In this chapter, we introduced the basic principles of optimization and applied those principles to a test application. When optimizing, the first thing to do is test and identify the bottlenecks in the application. We saw how to write and time a benchmark using the time Unix command, the Python timeit module, and the full-fledged pytest-benchmark package. We learned how to profile our application using cProfile, line_profiler, and memory_profiler, and how to analyze and navigate the profiling data graphically with KCachegrind.
In the next chapter, we will explore how to improve performance using algorithms and data structures available in the Python standard library. We will cover scaling, sample usage of several data structures, and learn techniques such as caching and memoization.
