6 design patterns for building high-performant observable services
Tags: monitoring, design, serverless
Published: 2023-01-19 16:58:00

Microservices, with their scalability, elasticity, reliability, and resiliency, have become a popular choice for software architecture. However, building a system with microservices also means that the system may have hundreds of distributed components, resulting in many potential points of failure.
Due to the scale and complexity of microservices, traditional design patterns may not provide the full-stack visibility needed to identify and resolve system issues. To effectively troubleshoot and improve performance, more comprehensive methods must be employed.

Observability allows developers to gain insight into potential issues among microservice components and understand what is happening within the production environment. This can help reduce delivery cycles, prevent downtime, and optimize performance.

There are several design patterns available for incorporating observability into microservices. This article highlights 6 of the most useful [observability design patterns](https://www.simform.com/blog/observability-design-patterns-for-microservices/) for microservices that CTOs can benefit from. Let's explore them in more brief.

###Distributed Tracing

Distributed tracing is a technique for tracking the flow of requests through a system that spans multiple services and components. By adding trace headers to requests, it becomes possible to see how a request flows through the different services and components of the system. This pattern is useful for building high-performance systems because it allows you to identify bottlenecks and slowdowns in the system, which can help to optimize the system and improve performance.

###Health Check API

A health check API is an endpoint that can be used to check the status of a service. This can include information such as whether the service is running, how many requests it is handling, and whether it is experiencing any errors. This pattern is useful for building high-performance systems because it allows you to quickly identify and diagnose issues in the system.

###Log Aggregation

Log aggregation is the process of collecting, storing and analyzing log data in a central location. By aggregating all the logs into a single place, it becomes easier to search and analyze the logs, which helps to identify and diagnose issues in the system. This pattern is useful for building high-performance systems because it allows you to quickly identify the source of an issue, which can reduce downtime and improve the overall performance of the system.

###Audit Logging

Audit logging is the process of logging important events in the system such as user actions, system events, and security incidents. This pattern is useful for building high-performance systems because it allows you to track the actions that were taken in the system, which can help to identify and diagnose issues in the system.

###Exception Tracking

Exception tracking is the process of collecting and analyzing information about exceptions that occur in the system. By collecting this information, it becomes possible to identify patterns and trends in the exceptions, which can help to identify and diagnose issues in the system. This pattern is useful for building high-performance systems because it allows you to quickly identify and diagnose issues in the system.

###Application Metrics

Application metrics are measurements of the performance of the system. By collecting metrics, it becomes possible to see the performance of the system over time, which can help to identify trends and patterns. This pattern is useful for building high-performance systems because it allows you to identify and address performance issues before they become a problem.

##Conclusion

Observability is an essential part of building high-performance systems. By using the above-said design patterns, you can collect, store, and analyze metrics, traces, and logs in a way that makes it easy to understand the system and identify issues within it. By understanding and applying these patterns, you will be able to design systems that are more robust, maintainable, and performant. If you have any queries or valuable suggestions regarding observability design patterns, feel free to connect with me in the comments below.