Zipkin and Jaeger are open source tools that make distributed computing and microservice-based architectures easier to monitor and manage.
Often the mark of a revolutionary idea is that at first, it seems totally stupid. Take Twitter: in 2006 when the service launched, most people had a hard time seeing its potential. To make matters worse, Twitter was not only weird, it was also unreliable. Twitter was a killer app for the mobile generation, but its backend systems couldn’t handle its sudden popularity and massive adoption. At the time, most backend software was written as a monolithic application; not only were these systems fragile, but they were hard to extend and maintain.
In order to create more resilient and scalable systems, online services adopted distributed architectures that decomposed into microservices. Like many revolutionary ideas, this both solved existing problems and created a number of new ones.. Specifically, microservices that operate in highly dispersed environments are much harder to monitor and debug.
In this article, we’ll examine two tools, Zipkin and Jaeger, that are designed to make distributed computing and microservice-based architectures easier to monitor and manage. We’ll look at what these tools provide, their strengths and weaknesses, and we’ll make recommendations on why you should choose one or the other.
Before we look at the tools, let’s take a deeper look at the problem and at the philosophy behind its solution. A monolithic application is like an old car: as soon as it starts making weird noises or something feels wrong, most of us can figure out what is wrong. If we have enough experience, we then dive under the hood and fix it. But a distributed system is more like a modern car: it will tell you something is wrong, but it gives you no indication of how to fix it without specialized tools or knowledge.
The lack of visibility of serverless into the underlying architecture and how the performance of that architecture impacts users is a significant challenge.
In this article we explore the serverless metrics that are critical to the health of your Amazon Web Services application.
How to best monitor your external and third party API integrations and hold partners accountable to SLAs
The anti-patterns unique to serverless and how observability can cushion the impact of anti-patterns creeping into your serverless architectures.
And to achieve observability in serverless applications, it's important ... Monitoring checks “known” metrics to evaluate the health of the system.