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
..
controllers	refactor to use external parser	2025-06-24 14:22:09 -05:00
integration	refactor and performance improvements	2025-07-10 10:53:34 -05:00
live	Optimize memory pruning performance for large datasets	2025-07-14 10:11:53 -05:00
plugs	fix geolocation hopefully	2025-07-10 17:12:49 -05:00
aprs_symbol_test.exs	format	2025-07-09 16:22:58 -05:00
time_helpers_test.exs	refactor to use external parser	2025-06-24 14:22:09 -05:00