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package spark
import org.scalatest.FunSuite
import org.scalatest.BeforeAndAfter
import org.scalatest.matchers.ShouldMatchers
import org.scalatest.prop.Checkers
import org.scalacheck.Arbitrary._
import org.scalacheck.Gen
import org.scalacheck.Prop._
import com.google.common.io.Files
import scala.collection.mutable.ArrayBuffer
import SparkContext._
class ShuffleSuite extends FunSuite with ShouldMatchers with BeforeAndAfter {
var sc: SparkContext = _
after {
if (sc != null) {
sc.stop()
sc = null
}
}
test("groupByKey") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)))
val groups = pairs.groupByKey().collect()
assert(groups.size === 2)
val valuesFor1 = groups.find(_._1 == 1).get._2
assert(valuesFor1.toList.sorted === List(1, 2, 3))
val valuesFor2 = groups.find(_._1 == 2).get._2
assert(valuesFor2.toList.sorted === List(1))
}
test("groupByKey with duplicates") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1)))
val groups = pairs.groupByKey().collect()
assert(groups.size === 2)
val valuesFor1 = groups.find(_._1 == 1).get._2
assert(valuesFor1.toList.sorted === List(1, 1, 2, 3))
val valuesFor2 = groups.find(_._1 == 2).get._2
assert(valuesFor2.toList.sorted === List(1))
}
test("groupByKey with negative key hash codes") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((-1, 1), (-1, 2), (-1, 3), (2, 1)))
val groups = pairs.groupByKey().collect()
assert(groups.size === 2)
val valuesForMinus1 = groups.find(_._1 == -1).get._2
assert(valuesForMinus1.toList.sorted === List(1, 2, 3))
val valuesFor2 = groups.find(_._1 == 2).get._2
assert(valuesFor2.toList.sorted === List(1))
}
test("groupByKey with many output partitions") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)))
val groups = pairs.groupByKey(10).collect()
assert(groups.size === 2)
val valuesFor1 = groups.find(_._1 == 1).get._2
assert(valuesFor1.toList.sorted === List(1, 2, 3))
val valuesFor2 = groups.find(_._1 == 2).get._2
assert(valuesFor2.toList.sorted === List(1))
}
test("reduceByKey") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1)))
val sums = pairs.reduceByKey(_+_).collect()
assert(sums.toSet === Set((1, 7), (2, 1)))
}
test("reduceByKey with collectAsMap") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1)))
val sums = pairs.reduceByKey(_+_).collectAsMap()
assert(sums.size === 2)
assert(sums(1) === 7)
assert(sums(2) === 1)
}
test("reduceByKey with many output partitons") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1)))
val sums = pairs.reduceByKey(_+_, 10).collect()
assert(sums.toSet === Set((1, 7), (2, 1)))
}
test("join") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
val joined = rdd1.join(rdd2).collect()
assert(joined.size === 4)
assert(joined.toSet === Set(
(1, (1, 'x')),
(1, (2, 'x')),
(2, (1, 'y')),
(2, (1, 'z'))
))
}
test("join all-to-all") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (1, 3)))
val rdd2 = sc.parallelize(Array((1, 'x'), (1, 'y')))
val joined = rdd1.join(rdd2).collect()
assert(joined.size === 6)
assert(joined.toSet === Set(
(1, (1, 'x')),
(1, (1, 'y')),
(1, (2, 'x')),
(1, (2, 'y')),
(1, (3, 'x')),
(1, (3, 'y'))
))
}
test("leftOuterJoin") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
val joined = rdd1.leftOuterJoin(rdd2).collect()
assert(joined.size === 5)
assert(joined.toSet === Set(
(1, (1, Some('x'))),
(1, (2, Some('x'))),
(2, (1, Some('y'))),
(2, (1, Some('z'))),
(3, (1, None))
))
}
test("rightOuterJoin") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
val joined = rdd1.rightOuterJoin(rdd2).collect()
assert(joined.size === 5)
assert(joined.toSet === Set(
(1, (Some(1), 'x')),
(1, (Some(2), 'x')),
(2, (Some(1), 'y')),
(2, (Some(1), 'z')),
(4, (None, 'w'))
))
}
test("join with no matches") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
val rdd2 = sc.parallelize(Array((4, 'x'), (5, 'y'), (5, 'z'), (6, 'w')))
val joined = rdd1.join(rdd2).collect()
assert(joined.size === 0)
}
test("join with many output partitions") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
val joined = rdd1.join(rdd2, 10).collect()
assert(joined.size === 4)
assert(joined.toSet === Set(
(1, (1, 'x')),
(1, (2, 'x')),
(2, (1, 'y')),
(2, (1, 'z'))
))
}
test("groupWith") {
sc = new SparkContext("local", "test")
val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1)))
val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w')))
val joined = rdd1.groupWith(rdd2).collect()
assert(joined.size === 4)
assert(joined.toSet === Set(
(1, (ArrayBuffer(1, 2), ArrayBuffer('x'))),
(2, (ArrayBuffer(1), ArrayBuffer('y', 'z'))),
(3, (ArrayBuffer(1), ArrayBuffer())),
(4, (ArrayBuffer(), ArrayBuffer('w')))
))
}
test("zero-partition RDD") {
sc = new SparkContext("local", "test")
val emptyDir = Files.createTempDir()
val file = sc.textFile(emptyDir.getAbsolutePath)
assert(file.splits.size == 0)
assert(file.collect().toList === Nil)
// Test that a shuffle on the file works, because this used to be a bug
assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil)
}
test("map-side combine") {
sc = new SparkContext("local", "test")
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1), (1, 1)), 2)
// Test with map-side combine on.
val sums = pairs.reduceByKey(_+_).collect()
assert(sums.toSet === Set((1, 8), (2, 1)))
// Turn off map-side combine and test the results.
val aggregator = new Aggregator[Int, Int, Int](
(v: Int) => v,
_+_,
_+_,
false)
val shuffledRdd = new ShuffledRDD(
pairs, aggregator, new HashPartitioner(2))
assert(shuffledRdd.collect().toSet === Set((1, 8), (2, 1)))
// Turn map-side combine off and pass a wrong mergeCombine function. Should
// not see an exception because mergeCombine should not have been called.
val aggregatorWithException = new Aggregator[Int, Int, Int](
(v: Int) => v, _+_, ShuffleSuite.mergeCombineException, false)
val shuffledRdd1 = new ShuffledRDD(
pairs, aggregatorWithException, new HashPartitioner(2))
assert(shuffledRdd1.collect().toSet === Set((1, 8), (2, 1)))
// Now run the same mergeCombine function with map-side combine on. We
// expect to see an exception thrown.
val aggregatorWithException1 = new Aggregator[Int, Int, Int](
(v: Int) => v, _+_, ShuffleSuite.mergeCombineException)
val shuffledRdd2 = new ShuffledRDD(
pairs, aggregatorWithException1, new HashPartitioner(2))
evaluating { shuffledRdd2.collect() } should produce [SparkException]
}
}
object ShuffleSuite {
def mergeCombineException(x: Int, y: Int): Int = {
throw new SparkException("Exception for map-side combine.")
x + y
}
}
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