Reading Time: 6 minutes Effects are the kind of utility that’s provided by the redux-saga package. when u invokes effects it returns object contains instructions which redux-saga interprets. The effects don’t have any special property outside the redux-saga application because it’s redux-saga that generates side effects like API call database or something else, The effect itself doesn’t actually do anything. If you don’t know how to create a Saga, Continue Reading
Reading Time: 5 minutes With the introduction of Java 9, Java community has started to show its support directly towards Reactive streams, which was earlier used by leveraging third-party libraries. Please visit my earlier blog on Reactive streams to understand the basic ideologies which are working behind it like the push-pull model or backpressure. As a part of this blog, we will explore how we can leverage the same Continue Reading
Reading Time: 5 minutes In Java, we are often tempted towards writing a code which is executed eagerly. There’s a good reason for that – eager code is easy to write and to reason about. But delaying commitments until the last responsible moment is a good agile practice. When executing the code, we can gain performance by being just a little lazy. In this blog, we will try to Continue Reading
Reading Time: 2 minutes Recently I got an invitation to present a guest lecture for faculty of Engineering colleges in ABES college of Engineering. I came up with the most trending topic i.e Reactive Architecture. We talked about what is this buzzing keywords and why does it came into existence. Also What are the challenges one were facing and how are the real world problems being solved by using Continue Reading
Reading Time: 5 minutes In the world of cloud computing, big data and IoT, system and application requirements have changed by leaps and bounds in recent years. Even the challenges being faced by developers and enterprises today are way different from the ones that they faced, say, a decade or two earlier. Find out why should modern enterprises opt for reactive systems today?
Reading Time: 3 minutes With a lot of buzz in the programming world about “reactive Programming”, a new concept following the same path has been introduced. This is “Reactive streams” backed up by the idea of backpressure. In this blog, we try to understand, what does it mean exactly? What are Reactive Streams? We are here talking about handling streams of data that needs to be handled in an Continue Reading
Reading Time: 4 minutes A few months back, I was working on interesting customer engagement. Lagom was getting popularity, therefore, I and my few team members wanted to explore it to see if it would suit our needs. Just reading its documentation was not enough to get the proper practical hands-on. So we thought to do a project. The idea was that we should not build a dummy project, Continue Reading
Reading Time: 3 minutes Error handling is one of the ways we ensure we are gracefully handling our failures. While working with streams of data, the Reactive Library Reactor provides us with multiple ways of dealing with situations where we need to handle our errors. In this blog, I wish to discuss a few of them with you.
Reading Time: 3 minutes I recently started working on the functional approach of Spring Boot Webflux. You can explore it more on my previous blog on Spring Boot Webflux. It is a new concept and you may not find many useful blogs on it unlike for annotation based controllers. However, going with some trial and error, I have come out with how one can test its router functions along Continue Reading
Reading Time: 3 minutes Reactive programming is a programming paradigm that promotes an asynchronous, non-blocking, event-driven approach to data processing. Reactive programming involves modeling data and events as observable data streams and implementing data processing routines to react to the changes in those streams. In the reactive style of programming, we make a request for the resource and start performing other things. When the data is available, we get Continue Reading
Reading Time: 3 minutes Consider a situation where a microservice is deployed on multiple pods and on condition one pod got restarted with any of the failure reason makes unreachable and at the same time interdependent services are registers its IP for communication. Now, since other pods of the service are alive but not able to communicate makes the communication failure as well as making the failure of every Continue Reading