Understanding Akka Streams, Back Pressure and Asynchronous Architectures
Where Akka Streams fits in your system architecture
As our deputy CTO Viktor Klang recalls in this Medium article announcing Reactive Streams 1.0.0, Reactive Streams started in 2013 with an discussion between various big thinkers about how to handle the issue of back pressure in streaming systems. Fast forward some years later and we have seen a tremendous rise in interest, attracting hard work and time from engineers working for companies like Netflix, Pivotal, Red Hat, MongoDB and others.
With so much activity in this area, the term 'streams' has been getting pretty overloaded recently–it's hard to know where to best use different technologies with streams in the name.
In this talk by noted hAkker Konrad Malawski, we'll disambiguate what streams are and what they aren't, taking a deeper look into Akka Streams (the implementation) and Reactive Streams (the standard).
You'll be introduced to a number of real life scenarios where applying back-pressure helps to keep your systems fast and healthy at the same time. While the focus is mainly on the Akka Streams implementation, the general principles apply to any kind of asynchronous, message-driven architectures.
Check out the slides
Read, code, meet up and start moving faster with Akka Streams
- Read - White Paper: An Introduction to Reactive Streams, Akka Streams and Akka HTTP for Enterprise Architects
- Code - Check out the Akka Streams documentation page on Akka.io
- Meet up - Come to our Reactive Roundtable sessions to learn, interact and share.
Or, if you want to start moving faster today, contact us to get started with Akka and Akka Streams with our new Proof of Value (POV) service: