Merge pull request #36 from kbastani/1.1.1-RELEASE

Merging 1.1.1-RELEASE into master

README.md

@@ -8,6 +8,8 @@ This docker image adds high-performance graph analytics to a [Neo4j graph databa
 
 *Closeness Centrality*
 
+*Betweenness Centrality*
+
 *Triangle Counting*
 
 *Connected Components*
@@ -23,13 +25,13 @@ The Neo4j Mazerunner service in this image is a [unmanaged extension](http://neo
 Installation requires 3 docker image deployments, each containing a separate linked component.
 
 * *Hadoop HDFS* (sequenceiq/hadoop-docker:2.4.1)
-* *Neo4j Graph Database* (kbastani/docker-neo4j:2.2.0)
+* *Neo4j Graph Database* (kbastani/docker-neo4j:2.2.1)
 * *Apache Spark Service* (kbastani/neo4j-graph-analytics:1.1.0)
 
 Pull the following docker images:
 
     docker pull sequenceiq/hadoop-docker:2.4.1
-    docker pull kbastani/docker-neo4j:2.2.0
+    docker pull kbastani/docker-neo4j:2.2.1
     docker pull kbastani/neo4j-graph-analytics:1.1.0
 
 After each image has been downloaded to your Docker server, run the following commands in order to create the linked containers.
@@ -43,13 +45,13 @@ After each image has been downloaded to your Docker server, run the following co
     # Create Neo4j database with links to HDFS and Mazerunner
     # Replace <user> and <neo4j-path>
     # with the location to your existing Neo4j database store directory
-    docker run -d -P -v /Users/<user>/<neo4j-path>/data:/opt/data --name graphdb --link mazerunner:mazerunner --link hdfs:hdfs kbastani/docker-neo4j:2.2.0
+    docker run -d -P -v /Users/<user>/<neo4j-path>/data:/opt/data --name graphdb --link mazerunner:mazerunner --link hdfs:hdfs kbastani/docker-neo4j:2.2.1
 
 ### Use Existing Neo4j Database
 
 To use an existing Neo4j database, make sure that the database store directory, typically `data/graph.db`, is available on your host OS. Read the [setup guide](https://github.com/kbastani/docker-neo4j#start-neo4j-container) for *kbastani/docker-neo4j* for additional details.
 
-> Note: The kbastani/docker-neo4j:2.2.0 image is running Neo4j 2.2.0. If you point it to an older database store, that database may become unable to be attached to a previous version of Neo4j. Make sure you back up your store files before proceeding.
+> Note: The kbastani/docker-neo4j:2.2.1 image is running Neo4j 2.2.1. If you point it to an older database store, that database may become unable to be attached to a previous version of Neo4j. Make sure you back up your store files before proceeding.
 
 ### Use New Neo4j Database
 
@@ -69,6 +71,7 @@ Replace `{analysis}` in the endpoint with one of the following analysis algorith
 
 - pagerank
 - closeness_centrality
+- betweenness_centrality
 - triangle_count
 - connected_components
 - strongly_connected_components
@@ -98,7 +101,7 @@ To begin graph analysis jobs on a particular metric, HTTP GET request on the fol
 
 * PageRank is used to find the relative importance of a node within a set of connected nodes.
 
-### Closeness Centrality (New)
+### Closeness Centrality
 
     http://172.17.0.21:7474/service/mazerunner/analysis/closeness_centrality/FOLLOWS
 
@@ -108,6 +111,16 @@ To begin graph analysis jobs on a particular metric, HTTP GET request on the fol
 
 * A key node centrality measure in networks is closeness centrality (Freeman, 1978; Opsahl et al., 2010; Wasserman and Faust, 1994). It is defined as the inverse of farness, which in turn, is the sum of distances to all other nodes.
 
+### Betweenness Centrality
+
+    http://172.17.0.21:7474/service/mazerunner/analysis/betweenness_centrality/FOLLOWS
+
+* Gets all nodes connected by the `FOLLOWS` relationship and updates each node with the property key `betweenness_centrality`.
+
+* The value of the `betweenness_centrality` property is a float data type, ex. `betweenness_centrality: 20.345`.
+
+* Betweenness centrality is an indicator of a node's centrality in a network. It is equal to the number of shortest paths from all vertices to all others that pass through that node. A node with high betweenness centrality has a large influence on the transfer of items through the network, under the assumption that item transfer follows the shortest paths.
+
 ### Triangle Counting
 
     http://172.17.0.21:7474/service/mazerunner/analysis/triangle_count/FOLLOWS

src/extension/pom.xml

@@ -6,10 +6,10 @@
 
     <groupId>org.mazerunner</groupId>
     <artifactId>extension</artifactId>
-    <version>1.1.0-RELEASE</version>
+    <version>1.1.1-RELEASE</version>
 
     <properties>
-        <neo4j.version>2.2.0</neo4j.version>
+        <neo4j.version>2.2.1</neo4j.version>
         <joda.version>2.3</joda.version>
         <guava.version>17.0</guava.version>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
@@ -30,7 +30,7 @@
         <dependency>
             <groupId>org.neo4j</groupId>
             <artifactId>neo4j-kernel</artifactId>
-            <version>2.2.0</version>
+            <version>2.2.1</version>
             <type>test-jar</type>
             <scope>test</scope>
         </dependency>

src/spark/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>org.mazerunner</groupId>
     <artifactId>spark</artifactId>
-    <version>1.1.0-RELEASE</version>
+    <version>1.1.1-RELEASE</version>
 
     <properties>
         <jetty.version>7.6.9.v20130131</jetty.version>
@@ -75,7 +75,7 @@
         <dependency>
             <groupId>org.apache.spark</groupId>
             <artifactId>spark-core_2.10</artifactId>
-            <version>1.3.0</version>
+            <version>1.3.1</version>
             <exclusions>
                 <exclusion>
                     <groupId>ch.qos.logback</groupId>
@@ -86,7 +86,7 @@
         <dependency>
             <groupId>org.apache.spark</groupId>
             <artifactId>spark-graphx_2.10</artifactId>
-            <version>1.3.0</version>
+            <version>1.3.1</version>
             <exclusions>
                 <exclusion>
                     <groupId>ch.qos.logback</groupId>
@@ -172,6 +172,17 @@
             <artifactId>gson</artifactId>
             <version>2.3</version>
         </dependency>
+        <dependency>
+            <groupId>org.scalatest</groupId>
+            <artifactId>scalatest_2.10</artifactId>
+            <version>2.0</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>com.github.mdr</groupId>
+            <artifactId>ascii-graphs_2.10</artifactId>
+            <version>0.0.6</version>
+        </dependency>
     </dependencies>
     <build>
         <resources>

src/spark/src/main/java/org/mazerunner/core/config/ConfigurationLoader.java

@@ -108,8 +108,8 @@ public void initialize() throws IOException {
 
     public void initializeTest()
     {
-        hadoopSitePath = "/Users/kennybastani/hadoop-1.0.4/conf/core-site.xml";
-        hadoopHdfsPath = "/Users/kennybastani/hadoop-1.0.4/conf/hdfs-site.xml";
+        hadoopSitePath = "/hadoop-2.4.1/conf/core-site.xml";
+        hadoopHdfsPath = "/hadoop-2.4.1/conf/hdfs-site.xml";
         hadoopHdfsUri = "hdfs://0.0.0.0:9000";
         mazerunnerRelationshipType = "CONNECTED_TO";
         rabbitmqNodename = "localhost";

src/spark/src/main/java/org/mazerunner/core/messaging/Worker.java

@@ -40,10 +40,10 @@ public class Worker {
     private String sparkAppName = "mazerunner";
 
     @Option(name="--spark.executor.memory",usage="Amount of memory to use per executor process, in the same format as JVM memory strings (e.g. 512m, 2g). ", metaVar = "<string>")
-    private String sparkExecutorMemory = "512m";
+    private String sparkExecutorMemory = "4092m";
 
     @Option(name="--spark.master",usage="The Spark master URL (e.g. spark://localhost:7077).",metaVar="<url>")
-    private String sparkMaster = "local";
+    private String sparkMaster = "local[8]";
 
     @Option(name="--hadoop.hdfs",usage="The HDFS URL (e.g. hdfs://0.0.0.0:9000).", metaVar = "<url>")
     private String hadoopHdfs = "hdfs://0.0.0.0:9000";

src/spark/src/main/java/org/mazerunner/core/processor/GraphProcessor.java

@@ -34,6 +34,7 @@ public class GraphProcessor {
     public static final String PAGERANK = "pagerank";
     public static final String STRONGLY_CONNECTED_COMPONENTS = "strongly_connected_components";
     public static final String CLOSENESS_CENTRALITY = "closeness_centrality";
+    public static final String BETWEENNESS_CENTRALITY = "betweenness_centrality";
 
     public static JavaSparkContext javaSparkContext = null;
 
@@ -63,9 +64,13 @@ public static void processEdgeList(ProcessorMessage processorMessage) throws IOE
                 results = algorithms.stronglyConnectedComponents(javaSparkContext.sc(), processorMessage.getPath());
                 break;
             case CLOSENESS_CENTRALITY:
-                // Route to StronglyConnectedComponents
+                // Route to ClosenessCentrality
                 results = algorithms.closenessCentrality(javaSparkContext.sc(), processorMessage.getPath());
                 break;
+            case BETWEENNESS_CENTRALITY:
+                // Route to BetweennessCentrality
+                results = algorithms.betweennessCentrality(javaSparkContext.sc(), processorMessage.getPath());
+                break;
             default:
                 // Analysis does not exist
                 System.out.println("Did not recognize analysis key: " + processorMessage.getAnalysis());

src/spark/src/main/scala/org/mazerunner/core/abstractions/PregelProgram.scala

@@ -0,0 +1,45 @@
+package org.mazerunner.core.abstractions
+
+import org.apache.spark.graphx._
+
+import scala.reflect.ClassTag
+
+/**
+ * The [[PregelProgram]] abstraction wraps Spark's Pregel API implementation from the [[GraphOps]]
+ * class into a model that is easier to write graph algorithms.
+ * @tparam VertexState is the generic type representing the state of a vertex
+ */
+abstract class PregelProgram[VertexState: ClassTag, VD: ClassTag, ED: ClassTag] protected () extends Serializable {
+
+  @transient val graph: Graph[VD, ED]
+
+  /**
+   * The vertex program receives a state update and acts to update its state
+   * @param id is the [[VertexId]] that this program will perform a state operation for
+   * @param state is the current state of this [[VertexId]]
+   * @param message is the state received from another vertex in the graph
+   * @return a [[VertexState]] resulting from a comparison between current state and incoming state
+   */
+  def vertexProgram(id : VertexId, state : VertexState, message : VertexState) : VertexState
+
+  /**
+   * The message broker sends and receives messages. It will initially receive one message for
+   * each vertex in the graph.
+   * @param triplet An edge triplet is an object containing a pair of connected vertex objects and edge object.
+   *                For example (v1)-[r]->(v2)
+   * @return The message broker returns a key value list, each containing a VertexId and a new message
+   */
+  def messageBroker(triplet :EdgeTriplet[VertexState, ED]) : Iterator[(VertexId, VertexState)]
+
+  /**
+   * This method is used to reduce or combine the set of all state outcomes produced by a vertexProgram
+   * for each vertex in each superstep iteration. Each vertex has a list of state updates received from
+   * other vertices in the graph via the messageBroker method. This method is used to reduce the list
+   * of state updates into a single state for the next superstep iteration.
+   * @param a A first [[VertexState]] representing a partial state of a vertex.
+   * @param b A second [[VertexState]] representing a different partial state of a vertex
+   * @return a merged [[VertexState]] representation from the two [[VertexState]] parameters
+   */
+  def combiner(a: VertexState, b: VertexState) : VertexState
+
+}