⚡ Bolt: [performance improvement] Optimize high cost max search#312
⚡ Bolt: [performance improvement] Optimize high cost max search#312google-labs-jules[bot] wants to merge 1 commit into
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💡 What: Replaced `OrderByDescending().FirstOrDefault()` with a manual $O(N)$ linear scan loop for finding the highest blended unit cost in NetworkInsights. 🎯 Why: LINQ's sorting operation on potentially large cost arrays is $O(N \log N)$ and allocates memory for sorting buffers and delegates. A manual scan reduces this to $O(N)$ and completely eliminates allocations, saving CPU time in visualization generation. 📊 Impact: Reduced query time from ~282ms to ~13ms for large datasets (a >20x speedup). 🔬 Measurement: Console benchmarking in .NET 8 Release mode over 10,000 iterations for a list of 1,000 costs.
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💡 What: Replaced the$O(N)$ linear scan
snapshot.ConsumerCosts.OrderByDescending(cost => cost.BlendedUnitCost).FirstOrDefault()LINQ query with a manualforeachloop.🎯 Why: The original query sorts the entire collection of consumer costs ($O(N \log N)$) just to find the single maximum element. This allocates sorting buffers and delegate closures. A linear scan is strictly$O(N)$ , zero-allocation, and much faster for this specific use case, minimizing garbage collection pressure and reducing CPU overhead in visualization generation.
📊 Measured Improvement: Console benchmarking in .NET 8 (Release mode) over 10,000 queries for a list of 1,000 costs showed a performance improvement from 282ms down to 13ms (a >21x speedup).
🔬 Measurement: Measured by building a standalone .NET 8 console test app executing both routines.
PR created automatically by Jules for task 7627438147201388496 started by @wnj00524