This article is a follow-up to Batch processing with Akka part 3.
In the last part of this series of articles I have a look at data processing within a network of computers. To do that the program was split into two logical parts, the one part – the master – contains the actors that read and write the data from and to files, the other part – the workers – contains the actors that do the actual processing.
This article is a follow-up to Batch processing with Akka part 2.
In the third part of this series I deal with error recovering for the case that records are lost during processing or in case the processing of a record takes too long time. The may happen for example when multiple computers are involved in the processing and network problems occur.
To handle such cases, the program checks in regular intervals if records, that have been sent into the system for processing, are processed in time.
This article is the follow-up to Batch processing with Akka part 1.
This first implementation does not care about error recovery, there is no special exception or error handling.
The records are sent into the actor system, where they are processed in parallel and written to the output file at the end. The actor system is running in a single VM, there is no load-balancing across several computers at the moment.
This is the first of a series of articles in which I’d like to report about the implementation of a batch processing by using the akka framework.
The first time I heard about akka was in the summer of 2012, but it’s only up to now that I find the time to do some deeper investigations. In the articles to follow I will tell about success and failures and what I find positive or negative.
