⚡️ Speed up function getHighlightPayload by 32%#44
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⚡️ Speed up function getHighlightPayload by 32%#44codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
getHighlightPayload by 32%#44codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
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The optimization achieves a **32% runtime improvement** (from 229μs to 173μs) by eliminating unnecessary array operations and reducing algorithmic complexity in two key areas: ## Primary Optimizations **1. Linear Scan Instead of Sort (39ms → 17ms in getHighlightPayload)** - **Original**: Created a new array with spread operator `[...filteredHighlights]` and sorted all viable highlights using `Array.sort()`, which is O(n log n) - **Optimized**: Uses a single linear pass to find the minimum distance highlight, reducing complexity to O(n) - **Impact**: Particularly effective when multiple highlights are viable (see test "should efficiently sort when multiple highlights are viable": 13.7μs → 3.33μs, 311% faster) **2. Single-Pass Filtering in getViableDropPositions (1.47ms → 0.28ms)** - **Original**: Used two separate `filter()` calls to split vertical/horizontal highlights, then two `forEach()` loops - effectively 4 passes through the array - **Optimized**: Combined into 2 sequential loops (one for vertical, one for horizontal), eliminating redundant array iterations - **Additional micro-optimizations**: - Pre-computed `pos.x` and `pos.y` to avoid repeated property access - Stored position values in local variables to reduce property lookups in tight loops - Pre-calculated multipliers outside the loop ## Performance Characteristics The optimization excels with: - **Multiple viable candidates** (311-530% faster): Linear scan vs sort shows dramatic improvement when many highlights match the drop zone criteria - **Large datasets** (59.9% faster with 500 highlights): Reduced passes through arrays compound savings at scale - **Mixed vertical/horizontal highlights** (530% faster with 300 mixed highlights): Single-pass approach eliminates redundant categorization Small regression in individual highlight tests (7-22% slower for single matches) is negligible given the overall 32% runtime improvement, especially since real-world UI interactions typically involve multiple overlapping drop zones where this optimization provides substantial gains.
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📄 32% (0.32x) speedup for
getHighlightPayloadinapp/client/src/layoutSystems/autolayout/utils/highlightSelectionUtils.ts⏱️ Runtime :
229 microseconds→173 microseconds(best of100runs)📝 Explanation and details
The optimization achieves a 32% runtime improvement (from 229μs to 173μs) by eliminating unnecessary array operations and reducing algorithmic complexity in two key areas:
Primary Optimizations
1. Linear Scan Instead of Sort (39ms → 17ms in getHighlightPayload)
[...filteredHighlights]and sorted all viable highlights usingArray.sort(), which is O(n log n)2. Single-Pass Filtering in getViableDropPositions (1.47ms → 0.28ms)
filter()calls to split vertical/horizontal highlights, then twoforEach()loops - effectively 4 passes through the arraypos.xandpos.yto avoid repeated property accessPerformance Characteristics
The optimization excels with:
Small regression in individual highlight tests (7-22% slower for single matches) is negligible given the overall 32% runtime improvement, especially since real-world UI interactions typically involve multiple overlapping drop zones where this optimization provides substantial gains.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
📊 Performance Profile
View detailed line-by-line performance analysis
To edit these changes
git checkout codeflash/optimize-getHighlightPayload-ml26yedeand push.