Efficient input/output (I/O) operations are crucial for the performance of Python applications, especially when dealing with large datasets or handling numerous file operations.
In this article, we’ll explore how to profile I/O performance in Python, identify bottlenecks, and implement optimizations for faster and more responsive applications.
Understanding I/O Performance
I/O performance refers to the speed at which a program can read from or write to external sources, such as files, networks, or databases. Profiling I/O performance involves measuring the time taken for I/O operations to complete and identifying areas for improvement.
Profiling Tools in Python
Python provides several built-in modules and tools for profiling code performance, including timeit and cProfile. These tools allow you to measure the execution time of specific code segments and identify performance bottlenecks.
