⚡️ Speed up function filterEntityGroupsBySearchTerm by 59%#53
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⚡️ Speed up function filterEntityGroupsBySearchTerm by 59%#53codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
filterEntityGroupsBySearchTerm by 59%#53codeflash-ai[bot] wants to merge 1 commit intoreleasefrom
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This optimization achieves a **58% runtime improvement** (from 810μs to 510μs) by replacing the functional `reduce()` pattern with a performance-optimized for-loop implementation.
**Key Performance Improvements:**
1. **Eliminated callback overhead**: The original `reduce()` creates a function closure for each invocation, adding call stack overhead on every iteration. The for-loop executes directly without function call penalties.
2. **Removed destructuring allocation**: The original code destructured each group with `const { items, ...rest } = group`, creating a temporary `rest` object with all group properties except `items` on every iteration (386 times according to line profiler). The optimized version uses direct property spreading `{ ...group, items: searchResults }`, which is more efficient as it doesn't create an intermediate object.
3. **Pre-allocated result array**: Declaring `const result: Array<Group<G, T>> = []` upfront allows V8's optimizer to better predict array growth patterns compared to the implicit accumulator in `reduce()`.
4. **Length caching**: `const len = groups.length` prevents repeated property lookups during loop condition checks, though this is a minor win with modern JIT compilers.
**Line Profiler Evidence:**
The original spent 17.9% of time on destructuring (`const { items, ...rest }`), which is completely eliminated in the optimized version. The for-loop structure also shows better instruction-level optimization as evidenced by the reduced relative time percentages across operations.
**Test Case Performance:**
The optimization particularly excels in scenarios with:
- **Large datasets with no matches**: 95.8% faster (490μs → 250μs) when searching through 50 groups with 20 items each
- **Unicode/special character searches**: 186-300% faster on short-circuit cases
- **Single character searches**: 197% faster (11.5μs → 3.87μs)
Edge cases like empty arrays show minor regression (~50% slower at microsecond scale), but these are negligible compared to the substantial gains in realistic workloads involving actual filtering operations where the 58% overall speedup matters most.
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📄 59% (0.59x) speedup for
filterEntityGroupsBySearchTerminapp/client/src/IDE/utils/filterEntityGroupsBySearchTerm.ts⏱️ Runtime :
810 microseconds→510 microseconds(best of10runs)📝 Explanation and details
This optimization achieves a 58% runtime improvement (from 810μs to 510μs) by replacing the functional
reduce()pattern with a performance-optimized for-loop implementation.Key Performance Improvements:
Eliminated callback overhead: The original
reduce()creates a function closure for each invocation, adding call stack overhead on every iteration. The for-loop executes directly without function call penalties.Removed destructuring allocation: The original code destructured each group with
const { items, ...rest } = group, creating a temporaryrestobject with all group properties exceptitemson every iteration (386 times according to line profiler). The optimized version uses direct property spreading{ ...group, items: searchResults }, which is more efficient as it doesn't create an intermediate object.Pre-allocated result array: Declaring
const result: Array<Group<G, T>> = []upfront allows V8's optimizer to better predict array growth patterns compared to the implicit accumulator inreduce().Length caching:
const len = groups.lengthprevents repeated property lookups during loop condition checks, though this is a minor win with modern JIT compilers.Line Profiler Evidence:
The original spent 17.9% of time on destructuring (
const { items, ...rest }), which is completely eliminated in the optimized version. The for-loop structure also shows better instruction-level optimization as evidenced by the reduced relative time percentages across operations.Test Case Performance:
The optimization particularly excels in scenarios with:
Edge cases like empty arrays show minor regression (~50% slower at microsecond scale), but these are negligible compared to the substantial gains in realistic workloads involving actual filtering operations where the 58% overall speedup matters most.
✅ 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-filterEntityGroupsBySearchTerm-ml2o2x7vand push.