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.
Long-running applications are not always busy doing work. In the case of, for instance, web, serverless or microservice applications, the work is done on every request, and the processes are idle the rest of the time. Similarly, if the program is processing messages from the queue, it may be idle when it is waiting for messages.
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.
The difference between a program’s exponential, linear, logarithmic and constant execution times is critical for various use cases. Even if an algorithm is purposely designed to satisfy a certain complexity class, there are multiple reasons why it might not. An underlying library, OS or even hardware can be the root cause of a performance problem.
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.
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.