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Posted by Carl Pulley
Wed, Jan 18, 2017

As part of a series of blog posts on , this blog post introduces the open source library . Here, we use this library to define a Chaos experiment (using ) that illustrates the impact that auto-downing can have when an Akka cluster is subjected to network partitioning. The previous post Can Real World Distributed Systems be Proven Correct? motivates and explains the need for performing fault injection on distributed applications. In future posts, we will consider more realistic use cases than considered here - so please stay tuned!

Posted by Carl Pulley
Sun, Mar 15, 2015

uses Akka streaming workflows to define a flexible and generic exercise classification pipeline. The classification pipeline is able to modularly include any machine learning classifier and is able to monitor the real-time streams of classification results using a linear dynamic logic.

This post provides a summary overview of this classification pipeline with future posts introducing the implementation details.

Posted by Martin Zapletal
Sun, Mar 8, 2015

Concepts such as event sourcing and CQRS allow an application to store all events that happen in the system using a persistence mechanism. The events can not be mutated and current state of the system in any point in history can be reconstructed by replaying all the events until that point. For performance reasons obviously the state can be cached using a snapshot. But the undisputable advantage of this approach is that the whole history of events (including user actions, behaviour or system messages - anything we decide to store) is available to us rather than just the current state. Event sourcing was thoroughly discussed before for instance in  or  and CQRS in  or . 

In this post we will discuss how we can store and further use these data by connecting Akka, Cassandra and Spark, focusing mostly on the configuration, Akka serialization and Akka-analytics project. Later I will follow up with another blog post building on top of this with an example of using machine learning techniques to obtain some insights to help optimize future decisions and application workflow.

Posted by Carl Pulley
Sat, Dec 20, 2014

Here we present a flexible and generic framework within which distributed applications, built upon a architecture, may be implemented and deployed.

We achieve this by deploying microservices to a cluster of machines (complete with for service discovery and for controlling services and specifying affinity rules).

Microservices are implemented using Akka actors that support clustering, Cassandra persistence and data sharding. Interaction with the microservices is mediated using a load balancer that round-robin connects (via circuit-breakers) to microservice REST endpoints.

Posted by Jan Machacek
Sat, Dec 13, 2014

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