The spark streaming API used in this repo boils down to the following snippet which demonstrates how an RDD has multiple Tweets in it (in this case, the tweets may have medical content, and that this blueprint implements a CTakes medical term extraction from an ongoing twitter stream, which uses the CTakes lexicon). But in any case, the thing to note is that RDDs of tweets have multiple tweets in them, so you have a nested processing loop, where in we (1) forEachRDD and (2) forEachTweet in the RDD.

    tweetStream.foreachRDD( transactions => {
        CassandraConnector(conf).withSessionDo {
        session => {
          val x=1
          Thread.sleep(1)
          transactions.foreach({
            xN =>
              System.out.println("Running Cassandra Insert..." + xN)
              System.out.println("Note that this can fail if cassandra isnt working...")
              val xNtxt=xN.toString+" "+xN.getText;
              session.executeAsync(s"INSERT INTO streaming_test.key_value (key, value) VALUES ('$xNtxt' , $x)")
          })
        }
      }
    })

I used this as my Demonstration at apachecon a few years ago to show how to build spark streaming apps.

This repo has two different resources.

• testing of spark docker containers, orchestrated via vagrant.
• spark streaming blueprint applications.

If interested just in the docker containers, checkout the deploy/ directory.

These are mostly unrelated. They just happen to be in the same repo.

To use it, import it into INtelliJ or your favorite IDE as an SBT project.

Then you should be able to run standard SBT tests/compile tasks etc inside your idea and also in standalone mode.

1. git clone jayunit100/SparkBluePrint

2. remove folders that dont apply to your project.

3. Now open intellij, and import.

4. Pick "SBT" project as the template

5. Run the Tester class via intelliJ

6. Change the .git/config to point to your repository.

• Set up a spark cluster w/ cassandra slaves. There is a WIP project under deploy/ which sets scaffolding for this up using dockerfiles and vagrant to create a n-node spark cluster w/ a cassandra sink.

• Then run sbt package, to create the jar.

• copy the jar into the shared directory defined in the vagrantfile in deploy (in general, just copy the file into your machine that submits the spark jobs).

• spark-submit the application jar w/ desired class name (details coming soon).

Feedback welcome !