, 9, 10)))
val coalesced3 = data.coalesce(10)
assert(coalesced3.collect().toList === (1 to 10).toList)
assert(coalesced3.glom().collect().map(_.toList).toList ===
(1 to 10).map(x => List(x)).toList)
// If we try to coalesce into more partitions than the original RDD, it should just
// keep the original number of partitions.
val coalesced4 = data.coalesce(20)
assert(coalesced4.collect().toList === (1 to 10).toList)
assert(coalesced4.glom().collect().map(_.toList).toList ===
(1 to 10).map(x => List(x)).toList)
// we can optionally shuffle to keep the upstream parallel
val coalesced5 = data.coalesce(1, shuffle = true)
val isEquals = coalesced5.dependencies.head.rdd.dependencies.head.rdd.
asInstanceOf[ShuffledRDD[_, _, _]] != null
assert(isEquals)
// when shuffling, we can increase the number of partitions
val coalesced6 = data.coalesce(20, shuffle = true)
assert(coalesced6.partitions.size === 20)
assert(coalesced6.collect().toSet === (1 to 10).toSet)
}
test("coalesced RDDs with locality") {
val data3 = sc.makeRDD(List((1, List("a", "c")), (2, List("a", "b", "c")), (3, List("b"))))
val coal3 = data3.coalesce(3)
val list3 = coal3.partitions.flatMap(_.asInstanceOf[CoalescedRDDPartition].preferredLocation)
assert(list3.sorted === Array("a", "b", "c"), "Locality preferences are dropped")
// RDD with locality preferences spread (non-randomly) over 6 machines, m0 through m5
val data = sc.makeRDD((1 to 9).map(i => (i, (i to (i + 2)).map{ j => "m" + (j%6)})))
val coalesced1 = data.coalesce(3)
assert(coalesced1.collect().toList.sorted === (1 to 9).toList, "Data got *lost* in coalescing")
val splits = coalesced1.glom().collect().map(_.toList).toList
assert(splits.length === 3, "Supposed to coalesce to 3 but got " + splits.length)
assert(splits.forall(_.length >= 1) === true, "Some partitions were empty")
// If we try to coalesce into more partitions than the original RDD, it should just
// keep the original number of partitions.
val coalesced4 = data.coalesce(20)
val listOfLists = coalesced4.glom().collect().map(_.toList).toList
val sortedList = listOfLists.sortWith{ (x, y) => !x.isEmpty && (y.isEmpty || (x(0) < y(0))) }
assert(sortedList === (1 to 9).
map{x => List(x)}.toList, "Tried coalescing 9 partitions to 20 but didn't get 9 back")
}
test("coalesced RDDs with locality, large scale (10K partitions)") {
// large scale experiment
import collection.mutable
val partitions = 10000
val numMachines = 50
val machines = mutable.ListBuffer[String]()
(1 to numMachines).foreach(machines += "m" + _)
val rnd = scala.util.Random
for (seed <- 1 to 5) {
rnd.setSeed(seed)
val blocks = (1 to partitions).map { i =>
(i, Array.fill(3)(machines(rnd.nextInt(machines.size))).toList)
}
val data2 = sc.makeRDD(blocks)
val coalesced2 = data2.coalesce(numMachines * 2)
// test that you get over 90% locality in each group
val minLocality = coalesced2.partitions
.map(part => part.asInstanceOf[CoalescedRDDPartition].localFraction)
.foldLeft(1.0)((perc, loc) => math.min(perc, loc))
assert(minLocality >= 0.90, "Expected 90% locality but got " +
(minLocality * 100.0).toInt + "%")
// test that the groups are load balanced with 100 +/- 20 elements in each
val maxImbalance = coalesced2.partitions
.map(part => part.asInstanceOf[CoalescedRDDPartition].parents.size)
.foldLeft(0)((dev, curr) => math.max(math.abs(100 - curr), dev))
assert(maxImbalance <= 20, "Expected 100 +/- 20 per partition, but got " + maxImbalance)
val data3 = sc.makeRDD(blocks).map(i => i * 2) // derived RDD to test *current* pref locs
val coalesced3 = data3.coalesce(numMachines * 2)
val minLocality2 = coalesced3.partitions
.map(part => part.asInstanceOf[CoalescedRDDPartition].localFraction)
.foldLeft(1.0)((perc, loc) => math.min(perc, loc))
assert(minLocality2 >= 0.90, "Expected 90% locality for derived RDD but got " +
(minLocality2 * 100.0).toInt + "%")
}
}
// Test for SPARK-2412 -- ensure that the second pass of the algorithm does not throw an exception
test("coalesced RDDs with locality, fail first pass") {
val initialPartitions = 1000
val targetLen = 50
val couponCount = 2 * (math.log(targetLen)*targetLen + targetLen + 0.5).toInt // = 492
val blocks = (1 to initialPartitions).map { i =>
(i, List(if (i > couponCount) "m2" else "m1"))
}
val data = sc.makeRDD(blocks)
val coalesced = data.coalesce(targetLen)
assert(coalesced.partitions.length == targetLen)
}
test("zipped RDDs") {
val nums = sc.makeRDD(Array(1, 2, 3, 4), 2)
val zipped = nums.zip(nums.map(_ + 1.0))
assert(zipped.glom().map(_.toList).collect().toList ===
List(List((1, 2.0), (2, 3.0)), List((3, 4.0), (4, 5.0))))
intercept[IllegalArgumentException] {
nums.zip(sc.parallelize(1 to 4, 1)).collect()
}
intercept[SparkException] {
nums.zip(sc.parallelize(1 to 5, 2)).collect()
}
}
test("partition pruning") {
val data = sc.parallelize(1 to 10, 10)
// Note that split number starts from 0, so > 8 means only 10th partition left.
val prunedRdd = new PartitionPruningRDD(data, splitNum => splitNum > 8)
assert(prunedRdd.partitions.size === 1)
val prunedData = prunedRdd.collect()
assert(prunedData.size === 1)
assert(prunedData(0) === 10)
}
test("collect large number of empty partitions") {
// Regression test for SPARK-4019
assert(sc.makeRDD(0 until 10, 1000).repartition(2001).collect().toSet === (0 until 10).toSet)
}
test("take") {
var nums = sc.makeRDD(Range(1, 1000), 1)
assert(nums.take(0).size === 0)
assert(nums.take(1) === Array(1))
assert(nums.take(3) === Array(1, 2, 3))
assert(nums.take(500) === (1 to 500).toArray)
assert(nums.take(501) === (1 to 501).toArray)
assert(nums.take(999) === (1 to 999).toArray)
assert(nums.take(1000) === (1 to 999).toArray)
nums = sc.makeRDD(Range(1, 1000), 2)
assert(nums.take(0).size === 0)
assert(nums.take(1) === Array(1))
assert(nums.take(3) === Array(1, 2, 3))
assert(nums.take(500) === (1 to 500).toArray)
assert(nums.take(501) === (1 to 501).toArray)
assert(nums.take(999) === (1 to 999).toArray)
assert(nums.take(1000) === (1 to 999).toArray)
nums = sc.makeRDD(Range(1, 1000), 100)
assert(nums.take(0).size === 0)
assert(nums.take(1) === Array(1))
assert(nums.take(3) === Array(1, 2, 3))
assert(nums.take(500) === (1 to 500).toArray)
assert(nums.take(501) === (1 to 501).toArray)
assert(nums.take(999) === (1 to 999).toArray)
assert(nums.take(1000) === (1 to 999).toArray)
nums = sc.makeRDD(Range(1, 1000), 1000)
assert(nums.take(0).size === 0)
assert(nums.take(1) === Array(1))
assert(nums.take(3) === Array(1, 2, 3))
assert(nums.take(500) === (1 to 500).toArray)
assert(nums.take(501) === (1 to 501).toArray)
assert(nums.take(999) === (1 to 999).toArray)
assert(nums.take(1000) === (1 to 999).toArray)
nums = sc.parallelize(1 to 2, 2)
assert(nums.take(2147483638).size === 2)
assert(nums.takeAsync(2147483638).get.size === 2)
}
test("top with predefined ordering") {
val nums = Array.range(1, 100000)
val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2)
val topK = ints.top(5)
assert(topK.size === 5)
assert(topK === nums.reverse.take(5))
}
test("top with custom ordering") {
val words = Vector("a", "b", "c", "d")
implicit val ord = implicitly[Ordering[String]].reverse
val rdd = sc.makeRDD(words, 2)
val topK = rdd.top(2)
assert(topK.size === 2)
assert(topK.sorted === Array("b", "a"))
}
test("takeOrdered with predefined ordering") {
val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
val rdd = sc.makeRDD(nums, 2)
val sortedLowerK = rdd.takeOrdered(5)
assert(sortedLowerK.size === 5)
assert(sortedLowerK === Array(1, 2, 3, 4, 5))
}
test("takeOrdered with limit 0") {
val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
val rdd = sc.makeRDD(nums, 2)
val sortedLowerK = rdd.takeOrdered(0)
assert(sortedLowerK.size === 0)
}
test("takeOrdered with custom ordering") {
val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
implicit val ord = implicitly[Ordering[Int]].reverse
val rdd = sc.makeRDD(nums, 2)
val sortedTopK = rdd.takeOrdered(5)
assert(sortedTopK.size === 5)
assert(sortedTopK === Array(10, 9, 8, 7, 6))
assert(sortedTopK === nums.sorted(ord).take(5))
}
test("isEmpty") {
assert(sc.emptyRDD.isEmpty())
assert(sc.parallelize(Seq[Int]()).isEmpty())
assert(!sc.parallelize(Seq(1)).isEmpty())
assert(sc.parallelize(Seq(1, 2, 3), 3).filter(_ < 0).isEmpty())
assert(!sc.parallelize(Seq(1, 2, 3), 3).filter(_ > 1).isEmpty())
}
test("sample preserves partitioner") {
val partitioner = new HashPartitioner(2)
val rdd = sc.parallelize(Seq((0, 1), (2, 3))).partitionBy(partitioner)
for (withReplacement <- Seq(true, false)) {
val sampled = rdd.sample(withReplacement, 1.0)
assert(sampled.partitioner === rdd.partitioner)
}
}
test("takeSample") {
val n = 1000000
val data = sc.parallelize(1 to n, 2)
for (num <- List(5, 20, 100)) {
val sample = data.takeSample(withReplacement = false, num = num)
assert(sample.size === num) // Got exactly num elements
assert(sample.toSet.size === num) // Elements are distinct
assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
}
for (seed <- 1 to 5) {
val sample = data.takeSample(withReplacement = false, 20, seed)
assert(sample.size === 20) // Got exactly 20 elements
assert(sample.toSet.size === 20) // Elements are distinct
assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
}
for (seed <- 1 to 5) {
val sample = data.takeSample(withReplacement = false, 100, seed)
assert(sample.size === 100) // Got only 100 elements
assert(sample.toSet.size === 100) // Elements are distinct
assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
}
for (seed <- 1 to 5) {
val sample = data.takeSample(withReplacement = true, 20, seed)
assert(sample.size === 20) // Got exactly 20 elements
assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
}
{
val sample = data.takeSample(withReplacement = true, num = 20)
assert(sample.size === 20) // Got exactly 100 elements
assert(sample.toSet.size <= 20, "sampling with replacement returned all distinct elements")
assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
}
{
val sample = data.takeSample(withReplacement = true, num = n)
assert(sample.size === n) // Got exactly 100 elements
// Chance of getting all distinct elements is astronomically low, so test we got < 100
assert(sample.toSet.size < n, "sampling with replacement returned all distinct elements")
assert(sample.forall(x => 1 <= x && x <= n), s"elements not in [1, $n]")
}
for (seed <- 1 to 5) {
val sample = data.takeSample(withReplacement = true, n, seed)
assert(sample.size === n) // Got exactly 100 elements
// Chance of getting all distinct elements is astronomically low, so test we got < 100
assert(sample.toSet.size < n, "sampling with replacement returned all distinct elements")
}
for (seed <- 1 to 5) {
val sample = data.takeSample(withReplacement = true, 2 * n, seed)
assert(sample.size === 2 * n) // Got exactly 200 elements
// Chance of getting all distinct elements is still quite low, so test we got < 100
assert(sample.toSet.size < n, "sampling with replacement returned all distinct elements")
}
}
test("takeSample from an empty rdd") {
val emptySet = sc.parallelize(Seq.empty[Int], 2)
val sample = emptySet.takeSample(false, 20, 1)
assert(sample.length === 0)
}
test("randomSplit") {
val n = 600
val data = sc.parallelize(1 to n, 2)
for(seed <- 1 to 5) {
val splits = data.randomSplit(Array(1.0, 2.0, 3.0), seed)
assert(splits.size == 3, "wrong number of splits")
assert(splits.flatMap(_.collect()).sorted.toList == data.collect().toList,
"incomplete or wrong split")
val s = splits.map(_.count())
assert(math.abs(s(0) - 100) < 50) // std = 9.13
assert(math.abs(s(1) - 200) < 50) // std = 11.55
assert(math.abs(s(2) - 300) < 50) // std = 12.25
}
}
test("runJob on an invalid partition") {
intercept[IllegalArgumentException] {
sc.runJob(sc.parallelize(1 to 10, 2), {iter: Iterator[Int] => iter.size}, Seq(0, 1, 2))
}
}
test("sort an empty RDD") {
val data = sc.emptyRDD[Int]
assert(data.sortBy(x => x).collect() === Array.empty)
}
test("sortByKey") {
val data = sc.parallelize(Seq("5|50|A", "4|60|C", "6|40|B"))
val col1 = Array("4|60|C", "5|50|A", "6|40|B")
val col2 = Array("6|40|B", "5|50|A", "4|60|C")
val col3 = Array("5|50|A", "6|40|B", "4|60|C")
assert(data.sortBy(_.split("\\|")(0)).collect() === col1)
assert(data.sortBy(_.split("\\|")(1)).collect() === col2)
assert(data.sortBy(_.split("\\|")(2)).collect() === col3)
}
test("sortByKey ascending parameter") {
val data = sc.parallelize(Seq("5|50|A", "4|60|C", "6|40|B"))
val asc = Array("4|60|C", "5|50|A", "6|40|B")
val desc = Array("6|40|B", "5|50|A", "4|60|C")
assert(data.sortBy(_.split("\\|")(0), true).collect() === asc)
assert(data.sortBy(_.split("\\|")(0), false).collect() === desc)
}
test("sortByKey with explicit ordering") {
val data = sc.parallelize(Seq("Bob|Smith|50",
"Jane|Smith|40",
"Thomas|Williams|30",
"Karen|Williams|60"))
val ageOrdered = Array("Thomas|Williams|30",
"Jane|Smith|40",
"Bob|Smith|50",
"Karen|Williams|60")
// last name, then first name
val nameOrdered = Array("Bob|Smith|50",
"Jane|Smith|40",
"Karen|Williams|60",
"Thomas|Williams|30")
val parse = (s: String) => {
val split = s.split("\\|")
Person(split(0), split(1), split(2).toInt)
}
import scala.reflect.classTag
assert(data.sortBy(parse, true, 2)(AgeOrdering, classTag[Person]).collect() === ageOrdered)
assert(data.sortBy(parse, true, 2)(NameOrdering, classTag[Person]).collect() === nameOrdered)
}
test("repartitionAndSortWithinPartitions") {
val data = sc.parallelize(Seq((0, 5), (3, 8), (2, 6), (0, 8), (3, 8), (1, 3)), 2)
val partitioner = new Partitioner {
def numPartitions: Int = 2
def getPartition(key: Any): Int = key.asInstanceOf[Int] % 2
}
val repartitioned = data.repartitionAndSortWithinPartitions(partitioner)
val partitions = repartitioned.glom().collect()
assert(partitions(0) === Seq((0, 5), (0, 8), (2, 6)))
assert(partitions(1) === Seq((1, 3), (3, 8), (3, 8)))
}
test("intersection") {
val all = sc.parallelize(1 to 10)
val evens = sc.parallelize(2 to 10 by 2)
val intersection = Array(2, 4, 6, 8, 10)
// intersection is commutative
assert(all.intersection(evens).collect().sorted === intersection)
assert(evens.intersection(all).collect().sorted === intersection)
}
test("intersection strips duplicates in an input") {
val a = sc.parallelize(Seq(1, 2, 3, 3))
val b = sc.parallelize(Seq(1, 1, 2, 3))
val intersection = Array(1, 2, 3)
assert(a.intersection(b).collect().sorted === intersection)
assert(b.intersection(a).collect().sorted === intersection)
}
test("zipWithIndex") {
val n = 10
val data = sc.parallelize(0 until n, 3)
val ranked = data.zipWithIndex()
ranked.collect().foreach { x =>
assert(x._1 === x._2)
}
}
test("zipWithIndex with a single partition") {
val n = 10
val data = sc.parallelize(0 until n, 1)
val ranked = data.zipWithIndex()
ranked.collect().foreach { x =>
assert(x._1 === x._2)
}
}
test("zipWithIndex chained with other RDDs (SPARK-4433)") {
val count = sc.parallelize(0 until 10, 2).zipWithIndex().repartition(4).count()
assert(count === 10)
}
test("zipWithUniqueId") {
val n = 10
val data = sc.parallelize(0 until n, 3)
val ranked = data.zipWithUniqueId()
val ids = ranked.map(_._1).distinct().collect()
assert(ids.length === n)
}
test("retag with implicit ClassTag") {
val jsc: JavaSparkContext = new JavaSparkContext(sc)
val jrdd: JavaRDD[String] = jsc.parallelize(Seq("A", "B", "C").asJava)
jrdd.rdd.retag.collect()
}
test("parent method") {
val rdd1 = sc.parallelize(1 to 10, 2)
val rdd2 = rdd1.filter(_ % 2 == 0)
val rdd3 = rdd2.map(_ + 1)
val rdd4 = new UnionRDD(sc, List(rdd1, rdd2, rdd3))
assert(rdd4.parent(0).isInstanceOf[ParallelCollectionRDD[_]])
assert(rdd4.parent[Int](1) === rdd2)
assert(rdd4.parent[Int](2) === rdd3)
}
test("getNarrowAncestors") {
val rdd1 = sc.parallelize(1 to 100, 4)
val rdd2 = rdd1.filter(_ % 2 == 0).map(_ + 1)
val rdd3 = rdd2.map(_ - 1).filter(_ < 50).map(i => (i, i))
val rdd4 = rdd3.reduceByKey(_ + _)
val rdd5 = rdd4.mapValues(_ + 1).mapValues(_ + 2).mapValues(_ + 3)
val ancestors1 = rdd1.getNarrowAncestors
val ancestors2 = rdd2.getNarrowAncestors
val ancestors3 = rdd3.getNarrowAncestors
val ancestors4 = rdd4.getNarrowAncestors
val ancestors5 = rdd5.getNarrowAncestors
// Simple dependency tree with a single branch
assert(ancestors1.size === 0)
assert(ancestors2.size === 2)
assert(ancestors2.count(_ === rdd1) === 1)
assert(ancestors2.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 1)
assert(ancestors3.size === 5)
assert(ancestors3.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 4)
// Any ancestors before the shuffle are not considered
assert(ancestors4.size === 0)
assert(ancestors4.count(_.isInstanceOf[ShuffledRDD[_, _, _]]) === 0)
assert(ancestors5.size === 3)
assert(ancestors5.count(_.isInstanceOf[ShuffledRDD[_, _, _]]) === 1)
assert(ancestors5.count(_ === rdd3) === 0)
assert(ancestors5.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 2)
}
test("getNarrowAncestors with multiple parents") {
val rdd1 = sc.parallelize(1 to 100, 5)
val rdd2 = sc.parallelize(1 to 200, 10).map(_ + 1)
val rdd3 = sc.parallelize(1 to 300, 15).filter(_ > 50)
val rdd4 = rdd1.map(i => (i, i))
val rdd5 = rdd2.map(i => (i, i))
val rdd6 = sc.union(rdd1, rdd2)
val rdd7 = sc.union(rdd1, rdd2, rdd3)
val rdd8 = sc.union(rdd6, rdd7)
val rdd9 = rdd4.join(rdd5)
val ancestors6 = rdd6.getNarrowAncestors
val ancestors7 = rdd7.getNarrowAncestors
val ancestors8 = rdd8.getNarrowAncestors
val ancestors9 = rdd9.getNarrowAncestors
// Simple dependency tree with multiple branches
assert(ancestors6.size === 3)
assert(ancestors6.count(_.isInstanceOf[ParallelCollectionRDD[_]]) === 2)
assert(ancestors6.count(_ === rdd2) === 1)
assert(ancestors7.size === 5)
assert(ancestors7.count(_.isInstanceOf[ParallelCollectionRDD[_]]) === 3)
assert(ancestors7.count(_ === rdd2) === 1)
assert(ancestors7.count(_ === rdd3) === 1)
// Dependency tree with duplicate nodes (e.g. rdd1 should not be reported twice)
assert(ancestors8.size === 7)
assert(ancestors8.count(_ === rdd2) === 1)
assert(ancestors8.count(_ === rdd3) === 1)
assert(ancestors8.count(_.isInstanceOf[UnionRDD[_]]) === 2)
assert(ancestors8.count(_.isInstanceOf[ParallelCollectionRDD[_]]) === 3)
assert(ancestors8.count(_ == rdd1) === 1)
assert(ancestors8.count(_ == rdd2) === 1)
assert(ancestors8.count(_ == rdd3) === 1)
// Any ancestors before the shuffle are not considered
assert(ancestors9.size === 2)
assert(ancestors9.count(_.isInstanceOf[CoGroupedRDD[_]]) === 1)
}
/**
* This tests for the pathological condition in which the RDD dependency graph is cyclical.
*
* Since RDD is part of the public API, applications may actually implement RDDs that allow
* such graphs to be constructed. In such cases, getNarrowAncestor should not simply hang.
*/
test("getNarrowAncestors with cycles") {
val rdd1 = new CyclicalDependencyRDD[Int]
val rdd2 = new CyclicalDependencyRDD[Int]
val rdd3 = new CyclicalDependencyRDD[Int]
val rdd4 = rdd3.map(_ + 1).filter(_ > 10).map(_ + 2).filter(_ % 5 > 1)
val rdd5 = rdd4.map(_ + 2).filter(_ > 20)
val rdd6 = sc.union(rdd1, rdd2, rdd3).map(_ + 4).union(rdd5).union(rdd4)
// Simple cyclical dependency
rdd1.addDependency(new OneToOneDependency[Int](rdd2))
rdd2.addDependency(new OneToOneDependency[Int](rdd1))
val ancestors1 = rdd1.getNarrowAncestors
val ancestors2 = rdd2.getNarrowAncestors
assert(ancestors1.size === 1)
assert(ancestors1.count(_ == rdd2) === 1)
assert(ancestors1.count(_ == rdd1) === 0)
assert(ancestors2.size === 1)
assert(ancestors2.count(_ == rdd1) === 1)
assert(ancestors2.count(_ == rdd2) === 0)
// Cycle involving a longer chain
rdd3.addDependency(new OneToOneDependency[Int](rdd4))
val ancestors3 = rdd3.getNarrowAncestors
val ancestors4 = rdd4.getNarrowAncestors
assert(ancestors3.size === 4)
assert(ancestors3.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 4)
assert(ancestors3.count(_ == rdd3) === 0)
assert(ancestors4.size === 4)
assert(ancestors4.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 3)
assert(ancestors4.count(_.isInstanceOf[CyclicalDependencyRDD[_]]) === 1)
assert(ancestors4.count(_ == rdd3) === 1)
assert(ancestors4.count(_ == rdd4) === 0)
// Cycles that do not involve the root
val ancestors5 = rdd5.getNarrowAncestors
assert(ancestors5.size === 6)
assert(ancestors5.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 5)
assert(ancestors5.count(_.isInstanceOf[CyclicalDependencyRDD[_]]) === 1)
assert(ancestors4.count(_ == rdd3) === 1)
// Complex cyclical dependency graph (combination of all of the above)
val ancestors6 = rdd6.getNarrowAncestors
assert(ancestors6.size === 12)
assert(ancestors6.count(_.isInstanceOf[UnionRDD[_]]) === 2)
assert(ancestors6.count(_.isInstanceOf[MapPartitionsRDD[_, _]]) === 7)
assert(ancestors6.count(_.isInstanceOf[CyclicalDependencyRDD[_]]) === 3)
}
test("task serialization exception should not hang scheduler") {
class BadSerializable extends Serializable {
@throws(classOf[IOException])
private def writeObject(out: ObjectOutputStream): Unit =
throw new KryoException("Bad serialization")
@throws(classOf[IOException])
private def readObject(in: ObjectInputStream): Unit = {}
}
// Note that in the original bug, SPARK-4349, that this verifies, the job would only hang if
// there were more threads in the Spark Context than there were number of objects in this
// sequence.
intercept[Throwable] {
sc.parallelize(Seq(new BadSerializable, new BadSerializable)).collect()
}
// Check that the context has not crashed
sc.parallelize(1 to 100).map(x => x*2).collect
}
/** A contrived RDD that allows the manual addition of dependencies after creation. */
private class CyclicalDependencyRDD[T: ClassTag] extends RDD[T](sc, Nil) {
private val mutableDependencies: ArrayBuffer[Dependency[_]] = ArrayBuffer.empty
override def compute(p: Partition, c: TaskContext): Iterator[T] = Iterator.empty
override def getPartitions: Array[Partition] = Array.empty
override def getDependencies: Seq[Dependency[_]] = mutableDependencies
def addDependency(dep: Dependency[_]) {
mutableDependencies += dep
}
}
test("RDD.partitions() fails fast when partitions indicies are incorrect (SPARK-13021)") {
class BadRDD[T: ClassTag](prev: RDD[T]) extends RDD[T](prev) {
override def compute(part: Partition, context: TaskContext): Iterator[T] = {
prev.compute(part, context)
}
override protected def getPartitions: Array[Partition] = {
prev.partitions.reverse // breaks contract, which is that `rdd.partitions(i).index == i`
}
}
val rdd = new BadRDD(sc.parallelize(1 to 100, 100))
val e = intercept[IllegalArgumentException] {
rdd.partitions
}
assert(e.getMessage.contains("partitions"))
}
test("nested RDDs are not supported (SPARK-5063)") {
val rdd: RDD[Int] = sc.parallelize(1 to 100)
val rdd2: RDD[Int] = sc.parallelize(1 to 100)
val thrown = intercept[SparkException] {
val nestedRDD: RDD[RDD[Int]] = rdd.mapPartitions { x => Seq(rdd2.map(x => x)).iterator }
nestedRDD.count()
}
assert(thrown.getMessage.contains("SPARK-5063"))
}
test("actions cannot be performed inside of transformations (SPARK-5063)") {
val rdd: RDD[Int] = sc.parallelize(1 to 100)
val rdd2: RDD[Int] = sc.parallelize(1 to 100)
val thrown = intercept[SparkException] {
rdd.map(x => x * rdd2.count).collect()
}
assert(thrown.getMessage.contains("SPARK-5063"))
}
test("cannot run actions after SparkContext has been stopped (SPARK-5063)") {
val existingRDD = sc.parallelize(1 to 100)
sc.stop()
val thrown = intercept[IllegalStateException] {
existingRDD.count()
}
assert(thrown.getMessage.contains("shutdown"))
}
test("cannot call methods on a stopped SparkContext (SPARK-5063)") {
sc.stop()
def assertFails(block: => Any): Unit = {
val thrown = intercept[IllegalStateException] {
block
}
assert(thrown.getMessage.contains("stopped"))
}
assertFails { sc.parallelize(1 to 100) }
assertFails { sc.textFile("/nonexistent-path") }
}
}
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