Python's built-in sets and dictionaries can have quadratic-time performance in certain edge cases, causing bottlenecks for developers working with large datasets. A library of optimized collections could prevent these slowdowns. Developers would pay for this to improve their app performance and reduce infrastructure costs. Start with a drop-in replacement for sets and dictionaries that handles the worst-case scenarios. The biggest risk is adoption—developers may prefer to rewrite their code rather than adopt a new library.
pythonperformancedatasets
Optimized Python collections for large datasets
Build a library of high-performance Python collections optimized for edge cases with quadratic-time performance. Target developers working with large datasets.
Why now
Python's popularity and increasing dataset sizes make performance tuning critical.
- Who for
- Python developers working with large datasets
- Business model
- Open-core model with paid enterprise features
- Effort
- A few weeks
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