As we make our performance improvements, we will need to continually monitor our changes to view impact. Many methods can be used to monitor the long-term performance of computer systems. A couple of examples of these methods would be the following:
- Brendan Gregg's USE Method: Utilization, saturation, and errors (www.brendangregg.com/usemethod.html)
- Tom Wilkie's RED Metrics: Requests, errors, and duration (https://www.weave.works/blog/the-red-method-key-metrics-for-microservices-architecture/)
- Google SRE's four Golden Signals: Latency, traffic, errors, and saturation (https://landing.google.com/sre/sre-book/chapters/monitoring-distributed-systems/)
We will discuss these concepts further in Chapter 15, Comparing Code Quality Across Versions. These paradigms help us to make smart decisions about the performance optimizations in our code as well as avoid premature optimization. Premature optimization plays as a very crucial aspect for many a computer programmers. Very frequently, we have to determine what fast enough is. We can waste our time trying to optimize a small segment of code when many other code paths have an opportunity to improve from a performance perspective. Go's simplicity allows for additional optimization without cognitive load overhead or an increase in code complexity. The algorithms that we will discuss in Chapter 2, Data Structures and Algorithms, will help us to avoid premature optimization.