Synopsis

The code & sample app can be found on Github

Risk Management in finance is one of the most common case studies for Grid Computing, and Value-at-Risk is most widely used risk measure. In this article I’m going to show how to scale-out Value-at-Risk calculation to multiple nodes with latest Akka middleware. In Part 1 I’m describing the problem and single-node solution, and in Part 2 I’m scaling it to multiple nodes.

[Part 2/2] Scale-out VaR calculation to multiple nodes

Go to Part 1 where Value at Risk calculator defined.

Synopsis

The code & sample app can be found on Github

Risk Management in finance is one of the most common case studies for Grid Computing, and Value-at-Risk is most widely used risk measure. In this article I’m going to show how to scale-out Value-at-Risk calculation to multiple nodes with latest Akka middleware. In Part 1 I’m describing the problem and single-node solution, and in Part 2 I’m scaling it to multiple nodes.

[Part 1] Introduction to Value at Risk calculation

Go to Part 2 where VaR calculation scaled-out to multiple nodes.

Synopsis

The code & sample app can be found on Github

Distributed micro-services architecture is hot trend right now, it’s widely adopted by , and Gilt. However it can be difficult if data model is already defined, and services accessed via existing API throughout all your code. I’ll show how it’s possible to split monoliths app into standalone services built with Finagle and SBinary for custom communication protocol.

I’m going to show it on example of small Fancy Movie Database application.

[Part 2/2] Spit application to distributed services

Go to Part 1 where fancy movie database application defined.

What is Finagle

Finagle is a protocol-agnostic, asynchronous RPC system for the JVM that makes it easy to build robust clients and servers in Java, Scala, or any JVM-hosted language.

Synopsis

The code & sample app can be found on Github

Distributed micro-services architecture is hot trend right now, it’s widely adopted by , and Gilt. However it can be difficult if data model is already defined, and services accessed via existing API throughout all your code. I’ll show how it’s possible to split monoliths app into standalone services built with Finagle and SBinary for custom communication protocol.

I’m going to show it on example of small Fancy Movie Database application.

[Part 1] Fancy Movie Database application

Go to Part 2 where fancy movie database application is divided into server and client using Finagle.

Let’s assume that we have some input process, and want to run some ‘heavy computation’ on each element. Obviously we want utilize all available cores and use thread pool. However scalaz-stream by default is deterministic and in following example all computation steps will run consecutively.