Merge branch 'master' of github.com:apache/spark into profiler

Author: davies

.gitignore

@@ -23,6 +23,7 @@ conf/*.cmd
 conf/*.properties
 conf/*.conf
 conf/*.xml
+conf/slaves
 docs/_site
 docs/api
 target/

.rat-excludes

@@ -19,6 +19,7 @@ log4j.properties
 log4j.properties.template
 metrics.properties.template
 slaves
+slaves.template
 spark-env.sh
 spark-env.cmd
 spark-env.sh.template

conf/slaves renamed to conf/slaves.template

@@ -18,10 +18,12 @@
 package org.apache.spark
 
 import java.io._
+import java.util.concurrent.ConcurrentHashMap
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
 import scala.collection.mutable.{HashSet, HashMap, Map}
 import scala.concurrent.Await
+import scala.collection.JavaConversions._
 
 import akka.actor._
 import akka.pattern.ask
@@ -84,6 +86,9 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
    * On the master, it serves as the source of map outputs recorded from ShuffleMapTasks.
    * On the workers, it simply serves as a cache, in which a miss triggers a fetch from the
    * master's corresponding HashMap.
+   *
+   * Note: because mapStatuses is accessed concurrently, subclasses should make sure it's a
+   * thread-safe map.
    */
   protected val mapStatuses: Map[Int, Array[MapStatus]]
 
@@ -339,7 +344,8 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf)
  * MapOutputTrackerMaster.
  */
 private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTracker(conf) {
-  protected val mapStatuses = new HashMap[Int, Array[MapStatus]]
+  protected val mapStatuses: Map[Int, Array[MapStatus]] =
+    new ConcurrentHashMap[Int, Array[MapStatus]]
 }
 
 private[spark] object MapOutputTracker {

core/src/main/scala/org/apache/spark/MapOutputTracker.scala

@@ -469,6 +469,22 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
     fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
   }
 
+  /**
+   * Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
+   * resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
+   * the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
+   * element (k, w) in `other`, the resulting RDD will either contain all pairs
+   * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
+   * in `this` have key k. Uses the given Partitioner to partition the output RDD.
+   */
+  def fullOuterJoin[W](other: JavaPairRDD[K, W], partitioner: Partitioner)
+  : JavaPairRDD[K, (Optional[V], Optional[W])] = {
+    val joinResult = rdd.fullOuterJoin(other, partitioner)
+    fromRDD(joinResult.mapValues{ case (v, w) =>
+      (JavaUtils.optionToOptional(v), JavaUtils.optionToOptional(w))
+    })
+  }
+
   /**
    * Simplified version of combineByKey that hash-partitions the resulting RDD using the existing
    * partitioner/parallelism level.
@@ -563,6 +579,38 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
     fromRDD(joinResult.mapValues{case (v, w) => (JavaUtils.optionToOptional(v), w)})
   }
 
+  /**
+   * Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
+   * resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
+   * the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
+   * element (k, w) in `other`, the resulting RDD will either contain all pairs
+   * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
+   * in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/
+   * parallelism level.
+   */
+  def fullOuterJoin[W](other: JavaPairRDD[K, W]): JavaPairRDD[K, (Optional[V], Optional[W])] = {
+    val joinResult = rdd.fullOuterJoin(other)
+    fromRDD(joinResult.mapValues{ case (v, w) =>
+      (JavaUtils.optionToOptional(v), JavaUtils.optionToOptional(w))
+    })
+  }
+
+  /**
+   * Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
+   * resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
+   * the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
+   * element (k, w) in `other`, the resulting RDD will either contain all pairs
+   * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
+   * in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions.
+   */
+  def fullOuterJoin[W](other: JavaPairRDD[K, W], numPartitions: Int)
+  : JavaPairRDD[K, (Optional[V], Optional[W])] = {
+    val joinResult = rdd.fullOuterJoin(other, numPartitions)
+    fromRDD(joinResult.mapValues{ case (v, w) =>
+      (JavaUtils.optionToOptional(v), JavaUtils.optionToOptional(w))
+    })
+  }
+
   /**
    * Return the key-value pairs in this RDD to the master as a Map.
    */

core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala

@@ -506,6 +506,23 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     }
   }
 
+  /**
+   * Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
+   * resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
+   * the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
+   * element (k, w) in `other`, the resulting RDD will either contain all pairs
+   * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
+   * in `this` have key k. Uses the given Partitioner to partition the output RDD.
+   */
+  def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner)
+      : RDD[(K, (Option[V], Option[W]))] = {
+    this.cogroup(other, partitioner).flatMapValues {
+      case (vs, Seq()) => vs.map(v => (Some(v), None))
+      case (Seq(), ws) => ws.map(w => (None, Some(w)))
+      case (vs, ws) => for (v <- vs; w <- ws) yield (Some(v), Some(w))
+    }
+  }
+
   /**
    * Simplified version of combineByKey that hash-partitions the resulting RDD using the
    * existing partitioner/parallelism level.
@@ -585,6 +602,31 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     rightOuterJoin(other, new HashPartitioner(numPartitions))
   }
 
+  /**
+   * Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
+   * resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
+   * the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
+   * element (k, w) in `other`, the resulting RDD will either contain all pairs
+   * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
+   * in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/
+   * parallelism level.
+   */
+  def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = {
+    fullOuterJoin(other, defaultPartitioner(self, other))
+  }
+
+  /**
+   * Perform a full outer join of `this` and `other`. For each element (k, v) in `this`, the
+   * resulting RDD will either contain all pairs (k, (Some(v), Some(w))) for w in `other`, or
+   * the pair (k, (Some(v), None)) if no elements in `other` have key k. Similarly, for each
+   * element (k, w) in `other`, the resulting RDD will either contain all pairs
+   * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements
+   * in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions.
+   */
+  def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = {
+    fullOuterJoin(other, new HashPartitioner(numPartitions))
+  }
+
   /**
    * Return the key-value pairs in this RDD to the master as a Map.
    *

core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala

@@ -69,8 +69,13 @@ private[spark] class ShuffleMapTask(
       return writer.stop(success = true).get
     } catch {
       case e: Exception =>
-        if (writer != null) {
-          writer.stop(success = false)
+        try {
+          if (writer != null) {
+            writer.stop(success = false)
+          }
+        } catch {
+          case e: Exception =>
+            log.debug("Could not stop writer", e)
         }
         throw e
     } finally {

core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala

@@ -121,7 +121,7 @@ final class ShuffleBlockFetcherIterator(
         }
 
         override def onBlockFetchFailure(e: Throwable): Unit = {
-          logError("Failed to get block(s) from ${req.address.host}:${req.address.port}", e)
+          logError(s"Failed to get block(s) from ${req.address.host}:${req.address.port}", e)
           // Note that there is a chance that some blocks have been fetched successfully, but we
           // still add them to the failed queue. This is fine because when the caller see a
           // FetchFailedException, it is going to fail the entire task anyway.

core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala

@@ -193,11 +193,13 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     assert(grouped2.join(grouped4).partitioner === grouped4.partitioner)
     assert(grouped2.leftOuterJoin(grouped4).partitioner === grouped4.partitioner)
     assert(grouped2.rightOuterJoin(grouped4).partitioner === grouped4.partitioner)
+    assert(grouped2.fullOuterJoin(grouped4).partitioner === grouped4.partitioner)
     assert(grouped2.cogroup(grouped4).partitioner === grouped4.partitioner)
 
     assert(grouped2.join(reduced2).partitioner === grouped2.partitioner)
     assert(grouped2.leftOuterJoin(reduced2).partitioner === grouped2.partitioner)
     assert(grouped2.rightOuterJoin(reduced2).partitioner === grouped2.partitioner)
+    assert(grouped2.fullOuterJoin(reduced2).partitioner === grouped2.partitioner)
     assert(grouped2.cogroup(reduced2).partitioner === grouped2.partitioner)
 
     assert(grouped2.map(_ => 1).partitioner === None)
@@ -218,6 +220,7 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     assert(intercept[SparkException]{ arrPairs.join(arrPairs) }.getMessage.contains("array"))
     assert(intercept[SparkException]{ arrPairs.leftOuterJoin(arrPairs) }.getMessage.contains("array"))
     assert(intercept[SparkException]{ arrPairs.rightOuterJoin(arrPairs) }.getMessage.contains("array"))
+    assert(intercept[SparkException]{ arrPairs.fullOuterJoin(arrPairs) }.getMessage.contains("array"))
     assert(intercept[SparkException]{ arrPairs.groupByKey() }.getMessage.contains("array"))
     assert(intercept[SparkException]{ arrPairs.countByKey() }.getMessage.contains("array"))
     assert(intercept[SparkException]{ arrPairs.countByKeyApprox(1) }.getMessage.contains("array"))

core/src/test/scala/org/apache/spark/PartitioningSuite.scala

@@ -298,6 +298,21 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext {
     ))
   }
 
+  test("fullOuterJoin") {
+    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.fullOuterJoin(rdd2).collect()
+    assert(joined.size === 6)
+    assert(joined.toSet === Set(
+      (1, (Some(1), Some('x'))),
+      (1, (Some(2), Some('x'))),
+      (2, (Some(1), Some('y'))),
+      (2, (Some(1), Some('z'))),
+      (3, (Some(1), None)),
+      (4, (None, Some('w')))
+    ))
+  }
+
   test("join with no matches") {
     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')))