Author: Himanshu Gupta

Akka Cluster in use (Part 5): Let’s stay in touch via Gossip

Reading Time: 3 minutes In our previous blog post, Managing an Akka Cluster, we learnt how to manage an Akka Cluster via Akka Management. In this blog post, we are going to learn, how the Node(s) within an Akka Cluster communicate with each other via Gossip Let’s Gossip A very popular way to transmit a message is via Gossip 😛 . As it doesn’t need much effort and also Continue Reading

integrating Cucumber with Akka-Http

Akka Cluster in use (Part 4): Managing a Cluster

Reading Time: 3 minutes Hello friends, I hope you all are safe in the COVID-19 pandemic and learning new tools and tech while staying at home. In our last blog post on Akka Cluster, we saw an Akka Cluster in action and learnt about how the node(s) react to new nodes in the Cluster. Now when we know how to create & setup an Akka Cluster, let’s learn, how to Continue Reading

Akka Cluster in use (Part 3): Setup a Local Akka Cluster

Reading Time: 4 minutes Hello friends, I hope you all are safe in COVID-19 pandemic and learning new tools and tech while staying at home. In our last blog post on Akka Cluster, we learnt about the configurations we need in order to form an Akka Cluster. But we didn’t saw it in action. Hence in this blog post, we will see one in action. Step 1: Download the Continue Reading

Akka Cluster in use (Part 2): Forming a Cluster

Reading Time: 3 minutes Hello friends, in our last post on Akka Cluster, we understood the purpose of an Akka Cluster. Now, next step is to understand: How to Form an Akka Cluster? But before we start forming an Akka Cluster, let us understand that: How Actors can communicate with each other over a cluster of machines (JVMs)? Now we know that each Actor in Akka has an Address. Continue Reading

Akka Cluster in use (Part 1): What is Akka Cluster?

Reading Time: 3 minutes In computer architecture, Amdahl’s law is a formula which tells us that how many times faster a program can be executed if it is parallelized. For example, if a program needs 20 hours using a single processor core, and a particular part of the program which takes one hour to execute cannot be parallelized, while the remaining 19 hours of execution time can be parallelized, Continue Reading

Introduction to Logging in R using log4r

Reading Time: 3 minutes One of the most important aspect of an application is Logging. Since logs provide visibility into the behavior of a running app. Hence logs play a vital role in maintenance and enhancement of an application. However, most of us are already aware with the importance of logging. That’s why we add them in our applications. But one thing that we are not aware of is Continue Reading

A Beginner’s Guide to Writing Acceptance Testing for Lagom Microservices with Cucumber

Reading Time: 3 minutes In software development, acceptance criteria is a way via which a client communicates their expectations to engineering team. Also, it acts as a list of conditions upon completion of which a software/app is marked as complete. Since acceptance criteria is an important part of software development, it becomes important to determine that the acceptance criteria is met by the software or not. This sub-discipline of Continue Reading

Flinkathon: Guide to setting up a Local Flink Custer

Reading Time: 3 minutes In our previous blog post, Flinkathon: First Step towards Flink’s DataStream API, we created our first streaming application using Apache Flink. It was easy, clean, and concise. However, the real power of Apache Flink is seen on a cluster, where data is processed in a distributed manner, with the advantage of multi-core/multi-memory systems. So, in this blog post, we will see how to set up Continue Reading

Flinkathon: First Step towards Flink’s DataStream API

Reading Time: 3 minutes In our previous blog posts: Flinkathon: Why Flink is better for Stateful Streaming applications? Flinkathon: What makes Flink better than Kafka Streams? We saw why Apache Flink is a better choice for streaming applications. In this blog post, we will explore how easy it is to express a streaming application using Apache Flink’s DataStream API. DataStream API DataStream API is used to develop regular programs Continue Reading

Flinkathon: Why Flink is better for Stateful Streaming applications?

Reading Time: 2 minutes Stream processing is a way to query a continuous stream of data and draw conclusions from it within the boundaries of a real-time scenario. For example, receiving an alert as soon as a fraudulent transaction is done via a credit/debit card. The 2 main types of stream processing done are: Stateless: Where every event is handled completely independent from the preceding events. Stateful: Where a Continue Reading

A Beginner’s Guide to Deploying a Lagom Microservice on Kubernetes

Reading Time: 4 minutes Both Lagom and Kubernetes are gaining popularity quite fast. Lagom is an open source framework for building reactive microservice systems in Java/Scala. And, Kubernetes (or K8s in short) is an open-source system for automating deployment, scaling, and management of containerized applications. Together they make an excellent stack for developing Reactive microservices of production grade. We have already seen a lot of blogs on Lagom on this Continue Reading

Structured Streaming: Philosophy behind it

Reading Time: 3 minutes In our previous blogs: Structured Streaming: What is it? & Structured Streaming: How it works? We got to know 2 major points about Structured Streaming – It is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. It treats the live data stream as a table that is being continuously appended/updated which allows us to express our streaming computation as Continue Reading

Structured Streaming: How it works?

Reading Time: 2 minutes In our previous blog post – Structured Streaming: What is it? we got to know that Structured Streaming is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. Now it’s time to learn  – How it works? So, in this blog post, we will look at the working of a structured stream via an example. So, let’s take a Continue Reading