Optimizing I/O workloads in Python typically involves understanding where the bottlenecks are and then applying strategies to reduce or manage these bottlenecks. Profiling is a crucial step in this process as it helps identify the parts of the code that are most resource-intensive. Here's a step-by-step guide to optimizing I/O workloads by profiling in Python:
Identify the I/O Workloads
Comprehending the type of your I/O workloads is essential as a first step. Do they involve disk I/O, such as file read/write operations, network I/O, which includes data transmission over a network, or database I/O, comprising database interactions? Distinct optimization techniques apply to each category. I have taken up the I/O bottlenecks related to Network and file read/write operations for this article.