mirror of
https://github.com/rjNemo/underscore
synced 2026-06-06 02:26:42 +00:00
Ruidy
75eddcdde5
Pre-allocates result slice with estimated capacity to reduce repeated allocations during append operations. Strategy: - Estimates capacity as len(values) * 2 - Assumes average of 2-3 items per mapped element - Simple heuristic that works well for typical use cases Performance improvements: - Time: 907.4 ns/op → 616.7 ns/op (32% faster) - Memory: 6,120 B/op → 4,992 B/op (18% less) - Allocations: 8 → 2 (75% reduction) Impact: - Significantly reduces allocation overhead - Better performance for typical flatmap operations - May over-allocate if mapper returns <2 items on average Added BenchmarkFlatmap to track performance. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
30 lines
592 B
Go
30 lines
592 B
Go
package underscore_test
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import (
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"testing"
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"github.com/stretchr/testify/assert"
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u "github.com/rjNemo/underscore"
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)
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func TestFlatmap(t *testing.T) {
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nums := []int{1, 2, 3, 4}
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transform := func(n int) []int { return []int{(n - 1) * n, (n) * n} }
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want := []int{0, 1, 2, 4, 6, 9, 12, 16}
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assert.Equal(t, want, u.Flatmap(nums, transform))
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}
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func BenchmarkFlatmap(b *testing.B) {
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data := make([]int, 100)
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for i := range data {
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data[i] = i
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}
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mapper := func(n int) []int { return []int{n, n * 2, n * 3} }
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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u.Flatmap(data, mapper)
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}
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}
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