Hey everyone! I'm dealing with a performance issue in my application. I've created a pandas pipeline that processes geospatial data from multiple sources every two minutes, performing operations like pivot tables and vectorized calculations. Currently, it's consuming a lot of CPU and RAM in a Kubernetes cluster, which affects other services, and sometimes it even gets throttled. My dataset is relatively small—about 5-10k rows and can have up to 150-170 columns. While it runs fine locally, it significantly slows down on the server, sometimes taking over a minute. I've heard a lot about Polars being faster, but I couldn't find solid data on its CPU efficiency compared to pandas. DuckDB has been recommended as well, but I'm concerned about the SQL syntax for calculations. I'm also considering whether a complete rewrite in Go would be worthwhile, although I'm aware it might lack some of the functionalities I currently use with pandas. Ultimately, I want to know if switching to Polars or DuckDB would provide better CPU efficiency and if they're worth the switch given the size of my pipeline. Also, would Apache Arrow be a helpful tool in my case?
4 Answers
From what I've seen, switching to Polars can indeed reduce CPU usage, especially for heavy I/O tasks. However, the significant rewrite effort could be a drawback. You might want to do some quick comparisons between pandas and a Polars implementation on less critical pieces of your pipeline before committing to an entire rewrite.
Polars is definitely worth a shot if you're aiming for better CPU efficiency—it has features that can handle various operations effectively, and it has a more modern syntax. But if your existing pandas code relies heavily on certain methods or libraries, the time to transition might be significant. You should also consider DuckDB, especially for SQL-like operations. It integrates well for geospatial tasks, which could be beneficial for you.
When working with geospatial data, DuckDB is pretty solid, and it handles queries fast. So it might complement your existing setup for specific tasks.
Running your workloads on a larger server might be a simpler solution than rewriting in another language or library. Remember, your dataset isn’t huge—typically optimizing your infrastructure could yield better results. You might find that spending a bit more on resources is worth it versus the time investment required for a full switch to Polars or DuckDB.
Exactly! The performance boost you’d get from better hardware could far outweigh the benefits you'll get from rewriting code right now.
Before making any moves, I recommend profiling your current code to identify bottlenecks. Sometimes simply optimizing what you already have can lead to significant improvements. You might want to avoid certain pandas operations like `iterrows`, which are slow. If Polars can speed things up, it's worth considering, but rewriting everything can be a big time investment, so ensure you have a solid plan.
I agree! Profiling can reveal where you can make quick wins without a full rewrite. Just be cautious about counting on major speed gains if your existing pandas setup is optimized already.
Have you tried that yet? I’m curious if you really see the CPU efficiency improvements with such a small dataset.