[GitHub] spark pull request: [SPARK-9057] [STREAMING] Twitter example joini...

[Benedicto <gi...@git.apache.org>]

Github user Benedicto commented on a diff in the pull request:

    https://github.com/apache/spark/pull/8431#discussion_r42384963
  
    --- Diff: 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)
    --- End diff --
    
    10 on this line should be 60


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