Merge pull request apache#66 from markhamstra/csd-1.4

SKIPME merging Apache branch-1.4 bug fixes

core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala

@@ -352,8 +352,8 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
 
   /**
    * Comparison function that defines the sort order for application attempts within the same
-   * application. Order is: running attempts before complete attempts, running attempts sorted
-   * by start time, completed attempts sorted by end time.
+   * application. Order is: attempts are sorted by descending start time.
+   * Most recent attempt state matches with current state of the app.
    *
    * Normally applications should have a single running attempt; but failure to call sc.stop()
    * may cause multiple running attempts to show up.
@@ -363,11 +363,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   private def compareAttemptInfo(
       a1: FsApplicationAttemptInfo,
       a2: FsApplicationAttemptInfo): Boolean = {
-    if (a1.completed == a2.completed) {
-      if (a1.completed) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime
-    } else {
-      !a1.completed
-    }
+    a1.startTime >= a2.startTime
   }
 
   /**

core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala

@@ -239,13 +239,12 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
     appListAfterRename.size should be (1)
   }
 
-  test("apps with multiple attempts") {
+  test("apps with multiple attempts with order") {
     val provider = new FsHistoryProvider(createTestConf())
 
-    val attempt1 = newLogFile("app1", Some("attempt1"), inProgress = false)
+    val attempt1 = newLogFile("app1", Some("attempt1"), inProgress = true)
     writeFile(attempt1, true, None,
-      SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1")),
-      SparkListenerApplicationEnd(2L)
+      SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1"))
       )
 
     updateAndCheck(provider) { list =>
@@ -255,7 +254,7 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
 
     val attempt2 = newLogFile("app1", Some("attempt2"), inProgress = true)
     writeFile(attempt2, true, None,
-      SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2"))
+      SparkListenerApplicationStart("app1", Some("app1"), 2L, "test", Some("attempt2"))
       )
 
     updateAndCheck(provider) { list =>
@@ -264,30 +263,29 @@ class FsHistoryProviderSuite extends SparkFunSuite with BeforeAndAfter with Matc
       list.head.attempts.head.attemptId should be (Some("attempt2"))
     }
 
-    val completedAttempt2 = newLogFile("app1", Some("attempt2"), inProgress = false)
-    attempt2.delete()
-    writeFile(attempt2, true, None,
-      SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")),
+    val attempt3 = newLogFile("app1", Some("attempt3"), inProgress = false)
+    writeFile(attempt3, true, None,
+      SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt3")),
       SparkListenerApplicationEnd(4L)
       )
 
     updateAndCheck(provider) { list =>
       list should not be (null)
       list.size should be (1)
-      list.head.attempts.size should be (2)
-      list.head.attempts.head.attemptId should be (Some("attempt2"))
+      list.head.attempts.size should be (3)
+      list.head.attempts.head.attemptId should be (Some("attempt3"))
     }
 
     val app2Attempt1 = newLogFile("app2", Some("attempt1"), inProgress = false)
-    writeFile(attempt2, true, None,
+    writeFile(attempt1, true, None,
       SparkListenerApplicationStart("app2", Some("app2"), 5L, "test", Some("attempt1")),
       SparkListenerApplicationEnd(6L)
       )
 
     updateAndCheck(provider) { list =>
       list.size should be (2)
       list.head.attempts.size should be (1)
-      list.last.attempts.size should be (2)
+      list.last.attempts.size should be (3)
       list.head.attempts.head.attemptId should be (Some("attempt1"))
 
       list.foreach { case app =>

graphx/src/main/scala/org/apache/spark/graphx/impl/GraphImpl.scala

@@ -332,9 +332,9 @@ object GraphImpl {
       edgeStorageLevel: StorageLevel,
       vertexStorageLevel: StorageLevel): GraphImpl[VD, ED] = {
     val edgeRDD = EdgeRDD.fromEdges(edges)(classTag[ED], classTag[VD])
-      .withTargetStorageLevel(edgeStorageLevel).cache()
+      .withTargetStorageLevel(edgeStorageLevel)
     val vertexRDD = VertexRDD(vertices, edgeRDD, defaultVertexAttr)
-      .withTargetStorageLevel(vertexStorageLevel).cache()
+      .withTargetStorageLevel(vertexStorageLevel)
     GraphImpl(vertexRDD, edgeRDD)
   }
 
@@ -346,9 +346,14 @@ object GraphImpl {
   def apply[VD: ClassTag, ED: ClassTag](
       vertices: VertexRDD[VD],
       edges: EdgeRDD[ED]): GraphImpl[VD, ED] = {
+
+    vertices.cache()
+
     // Convert the vertex partitions in edges to the correct type
     val newEdges = edges.asInstanceOf[EdgeRDDImpl[ED, _]]
       .mapEdgePartitions((pid, part) => part.withoutVertexAttributes[VD])
+      .cache()
+
     GraphImpl.fromExistingRDDs(vertices, newEdges)
   }
 

pom.xml

@@ -146,7 +146,7 @@
     <chill.version>0.5.0</chill.version>
     <ivy.version>2.4.0</ivy.version>
     <oro.version>2.0.8</oro.version>
-    <codahale.metrics.version>3.1.0</codahale.metrics.version>
+    <codahale.metrics.version>3.1.2</codahale.metrics.version>
     <avro.version>1.7.7</avro.version>
     <avro.mapred.classifier>hadoop2</avro.mapred.classifier>
     <jets3t.version>0.7.1</jets3t.version>

python/pyspark/rdd.py

@@ -849,6 +849,9 @@ def func(iterator):
             for obj in iterator:
                 acc = op(obj, acc)
             yield acc
+        # collecting result of mapPartitions here ensures that the copy of
+        # zeroValue provided to each partition is unique from the one provided
+        # to the final reduce call
         vals = self.mapPartitions(func).collect()
         return reduce(op, vals, zeroValue)
 
@@ -878,8 +881,11 @@ def func(iterator):
             for obj in iterator:
                 acc = seqOp(acc, obj)
             yield acc
-
-        return self.mapPartitions(func).fold(zeroValue, combOp)
+        # collecting result of mapPartitions here ensures that the copy of
+        # zeroValue provided to each partition is unique from the one provided
+        # to the final reduce call
+        vals = self.mapPartitions(func).collect()
+        return reduce(combOp, vals, zeroValue)
 
     def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
         """

python/pyspark/tests.py

@@ -529,10 +529,127 @@ def test_deleting_input_files(self):
 
     def test_sampling_default_seed(self):
         # Test for SPARK-3995 (default seed setting)
-        data = self.sc.parallelize(range(1000), 1)
+        data = self.sc.parallelize(xrange(1000), 1)
         subset = data.takeSample(False, 10)
         self.assertEqual(len(subset), 10)
 
+    def test_aggregate_mutable_zero_value(self):
+        # Test for SPARK-9021; uses aggregate and treeAggregate to build dict
+        # representing a counter of ints
+        # NOTE: dict is used instead of collections.Counter for Python 2.6
+        # compatibility
+        from collections import defaultdict
+
+        # Show that single or multiple partitions work
+        data1 = self.sc.range(10, numSlices=1)
+        data2 = self.sc.range(10, numSlices=2)
+
+        def seqOp(x, y):
+            x[y] += 1
+            return x
+
+        def comboOp(x, y):
+            for key, val in y.items():
+                x[key] += val
+            return x
+
+        counts1 = data1.aggregate(defaultdict(int), seqOp, comboOp)
+        counts2 = data2.aggregate(defaultdict(int), seqOp, comboOp)
+        counts3 = data1.treeAggregate(defaultdict(int), seqOp, comboOp, 2)
+        counts4 = data2.treeAggregate(defaultdict(int), seqOp, comboOp, 2)
+
+        ground_truth = defaultdict(int, dict((i, 1) for i in range(10)))
+        self.assertEqual(counts1, ground_truth)
+        self.assertEqual(counts2, ground_truth)
+        self.assertEqual(counts3, ground_truth)
+        self.assertEqual(counts4, ground_truth)
+
+    def test_aggregate_by_key_mutable_zero_value(self):
+        # Test for SPARK-9021; uses aggregateByKey to make a pair RDD that
+        # contains lists of all values for each key in the original RDD
+
+        # list(range(...)) for Python 3.x compatibility (can't use * operator
+        # on a range object)
+        # list(zip(...)) for Python 3.x compatibility (want to parallelize a
+        # collection, not a zip object)
+        tuples = list(zip(list(range(10))*2, [1]*20))
+        # Show that single or multiple partitions work
+        data1 = self.sc.parallelize(tuples, 1)
+        data2 = self.sc.parallelize(tuples, 2)
+
+        def seqOp(x, y):
+            x.append(y)
+            return x
+
+        def comboOp(x, y):
+            x.extend(y)
+            return x
+
+        values1 = data1.aggregateByKey([], seqOp, comboOp).collect()
+        values2 = data2.aggregateByKey([], seqOp, comboOp).collect()
+        # Sort lists to ensure clean comparison with ground_truth
+        values1.sort()
+        values2.sort()
+
+        ground_truth = [(i, [1]*2) for i in range(10)]
+        self.assertEqual(values1, ground_truth)
+        self.assertEqual(values2, ground_truth)
+
+    def test_fold_mutable_zero_value(self):
+        # Test for SPARK-9021; uses fold to merge an RDD of dict counters into
+        # a single dict
+        # NOTE: dict is used instead of collections.Counter for Python 2.6
+        # compatibility
+        from collections import defaultdict
+
+        counts1 = defaultdict(int, dict((i, 1) for i in range(10)))
+        counts2 = defaultdict(int, dict((i, 1) for i in range(3, 8)))
+        counts3 = defaultdict(int, dict((i, 1) for i in range(4, 7)))
+        counts4 = defaultdict(int, dict((i, 1) for i in range(5, 6)))
+        all_counts = [counts1, counts2, counts3, counts4]
+        # Show that single or multiple partitions work
+        data1 = self.sc.parallelize(all_counts, 1)
+        data2 = self.sc.parallelize(all_counts, 2)
+
+        def comboOp(x, y):
+            for key, val in y.items():
+                x[key] += val
+            return x
+
+        fold1 = data1.fold(defaultdict(int), comboOp)
+        fold2 = data2.fold(defaultdict(int), comboOp)
+
+        ground_truth = defaultdict(int)
+        for counts in all_counts:
+            for key, val in counts.items():
+                ground_truth[key] += val
+        self.assertEqual(fold1, ground_truth)
+        self.assertEqual(fold2, ground_truth)
+
+    def test_fold_by_key_mutable_zero_value(self):
+        # Test for SPARK-9021; uses foldByKey to make a pair RDD that contains
+        # lists of all values for each key in the original RDD
+
+        tuples = [(i, range(i)) for i in range(10)]*2
+        # Show that single or multiple partitions work
+        data1 = self.sc.parallelize(tuples, 1)
+        data2 = self.sc.parallelize(tuples, 2)
+
+        def comboOp(x, y):
+            x.extend(y)
+            return x
+
+        values1 = data1.foldByKey([], comboOp).collect()
+        values2 = data2.foldByKey([], comboOp).collect()
+        # Sort lists to ensure clean comparison with ground_truth
+        values1.sort()
+        values2.sort()
+
+        # list(range(...)) for Python 3.x compatibility
+        ground_truth = [(i, list(range(i))*2) for i in range(10)]
+        self.assertEqual(values1, ground_truth)
+        self.assertEqual(values2, ground_truth)
+
     def test_aggregate_by_key(self):
         data = self.sc.parallelize([(1, 1), (1, 1), (3, 2), (5, 1), (5, 3)], 2)
 
@@ -624,8 +741,8 @@ def test_zip_with_different_serializers(self):
 
     def test_zip_with_different_object_sizes(self):
         # regress test for SPARK-5973
-        a = self.sc.parallelize(range(10000)).map(lambda i: '*' * i)
-        b = self.sc.parallelize(range(10000, 20000)).map(lambda i: '*' * i)
+        a = self.sc.parallelize(xrange(10000)).map(lambda i: '*' * i)
+        b = self.sc.parallelize(xrange(10000, 20000)).map(lambda i: '*' * i)
         self.assertEqual(10000, a.zip(b).count())
 
     def test_zip_with_different_number_of_items(self):
@@ -647,7 +764,7 @@ def test_zip_with_different_number_of_items(self):
             self.assertRaises(Exception, lambda: a.zip(b).count())
 
     def test_count_approx_distinct(self):
-        rdd = self.sc.parallelize(range(1000))
+        rdd = self.sc.parallelize(xrange(1000))
         self.assertTrue(950 < rdd.countApproxDistinct(0.03) < 1050)
         self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.03) < 1050)
         self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.03) < 1050)
@@ -777,7 +894,7 @@ def test_distinct(self):
     def test_external_group_by_key(self):
         self.sc._conf.set("spark.python.worker.memory", "1m")
         N = 200001
-        kv = self.sc.parallelize(range(N)).map(lambda x: (x % 3, x))
+        kv = self.sc.parallelize(xrange(N)).map(lambda x: (x % 3, x))
         gkv = kv.groupByKey().cache()
         self.assertEqual(3, gkv.count())
         filtered = gkv.filter(lambda kv: kv[0] == 1)
@@ -871,7 +988,7 @@ def test_narrow_dependency_in_join(self):
 
     # Regression test for SPARK-6294
     def test_take_on_jrdd(self):
-        rdd = self.sc.parallelize(range(1 << 20)).map(lambda x: str(x))
+        rdd = self.sc.parallelize(xrange(1 << 20)).map(lambda x: str(x))
         rdd._jrdd.first()
 
     def test_sortByKey_uses_all_partitions_not_only_first_and_last(self):
@@ -1503,13 +1620,13 @@ def run():
             self.fail("daemon had been killed")
 
         # run a normal job
-        rdd = self.sc.parallelize(range(100), 1)
+        rdd = self.sc.parallelize(xrange(100), 1)
         self.assertEqual(100, rdd.map(str).count())
 
     def test_after_exception(self):
         def raise_exception(_):
             raise Exception()
-        rdd = self.sc.parallelize(range(100), 1)
+        rdd = self.sc.parallelize(xrange(100), 1)
         with QuietTest(self.sc):
             self.assertRaises(Exception, lambda: rdd.foreach(raise_exception))
         self.assertEqual(100, rdd.map(str).count())
@@ -1525,22 +1642,22 @@ def test_after_jvm_exception(self):
         with QuietTest(self.sc):
             self.assertRaises(Exception, lambda: filtered_data.count())
 
-        rdd = self.sc.parallelize(range(100), 1)
+        rdd = self.sc.parallelize(xrange(100), 1)
         self.assertEqual(100, rdd.map(str).count())
 
     def test_accumulator_when_reuse_worker(self):
         from pyspark.accumulators import INT_ACCUMULATOR_PARAM
         acc1 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
-        self.sc.parallelize(range(100), 20).foreach(lambda x: acc1.add(x))
+        self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc1.add(x))
         self.assertEqual(sum(range(100)), acc1.value)
 
         acc2 = self.sc.accumulator(0, INT_ACCUMULATOR_PARAM)
-        self.sc.parallelize(range(100), 20).foreach(lambda x: acc2.add(x))
+        self.sc.parallelize(xrange(100), 20).foreach(lambda x: acc2.add(x))
         self.assertEqual(sum(range(100)), acc2.value)
         self.assertEqual(sum(range(100)), acc1.value)
 
     def test_reuse_worker_after_take(self):
-        rdd = self.sc.parallelize(range(100000), 1)
+        rdd = self.sc.parallelize(xrange(100000), 1)
         self.assertEqual(0, rdd.first())
 
         def count():