diff options
Diffstat (limited to 'examples')
10 files changed, 58 insertions, 41 deletions
diff --git a/examples/pom.xml b/examples/pom.xml index 7e41bef252..cb4f7ee33b 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -72,6 +72,31 @@ <scope>provided</scope> </dependency> <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-twitter_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-kafka_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-flume_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-zeromq_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-mqtt_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase</artifactId> <version>0.94.6</version> @@ -87,21 +112,6 @@ </exclusions> </dependency> <dependency> - <groupId>com.sksamuel.kafka</groupId> - <artifactId>kafka_${scala.binary.version}</artifactId> - <version>0.8.0-beta1</version> - <exclusions> - <exclusion> - <groupId>com.sun.jmx</groupId> - <artifactId>jmxri</artifactId> - </exclusion> - <exclusion> - <groupId>com.sun.jdmk</groupId> - <artifactId>jmxtools</artifactId> - </exclusion> - </exclusions> - </dependency> - <dependency> <groupId>org.eclipse.jetty</groupId> <artifactId>jetty-server</artifactId> </dependency> @@ -174,7 +184,7 @@ <artifactId>maven-shade-plugin</artifactId> <configuration> <shadedArtifactAttached>false</shadedArtifactAttached> - <outputFile>${project.build.directory}/scala-${scala.version}/${project.artifactId}-assembly-${project.version}.jar</outputFile> + <outputFile>${project.build.directory}/scala-${scala.binary.version}/${project.artifactId}-assembly-${project.version}.jar</outputFile> <artifactSet> <includes> <include>*:*</include> diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java index fd683ce0d3..b11cfa667e 100644 --- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java +++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java @@ -20,7 +20,8 @@ package org.apache.spark.streaming.examples; import org.apache.spark.api.java.function.Function; import org.apache.spark.streaming.*; import org.apache.spark.streaming.api.java.*; -import org.apache.spark.streaming.dstream.SparkFlumeEvent; +import org.apache.spark.streaming.flume.FlumeUtils; +import org.apache.spark.streaming.flume.SparkFlumeEvent; /** * Produces a count of events received from Flume. @@ -52,11 +53,10 @@ public final class JavaFlumeEventCount { Duration batchInterval = new Duration(2000); - JavaStreamingContext sc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval, + JavaStreamingContext ssc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval, System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaFlumeEventCount.class)); - - JavaDStream<SparkFlumeEvent> flumeStream = sc.flumeStream("localhost", port); + JavaDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port); flumeStream.count(); @@ -67,6 +67,6 @@ public final class JavaFlumeEventCount { } }).print(); - sc.start(); + ssc.start(); } } diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java index d8b4f4dddd..16b8a948e6 100644 --- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java +++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java @@ -30,6 +30,7 @@ import org.apache.spark.streaming.Duration; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; import org.apache.spark.streaming.api.java.JavaStreamingContext; +import org.apache.spark.streaming.kafka.KafkaUtils; import scala.Tuple2; /** @@ -59,7 +60,7 @@ public final class JavaKafkaWordCount { } // Create the context with a 1 second batch size - JavaStreamingContext ssc = new JavaStreamingContext(args[0], "KafkaWordCount", + JavaStreamingContext jssc = new JavaStreamingContext(args[0], "KafkaWordCount", new Duration(2000), System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaKafkaWordCount.class)); @@ -70,7 +71,7 @@ public final class JavaKafkaWordCount { topicMap.put(topic, numThreads); } - JavaPairDStream<String, String> messages = ssc.kafkaStream(args[1], args[2], topicMap); + JavaPairDStream<String, String> messages = KafkaUtils.createStream(jssc, args[1], args[2], topicMap); JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() { @Override @@ -100,6 +101,6 @@ public final class JavaKafkaWordCount { }); wordCounts.print(); - ssc.start(); + jssc.start(); } } diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala index 5ef1928294..ae3709b3d9 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala @@ -20,6 +20,7 @@ package org.apache.spark.streaming.examples import org.apache.spark.util.IntParam import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ +import org.apache.spark.streaming.flume._ /** * Produces a count of events received from Flume. @@ -51,7 +52,7 @@ object FlumeEventCount { System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) // Create a flume stream - val stream = ssc.flumeStream(host,port,StorageLevel.MEMORY_ONLY) + val stream = FlumeUtils.createStream(ssc, host,port,StorageLevel.MEMORY_ONLY_SER_2) // Print out the count of events received from this server in each batch stream.count().map(cnt => "Received " + cnt + " flume events." ).print() diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala index 197461655e..31a94bd224 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala @@ -24,6 +24,7 @@ import kafka.producer._ import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ import org.apache.spark.streaming.util.RawTextHelper._ +import org.apache.spark.streaming.kafka._ /** * Consumes messages from one or more topics in Kafka and does wordcount. @@ -52,7 +53,7 @@ object KafkaWordCount { ssc.checkpoint("checkpoint") val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap - val lines = ssc.kafkaStream(zkQuorum, group, topicpMap).map(_._2) + val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicpMap).map(_._2) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1l)) .reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala index 2d02ef77c0..325290b66f 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala @@ -17,11 +17,6 @@ package org.apache.spark.streaming.examples -import org.apache.spark.streaming.{ Seconds, StreamingContext } -import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.streaming.dstream.MQTTReceiver -import org.apache.spark.storage.StorageLevel - import org.eclipse.paho.client.mqttv3.MqttClient import org.eclipse.paho.client.mqttv3.MqttClientPersistence import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence @@ -29,6 +24,11 @@ import org.eclipse.paho.client.mqttv3.MqttException import org.eclipse.paho.client.mqttv3.MqttMessage import org.eclipse.paho.client.mqttv3.MqttTopic +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{Seconds, StreamingContext} +import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.mqtt._ + /** * A simple Mqtt publisher for demonstration purposes, repeatedly publishes * Space separated String Message "hello mqtt demo for spark streaming" @@ -97,7 +97,7 @@ object MQTTWordCount { val ssc = new StreamingContext(master, "MqttWordCount", Seconds(2), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val lines = ssc.mqttStream(brokerUrl, topic, StorageLevel.MEMORY_ONLY) + val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2) val words = lines.flatMap(x => x.toString.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala index 9d21d3178f..3ccdc908e2 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala @@ -23,6 +23,8 @@ import com.twitter.algebird._ import org.apache.spark.streaming.StreamingContext._ import org.apache.spark.SparkContext._ +import org.apache.spark.streaming.twitter._ + /** * Illustrates the use of the Count-Min Sketch, from Twitter's Algebird library, to compute * windowed and global Top-K estimates of user IDs occurring in a Twitter stream. @@ -33,7 +35,7 @@ import org.apache.spark.SparkContext._ * <p> * <p> * <a href="http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/"> - * This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a datastructure + * This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a data structure * for approximate frequency estimation in data streams (e.g. Top-K elements, frequency of any given element, etc), * that uses space sub-linear in the number of elements in the stream. Once elements are added to the CMS, the * estimated count of an element can be computed, as well as "heavy-hitters" that occur more than a threshold @@ -61,7 +63,7 @@ object TwitterAlgebirdCMS { val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER_2) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala index 5111e6f62a..c7e83e76b0 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala @@ -21,7 +21,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext} import org.apache.spark.storage.StorageLevel import com.twitter.algebird.HyperLogLog._ import com.twitter.algebird.HyperLogLogMonoid -import org.apache.spark.streaming.dstream.TwitterInputDStream +import org.apache.spark.streaming.twitter._ /** * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute @@ -50,7 +50,7 @@ object TwitterAlgebirdHLL { val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala index 7a3df687b7..e2b0418d55 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala @@ -20,6 +20,7 @@ package org.apache.spark.streaming.examples import org.apache.spark.streaming.{Seconds, StreamingContext} import StreamingContext._ import org.apache.spark.SparkContext._ +import org.apache.spark.streaming.twitter._ /** * Calculates popular hashtags (topics) over sliding 10 and 60 second windows from a Twitter @@ -39,7 +40,7 @@ object TwitterPopularTags { val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val stream = ssc.twitterStream(None, filters) + val stream = TwitterUtils.createStream(ssc, None, filters) val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#"))) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala index beda73a71b..03902ec353 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala @@ -20,11 +20,13 @@ package org.apache.spark.streaming.examples import akka.actor.ActorSystem import akka.actor.actorRef2Scala import akka.zeromq._ -import org.apache.spark.streaming.{ Seconds, StreamingContext } -import org.apache.spark.streaming.StreamingContext._ import akka.zeromq.Subscribe import akka.util.ByteString +import org.apache.spark.streaming.{Seconds, StreamingContext} +import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.zeromq._ + /** * A simple publisher for demonstration purposes, repeatedly publishes random Messages * every one second. @@ -83,11 +85,10 @@ object ZeroMQWordCount { def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator //For this stream, a zeroMQ publisher should be running. - val lines = ssc.zeroMQStream(url, Subscribe(topic), bytesToStringIterator) + val lines = ZeroMQUtils.createStream(ssc, url, Subscribe(topic), bytesToStringIterator _) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.print() ssc.start() } - } |