Monitoring Microservices: A Step By Step Guide

Microservices are an architectural approach for developing applications. They are distributed and loosely coupled to support independent deployment, scalability of an application that helps developers to rapidly deliver more functionality and reliability.  Therefore, the increasing need for microservices can be summarized in one word - speed. 

Unsurprisingly, it has changed undulate effects within software management, including monitoring systems. So, today we will be discussing the radical changes required to monitor microservices efficiently in production environments. We’ll check why we need to monitor systems, what the challenges are in monitoring microservices, and how to overcome them.

Monitoring is a critical part of every IT system. However, the challenges that are associated with microservices are comparatively new and different. For instance, deployed as a single application, a monolithic system has dependencies and failures that are completely different from what one can find in microservices. 

Although the reason behind monitoring microservices is similar to any other type of distributed system, i.e., failure, being a system with multiple services, microservices-based applications require more intensive and different monitoring methods. 

No one wants their system to fail. However, it’s not only one reason why you should be monitoring microservices. Systems are not only about ups and downs - but complex systems can also operate in a degraded state while impacting the performance. These states can often proclaim the upcoming failures in the system. That’s why monitoring the system behavior can alert the developers about the degraded state prior to complete failure. 

Additionally, system monitoring can produce insightful information that can help in enhancing the performance of services. The data, including performance and failure, can be used to determine certain failure patterns of the system and resolve them. 

The cloud-native architecture that is based on Kubernetes and Containers like Docker has become popular for driving microservices. However, they have also added the complexity layer in the microservice system. 

In the containerized workloads, you will have to monitor infrastructure metrics ( Host Memory & CPU), the runtime of Kubernetes & Containers (Node Resource Utilization and Running Pods), and application metrics (request rate and duration background). 

With dozens of microservices where each one is running its database and programming language while being scaled, deployed, and upgraded independently, a lack of monitoring can cause unpredictable issues and poor performance of the system. 

Till now, we have gained a bit of insight into the microservices architecture and the need of monitoring them, but what is the right way to do so? Let’s check!

To focus your idea on limiting, you will have to sharpen one to three of the important metrics (a measure of quantitative assessments for tracking and comparing performance) you want to measure. For example, tools like Retrace, LightStep, or Splunk APM can perform metrics, alerts, error tracking, and centralized logging. And if you haven’t still decided what monitoring tool you should be using for microservice-based applications, reviewing the features can be intimidating. 

In order to understand the metrics to concentrate on, you will have to acknowledge the need of your business. You should know from where the most customer or operational complaints are coming, is it from downtime of the services or requests are too low, and so on. Simply put, the more deeply you will know your business, the easier it will be to choose tools for required metrics. 

With the three metrics you have opted in mind, you can easily choose a monitoring tool. Moreover, if you have a demo instance, you can explore the content to understand the way it works.  

Regardless of the metrics based on which you have chosen your monitoring system, you may want an easier overview of an entire system, including databases, runtimes, and other back-end components. Other than that you will also need a dashboard to correlate services to acknowledge the relationship between them that can’t be seen easily through code. 

The monitoring tool should be able to divide different dimensions while pinpointing the potential errors and problems. It must also be a system for centralized logging rather than just a monitoring tool. This tool must also enable you to log multiple services in one place. 

But in some cases, your monitoring and logging tool can be individual as long as there’s a way to correlate logs with the data in the monitor. 

A good tool will tend to automatically instrument your services, that is, all you need to do is add a library and configure properties to connect with the accurate server. Another thing to assure here is that the chosen tool for monitoring also supports the framework and programming language you are opting for. Otherwise, you will have to manually find the suture in the life cycle of the framework’s request and instrument by yourself. 

Most tools out there with auto-instrumentation also allow customizing your application in places that are evolved from less ideal circumstances. After instrumenting and configuring, you can easily run services locally while pointing them to the server of the monitoring tool. 

With microservices, it can be hard to trace events throughout your system that makes cross-services bugs harder to find. To avoid that, you can use trace IDs in every service of your microservices architecture-based application. Tools like Open Tracking can help in doing so, and multiple frameworks have instrumental libraries to support it as well. It will make query logs across multiple services and identifying problems that may reside in your system easier and efficient. 

Now that you know why monitoring microservices is essential, it’s time for you to adopt these steps for your microservices-based application sooner than later.

All you will need to do is get a tool to monitor services side by side, add trace data to every service to understand the way they interact with one another. Besides, it will allow you to make smart decisions on architecture and scaling as well.