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authorSandy Ryza <sandy@cloudera.com>2014-09-08 11:20:00 -0700
committerMatei Zaharia <matei@databricks.com>2014-09-08 11:20:00 -0700
commit16a73c2473181e03d88001aa3e08e6ffac92eb8b (patch)
treefc6746e31bc239087505248e0efc1ad58f383f2f /core/src
parente16a8e7db5a3b1065b14baf89cb723a59b99226b (diff)
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SPARK-2978. Transformation with MR shuffle semantics
I didn't add this to the transformations list in the docs because it's kind of obscure, but would be happy to do so if others think it would be helpful. Author: Sandy Ryza <sandy@cloudera.com> Closes #2274 from sryza/sandy-spark-2978 and squashes the following commits: 4a5332a [Sandy Ryza] Fix Java test c04b447 [Sandy Ryza] Fix Python doc and add back deleted code 433ad5b [Sandy Ryza] Add Java test 4c25a54 [Sandy Ryza] Add s at the end and a couple other fixes 9b0ba99 [Sandy Ryza] Fix compilation 36e0571 [Sandy Ryza] Fix import ordering 48c12c2 [Sandy Ryza] Add Java version and additional doc e5381cd [Sandy Ryza] Fix python style warnings f147634 [Sandy Ryza] SPARK-2978. Transformation with MR shuffle semantics
Diffstat (limited to 'core/src')
-rw-r--r--core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala26
-rw-r--r--core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala14
-rw-r--r--core/src/test/java/org/apache/spark/JavaAPISuite.java30
-rw-r--r--core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala14
4 files changed, 83 insertions, 1 deletions
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index feeb6c02ca..880f61c497 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -759,6 +759,32 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
}
/**
+ * Repartition the RDD according to the given partitioner and, within each resulting partition,
+ * sort records by their keys.
+ *
+ * This is more efficient than calling `repartition` and then sorting within each partition
+ * because it can push the sorting down into the shuffle machinery.
+ */
+ def repartitionAndSortWithinPartitions(partitioner: Partitioner): JavaPairRDD[K, V] = {
+ val comp = com.google.common.collect.Ordering.natural().asInstanceOf[Comparator[K]]
+ repartitionAndSortWithinPartitions(partitioner, comp)
+ }
+
+ /**
+ * Repartition the RDD according to the given partitioner and, within each resulting partition,
+ * sort records by their keys.
+ *
+ * This is more efficient than calling `repartition` and then sorting within each partition
+ * because it can push the sorting down into the shuffle machinery.
+ */
+ def repartitionAndSortWithinPartitions(partitioner: Partitioner, comp: Comparator[K])
+ : JavaPairRDD[K, V] = {
+ implicit val ordering = comp // Allow implicit conversion of Comparator to Ordering.
+ fromRDD(
+ new OrderedRDDFunctions[K, V, (K, V)](rdd).repartitionAndSortWithinPartitions(partitioner))
+ }
+
+ /**
* Sort the RDD by key, so that each partition contains a sorted range of the elements in
* ascending order. Calling `collect` or `save` on the resulting RDD will return or output an
* ordered list of records (in the `save` case, they will be written to multiple `part-X` files
diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
index e98bad2026..d0dbfef35d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala
@@ -19,7 +19,7 @@ package org.apache.spark.rdd
import scala.reflect.ClassTag
-import org.apache.spark.{Logging, RangePartitioner}
+import org.apache.spark.{Logging, Partitioner, RangePartitioner}
import org.apache.spark.annotation.DeveloperApi
/**
@@ -64,4 +64,16 @@ class OrderedRDDFunctions[K : Ordering : ClassTag,
new ShuffledRDD[K, V, V](self, part)
.setKeyOrdering(if (ascending) ordering else ordering.reverse)
}
+
+ /**
+ * Repartition the RDD according to the given partitioner and, within each resulting partition,
+ * sort records by their keys.
+ *
+ * This is more efficient than calling `repartition` and then sorting within each partition
+ * because it can push the sorting down into the shuffle machinery.
+ */
+ def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = {
+ new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering)
+ }
+
}
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index e1c13de04a..be99dc501c 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -190,6 +190,36 @@ public class JavaAPISuite implements Serializable {
}
@Test
+ public void repartitionAndSortWithinPartitions() {
+ List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
+ pairs.add(new Tuple2<Integer, Integer>(0, 5));
+ pairs.add(new Tuple2<Integer, Integer>(3, 8));
+ pairs.add(new Tuple2<Integer, Integer>(2, 6));
+ pairs.add(new Tuple2<Integer, Integer>(0, 8));
+ pairs.add(new Tuple2<Integer, Integer>(3, 8));
+ pairs.add(new Tuple2<Integer, Integer>(1, 3));
+
+ JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
+
+ Partitioner partitioner = new Partitioner() {
+ public int numPartitions() {
+ return 2;
+ }
+ public int getPartition(Object key) {
+ return ((Integer)key).intValue() % 2;
+ }
+ };
+
+ JavaPairRDD<Integer, Integer> repartitioned =
+ rdd.repartitionAndSortWithinPartitions(partitioner);
+ List<List<Tuple2<Integer, Integer>>> partitions = repartitioned.glom().collect();
+ Assert.assertEquals(partitions.get(0), Arrays.asList(new Tuple2<Integer, Integer>(0, 5),
+ new Tuple2<Integer, Integer>(0, 8), new Tuple2<Integer, Integer>(2, 6)));
+ Assert.assertEquals(partitions.get(1), Arrays.asList(new Tuple2<Integer, Integer>(1, 3),
+ new Tuple2<Integer, Integer>(3, 8), new Tuple2<Integer, Integer>(3, 8)));
+ }
+
+ @Test
public void emptyRDD() {
JavaRDD<String> rdd = sc.emptyRDD();
Assert.assertEquals("Empty RDD shouldn't have any values", 0, rdd.count());
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
index 499dcda3da..c1b501a75c 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala
@@ -682,6 +682,20 @@ class RDDSuite extends FunSuite with SharedSparkContext {
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)