In my ongoing workings with Akka, i recently wrote an Word count map reduce example. This example implements the Map Reduce model, which is very good fit for a scale out design approach.
Flow
- The client system (FileReadActor) reads a text file and sends each line of text as a message to the ClientActor.
- The ClientActor has the reference to the RemoteActor ( WCMapReduceActor ) and the message is passed on to the remote actor
- The server (WCMapReduceActor) gets the message. The Actor uses the PriorityMailBox to decide the priority of the message and filters the queue accordingly. In this case, the PriorityMailBox is used to segregate the message between the mapreduce requests and getting the list of results (DISPLAY_LIST)message from the aggregate actor.
- The WCMapReduceActor sends across the messages to the MapActor (uses RoundRobinRouter dispatcher) for mapping the words
- After mapping the words, the message is send across to the ReduceActor(uses RoundRobinRouter dispatcher) for reducing the words
- The reduced result(s) are send to the Aggregate Actor that does an in-memory aggregation of the result
The following picture details how the program has been structured
The code base for the program is available at the following location – https://github.com/write2munish/Akka-Essentials.
For more information on MapReduce, read the post MapReduce for dummies.
Reference: Word Count MapReduce with Akka from our JCG partner Munish K Gupta at the Akka Essentials blog.
