Microservices Monitoring Patterns

Microservices Monitoring Patterns

This block of templates covers possible options for building monitoring of the operation of microservices.

Log Aggregation Template

It is good practice when developing microservices to keep logs for each instance of the service. Logs can contain errors, warnings, informational and debug messages. But with the increase in the number of services, the analysis of logs distributed across different hosts becomes difficult.

The Log Aggregation pattern suggests using a centralized logging service that will collect logs from each service instance. This will provide users with a single point to search, analyze logs and set up alerts triggered when specific messages appear in them.

Distributed Tracing Pattern

In a microservice architecture, several interconnected microservices may need to run to fulfill client requests. Each service processes a request by performing one or more operations, including accessing a database, posting messages, and so on. As the number of services increases, it becomes more challenging to track down where errors occur.

The Distributed Tracing pattern is designed to solve this problem. He suggests assigning a unique identifier (TraceId) to each external request, which will be transmitted to all services participating in the processing of the request and recorded in the logs. This will allow developers to see how an individual request is processed by searching the aggregated logs for its external ID.

Health Check Template

Sometimes, a service instance that can no longer handle external requests remains available to other subsystems. For example, a service can exhaust the pool of connections to the database – in fact, it becomes inoperative. However, it is still able to accept external requests, albeit without further correct processing. In such cases, the monitoring system should issue a timely warning, and the load balancer, service registry, and other subsystems should not send requests to the failed instance.

The Health Check pattern is designed to solve this problem. He suggests defining an endpoint for each service that can be used to check health, such as / health. This API should check host status, connections to other services, infrastructure, and any other business logic. The client — a monitoring service, service registry, or load balancer — will periodically contact the endpoint to verify the health of the service instance.

Why It Is Essential To Apply Design Patterns In A Microservice Architecture

When switching to microservices, many architectural decisions will have to be made, determining the final product’s effectiveness. Knowing and choosing the proper patterns simplifies and speeds up this process. It’s always better to rely on years of experience from other developers than to try to invent your solution from scratch.

Here are just a few of the benefits that can be achieved by using microservice design patterns:

  • Reducing design errors in microservices – without the need for refactoring in the future.
  • Faster and better migration of monoliths to microservice architecture.
  • Prevention of unnecessary calls and inefficient use of resources.
  • No problems with connecting new services, integrating them and databases.
  • Better scalability: Adding additional services makes it easy to maintain dependencies.
  • Increased resiliency.
  • Minimizing security threats, including hiding microservice endpoints.
  • Reduced maintenance and debugging work.

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