Microservices are becoming increasingly popular in software development. The reason for this lies in the ever-increasing complexity of IT infrastructures. To keep track of microservice environments, real-time monitoring continuing to help. The traditional way of managing and expanding monolithic enterprise applications becomes problematic with their increasing size and complexity. Switching to microservices provides a solution: applications are structured as a collection of loosely coupled services. This makes it easier to create, extend and scale services.
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Monitoring the Microservices Environment Required to Address Complexity
For many developers, containers are the predestined architecture for microservices. Especially Docker containers have made it almost the best option. Most of them are designed so that several containers together form an application. If you look at that topic, you will not miss Kubernetes – the open-source system is the new standard for container orchestration. Renowned public cloud providers such as IBM, Google, AWS and Azure use Kubernetes effectively. Even on-premise solutions like OpenShift rely on the orchestration solution are using them. However, microservices can also be used without a container: Amazon’s AWS Lambda serverless approach, for example, enables the deployment of individual written functions in Java, JavaScript and Python (Function-as-a-Service). Which solution makes more sense depends on the available resources, the requirements of the application and the required security standards.
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But a microservice architecture also brings with it challenges. While smaller code blocks are easier to understand and manage for developers, the applications themselves are made up of many more components – and these components have more connections. These dependencies increase the overall complexity of the application, which can lead to problems. A special focus should be put on Application Performance Monitoring (APM). Because so many moving parts are involved in a single transaction, and every millisecond delay can affect usability, troubleshooting performance issues becomes more complex than ever.
Close Monitoring Helps
To quickly identify and fix problems and errors, it is necessary to closely monitor the microservice architecture. A comprehensive overview of the entire structure and deep insights into all commands and transactions is based on three pillars:
- Monitoring the relationships between the applications and the infrastructure in which they run;
- Monitoring the application architecture: how the different components are related and
- Track each transaction to detect errors.
Infrastructure and applications are inseparable and influence each other. Ideally, the monitoring tool used shows the performance data in real time in a view. In this way, correlations can be detected quickly and larger failures avoided. In addition, it is necessary to monitor the connections of the individual instances with each other and their general state. Again, it is true that ideally these metrics are monitored directly. So it is easier in an emergency to identify the trigger of a problem. In addition, one should always keep an eye on upstream and downstream applications: Has the problem possibly originated in an upstream application? Or does it affect downstream services?
At the code level, tracing help to see where performance issues arise during a transaction. This monitoring is all about accuracy – and the ability to gain deep insights into the monitoring details. By using automatic code-level instrumentation to highlight the relationships between code, performance, and errors that occur we can improve the coding. Distributed tracing continues this technique adapted to microservice architectures. In an architecture based on microservices, where a request goes through many different services until a full page is displayed to the user, this approach is essential. A Distributed Trace provides information about all services, data stores, and other components involved in a request. This gives you a comprehensive understanding of the relationships between all the processes involved, how long they last and how they are interdependent. The true strength of Distributed Tracking is that traces can be traced across services, containers or applications. This is a big advantage, especially for microservices that are hosted externally. For example, at AWS Lambda, there is the difficulty that development teams have too little insight into what really happens in the lambda function and what dependency such a function has on other native services. With Distributed Tracing, developer teams can bring light into this black box.
Conclusion
Microservices provide developer teams with exactly what they need in the digitized and disruptive business world: speed, scalability, flexibility, productivity and reliability of applications. But microservice-based structures bring with them a high degree of complexity. The challenge for developers is to master this complexity through continuous monitoring with meaningful analyzes. They show the development teams where problems exist or processes can be improved. Every failure of a service costs time, money and sometimes customer confidence. Therefore, it makes sense to invest in the speed, flexibility and reliability of microservice architectures and their stringent maintenance.
