Implementing Master Slave / Grid Computing Pattern in Akka

Master Slave pattern is a prime example of fault tolerance and parallel computation. The idea behind the pattern is to partition the work into identical sub tasks which are then delegated to Slaves. These slave node or instances will process the work task and send back the result to the master. The master will then compile the results received from all the slave nodes. Key here is that the Slave nodes are only aware on how to process the task and not aware of what happens to the output.

The Master Slave pattern is analogous to the Grid Computing pattern where a control node distributes the work to other nodes. Idea is to make use of the nodes on the network for their computing power. SETI@Home was one of the earliest pioneers in using this model.

I have build a similar example with difference being that worker nodes get started on Remote Nodes, Worker Nodes register with Master(WorkServer) and then subsequently start processing work packets. If there is no worker slave registered with Master(WorkServer), the master waits the workers to register. The workers can register at any time and will start getting work packets from there on.

Processing 10 million messages with Akka

Akka Actors promise concurrency. What better way to simulate that and see if how much time it takes to process 10 million messages using commodity hardware and software without any low level tunings. I wrote the entire 10 million messages processing in Java and the overall results astonished me.

When I ran the program on my iMac machine with an intel i5 - 4 core, 4 Gb RAM machine and JVM heap at 1024Mb, the program processed 10 million machines in 23 secs. I ran the program multiple times and the average time was in the range of 25 secs. So the through put I received was almost in the range of 400K messages per second which is phenomenal.

Word Count MapReduce with Akka

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 

1. The client system (FileReadActor) reads a text file and sends each line of text as a message to the ClientActor. 
2. The ClientActor has the reference to the RemoteActor ( WCMapReduceActor ) and the message is passed on to the remote actor
3. 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. 
4. The  WCMapReduceActor sends across the messages to the MapActor (uses RoundRobinRouter dispatcher) for mapping the words
5.  After mapping the words, the message is send across to the ReduceActor(uses RoundRobinRouter dispatcher) for reducing the words  
6. The reduced result(s) are send to the Aggregate Actor that does an in-memory aggregation of the result