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What semantic monitoring can and can't do for microservices

Semantic monitoring can identify microservices performance problems, catch bugs in transactions and ensure uptime. But when you test in production, you'd better know what you're doing.

It's complex and challenging to monitor applications built on a microservices architecture. Simultaneous service execution can obscure the root cause of a failure. Microservices might appear healthy in monitoring tools but could experience issues in the integration points between them.

Semantic monitoring in microservices provides insights into business transaction functionality, service availability and overall application performance. Also known as synthetic transaction monitoring, it takes place in production but is a type of application testing. Synthetic transactions are just one microservices monitoring technique, and IT teams should understand what this approach provides and what other tracking technologies are necessary for overall application health support.

How semantic monitoring works

Semantic monitoring uses synthetic transactions and emulates end-user flows through a microservices-based application to track performance and alert on anomalies.

Semantic monitoring runs a set of automated tests against an application on production systems at regular intervals. These tests mimic real uses, such as purchasing an item from a website or completing a confidential form. The results generate data on the application's performance, availability, functionality and response times.

Semantic monitoring in microservices

Semantic monitoring provides benefits in three specific scenarios.

First, semantic monitoring can measure and analyze application performance trends. It collects and uses performance data to determine a baseline for application performance and to identify areas that need improvement. From there, teams can compile the information they need to create performance improvement strategies.

Semantic monitoring also helps teams detect and fix issues before users notice. It activates APIs, websites and mobile applications, and then sends alerts about performance problems or lost availability. With these alerts, the IT team can identify and fix a problem before it grows.

Finally, semantic monitoring can emulate and monitor the performance of business processes or user transactions, such as logins, data searches, item additions and deletions. It ultimately helps validate these business transactions to see if they perform as expected in the production environment.

What semantic monitoring cannot do

It's time-consuming for developers and testers to create and maintain semantic monitoring scripts. Don't use the technique to analyze each page in the application. Instead, focus on the critical transactions and processes of an application.

Don't expect to troubleshoot server or network problems with this type of microservices monitoring. A microservice is only as good as the software stack supporting its operation, and there are many strategies for IT monitoring to ensure stack health. Semantic scripts cannot simulate the network or a particular geographical location or all possible end-user activities.

It's also not the only form of microservices monitoring that organizations should implement. Service layer monitoring checks if services are working as expected, such as whether they scale when needed. Distributed tracing follows a user request through the microservices it triggers in an application to discover problems, especially in dependencies.

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