My Tech Sharing

  As many applications have started the journey of modernisation from monolithic to microservices architecture, observability tool has now become essential to troubleshoot and understand issues happened in application. Business logic will no longer sit within the monolithic code, but has now spread across multiple microservices which can be hard to trace if we don't have a proper tool. Jaeger ( https://www.jaegertracing.io/ ) is an open source project developed by Uber Technologies which is meant for us to perform distributed tracing across APIs. In today's sharing, I am going to implement Jaeger distributed tracing via microservices developed using Node.JS. I am going to use threadfin-http (  https://github.com/joshua-lim/threadfin-http ) ; a simple Node.JS http server to illustrate this. If you are not a developer, just want to explore the features of Jaeger without doing much coding, you can download the hot r.o.d example ( https://github.com/jaegertracing/jaeger/tree/master

Build a Batch Framework with Go Recently, I have just picked up Golang and decided to use what I learnt to build a Batch Framework with Go. Batch Framework - Batch103 I called this project Batch103 ( S tarted as Batch101. 103 is the 3rd version I decided to publish ) ; the project is a simple batch framework inspired by SpringBatch - a popular batch framework in Java world. If you familiar with SpringBatch, you may find many similar features you used to see in SpringBatch. The source code of the framework is available at github  (https://github.com/pengyeng/batch103.git )  I will illustrates the high level  architecture of Batch103 using a diagram. (Please refer the overall architecture diagram) The framework comes with a Job Launcher which provides  a implementation class to launch the batch job. Job Launcher needs 3 key components to operate; which are : Reader - handling the input of batch job Processor - handling the logic to process the data Writer - handling the output of batch j

Prometheus is probably one of the most popular monitoring tools for microservices architecture. With Prometheus, microservices environment like docker can be easily monitor by connecting Prometheus to docker swarm cluster and docker daemon. There are many articles available to talk about setting up Prometheus. The question I have is how about the microservice deployed in environment such docker swarm? how can we effectively monitor these applications? I have recently found my answer through a npm library called prom-client (  https://www.npmjs.com/package/prom-client  ). This is an amazing client library which allows me to connect my Node.JS application with Prometheus. To share this with you, I am going to take you through a demo by creating a 2 nodes docker swarm and deploy my threadfin-http as a docker service. On top of that, I also going to deploy prometheus and grafana to enable monitoring and dashboard for visualization. Well… to kick start this, I am going to use threadfin-http