aprs.me/test
Graham McIntire 1934b8d014
Optimize memory pruning performance for large datasets
- Implemented dual-strategy pruning based on overage size
- Small overages (<10 packets): Only remove exactly what's needed
- Large overages: Batch prune with 20% buffer to reduce frequency
- Used Map.drop for more efficient bulk deletion
- Added Stream for lazy evaluation in small overage case
- Reduces complexity from O(n log n) on every insertion to:
  - O(k log k) for small overages where k << n
  - Amortized O(n log n) for large overages with buffering
- This significantly improves performance when managing thousands of packets

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-07-14 10:11:53 -05:00
..
aprsme Fix failing tests and improve movement tracking 2025-07-14 10:03:04 -05:00
aprsme_web Optimize memory pruning performance for large datasets 2025-07-14 10:11:53 -05:00
assets/js use postgis for testing 2025-07-09 16:48:55 -05:00
integration performance improvements 2025-07-08 10:03:52 -05:00
support refactor 2025-07-13 16:49:20 -05:00
types refactor to use external parser 2025-06-24 14:22:09 -05:00
test_helper.exs refactor and performance improvements 2025-07-10 10:53:34 -05:00