Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/1434#discussion_r15668266
--- Diff:
examples/src/main/scala/org/apache/spark/examples/streaming/KinesisWordCount.scala
---
@@ -0,0 +1,369 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.examples.streaming
+
+import java.nio.ByteBuffer
+
+import scala.util.Random
+
+import org.apache.log4j.Level
+import org.apache.log4j.Logger
+import org.apache.spark.Logging
+import org.apache.spark.SparkConf
+import org.apache.spark.SparkContext.rddToOrderedRDDFunctions
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.Milliseconds
+import org.apache.spark.streaming.StreamingContext
+import org.apache.spark.streaming.StreamingContext.toPairDStreamFunctions
+import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.kinesis.KinesisStringRecordSerializer
+import org.apache.spark.streaming.kinesis.KinesisUtils
+
+import com.amazonaws.auth.DefaultAWSCredentialsProviderChain
+import com.amazonaws.services.kinesis.AmazonKinesisClient
+import
com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+import com.amazonaws.services.kinesis.model.PutRecordRequest
+
+/**
+ * Kinesis Spark Streaming WordCount example.
+ *
+ * See
http://spark.apache.org/docs/latest/streaming-programming-guide.html for more
details on
+ * the Kinesis Spark Streaming integration.
+ *
+ * This example spins up 1 Kinesis Worker (Spark Streaming Receivers) per
shard of the
+ * given stream.
+ * It then starts pulling from the last checkpointed sequence number of
the given
+ * <stream-name> and <endpoint-url>.
+ *
+ * Valid endpoint urls:
http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region
+ *
+ * This code uses the DefaultAWSCredentialsProviderChain and searches for
credentials
+ * in the following order of precedence:
+ * Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY
+ * Java System Properties - aws.accessKeyId and aws.secretKey
+ * Credential profiles file - default location (~/.aws/credentials) shared
by all AWS SDKs
+ * Instance profile credentials - delivered through the Amazon EC2
metadata service
+ *
+ * Usage: KinesisWordCount <stream-name> <endpoint-url>
+ * <stream-name> is the name of the Kinesis stream (ie. mySparkStream)
+ * <endpoint-url> is the endpoint of the Kinesis service
+ * (ie. https://kinesis.us-east-1.amazonaws.com)
+ *
+ * Example:
+ * $ export AWS_ACCESS_KEY_ID=<your-access-key>
+ * $ export AWS_SECRET_KEY=<your-secret-key>
+ * $ $SPARK_HOME/bin/run-example \
+ * org.apache.spark.examples.streaming.KinesisWordCount
mySparkStream \
+ * https://kinesis.us-east-1.amazonaws.com
+ *
+ * There is a companion helper class below called KinesisWordCountProducer
which puts
+ * dummy data onto the Kinesis stream.
+ * Usage instructions for KinesisWordCountProducer are provided in that
class definition.
+ */
+object KinesisWordCount extends Logging {
+ val WordSeparator = " "
+
+ def main(args: Array[String]) {
+/**
+ * Check that all required args were passed in.
+ */
+ if (args.length < 2) {
+ System.err.println("Usage: KinesisWordCount <stream-name>
<endpoint-url>")
+ System.exit(1)
+ }
+
+ /**
+ * (This was lifted from the StreamingExamples.scala in order to avoid
the dependency
+ * on the spark-examples artifact.)
+ * Set reasonable logging levels for streaming if the user has not
configured log4j.
+ */
+ val log4jInitialized =
Logger.getRootLogger.getAllAppenders.hasMoreElements()
+ if (!log4jInitialized) {
+ /**
+ * We first log something to initialize Spark's default logging,
+ * then we override the logging level.
+ * */
+ logInfo("Setting log level to [INFO] for streaming example." +
+ " To override add a custom log4j.properties to the classpath.")
+
+ Logger.getRootLogger().setLevel(Level.INFO)
+
Logger.getLogger("org.apache.spark.examples.streaming").setLevel(Level.DEBUG);
+ }
+
+ /** Populate the appropriate variables from the given args */
+ val Array(stream, endpoint) = args
+ val batchIntervalMillis = 2000
+
+ /** Create a Kinesis client in order to determine the number of shards
for the given stream */
+ val kinesisClient = new AmazonKinesisClient(new
DefaultAWSCredentialsProviderChain())
+ kinesisClient.setEndpoint(endpoint)
+
+ /** Determine the number of shards from the stream */
+ val numShards =
kinesisClient.describeStream(stream).getStreamDescription().getShards().size()
+
+ /** In this example, we're going to create 1 Kinesis
Worker/Receiver/DStream for each shard.*/
+ val numStreams = numShards
+
+ /**
+ * Must add 1 more thread than the number of receivers or the output
won't show properly
+ * from the driver
+ */
+ val numSparkThreads = numStreams + 1
+
+ /** Set the app name */
+ val appName = "KinesisWordCount"
+
+ /** Setup the Spark config. */
+ val sparkConfig = new
SparkConf().setAppName(appName).setMaster(s"local[$numSparkThreads]")
+
+ /**
+ * Set the batch interval.
+ * Records will be pulled from the Kinesis stream and stored as a
single DStream within Spark
+ * every batch interval.
+ */
+ val batchInterval = Milliseconds(batchIntervalMillis)
+
+ /**
+ * It's recommended that you perform a Spark checkpoint between 5 and
10 times the batch
+ * interval.
+ * While this is the Spark checkpoint interval, we're going to use it
for the Kinesis
+ * checkpoint interval, as well.
+ * For example purposes, we'll just use the batchInterval.
+ */
+ val checkpointInterval = batchInterval
+
+ /** Setup the StreamingContext */
+ val ssc = new StreamingContext(sparkConfig, batchInterval)
+
+ /** Setup the checkpoint directory used by Spark Streaming */
+ ssc.checkpoint("/tmp/checkpoint");
+
+ /**
+ * Create the same number of Kinesis Receivers/DStreams as stream
shards, then union
+ * them all.
+ */
+ var allStreams: DStream[Array[Byte]] = KinesisUtils.createStream(ssc,
appName, stream,
+ endpoint, checkpointInterval.milliseconds,
InitialPositionInStream.LATEST)
+ /** Set the checkpoint interval */
+ allStreams.checkpoint(checkpointInterval)
+ for (i <- 1 until numStreams) {
+ /** Create a new Receiver/DStream for each stream shard */
+ val dStream = KinesisUtils.createStream(ssc, appName, stream,
endpoint,
+ checkpointInterval.milliseconds, InitialPositionInStream.LATEST)
+ /** Set the Spark checkpoint interval */
+ dStream.checkpoint(checkpointInterval)
+
+ /** Union with the existing streams */
+ allStreams = allStreams.union(dStream)
--- End diff --
The better way to do this Union is to do the followings.
```
val kafkaStreams = (0 until numStream).map { i =>
KinesisUtils.createStream(ssc, ...
}
val unifiedStream = ssc.union(kafkaStreams)
```
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