[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.

1 files changed, 77 insertions, 0 deletions

diff --git a/examples/src/main/python/streaming/network_wordjoinsentiments.py b/examples/src/main/python/streaming/network_wordjoinsentiments.py
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+++ b/examples/src/main/python/streaming/network_wordjoinsentiments.py
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+#
+# 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()