Performance profiling is essential for optimizing and fixing an application’s resource consumption and latency. A performance issue without an execution profile is like an error without a stack trace. It will lead to a lot of manual work to get to the root cause.
Profilers provide application developers with critical execution insights that enable resolving performance issues, locating memory leaks, thread contention and more. Although hot code stack traces and call graphs generated by the profilers are usually self-explanatory, it is sometimes necessary to understand how profilers work underneath to infer even greater details about applications from the generated profiles.
Go is a relatively new open source programming language created by Google. Considering its recent growth, many developers and organizations have naturally become interested in it. With its main features, such as compiled-language performance, powerful and simple concurrency model and cross-compilation support, it can be considered as a true cloud language.
Mutexes are often a source of contention, resulting in performance issues or deadlocks. This is no different in Go. Locating the root cause is often very challenging. In obvious deadlock situations where all goroutines are waiting, the runtime may be able to detect/predict mutex-related issues and panic. Generally, the problems will manifest themselves at the application logic level.
Identifying CPU hots spots with line-of-code precision is critical when it comes to troubleshooting performance issues, identifying performance bottlenecks or improving response for a better customer experience.
Memory leaks are very common in almost any language, including garbage collected languages. Go is not an exception. A reference to an object, if not properly managed, may be left assigned even if unused. This usually happens on an application logic level, but can also be an issue inside of an imported package.
Production environments are different from development and staging. Whether optimizing application performance or trying to find a problem root cause, it is often necessary to use Go’s profiling tools in production.