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The Principles of Monitoring Microservices


The term microservices refers to a very specific type of architectural style that structures an application not as a monolith, but as a series of smaller and more loosely coupled services. One of the major benefits of microservices is that each service is independently deployable – thus making them highly maintainable and testable as well.

Over the last few years, this approach to application development has become incredibly popular because it allows for each service to be better organized around a business’ capabilities, leads to a faster time-to-market for most software and ultimately creates the most important benefit of all: a superior experience for end users.

Having said that, the microservices style in and of itself is not necessarily enough to accomplish your larger goals. If you truly want to embrace all of the benefits of this approach with as few of the downsides as possible, you’ll need to monitor those microservices moving forward.

Why Should You Monitor Microservices

The most obvious reason as to why microservices monitoring is important has to do with what you’re trying to prevent: a system failure that ultimately disrupts the use of your application. If you’re proactively monitoring the behavior of your systems, you’re in a much better position to identify (and correct) small issues today before they have a chance to become much bigger and more expensive ones down the road.

Note that when it comes to observability vs. monitoring, these are two closely related concepts that you shouldn’t actually use interchangeably. Monitoring is the process of collecting and displaying data related to your application. Observability is what happens when that data is then made available to development people for a larger context. So if monitoring tells you what is going on within an application, observability helps tell you why.

But there are a number of other benefits as well, chief among them being the fact that monitoring microservices over a long period of time generates valuable data that can be used to improve application performance in meaningful ways. Performance data can be analyzed to look for trends and patterns that would have otherwise gone undiscovered.

Microservices Monitoring Metrics

Generally speaking, there are a number of key metrics that you should be monitoring to gain a better understanding of exactly what is happening across your microservices architecture. Not only are they an important part of the DevOps process overall, but collectively they also generate the type of real-time insight needed to make better and more informed decisions across the board.

Request Rate

This particular metric refers to the total number of requests that each microservice in your architecture is serving. This is typically measured on a “per second” scale.

Request Errors

As the name suggests, this refers to the total number of failed requests that your microservices are experiencing per second.

Request Duration

This metric goes into more detail about the total amount of time that each request takes to either be considered a failure or a success.

These are metrics that go into more detail about the actual operation of your microservices infrastructure. Examples of metrics that would fall into this category include your ability to measure the average execution time of your top ten most frequently executed database queries. The average response time for each service endpoint would be a part of this group as well.

System Event Metrics

Since new code deployments are one of the leading causes of system failures, it stands to reason that you would want to log your deployments in relation to the time-series data being recorded by your microservices. This will allow you to take a closer look at scaling events, configuration updates and more to correlate them with whatever system behavior you’re trying to learn more about.

Best Practices for Microservice Monitoring

The most important best practice for microservices monitoring involves making sure you’re using the right tools to do so in the first place. Options like Raygun APM, for example, offer both instrumentation and data collection processes as well as a comprehensive dashboard that you can use to visualize the data your metrics are creating.

There are other more specialized tools that you can also use depending on what you’re trying to look at in the moment. Zipkin is a tool designed specifically to trace calls between microservices and is particularly helpful when it comes to addressing latency issues. Monitoring Grafana is an option that can help you build visual dashboards to collate all of the data being collected by various other tools into something you can actually interpret and use productively.

In the End

Microservices monitoring is important to gather the type of information you need to prevent a larger system failure, yes. But it also brings with it the benefit of context in a way that you might not otherwise have access to.

Knowing “what” is going on with your microservices application is one small part of a much larger story. You also need to know “why” things are occurring so you know what, if any, action needs to be taken. This ultimately results in better applications that are easier to maintain and deploy and that offer a better experience to end users, which is and should always be your number one priority.

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