google-labs-jules[bot] da3d26c1ab feat: Add performance audit report

This commit introduces a comprehensive performance audit of the Poindexter library, as requested. The audit is documented in the `AUDIT-PERFORMANCE.md` file and covers the following areas:

- Database Performance (N/A)
- Memory Usage
- Concurrency
- API Performance (N/A)
- Build/Deploy Performance

The audit includes an analysis of the two k-d tree backends (`linear` and `gonum`) and is supported by benchmark data and memory profiles, which are also included in this commit. The findings provide a clear overview of the library's performance characteristics and identify areas for potential optimization.

Co-authored-by: Snider <631881+Snider@users.noreply.github.com>

2026-02-02 01:17:57 +00:00

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Performance Audit

Database Performance

Not applicable. This is a library and does not have a database.

Memory Usage

Memory Leaks: No memory leaks were identified. The memory usage scales predictably with the size of the dataset and the complexity of the queries.
Large Object Loading: The primary memory usage comes from loading the dataset into the k-d tree. For very large datasets, this could be a concern, but it's an inherent part of the library's design. No unnecessary large objects are loaded.
Cache Efficiency: The linear backend has poor cache efficiency for large datasets as it must scan all points for every query. The gonum backend has better cache efficiency due to the spatial partitioning of the k-d tree, which allows it to prune large parts of the search space.
Garbage Collection: The benchmarks show that the Radius and KNearest functions in the linear backend cause the most allocations, which can lead to GC pressure. The gonum backend is more efficient in this regard, with fewer allocations for the same operations.

Concurrency

Blocking Operations: The library's operations are CPU-bound and will block the calling goroutine. This is expected behavior for a data structure library.
Lock Contention: The library does not use any internal locking, so there is no lock contention. However, this also means the KDTree is not safe for concurrent use. The documentation correctly states that users must provide their own synchronization, for example, by using a mutex.
Thread Pool Sizing: Not applicable. The library does not manage its own thread pool.
Async Opportunities: The core k-d tree operations are inherently synchronous. While it's possible to wrap the library's functions in goroutines to perform queries in parallel, this is left to the user to implement. The library itself does not offer any async APIs.

API Performance

Not applicable. This is a library and does not have an API.

Build/Deploy Performance

Build Time: The build process is fast and efficient. The Makefile provides convenient targets for common tasks, and the Go compiler is known for its speed. No build performance issues were identified.
Asset Size: As this is a library, there are no assets to consider. The compiled code size is minimal. The WASM module is the only distributable asset, and its size is reasonable for its functionality.
Cold Start: Not applicable. This is a library and does not have a cold start time.

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