[TINKERPOP-3133] Allow customize the output partition

set the repartition number by, for example, 'gremlin.spark.outputRepartition=500'.
only integer values larger than 0 is valid input. Otherwise the
repartition will be skipped silently.

docs/src/reference/implementations-spark.asciidoc

@@ -97,6 +97,9 @@ This can save a significant amount of time and space resources. If the `InputRDD
 `SparkGraphComputer` will partition the graph using a `org.apache.spark.HashPartitioner` with the number of partitions
 being either the number of existing partitions in the input (i.e. input splits) or the user specified number of `GraphComputer.workers()`.
 
+If the provider/user finds there are many small HDFS files generated by `OutputRDD`. The option `gremlin.spark.outputRepartition`
+can help to repartition the output according to the specified number. The option is disabled by default.
+
 ===== Storage Levels
 
 The `SparkGraphComputer` uses `MEMORY_ONLY` to cache the input graph and the output graph by default. Users should be aware of the impact of

hadoop-gremlin/src/main/java/org/apache/tinkerpop/gremlin/hadoop/Constants.java

@@ -74,6 +74,7 @@ private Constants() {
     public static final String GREMLIN_SPARK_SKIP_PARTITIONER = "gremlin.spark.skipPartitioner"; // don't partition the loadedGraphRDD
     public static final String GREMLIN_SPARK_SKIP_GRAPH_CACHE = "gremlin.spark.skipGraphCache";  // don't cache the loadedGraphRDD (ignores graphStorageLevel)
     public static final String GREMLIN_SPARK_DONT_DELETE_NON_EMPTY_OUTPUT = "gremlin.spark.dontDeleteNonEmptyOutput"; // don't delete the output if it is not empty
+    public static final String GREMLIN_SPARK_OUTPUT_REPARTITION = "gremlin.spark.outputRepartition"; // allow set the repartition number of the outputRDD to reduce HDFS small files
     public static final String SPARK_SERIALIZER = "spark.serializer";
     public static final String SPARK_KRYO_REGISTRATOR = "spark.kryo.registrator";
     public static final String SPARK_KRYO_REGISTRATION_REQUIRED = "spark.kryo.registrationRequired";

spark-gremlin/src/main/java/org/apache/tinkerpop/gremlin/spark/structure/io/OutputFormatRDD.java

@@ -48,7 +48,8 @@ public void writeGraphRDD(final Configuration configuration, final JavaPairRDD<O
         final String outputLocation = hadoopConfiguration.get(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION);
         if (null != outputLocation) {
             // map back to a <nullwritable,vertexwritable> stream for output
-            graphRDD.mapToPair(tuple -> new Tuple2<>(NullWritable.get(), tuple._2()))
+            JavaPairRDD<Object, VertexWritable> javaPairRDD = repartitionJavaPairRDD(hadoopConfiguration.get(Constants.GREMLIN_SPARK_OUTPUT_REPARTITION), graphRDD);
+            javaPairRDD.mapToPair(tuple -> new Tuple2<>(NullWritable.get(), tuple._2()))
                     .saveAsNewAPIHadoopFile(Constants.getGraphLocation(outputLocation),
                             NullWritable.class,
                             VertexWritable.class,
@@ -62,7 +63,8 @@ public <K, V> Iterator<KeyValue<K, V>> writeMemoryRDD(final Configuration config
         final String outputLocation = hadoopConfiguration.get(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION);
         if (null != outputLocation) {
             // map back to a Hadoop stream for output
-            memoryRDD.mapToPair(keyValue -> new Tuple2<>(new ObjectWritable<>(keyValue._1()), new ObjectWritable<>(keyValue._2())))
+            JavaPairRDD<K, V> javaPairRDD = repartitionJavaPairRDD(hadoopConfiguration.get(Constants.GREMLIN_SPARK_OUTPUT_REPARTITION), memoryRDD);
+            javaPairRDD.mapToPair(keyValue -> new Tuple2<>(new ObjectWritable<>(keyValue._1()), new ObjectWritable<>(keyValue._2())))
                     .saveAsNewAPIHadoopFile(Constants.getMemoryLocation(outputLocation, memoryKey),
                             ObjectWritable.class,
                             ObjectWritable.class,
@@ -75,4 +77,4 @@ public <K, V> Iterator<KeyValue<K, V>> writeMemoryRDD(final Configuration config
         }
         return Collections.emptyIterator();
     }
-}
+}

spark-gremlin/src/main/java/org/apache/tinkerpop/gremlin/spark/structure/io/OutputRDD.java

@@ -55,4 +55,17 @@ public interface OutputRDD {
     public default <K, V> Iterator<KeyValue<K, V>> writeMemoryRDD(final Configuration configuration, final String memoryKey, final JavaPairRDD<K, V> memoryRDD) {
         return Collections.emptyIterator();
     }
+
+    /**
+     * Allow users to customize the RDD partitions to reduce HDFS small files
+     */
+    public default <K, V> JavaPairRDD<K, V> repartitionJavaPairRDD(final String repartitionString, JavaPairRDD<K, V> graphRDD) {
+        JavaPairRDD<K, V> javaPairRDD = graphRDD;
+        final int repartition = null == repartitionString ? -1 : Integer.parseInt(repartitionString);
+        if (repartition > 0) {
+            javaPairRDD = javaPairRDD.repartition(repartition);
+        }
+        return javaPairRDD;
+    }
+
 }

spark-gremlin/src/main/java/org/apache/tinkerpop/gremlin/spark/structure/io/PersistedOutputRDD.java

@@ -51,15 +51,16 @@ public void writeGraphRDD(final Configuration configuration, final JavaPairRDD<O
         SparkContextStorage.open(configuration).rm(configuration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION));  // this might be bad cause it unpersists the job RDD
         // determine which storage level to persist the RDD as with MEMORY_ONLY being the default cache()
         final StorageLevel storageLevel = StorageLevel.fromString(configuration.getString(Constants.GREMLIN_SPARK_PERSIST_STORAGE_LEVEL, "MEMORY_ONLY"));
+        final JavaPairRDD<Object, VertexWritable> javaPairRDD = repartitionJavaPairRDD(configuration.getString(Constants.GREMLIN_SPARK_OUTPUT_REPARTITION), graphRDD);
         if (!configuration.getBoolean(Constants.GREMLIN_HADOOP_GRAPH_WRITER_HAS_EDGES, true))
-            graphRDD.mapValues(vertex -> {
+            javaPairRDD.mapValues(vertex -> {
                 vertex.get().dropEdges(Direction.BOTH);
                 return vertex;
             }).setName(Constants.getGraphLocation(configuration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION))).persist(storageLevel)
                     // call action to eager store rdd
                     .count();
         else
-            graphRDD.setName(Constants.getGraphLocation(configuration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION))).persist(storageLevel)
+            javaPairRDD.setName(Constants.getGraphLocation(configuration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION))).persist(storageLevel)
                     // call action to eager store rdd
                     .count();
         Spark.refresh(); // necessary to do really fast so the Spark GC doesn't clear out the RDD
@@ -73,11 +74,12 @@ public <K, V> Iterator<KeyValue<K, V>> writeMemoryRDD(final Configuration config
             throw new IllegalArgumentException("There is no provided " + Constants.GREMLIN_HADOOP_OUTPUT_LOCATION + " to write the persisted RDD to");
         final String memoryRDDName = Constants.getMemoryLocation(configuration.getString(Constants.GREMLIN_HADOOP_OUTPUT_LOCATION), memoryKey);
         Spark.removeRDD(memoryRDDName);
-        memoryRDD.setName(memoryRDDName).persist(StorageLevel.fromString(configuration.getString(Constants.GREMLIN_SPARK_PERSIST_STORAGE_LEVEL, "MEMORY_ONLY")))
+        final JavaPairRDD<K, V> javaPairRDD = repartitionJavaPairRDD(configuration.getString(Constants.GREMLIN_SPARK_OUTPUT_REPARTITION), memoryRDD);
+        javaPairRDD.setName(memoryRDDName).persist(StorageLevel.fromString(configuration.getString(Constants.GREMLIN_SPARK_PERSIST_STORAGE_LEVEL, "MEMORY_ONLY")))
                 // call action to eager store rdd
                 .count();
         Spark.refresh(); // necessary to do really fast so the Spark GC doesn't clear out the RDD
-        return IteratorUtils.map(memoryRDD.collect().iterator(), tuple -> new KeyValue<>(tuple._1(), tuple._2()));
+        return IteratorUtils.map(javaPairRDD.collect().iterator(), tuple -> new KeyValue<>(tuple._1(), tuple._2()));
     }
 
     @Override