Merge branch 'master' into openstack

core/pom.xml

@@ -70,6 +70,7 @@
     <dependency>
       <groupId>org.apache.commons</groupId>
       <artifactId>commons-math3</artifactId>
+      <version>3.3</version>
       <scope>test</scope>
     </dependency>
     <dependency>

core/src/main/scala/org/apache/spark/deploy/ExecutorState.scala

@@ -19,9 +19,9 @@ package org.apache.spark.deploy
 
 private[spark] object ExecutorState extends Enumeration {
 
-  val LAUNCHING, LOADING, RUNNING, KILLED, FAILED, LOST = Value
+  val LAUNCHING, LOADING, RUNNING, KILLED, FAILED, LOST, EXITED = Value
 
   type ExecutorState = Value
 
-  def isFinished(state: ExecutorState): Boolean = Seq(KILLED, FAILED, LOST).contains(state)
+  def isFinished(state: ExecutorState): Boolean = Seq(KILLED, FAILED, LOST, EXITED).contains(state)
 }

core/src/main/scala/org/apache/spark/deploy/master/Master.scala

@@ -303,10 +303,11 @@ private[spark] class Master(
             appInfo.removeExecutor(exec)
             exec.worker.removeExecutor(exec)
 
+            val normalExit = exitStatus.exists(_ == 0)
             // Only retry certain number of times so we don't go into an infinite loop.
-            if (appInfo.incrementRetryCount < ApplicationState.MAX_NUM_RETRY) {
+            if (!normalExit && appInfo.incrementRetryCount < ApplicationState.MAX_NUM_RETRY) {
               schedule()
-            } else {
+            } else if (!normalExit) {
               logError("Application %s with ID %s failed %d times, removing it".format(
                 appInfo.desc.name, appInfo.id, appInfo.retryCount))
               removeApplication(appInfo, ApplicationState.FAILED)

core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala

@@ -154,11 +154,10 @@ private[spark] class ExecutorRunner(
       Files.write(header, stderr, Charsets.UTF_8)
       stderrAppender = FileAppender(process.getErrorStream, stderr, conf)
 
-      // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run
-      // long-lived processes only. However, in the future, we might restart the executor a few
-      // times on the same machine.
+      // Wait for it to exit; executor may exit with code 0 (when driver instructs it to shutdown)
+      // or with nonzero exit code
       val exitCode = process.waitFor()
-      state = ExecutorState.FAILED
+      state = ExecutorState.EXITED
       val message = "Command exited with code " + exitCode
       worker ! ExecutorStateChanged(appId, execId, state, Some(message), Some(exitCode))
     } catch {

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

@@ -139,10 +139,13 @@ class HadoopRDD[K, V](
       // Create a JobConf that will be cached and used across this RDD's getJobConf() calls in the
       // local process. The local cache is accessed through HadoopRDD.putCachedMetadata().
       // The caching helps minimize GC, since a JobConf can contain ~10KB of temporary objects.
-      val newJobConf = new JobConf(broadcastedConf.value.value)
-      initLocalJobConfFuncOpt.map(f => f(newJobConf))
-      HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
-      newJobConf
+      // synchronize to prevent ConcurrentModificationException (Spark-1097, Hadoop-10456)
+      broadcastedConf.synchronized {
+        val newJobConf = new JobConf(broadcastedConf.value.value)
+        initLocalJobConfFuncOpt.map(f => f(newJobConf))
+        HadoopRDD.putCachedMetadata(jobConfCacheKey, newJobConf)
+        newJobConf
+      }
     }
   }
 

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

@@ -787,8 +787,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val outfmt = job.getOutputFormatClass
     val jobFormat = outfmt.newInstance
 
-    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true) &&
-      jobFormat.isInstanceOf[NewFileOutputFormat[_, _]]) {
+    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
       jobFormat.checkOutputSpecs(job)
     }
@@ -854,8 +853,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " +
       valueClass.getSimpleName + ")")
 
-    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true) &&
-      outputFormatInstance.isInstanceOf[FileOutputFormat[_, _]]) {
+    if (self.conf.getBoolean("spark.hadoop.validateOutputSpecs", true)) {
       // FileOutputFormat ignores the filesystem parameter
       val ignoredFs = FileSystem.get(conf)
       conf.getOutputFormat.checkOutputSpecs(ignoredFs, conf)

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

@@ -117,6 +117,15 @@ private object ParallelCollectionRDD {
     if (numSlices < 1) {
       throw new IllegalArgumentException("Positive number of slices required")
     }
+    // Sequences need to be sliced at the same set of index positions for operations
+    // like RDD.zip() to behave as expected
+    def positions(length: Long, numSlices: Int): Iterator[(Int, Int)] = {
+      (0 until numSlices).iterator.map(i => {
+        val start = ((i * length) / numSlices).toInt
+        val end = (((i + 1) * length) / numSlices).toInt
+        (start, end)
+      })
+    }
     seq match {
       case r: Range.Inclusive => {
         val sign = if (r.step < 0) {
@@ -128,30 +137,28 @@ private object ParallelCollectionRDD {
           r.start, r.end + sign, r.step).asInstanceOf[Seq[T]], numSlices)
       }
       case r: Range => {
-        (0 until numSlices).map(i => {
-          val start = ((i * r.length.toLong) / numSlices).toInt
-          val end = (((i + 1) * r.length.toLong) / numSlices).toInt
-          new Range(r.start + start * r.step, r.start + end * r.step, r.step)
-        }).asInstanceOf[Seq[Seq[T]]]
+        positions(r.length, numSlices).map({
+          case (start, end) =>
+            new Range(r.start + start * r.step, r.start + end * r.step, r.step)
+        }).toSeq.asInstanceOf[Seq[Seq[T]]]
       }
       case nr: NumericRange[_] => {
         // For ranges of Long, Double, BigInteger, etc
         val slices = new ArrayBuffer[Seq[T]](numSlices)
-        val sliceSize = (nr.size + numSlices - 1) / numSlices // Round up to catch everything
         var r = nr
-        for (i <- 0 until numSlices) {
+        for ((start, end) <- positions(nr.length, numSlices)) {
+          val sliceSize = end - start
           slices += r.take(sliceSize).asInstanceOf[Seq[T]]
           r = r.drop(sliceSize)
         }
         slices
       }
       case _ => {
         val array = seq.toArray // To prevent O(n^2) operations for List etc
-        (0 until numSlices).map(i => {
-          val start = ((i * array.length.toLong) / numSlices).toInt
-          val end = (((i + 1) * array.length.toLong) / numSlices).toInt
-          array.slice(start, end).toSeq
-        })
+        positions(array.length, numSlices).map({
+          case (start, end) =>
+            array.slice(start, end).toSeq
+        }).toSeq
       }
     }
   }

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

@@ -26,7 +26,7 @@ class RDDInfo(
     val id: Int,
     val name: String,
     val numPartitions: Int,
-    val storageLevel: StorageLevel)
+    var storageLevel: StorageLevel)
   extends Ordered[RDDInfo] {
 
   var numCachedPartitions = 0
@@ -36,8 +36,8 @@ class RDDInfo(
 
   override def toString = {
     import Utils.bytesToString
-    ("RDD \"%s\" (%d) Storage: %s; CachedPartitions: %d; TotalPartitions: %d; MemorySize: %s; " +
-      "TachyonSize: %s; DiskSize: %s").format(
+    ("RDD \"%s\" (%d) StorageLevel: %s; CachedPartitions: %d; TotalPartitions: %d; " +
+      "MemorySize: %s; TachyonSize: %s; DiskSize: %s").format(
         name, id, storageLevel.toString, numCachedPartitions, numPartitions,
         bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize))
   }

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

@@ -89,10 +89,13 @@ private[spark] object StorageUtils {
       // Add up memory, disk and Tachyon sizes
       val persistedBlocks =
         blocks.filter { status => status.memSize + status.diskSize + status.tachyonSize > 0 }
+      val _storageLevel =
+        if (persistedBlocks.length > 0) persistedBlocks(0).storageLevel else StorageLevel.NONE
       val memSize = persistedBlocks.map(_.memSize).reduceOption(_ + _).getOrElse(0L)
       val diskSize = persistedBlocks.map(_.diskSize).reduceOption(_ + _).getOrElse(0L)
       val tachyonSize = persistedBlocks.map(_.tachyonSize).reduceOption(_ + _).getOrElse(0L)
       rddInfoMap.get(rddId).map { rddInfo =>
+        rddInfo.storageLevel = _storageLevel
         rddInfo.numCachedPartitions = persistedBlocks.length
         rddInfo.memSize = memSize
         rddInfo.diskSize = diskSize

core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala

@@ -111,6 +111,24 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers {
     assert(slices.forall(_.isInstanceOf[Range]))
   }
 
+  test("identical slice sizes between Range and NumericRange") {
+    val r = ParallelCollectionRDD.slice(1 to 7, 4)
+    val nr = ParallelCollectionRDD.slice(1L to 7L, 4)
+    assert(r.size === 4)
+    for (i <- 0 until r.size) {
+      assert(r(i).size === nr(i).size)
+    }
+  }
+
+  test("identical slice sizes between List and NumericRange") {
+    val r = ParallelCollectionRDD.slice(List(1, 2), 4)
+    val nr = ParallelCollectionRDD.slice(1L to 2L, 4)
+    assert(r.size === 4)
+    for (i <- 0 until r.size) {
+      assert(r(i).size === nr(i).size)
+    }
+  }
+
   test("large ranges don't overflow") {
     val N = 100 * 1000 * 1000
     val data = 0 until N

docs/programming-guide.md

@@ -377,13 +377,15 @@ Some notes on reading files with Spark:
 
 * The `textFile` method also takes an optional second argument for controlling the number of slices of the file. By default, Spark creates one slice for each block of the file (blocks being 64MB by default in HDFS), but you can also ask for a higher number of slices by passing a larger value. Note that you cannot have fewer slices than blocks.
 
-Apart from reading files as a collection of lines,
-`SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+Apart from text files, Spark's Python API also supports several other data formats:
 
-### SequenceFile and Hadoop InputFormats
+* `SparkContext.wholeTextFiles` lets you read a directory containing multiple small text files, and returns each of them as (filename, content) pairs. This is in contrast with `textFile`, which would return one record per line in each file.
+
+* `RDD.saveAsPickleFile` and `SparkContext.pickleFile` support saving an RDD in a simple format consisting of pickled Python objects. Batching is used on pickle serialization, with default batch size 10.
 
-In addition to reading text files, PySpark supports reading ```SequenceFile``` 
-and any arbitrary ```InputFormat```.
+* Details on reading `SequenceFile` and arbitrary Hadoop `InputFormat` are given below.
+
+### SequenceFile and Hadoop InputFormats
 
 **Note** this feature is currently marked ```Experimental``` and is intended for advanced users. It may be replaced in future with read/write support based on SparkSQL, in which case SparkSQL is the preferred approach.
 
@@ -760,6 +762,11 @@ val counts = pairs.reduceByKey((a, b) => a + b)
 We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
 `counts.collect()` to bring them back to the driver program as an array of objects.
 
+**Note:** when using custom objects as the key in key-value pair operations, you must be sure that a
+custom `equals()` method is accompanied with a matching `hashCode()` method.  For full details, see
+the contract outlined in the [Object.hashCode()
+documentation](http://docs.oracle.com/javase/7/docs/api/java/lang/Object.html#hashCode()).
+
 </div>
 
 <div data-lang="java" markdown="1">
@@ -792,6 +799,10 @@ JavaPairRDD<String, Integer> counts = pairs.reduceByKey((a, b) -> a + b);
 We could also use `counts.sortByKey()`, for example, to sort the pairs alphabetically, and finally
 `counts.collect()` to bring them back to the driver program as an array of objects.
 
+**Note:** when using custom objects as the key in key-value pair operations, you must be sure that a
+custom `equals()` method is accompanied with a matching `hashCode()` method.  For full details, see
+the contract outlined in the [Object.hashCode()
+documentation](http://docs.oracle.com/javase/7/docs/api/java/lang/Object.html#hashCode()).
 
 </div>
 
@@ -888,7 +899,7 @@ for details.
 </tr>
 <tr>
   <td> <b>reduceByKey</b>(<i>func</i>, [<i>numTasks</i>]) </td>
-  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
+  <td> When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function <i>func</i>, which must be of type (V,V) => V. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument. </td>
 </tr>
 <tr>
   <td> <b>aggregateByKey</b>(<i>zeroValue</i>)(<i>seqOp</i>, <i>combOp</i>, [<i>numTasks</i>]) </td>
@@ -1056,7 +1067,10 @@ storage levels is:
   <td> Store RDD in serialized format in <a href="http://tachyon-project.org">Tachyon</a>.
     Compared to MEMORY_ONLY_SER, OFF_HEAP reduces garbage collection overhead and allows executors
     to be smaller and to share a pool of memory, making it attractive in environments with
-    large heaps or multiple concurrent applications.
+    large heaps or multiple concurrent applications. Furthermore, as the RDDs reside in Tachyon,
+    the crash of an executor does not lead to losing the in-memory cache. In this mode, the memory 
+    in Tachyon is discardable. Thus, Tachyon does not attempt to reconstruct a block that it evicts
+    from memory.
   </td>
 </tr>
 </table>

docs/running-on-yarn.md

@@ -67,6 +67,20 @@ Most of the configs are the same for Spark on YARN as for other deployment modes
     The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.executor.memoryOverhead</code></td>
+  <td>384</code></td>
+  <td>
+    The amount of off heap memory (in megabytes) to be allocated per executor. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.yarn.driver.memoryOverhead</code></td>
+  <td>384</code></td>
+  <td>
+    The amount of off heap memory (in megabytes) to be allocated per driver. This is memory that accounts for things like VM overheads, interned strings, other native overheads, etc.
+  </td>
+</tr>
 </table>
 
 By default, Spark on YARN will use a Spark jar installed locally, but the Spark JAR can also be in a world-readable location on HDFS. This allows YARN to cache it on nodes so that it doesn't need to be distributed each time an application runs. To point to a JAR on HDFS, `export SPARK_JAR=hdfs:///some/path`.

docs/streaming-programming-guide.md

@@ -950,7 +950,7 @@ is 200 milliseconds.
 
 An alternative to receiving data with multiple input streams / receivers is to explicitly repartition
 the input data stream (using `inputStream.repartition(<number of partitions>)`).
-This distributes the received batches of data across all the machines in the cluster
+This distributes the received batches of data across specified number of machines in the cluster
 before further processing.
 
 ### Level of Parallelism in Data Processing

python/pyspark/sql.py

@@ -16,6 +16,7 @@
 #
 
 from pyspark.rdd import RDD
+from pyspark.serializers import BatchedSerializer, PickleSerializer
 
 from py4j.protocol import Py4JError
 
@@ -76,12 +77,25 @@ def inferSchema(self, rdd):
         """Infer and apply a schema to an RDD of L{dict}s.
 
         We peek at the first row of the RDD to determine the fields names
-        and types, and then use that to extract all the dictionaries.
+        and types, and then use that to extract all the dictionaries. Nested
+        collections are supported, which include array, dict, list, set, and
+        tuple.
 
         >>> srdd = sqlCtx.inferSchema(rdd)
         >>> srdd.collect() == [{"field1" : 1, "field2" : "row1"}, {"field1" : 2, "field2": "row2"},
         ...                    {"field1" : 3, "field2": "row3"}]
         True
+
+        >>> from array import array
+        >>> srdd = sqlCtx.inferSchema(nestedRdd1)
+        >>> srdd.collect() == [{"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
+        ...                    {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}]
+        True
+
+        >>> srdd = sqlCtx.inferSchema(nestedRdd2)
+        >>> srdd.collect() == [{"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
+        ...                    {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}]
+        True
         """
         if (rdd.__class__ is SchemaRDD):
             raise ValueError("Cannot apply schema to %s" % SchemaRDD.__name__)
@@ -346,7 +360,8 @@ def _toPython(self):
         # TODO: This is inefficient, we should construct the Python Row object
         # in Java land in the javaToPython function. May require a custom
         # pickle serializer in Pyrolite
-        return RDD(jrdd, self._sc, self._sc.serializer).map(lambda d: Row(d))
+        return RDD(jrdd, self._sc, BatchedSerializer(
+                        PickleSerializer())).map(lambda d: Row(d))
 
     # We override the default cache/persist/checkpoint behavior as we want to cache the underlying
     # SchemaRDD object in the JVM, not the PythonRDD checkpointed by the super class
@@ -411,6 +426,7 @@ def subtract(self, other, numPartitions=None):
 
 def _test():
     import doctest
+    from array import array
     from pyspark.context import SparkContext
     globs = globals().copy()
     # The small batch size here ensures that we see multiple batches,
@@ -420,6 +436,12 @@ def _test():
     globs['sqlCtx'] = SQLContext(sc)
     globs['rdd'] = sc.parallelize([{"field1" : 1, "field2" : "row1"},
         {"field1" : 2, "field2": "row2"}, {"field1" : 3, "field2": "row3"}])
+    globs['nestedRdd1'] = sc.parallelize([
+        {"f1" : array('i', [1, 2]), "f2" : {"row1" : 1.0}},
+        {"f1" : array('i', [2, 3]), "f2" : {"row2" : 2.0}}])
+    globs['nestedRdd2'] = sc.parallelize([
+        {"f1" : [[1, 2], [2, 3]], "f2" : set([1, 2]), "f3" : (1, 2)},
+        {"f1" : [[2, 3], [3, 4]], "f2" : set([2, 3]), "f3" : (2, 3)}])
     (failure_count, test_count) = doctest.testmod(globs=globs,optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count: