Real Time Application(Twitter)

Let us analyze a real time application to get the latest twitter feeds and its hashtags. Earlier, we have seen integration of Storm and Spark with Kafka. In both the scenarios, we created a Kafka Producer (using cli) to send message to the Kafka ecosystem. Then, the storm and spark inte-gration reads the messages by using the Kafka consumer and injects it into storm and spark ecosystem respectively. So, practically we need to create a Kafka Producer, which should −

  • Read the twitter feeds using “Twitter Streaming API”,
  • Process the feeds,
  • Extract the HashTags and
  • Send it to Kafka.

Once the HashTags are received by Kafka, the Storm / Spark integration receive the infor-mation and send it to Storm / Spark ecosystem.

Twitter Streaming API

The “Twitter Streaming API” can be accessed in any programming language. The “twitter4j” is an open source, unofficial Java library, which provides a Java based module to easily access the “Twitter Streaming API”. The “twitter4j” provides a listener based framework to access the tweets. To access the “Twitter Streaming API”, we need to sign in for Twitter developer account and should get the following OAuth authentication details.

  • Customerkey
  • CustomerSecret
  • AccessToken
  • AccessTookenSecret

Once the developer account is created, download the “twitter4j” jar files and place it in the java class path.

The Complete Twitter Kafka producer coding ( is listed below −

import java.util.Arrays;
import java.util.Properties;
import java.util.concurrent.LinkedBlockingQueue;

import twitter4j.*;
import twitter4j.conf.*;

import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

public class KafkaTwitterProducer {
   public static void main(String[] args) throws Exception {
      LinkedBlockingQueue<Status> queue = new LinkedBlockingQueue<Sta-tus>(1000);
      if(args.length < 5){
            "Usage: KafkaTwitterProducer <twitter-consumer-key>
            <twitter-consumer-secret> <twitter-access-token>
            <topic-name> <twitter-search-keywords>");
      String consumerKey = args[0].toString();
      String consumerSecret = args[1].toString();
      String accessToken = args[2].toString();
      String accessTokenSecret = args[3].toString();
      String topicName = args[4].toString();
      String[] arguments = args.clone();
      String[] keyWords = Arrays.copyOfRange(arguments, 5, arguments.length);

      ConfigurationBuilder cb = new ConfigurationBuilder();

      TwitterStream twitterStream = new TwitterStreamFactory(;
      StatusListener listener = new StatusListener() {
         public void onStatus(Status status) {      

            // System.out.println("@" + status.getUser().getScreenName() 
               + " - " + status.getText());
            // System.out.println("@" + status.getUser().getScreen-Name());

            /*for(URLEntity urle : status.getURLEntities()) {

            /*for(HashtagEntity hashtage : status.getHashtagEntities()) {
         public void onDeletionNotice(StatusDeletionNotice statusDeletion-Notice) {
            // System.out.println("Got a status deletion notice id:" 
               + statusDeletionNotice.getStatusId());
         public void onTrackLimitationNotice(int numberOfLimitedStatuses) {
            // System.out.println("Got track limitation notice:" + 

         public void onScrubGeo(long userId, long upToStatusId) {
            // System.out.println("Got scrub_geo event userId:" + userId + 
            "upToStatusId:" + upToStatusId);
         public void onStallWarning(StallWarning warning) {
            // System.out.println("Got stall warning:" + warning);
         public void onException(Exception ex) {
      FilterQuery query = new FilterQuery().track(keyWords);

      //Add Kafka producer config settings
      Properties props = new Properties();
      props.put("bootstrap.servers", "localhost:9092");
      props.put("acks", "all");
      props.put("retries", 0);
      props.put("batch.size", 16384);
      props.put("", 1);
      props.put("buffer.memory", 33554432);
      Producer<String, String> producer = new KafkaProducer<String, String>(props);
      int i = 0;
      int j = 0;
      while(i < 10) {
         Status ret = queue.poll();
         if (ret == null) {
         }else {
            for(HashtagEntity hashtage : ret.getHashtagEntities()) {
               System.out.println("Hashtag: " + hashtage.getText());
               producer.send(new ProducerRecord<String, String>(
                  top-icName, Integer.toString(j++), hashtage.getText()));


Compile the application using the following command −

javac -cp “/path/to/kafka/libs/*”:”/path/to/twitter4j/lib/*”:.


Open two consoles. Run the above compiled application as shown below in one console.

java -cp “/path/to/kafka/libs/*”:”/path/to/twitter4j/lib/*”:
. KafkaTwitterProducer <twitter-consumer-key>
my-first-topic food

Run any one of the Spark / Storm application explained in the previous chapter in another win-dow. The main point to note is that the topic used should be same in both cases. Here, we have used “my-first-topic” as the topic name.


The output of this application will depend on the keywords and the current feed of the twitter. A sample output is specified below (storm integration).

. . .
food : 1
foodie : 2
burger : 1
. . .