aprs.me/lib
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 stations that heard call 2025-07-13 18:55:51 -05:00
aprsme_web Optimize memory pruning performance for large datasets 2025-07-14 10:11:53 -05:00
aprs_web.ex upgrade more with modern phoenix 2025-07-05 08:04:06 -05:00
aprsme.ex refactor to use external parser 2025-06-24 14:22:09 -05:00