More work on StreamingContext

streaming/src/main/scala/spark/streaming/api/java/JavaStreamingContext.scala

@@ -9,6 +9,7 @@ import spark.storage.StorageLevel
 import spark.api.java.function.{Function => JFunction, Function2 => JFunction2}
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 import java.io.InputStream
+import java.util.{Map => JMap}
 
 class JavaStreamingContext(val ssc: StreamingContext) {
   def this(master: String, frameworkName: String, batchDuration: Time) =
@@ -17,10 +18,31 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   // TODOs:
   // - Test StreamingContext functions
   // - Test to/from Hadoop functions
-  // - Add checkpoint()/remember()
-  // - Support creating your own streams
+  // - Support registering InputStreams
   // - Add Kafka Stream
 
+
+  /**
+   * Create an input stream that pulls messages form a Kafka Broker.
+   * @param hostname Zookeper hostname.
+   * @param port Zookeper port.
+   * @param groupId The group id for this consumer.
+   * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
+   * in its own thread.
+   * @param initialOffsets Optional initial offsets for each of the partitions to consume.
+   * By default the value is pulled from zookeper.
+   * @param storageLevel RDD storage level. Defaults to memory-only.
+   */
+  def kafkaStream[T](
+    hostname: String,
+    port: Int,
+    groupId: String,
+    topics: JMap[String, Int])
+  : DStream[T] = {
+    implicit val cmt: ClassManifest[T] =
+      implicitly[ClassManifest[AnyRef]].asInstanceOf[ClassManifest[T]]
+    ssc.kafkaStream(hostname, port, groupId, Map(topics.toSeq: _*))
+  }
   /**
    * Create a input stream from network source hostname:port. Data is received using
    * a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited
@@ -162,6 +184,27 @@ class JavaStreamingContext(val ssc: StreamingContext) {
     ssc.registerOutputStream(outputStream.dstream)
   }
 
+  /**
+   * Sets the context to periodically checkpoint the DStream operations for master
+   * fault-tolerance. By default, the graph will be checkpointed every batch interval.
+   * @param directory HDFS-compatible directory where the checkpoint data will be reliably stored
+   * @param interval checkpoint interval
+   */
+  def checkpoint(directory: String, interval: Time = null) {
+    ssc.checkpoint(directory, interval)
+  }
+
+  /**
+   * Sets each DStreams in this context to remember RDDs it generated in the last given duration.
+   * DStreams remember RDDs only for a limited duration of time and releases them for garbage
+   * collection. This method allows the developer to specify how to long to remember the RDDs (
+   * if the developer wishes to query old data outside the DStream computation).
+   * @param duration Minimum duration that each DStream should remember its RDDs
+   */
+  def remember(duration: Time) {
+    ssc.remember(duration)
+  }
+
   /**
    * Starts the execution of the streams.
    */