Less Java

Introduction Welcome to event streaming unplugged, this is the first in a series of articles covering the topic of realtime evemt stream processing. This is a practical programming series with resources at unplugged part 1 . What you will learn in these articles: The basics of unbounded event processing Functional reactive programming introducing concepts like map, filtering, groupBy, flatMap Constructing directd acyclic execution graphs Managing event streams of heterogenous types Imperative event programming, integrating application code Input and output Building, testing and deploying Auditing, logging and debugging event execution flow This article starts with a simple hello world and then moves onto a more complex example. The second example calculates the mark to market value for a set of traded currency pairs. Two independent event streams, instrument market price and executed trades are processed. Batches of trades and reset signals are also supported. A more complex example se

Introduction In this article I am combining two products Lombok and Fluxtion to demonstrate how tools can reduce both the code written and time to delivery while improving the readability of the code. The use of var from java 10 improves the situation even further. Both products and var use inference at build time to accelerate development. Fluxtion's ethos is to minimise waste, our goal here is to remove boiler plate code, reduce code-noise, and simplify integration tasks. We want to expends as little development time as possible while still delivering an efficient and high performance solution capable of processing millions of messages per second. Using the techniques described I compare a Fluxtion/Lombok implementation to a scala example using Akka streams, the Java version requires less code and is simpler to build. Housekeeping, apologies for not acknowledging Richard Warburton of  Opsian , in my first blog . Code Signal to Noise ratio When we code we address tw