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Requirements

  • Apache Maven 3.x
  • JVM 6 or 7
  • Internet connection for downloading maven dependencies and reading documentation
  • IDE (Recommended: IntelliJ IDEA)

General Info

  • The folder cheating contains the entire solution, while the folder storm-assignment is the working directory for this workshop.
  • Take a look at our wiki for some guide, e.g. for the provided image file

STEP ONE

Download workshop projects

  1. git clone https://github.com/kantega/storm-twitter-workshop

For Eclipse-users only

  1. Create Eclipse projects by running the mvn eclipse:eclipse command from the project root ("storm-twitter-workshop")
  2. Run mvn -Declipse.workspace="your Eclipse Workspace" eclipse:configure-workspace and open this workspace from Eclipse
  3. Import the storm-assignment project into Eclipse.

Twitter Developer: Create keys and tokens

  1. Go to Twitter developer site and log in. Create new account if you do not already have one

  2. Click on the arrow on top right beside your account image and select "My applications".

  3. Create a new application. If you already have one, you can use credentials from that application and skip this and the next step.

  4. Fill in the details. You can use dummy values for all required fields.

  5. In the application detail page note the Consumer key and Consumer secret fields under OAuth settings

  6. Create a new access token at the bottom of the page. The Access token and Access token secret will be shown after a moment.

  7. Add these tokens to BOTH
    storm-assignment / src / main / java / storm / starter / TwitterFunTopology.java and
    cheating / src / main / java / storm / starter / CheatingTwitterFunTopology.java
    OR send them as command line arguments through maven by adding the following in step 8 -Dexec.args="CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET"

  8. Test your credentials by running the main class in cheating

     cd cheating   
 mvn compile exec:java -Dexec.classpathScope=compile -Dexec.mainClass=storm.starter.CheatingTwitterFunTopology

  9. You should now start getting continuous output for 10 seconds. After this a file tweet.txt should have been generated that contains some twitter messages. If this file is empty, look at console output.

Now, you are ready for coding. To the below steps on the storm-assignment folder and fill out the TODOs.

STEP TWO

Storm: My first topology

Goal: Print out tweets
Modify TwitterFunTopology.java: Define your Twitter query (location and topic based). See the provided TwitterSpout class and our Reading tweet streams using Twitter4j wiki page for understanding how the tweets are fetched. Build a topology that uses your twitter query and routes the twitter messages to the FileWriterBolt (Replace the todo's with implementations). Run the main method in TwitterFunTopology.java. NOTE: Do not create more than 1 TwitterSpout workers or else you may encounter Twitter API limits!

STEP THREE

Storm: Extracting hashtags

Goal: Build a topology that extracts and prints the hashtags from Norwegian tweets. We do this by introducing a new bolt (HashtagExtractionBolt). (Replace the todo's with implementations). Print the hashtags to a FileWriterBolt.
Hint: Take a look at FilterQuery, however, note that the language feature may not yet implementet by Twitter4j. You can use the LanguageDetectionBolt to find the correct language, but it can give inaccurate results for a small language as Norwegain. You can use the location filter of FilterQuery get better accuracy. Again take a look at Basic Twitter stream reading using Twitter4j. Twitter4j documentation is poor, so it is recommended to use Twitters own documentation. In our Twitter resource we have listed relevant documentation from Twitter. Be sure to check that out! TODO: Explain location coordinate usage.

STEP FOUR

Storm: Count the popularity of hashtags

Goal: Build a topology that counts occurrences of the hashtags. Hint: We do this by introducing a new bolt; the RollingCountBolt. Print the results to a FileWriterBolt. New concept: Sliding Windows - Time or size based frame for calculation objects

STEP FIVE

Storm: Rank the 10 most popular hashtags continuously

Goal: Show what people are discussing right now. Print a top 10 of the most popular hashtags to a FileWriterBolt. Problem: How do we create such ranking if we do not store every event or hashtag? Hint: We do this by introducing two new bolts; the IntermediateRankingsBolt and the TotalRankingsBolt. We will route tuples from HashtagExtractionBolt --> RollingCountBolt --> IntermediateRankingsBolt --> TotalRankingsBolt --> FileWriterBolt Example of routing:

builder.setBolt("hashtags", new HashtagExtractionBolt(), 4).shuffleGrouping("YOUR SPOUT ID");
builder.setBolt("hashtag-counter", new RollingCountBolt(9, 3), 4).fieldsGrouping("hashtags", new Fields("entity"));
builder.setBolt("hashtag-intermediate-ranking", new IntermediateRankingsBolt(100), 4).fieldsGrouping("hashtag-counter", new Fields("obj"));
builder.setBolt("hashtag-total-ranking", new TotalRankingsBolt(100)).globalGrouping("hashtag-intermediate-ranking");
builder.setBolt("hashtag-ranking-print", new FileWriterBolt("HASHTAG_RANKING.txt")).shuffleGrouping("hashtag-total-ranking");

Information about how these bolts work: http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/ New concept: Tick tuples - When we require a bolt to “do something” at a fixed interval GlobalGrouping - We're using a TotalRankingsBolt-worker. Data from IntermediateRankingsBolt to TotalRankingsBolt is routed using globalGrouping

STEP SIX

Storm: Extracting sentiment values

Sentiment values are numerical measures of how positive or negative a given text is. By extracting sentiment values from tweets we can chart how positive people are to certain entities. A common approach for extracting sentiment values is to apply dictionaries where different words are associated with a sentiment value. Such dictionaries are customized for each language. Goal: Extract the sentiment value for each of the tweets in your stream. Print the sentiment value to a FileWriterBolt. To accomplish this task we need a topology with a bolt for language detection and a bolt for extracting the sentiment value.

STEP SEVEN

Storm: Calculate a running average value for sentiment

Goal: Calculate a running average sentiment value for the last 50 tweets. Print the average sentiment value to a FileWriterBolt. To accomplish this will add the bolt to our topology: AverageWindowBolt.

STEP EIGHT

Storm: Its your playground!

Be creative - Form new twitter queries, create new bolts, combine them and extract knowledge! For example:

  • Find out where people are most happy, Oslo, Trondheim, Stavanger or Bergen?
  • How happy are people about the weather, the public transport, etc.?
  • Who is most popular: Jens Stoltenberg or Erna Solberg? What are their associated sentiment values?