Refactoring nested loops for better performance in a large data processing application
Can someone help me understand I'm converting an old project and I need some guidance on I'm sure I'm missing something obvious here, but I'm sure I'm missing something obvious here, but I've been struggling with this for a few days now and could really use some help... Currently developing a data processing application that utilizes multiple nested loops to handle large datasets. The existing implementation has become a bottleneck, especially when processing over a million records. After profiling, it became evident that the nested iteration over lists was responsible for significant performance degradation. Here’s a simplified version of the critical section: ```python for item in data: for sub_item in item['sub_items']: process(sub_item) ``` The `process` function is computationally intensive, and this structure is inefficient for the volume of data. I’ve explored several refactoring strategies, such as using `itertools.chain` to flatten the data structure before processing: ```python from itertools import chain for sub_item in chain.from_iterable(item['sub_items'] for item in data): process(sub_item) ``` While this approach improved readability and slightly reduced overhead, I’m still not satisfied with the performance gains. I also attempted using `multiprocessing` to parallelize the processing: ```python from multiprocessing import Pool with Pool() as pool: pool.map(process, chain.from_iterable(item['sub_items'] for item in data)) ``` This did yield a noticeable performance boost, but now the memory usage has spiked. I suspect that creating multiple processes is causing overhead that negates the gains from parallel execution. I am considering implementing a generator-based approach to yield items one at a time, potentially reducing memory usage: ```python def generate_sub_items(data): for item in data: for sub_item in item['sub_items']: yield sub_item for sub_item in generate_sub_items(data): process(sub_item) ``` Before I implement this change, I’m curious if there are better design patterns or best practices specific to Python for handling such scenarios that efficiently balance performance and memory usage. Any recommendations on optimizing nested loop performance in data processing applications would be greatly appreciated. For context: I'm using Python on Linux. How would you solve this? For context: I'm using Python on Linux. Thanks in advance! What am I doing wrong? I'm working in a Ubuntu 20.04 environment. I'm working on a application that needs to handle this. What's the best practice here?