Merge branch 'master' into SPARK-16323

Author: techaddict

core/src/main/java/org/apache/spark/shuffle/sort/ShuffleExternalSorter.java

@@ -376,7 +376,8 @@ public void insertRecord(Object recordBase, long recordOffset, int length, int p
     // for tests
     assert(inMemSorter != null);
     if (inMemSorter.numRecords() >= numElementsForSpillThreshold) {
-      logger.info("Spilling data because number of spilledRecords crossed the threshold " + numElementsForSpillThreshold);
+      logger.info("Spilling data because number of spilledRecords crossed the threshold " +
+        numElementsForSpillThreshold);
       spill();
     }
 

core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java

@@ -27,7 +27,6 @@
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import org.apache.spark.SparkEnv;
 import org.apache.spark.TaskContext;
 import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.memory.MemoryConsumer;
@@ -99,8 +98,8 @@ public static UnsafeExternalSorter createWithExistingInMemorySorter(
       long numElementsForSpillThreshold,
       UnsafeInMemorySorter inMemorySorter) throws IOException {
     UnsafeExternalSorter sorter = new UnsafeExternalSorter(taskMemoryManager, blockManager,
-      serializerManager, taskContext, recordComparator, prefixComparator, initialSize, numElementsForSpillThreshold,
-        pageSizeBytes, inMemorySorter, false /* ignored */);
+      serializerManager, taskContext, recordComparator, prefixComparator, initialSize,
+        numElementsForSpillThreshold, pageSizeBytes, inMemorySorter, false /* ignored */);
     sorter.spill(Long.MAX_VALUE, sorter);
     // The external sorter will be used to insert records, in-memory sorter is not needed.
     sorter.inMemSorter = null;
@@ -119,8 +118,8 @@ public static UnsafeExternalSorter create(
       long numElementsForSpillThreshold,
       boolean canUseRadixSort) {
     return new UnsafeExternalSorter(taskMemoryManager, blockManager, serializerManager,
-      taskContext, recordComparator, prefixComparator, initialSize, pageSizeBytes, numElementsForSpillThreshold, null,
-      canUseRadixSort);
+      taskContext, recordComparator, prefixComparator, initialSize, pageSizeBytes,
+      numElementsForSpillThreshold, null, canUseRadixSort);
   }
 
   private UnsafeExternalSorter(
@@ -387,7 +386,8 @@ public void insertRecord(
 
     assert(inMemSorter != null);
     if (inMemSorter.numRecords() >= numElementsForSpillThreshold) {
-      logger.info("Spilling data because number of spilledRecords crossed the threshold " + numElementsForSpillThreshold);
+      logger.info("Spilling data because number of spilledRecords crossed the threshold " +
+        numElementsForSpillThreshold);
       spill();
     }
 

docs/graphx-programming-guide.md

@@ -603,29 +603,7 @@ slightly unreliable and instead opted for more explicit user control.
 In the following example we use the [`aggregateMessages`][Graph.aggregateMessages] operator to
 compute the average age of the more senior followers of each user.
 
-{% highlight scala %}
-// Import random graph generation library
-import org.apache.spark.graphx.util.GraphGenerators
-// Create a graph with "age" as the vertex property.  Here we use a random graph for simplicity.
-val graph: Graph[Double, Int] =
-  GraphGenerators.logNormalGraph(sc, numVertices = 100).mapVertices( (id, _) => id.toDouble )
-// Compute the number of older followers and their total age
-val olderFollowers: VertexRDD[(Int, Double)] = graph.aggregateMessages[(Int, Double)](
-  triplet => { // Map Function
-    if (triplet.srcAttr > triplet.dstAttr) {
-      // Send message to destination vertex containing counter and age
-      triplet.sendToDst(1, triplet.srcAttr)
-    }
-  },
-  // Add counter and age
-  (a, b) => (a._1 + b._1, a._2 + b._2) // Reduce Function
-)
-// Divide total age by number of older followers to get average age of older followers
-val avgAgeOfOlderFollowers: VertexRDD[Double] =
-  olderFollowers.mapValues( (id, value) => value match { case (count, totalAge) => totalAge / count } )
-// Display the results
-avgAgeOfOlderFollowers.collect.foreach(println(_))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/graphx/AggregateMessagesExample.scala %}
 
 > The `aggregateMessages` operation performs optimally when the messages (and the sums of
 > messages) are constant sized (e.g., floats and addition instead of lists and concatenation).
@@ -793,29 +771,7 @@ second argument list contains the user defined functions for receiving messages
 We can use the Pregel operator to express computation such as single source
 shortest path in the following example.
 
-{% highlight scala %}
-import org.apache.spark.graphx._
-// Import random graph generation library
-import org.apache.spark.graphx.util.GraphGenerators
-// A graph with edge attributes containing distances
-val graph: Graph[Long, Double] =
-  GraphGenerators.logNormalGraph(sc, numVertices = 100).mapEdges(e => e.attr.toDouble)
-val sourceId: VertexId = 42 // The ultimate source
-// Initialize the graph such that all vertices except the root have distance infinity.
-val initialGraph = graph.mapVertices((id, _) => if (id == sourceId) 0.0 else Double.PositiveInfinity)
-val sssp = initialGraph.pregel(Double.PositiveInfinity)(
-  (id, dist, newDist) => math.min(dist, newDist), // Vertex Program
-  triplet => {  // Send Message
-    if (triplet.srcAttr + triplet.attr < triplet.dstAttr) {
-      Iterator((triplet.dstId, triplet.srcAttr + triplet.attr))
-    } else {
-      Iterator.empty
-    }
-  },
-  (a,b) => math.min(a,b) // Merge Message
-  )
-println(sssp.vertices.collect.mkString("\n"))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/graphx/SSSPExample.scala %}
 
 <a name="graph_builders"></a>
 
@@ -1009,64 +965,19 @@ GraphX comes with static and dynamic implementations of PageRank as methods on t
 
 GraphX also includes an example social network dataset that we can run PageRank on. A set of users is given in `data/graphx/users.txt`, and a set of relationships between users is given in `data/graphx/followers.txt`. We compute the PageRank of each user as follows:
 
-{% highlight scala %}
-// Load the edges as a graph
-val graph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt")
-// Run PageRank
-val ranks = graph.pageRank(0.0001).vertices
-// Join the ranks with the usernames
-val users = sc.textFile("data/graphx/users.txt").map { line =>
-  val fields = line.split(",")
-  (fields(0).toLong, fields(1))
-}
-val ranksByUsername = users.join(ranks).map {
-  case (id, (username, rank)) => (username, rank)
-}
-// Print the result
-println(ranksByUsername.collect().mkString("\n"))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/graphx/PageRankExample.scala %}
 
 ## Connected Components
 
 The connected components algorithm labels each connected component of the graph with the ID of its lowest-numbered vertex. For example, in a social network, connected components can approximate clusters. GraphX contains an implementation of the algorithm in the [`ConnectedComponents` object][ConnectedComponents], and we compute the connected components of the example social network dataset from the [PageRank section](#pagerank) as follows:
 
-{% highlight scala %}
-// Load the graph as in the PageRank example
-val graph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt")
-// Find the connected components
-val cc = graph.connectedComponents().vertices
-// Join the connected components with the usernames
-val users = sc.textFile("data/graphx/users.txt").map { line =>
-  val fields = line.split(",")
-  (fields(0).toLong, fields(1))
-}
-val ccByUsername = users.join(cc).map {
-  case (id, (username, cc)) => (username, cc)
-}
-// Print the result
-println(ccByUsername.collect().mkString("\n"))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/graphx/ConnectedComponentsExample.scala %}
 
 ## Triangle Counting
 
 A vertex is part of a triangle when it has two adjacent vertices with an edge between them. GraphX implements a triangle counting algorithm in the [`TriangleCount` object][TriangleCount] that determines the number of triangles passing through each vertex, providing a measure of clustering. We compute the triangle count of the social network dataset from the [PageRank section](#pagerank). *Note that `TriangleCount` requires the edges to be in canonical orientation (`srcId < dstId`) and the graph to be partitioned using [`Graph.partitionBy`][Graph.partitionBy].*
 
-{% highlight scala %}
-// Load the edges in canonical order and partition the graph for triangle count
-val graph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt", true).partitionBy(PartitionStrategy.RandomVertexCut)
-// Find the triangle count for each vertex
-val triCounts = graph.triangleCount().vertices
-// Join the triangle counts with the usernames
-val users = sc.textFile("data/graphx/users.txt").map { line =>
-  val fields = line.split(",")
-  (fields(0).toLong, fields(1))
-}
-val triCountByUsername = users.join(triCounts).map { case (id, (username, tc)) =>
-  (username, tc)
-}
-// Print the result
-println(triCountByUsername.collect().mkString("\n"))
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/graphx/TriangleCountingExample.scala %}
 
 
 # Examples
@@ -1076,36 +987,4 @@ to important relationships and users, run page-rank on the sub-graph, and
 then finally return attributes associated with the top users.  I can do
 all of this in just a few lines with GraphX:
 
-{% highlight scala %}
-// Connect to the Spark cluster
-val sc = new SparkContext("spark://master.amplab.org", "research")
-
-// Load my user data and parse into tuples of user id and attribute list
-val users = (sc.textFile("data/graphx/users.txt")
-  .map(line => line.split(",")).map( parts => (parts.head.toLong, parts.tail) ))
-
-// Parse the edge data which is already in userId -> userId format
-val followerGraph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt")
-
-// Attach the user attributes
-val graph = followerGraph.outerJoinVertices(users) {
-  case (uid, deg, Some(attrList)) => attrList
-  // Some users may not have attributes so we set them as empty
-  case (uid, deg, None) => Array.empty[String]
-}
-
-// Restrict the graph to users with usernames and names
-val subgraph = graph.subgraph(vpred = (vid, attr) => attr.size == 2)
-
-// Compute the PageRank
-val pagerankGraph = subgraph.pageRank(0.001)
-
-// Get the attributes of the top pagerank users
-val userInfoWithPageRank = subgraph.outerJoinVertices(pagerankGraph.vertices) {
-  case (uid, attrList, Some(pr)) => (pr, attrList.toList)
-  case (uid, attrList, None) => (0.0, attrList.toList)
-}
-
-println(userInfoWithPageRank.vertices.top(5)(Ordering.by(_._2._1)).mkString("\n"))
-
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/graphx/ComprehensiveExample.scala %}

examples/src/main/scala/org/apache/spark/examples/graphx/AggregateMessagesExample.scala

@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.graphx
+
+// $example on$
+import org.apache.spark.graphx.{Graph, VertexRDD}
+import org.apache.spark.graphx.util.GraphGenerators
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+/**
+ * An example use the [`aggregateMessages`][Graph.aggregateMessages] operator to
+ * compute the average age of the more senior followers of each user
+ * Run with
+ * {{{
+ * bin/run-example graphx.AggregateMessagesExample
+ * }}}
+ */
+object AggregateMessagesExample {
+
+  def main(args: Array[String]): Unit = {
+    // Creates a SparkSession.
+    val spark = SparkSession
+      .builder
+      .appName(s"${this.getClass.getSimpleName}")
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // $example on$
+    // Create a graph with "age" as the vertex property.
+    // Here we use a random graph for simplicity.
+    val graph: Graph[Double, Int] =
+      GraphGenerators.logNormalGraph(sc, numVertices = 100).mapVertices( (id, _) => id.toDouble )
+    // Compute the number of older followers and their total age
+    val olderFollowers: VertexRDD[(Int, Double)] = graph.aggregateMessages[(Int, Double)](
+      triplet => { // Map Function
+        if (triplet.srcAttr > triplet.dstAttr) {
+          // Send message to destination vertex containing counter and age
+          triplet.sendToDst(1, triplet.srcAttr)
+        }
+      },
+      // Add counter and age
+      (a, b) => (a._1 + b._1, a._2 + b._2) // Reduce Function
+    )
+    // Divide total age by number of older followers to get average age of older followers
+    val avgAgeOfOlderFollowers: VertexRDD[Double] =
+      olderFollowers.mapValues( (id, value) =>
+        value match { case (count, totalAge) => totalAge / count } )
+    // Display the results
+    avgAgeOfOlderFollowers.collect.foreach(println(_))
+    // $example off$
+
+    spark.stop()
+  }
+}
+// scalastyle:on println

examples/src/main/scala/org/apache/spark/examples/graphx/ComprehensiveExample.scala

@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.graphx
+
+// $example on$
+import org.apache.spark.graphx.GraphLoader
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+/**
+ * Suppose I want to build a graph from some text files, restrict the graph
+ * to important relationships and users, run page-rank on the sub-graph, and
+ * then finally return attributes associated with the top users.
+ * This example do all of this in just a few lines with GraphX.
+ *
+ * Run with
+ * {{{
+ * bin/run-example graphx.ComprehensiveExample
+ * }}}
+ */
+object ComprehensiveExample {
+
+  def main(args: Array[String]): Unit = {
+    // Creates a SparkSession.
+    val spark = SparkSession
+      .builder
+      .appName(s"${this.getClass.getSimpleName}")
+      .getOrCreate()
+    val sc = spark.sparkContext
+
+    // $example on$
+    // Load my user data and parse into tuples of user id and attribute list
+    val users = (sc.textFile("data/graphx/users.txt")
+      .map(line => line.split(",")).map( parts => (parts.head.toLong, parts.tail) ))
+
+    // Parse the edge data which is already in userId -> userId format
+    val followerGraph = GraphLoader.edgeListFile(sc, "data/graphx/followers.txt")
+
+    // Attach the user attributes
+    val graph = followerGraph.outerJoinVertices(users) {
+      case (uid, deg, Some(attrList)) => attrList
+      // Some users may not have attributes so we set them as empty
+      case (uid, deg, None) => Array.empty[String]
+    }
+
+    // Restrict the graph to users with usernames and names
+    val subgraph = graph.subgraph(vpred = (vid, attr) => attr.size == 2)
+
+    // Compute the PageRank
+    val pagerankGraph = subgraph.pageRank(0.001)
+
+    // Get the attributes of the top pagerank users
+    val userInfoWithPageRank = subgraph.outerJoinVertices(pagerankGraph.vertices) {
+      case (uid, attrList, Some(pr)) => (pr, attrList.toList)
+      case (uid, attrList, None) => (0.0, attrList.toList)
+    }
+
+    println(userInfoWithPageRank.vertices.top(5)(Ordering.by(_._2._1)).mkString("\n"))
+    // $example off$
+
+    spark.stop()
+  }
+}
+// scalastyle:on println