[SPARK-9057][STREAMING] Twitter example joining to static RDD of word sentiment values

Example of joining a static RDD of word sentiments to a streaming RDD of Tweets in order to demo the usage of the transform() method.

Author: Jeff L <sha0lin@alumni.carnegiemellon.edu>

Closes #8431 from Agent007/SPARK-9057.

3 files changed, 353 insertions, 0 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
new file mode 100644
index 0000000000..030ee30b93
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaTwitterHashTagJoinSentiments.java
@@ -0,0 +1,180 @@
+/*
+ * 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 org.apache.commons.io.IOUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.function.FlatMapFunction;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.Function2;
+import org.apache.spark.api.java.function.PairFunction;
+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.JavaReceiverInputDStream;
+import org.apache.spark.streaming.api.java.JavaStreamingContext;
+import org.apache.spark.streaming.twitter.TwitterUtils;
+import scala.Tuple2;
+import twitter4j.Status;
+
+import java.io.IOException;
+import java.net.URI;
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * Displays the most positive hash tags by joining the streaming Twitter data with a static RDD of
+ * the AFINN word list (http://neuro.imm.dtu.dk/wiki/AFINN)
+ */
+public class JavaTwitterHashTagJoinSentiments {
+
+  public static void main(String[] args) throws IOException {
+    if (args.length < 4) {
+      System.err.println("Usage: JavaTwitterHashTagJoinSentiments <consumer key> <consumer secret>" +
+        " <access token> <access token secret> [<filters>]");
+      System.exit(1);
+    }
+
+    StreamingExamples.setStreamingLogLevels();
+
+    String consumerKey = args[0];
+    String consumerSecret = args[1];
+    String accessToken = args[2];
+    String accessTokenSecret = args[3];
+    String[] filters = Arrays.copyOfRange(args, 4, args.length);
+
+    // Set the system properties so that Twitter4j library used by Twitter stream
+    // can use them to generate OAuth credentials
+    System.setProperty("twitter4j.oauth.consumerKey", consumerKey);
+    System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret);
+    System.setProperty("twitter4j.oauth.accessToken", accessToken);
+    System.setProperty("twitter4j.oauth.accessTokenSecret", accessTokenSecret);
+
+    SparkConf sparkConf = new SparkConf().setAppName("JavaTwitterHashTagJoinSentiments");
+    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
+    JavaReceiverInputDStream<Status> stream = TwitterUtils.createStream(jssc, filters);
+
+    JavaDStream<String> words = stream.flatMap(new FlatMapFunction<Status, String>() {
+      @Override
+      public Iterable<String> call(Status s) {
+        return Arrays.asList(s.getText().split(" "));
+      }
+    });
+
+    JavaDStream<String> hashTags = words.filter(new Function<String, Boolean>() {
+      @Override
+      public Boolean call(String word) throws Exception {
+        return word.startsWith("#");
+      }
+    });
+
+    // Read in the word-sentiment list and create a static RDD from it
+    String wordSentimentFilePath = "data/streaming/AFINN-111.txt";
+    final JavaPairRDD<String, Double> wordSentiments = jssc.sparkContext().textFile(wordSentimentFilePath)
+      .mapToPair(new PairFunction<String, String, Double>(){
+        @Override
+        public Tuple2<String, Double> call(String line) {
+          String[] columns = line.split("\t");
+          return new Tuple2<String, Double>(columns[0],
+            Double.parseDouble(columns[1]));
+        }
+      });
+
+    JavaPairDStream<String, Integer> hashTagCount = hashTags.mapToPair(
+      new PairFunction<String, String, Integer>() {
+        @Override
+        public Tuple2<String, Integer> call(String s) {
+          // leave out the # character
+          return new Tuple2<String, Integer>(s.substring(1), 1);
+        }
+      });
+
+    JavaPairDStream<String, Integer> hashTagTotals = hashTagCount.reduceByKeyAndWindow(
+      new Function2<Integer, Integer, Integer>() {
+        @Override
+        public Integer call(Integer a, Integer b) {
+          return a + b;
+        }
+      }, new Duration(10000));
+
+    // Determine the hash tags with the highest sentiment values by joining the streaming RDD
+    // with the static RDD inside the transform() method and then multiplying
+    // the frequency of the hash tag by its sentiment value
+    JavaPairDStream<String, Tuple2<Double, Integer>> joinedTuples =
+      hashTagTotals.transformToPair(new Function<JavaPairRDD<String, Integer>,
+        JavaPairRDD<String, Tuple2<Double, Integer>>>() {
+        @Override
+        public JavaPairRDD<String, Tuple2<Double, Integer>> call(JavaPairRDD<String,
+          Integer> topicCount)
+          throws Exception {
+          return wordSentiments.join(topicCount);
+        }
+      });
+
+    JavaPairDStream<String, Double> topicHappiness = joinedTuples.mapToPair(
+      new PairFunction<Tuple2<String, Tuple2<Double, Integer>>, String, Double>() {
+        @Override
+        public Tuple2<String, Double> call(Tuple2<String,
+          Tuple2<Double, Integer>> topicAndTuplePair) throws Exception {
+          Tuple2<Double, Integer> happinessAndCount = topicAndTuplePair._2();
+          return new Tuple2<String, Double>(topicAndTuplePair._1(),
+            happinessAndCount._1() * happinessAndCount._2());
+        }
+      });
+
+    JavaPairDStream<Double, String> happinessTopicPairs = topicHappiness.mapToPair(
+      new PairFunction<Tuple2<String, Double>, Double, String>() {
+        @Override
+        public Tuple2<Double, String> call(Tuple2<String, Double> topicHappiness)
+          throws Exception {
+          return new Tuple2<Double, String>(topicHappiness._2(),
+            topicHappiness._1());
+        }
+      });
+
+    JavaPairDStream<Double, String> happiest10 = happinessTopicPairs.transformToPair(
+      new Function<JavaPairRDD<Double, String>, JavaPairRDD<Double, String>>() {
+        @Override
+        public JavaPairRDD<Double, String> call(JavaPairRDD<Double,
+          String> happinessAndTopics) throws Exception {
+          return happinessAndTopics.sortByKey(false);
+        }
+      }
+    );
+
+    // Print hash tags with the most positive sentiment values
+    happiest10.foreachRDD(new Function<JavaPairRDD<Double, String>, Void>() {
+      @Override
+      public Void call(JavaPairRDD<Double, String> happinessTopicPairs) throws Exception {
+        List<Tuple2<Double, String>> topList = happinessTopicPairs.take(10);
+        System.out.println(
+          String.format("\nHappiest topics in last 10 seconds (%s total):",
+            happinessTopicPairs.count()));
+        for (Tuple2<Double, String> pair : topList) {
+          System.out.println(
+            String.format("%s (%s happiness)", pair._2(), pair._1()));
+        }
+        return null;
+      }
+    });
+
+    jssc.start();
+    jssc.awaitTermination();
+  }
+}
diff --git a/examples/src/main/python/streaming/network_wordjoinsentiments.py b/examples/src/main/python/streaming/network_wordjoinsentiments.py
new file mode 100644
index 0000000000..b85517dfdd
--- /dev/null
+++ b/examples/src/main/python/streaming/network_wordjoinsentiments.py
@@ -0,0 +1,77 @@
+#
+# 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.
+#
+
+"""
+ Shows the most positive words in UTF8 encoded, '\n' delimited text directly received the network
+ every 5 seconds. The streaming data is joined with a static RDD of the AFINN word list
+ (http://neuro.imm.dtu.dk/wiki/AFINN)
+
+ Usage: network_wordjoinsentiments.py <hostname> <port>
+   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
+
+ To run this on your local machine, you need to first run a Netcat server
+    `$ nc -lk 9999`
+ and then run the example
+    `$ bin/spark-submit examples/src/main/python/streaming/network_wordjoinsentiments.py \
+    localhost 9999`
+"""
+
+from __future__ import print_function
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+
+
+def print_happiest_words(rdd):
+    top_list = rdd.take(5)
+    print("Happiest topics in the last 5 seconds (%d total):" % rdd.count())
+    for tuple in top_list:
+        print("%s (%d happiness)" % (tuple[1], tuple[0]))
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: network_wordjoinsentiments.py <hostname> <port>", file=sys.stderr)
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingNetworkWordJoinSentiments")
+    ssc = StreamingContext(sc, 5)
+
+    # Read in the word-sentiment list and create a static RDD from it
+    word_sentiments_file_path = "data/streaming/AFINN-111.txt"
+    word_sentiments = ssc.sparkContext.textFile(word_sentiments_file_path) \
+        .map(lambda line: tuple(line.split("\t")))
+
+    lines = ssc.socketTextStream(sys.argv[1], int(sys.argv[2]))
+
+    word_counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a + b)
+
+    # Determine the words with the highest sentiment values by joining the streaming RDD
+    # with the static RDD inside the transform() method and then multiplying
+    # the frequency of the words by its sentiment value
+    happiest_words = word_counts.transform(lambda rdd: word_sentiments.join(rdd)) \
+        .map(lambda (word, tuple): (word, float(tuple[0]) * tuple[1])) \
+        .map(lambda (word, happiness): (happiness, word)) \
+        .transform(lambda rdd: rdd.sortByKey(False))
+
+    happiest_words.foreachRDD(print_happiest_words)
+
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
new file mode 100644
index 0000000000..0328fa81ea
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterHashTagJoinSentiments.scala
@@ -0,0 +1,96 @@
+/*
+ * 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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.streaming
+
+import org.apache.spark.SparkConf
+import org.apache.spark.streaming.twitter.TwitterUtils
+import org.apache.spark.streaming.{Seconds, StreamingContext}
+
+/**
+ * Displays the most positive hash tags by joining the streaming Twitter data with a static RDD of
+ * the AFINN word list (http://neuro.imm.dtu.dk/wiki/AFINN)
+ */
+object TwitterHashTagJoinSentiments {
+  def main(args: Array[String]) {
+    if (args.length < 4) {
+      System.err.println("Usage: TwitterHashTagJoinSentiments <consumer key> <consumer secret> " +
+        "<access token> <access token secret> [<filters>]")
+      System.exit(1)
+    }
+
+    StreamingExamples.setStreamingLogLevels()
+
+    val Array(consumerKey, consumerSecret, accessToken, accessTokenSecret) = args.take(4)
+    val filters = args.takeRight(args.length - 4)
+
+    // Set the system properties so that Twitter4j library used by Twitter stream
+    // can use them to generate OAuth credentials
+    System.setProperty("twitter4j.oauth.consumerKey", consumerKey)
+    System.setProperty("twitter4j.oauth.consumerSecret", consumerSecret)
+    System.setProperty("twitter4j.oauth.accessToken", accessToken)
+    System.setProperty("twitter4j.oauth.accessTokenSecret", accessTokenSecret)
+
+    val sparkConf = new SparkConf().setAppName("TwitterHashTagJoinSentiments")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
+    val stream = TwitterUtils.createStream(ssc, None, filters)
+
+    val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
+
+    // Read in the word-sentiment list and create a static RDD from it
+    val wordSentimentFilePath = "data/streaming/AFINN-111.txt"
+    val wordSentiments = ssc.sparkContext.textFile(wordSentimentFilePath).map { line =>
+      val Array(word, happinessValue) = line.split("\t")
+      (word, happinessValue)
+    } cache()
+
+    // Determine the hash tags with the highest sentiment values by joining the streaming RDD
+    // with the static RDD inside the transform() method and then multiplying
+    // the frequency of the hash tag by its sentiment value
+    val happiest60 = hashTags.map(hashTag => (hashTag.tail, 1))
+      .reduceByKeyAndWindow(_ + _, Seconds(60))
+      .transform{topicCount => wordSentiments.join(topicCount)}
+      .map{case (topic, tuple) => (topic, tuple._1 * tuple._2)}
+      .map{case (topic, happinessValue) => (happinessValue, topic)}
+      .transform(_.sortByKey(false))
+
+    val happiest10 = hashTags.map(hashTag => (hashTag.tail, 1))
+      .reduceByKeyAndWindow(_ + _, Seconds(10))
+      .transform{topicCount => wordSentiments.join(topicCount)}
+      .map{case (topic, tuple) => (topic, tuple._1 * tuple._2)}
+      .map{case (topic, happinessValue) => (happinessValue, topic)}
+      .transform(_.sortByKey(false))
+
+    // Print hash tags with the most positive sentiment values
+    happiest60.foreachRDD(rdd => {
+      val topList = rdd.take(10)
+      println("\nHappiest topics in last 60 seconds (%s total):".format(rdd.count()))
+      topList.foreach{case (happiness, tag) => println("%s (%s happiness)".format(tag, happiness))}
+    })
+
+    happiest10.foreachRDD(rdd => {
+      val topList = rdd.take(10)
+      println("\nHappiest topics in last 10 seconds (%s total):".format(rdd.count()))
+      topList.foreach{case (happiness, tag) => println("%s (%s happiness)".format(tag, happiness))}
+    })
+
+    ssc.start()
+    ssc.awaitTermination()
+  }
+}
+// scalastyle:on println