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Author SHA1 Message Date
d622c8cba8
docs: update ACTION_PLAN.md with Issue 12 completion
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Mark Issue 12 (Add Stress Tests) as completed with commit hash 6576c4f.
Update overall progress to 10/25 issues completed.

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 14:39:05 +01:00
6576c4fea7
test: add comprehensive stress tests for large data and concurrency
- Add large data tests (1M elements) for Filter, Map, Partition, Unique
- Add concurrency stress tests with high worker counts (50-100)
- Add race condition tests with 100 iterations
- Add concurrent function call tests (10 goroutines)
- Add context cancellation tests for ParallelMap
- All tests skip in short mode to keep CI fast

All tests pass including race detector validation.

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 14:38:08 +01:00
07d05425bb
perf: improve GroupBy map initialization
Fixes useless capacity hint of 0 for map initialization.
Uses estimated capacity of len(values)/10 to reduce map
resizing operations.

Changes:
- Before: make(map[K][]V, 0) - capacity 0 is meaningless for maps
- After: make(map[K][]V, len(values)/10) - reasonable estimate

Impact:
- Reduces map resizing overhead during population
- Assumes ~10% unique keys (reasonable for grouping operations)
- May over-allocate for high cardinality, but acceptable trade-off

This is a minor optimization that avoids repeated map growth
when keys are added.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 14:32:05 +01:00
75eddcdde5
perf: improve Flatmap allocation strategy
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>
2025-11-14 14:29:40 +01:00
b04e545d03
test: add comprehensive benchmarks for core functions
Adds performance benchmarks for core collection functions to enable
tracking of performance regressions and optimization opportunities.

Benchmarks added:
- Map: 1000 element transformation
- Reduce: 1000 element sum
- Partition: 1000 element split
- Unique/UniqueInPlace: Comparison with many duplicates
- ParallelMap: Multiple worker counts (1, 2, 4, 8)
- MapVsParallelMap: Direct comparison (10k elements)

Key findings from benchmarks:
- Map: 1363 ns/op, 1 alloc (excellent)
- Reduce: 335 ns/op, 0 allocs (excellent)
- Partition: 3411 ns/op, 2 allocs (good - both slices)
- ParallelMap overhead: ~240x slower for simple operations
- ParallelMap is best for CPU-intensive operations (>1ms per element)

Use cases clarified:
- Regular Map for simple/fast operations
- ParallelMap for expensive operations with 100+ elements
- Optimal workers: 1-4 for most workloads

All tests pass 
Coverage maintained 

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 14:27:03 +01:00
40ac16261e
feat!: fix Drop semantics and add RemoveAt function
BREAKING CHANGE: Drop function now correctly drops first N elements
instead of removing element at specific index.

Changes:
- Renamed old Drop behavior to RemoveAt function
- Implemented correct Drop semantics (drop first N elements)
- Added comprehensive tests for both functions

Drop (NEW behavior):
- Drop([]int{1,2,3,4,5}, 2) → [3,4,5] (drops first 2 elements)
- Returns empty slice if n >= len(values)
- Returns original slice if n <= 0

RemoveAt (OLD Drop behavior):
- RemoveAt([]int{1,2,3,4,5}, 2) → [1,2,4,5] (removes index 2)
- Returns original slice if index out of bounds
- Pre-allocates with capacity len(values)-1

Tests added:
- Drop: 5 tests (basic, none, all, empty, single)
- RemoveAt: 6 tests (basic, first, last, bounds, empty, single)

Documentation updated:
- README.md: Added RemoveAt to function list
- CLAUDE.md: Marked Drop semantics as fixed
- ACTION_PLAN.md: Updated completion status

Migration guide:
- Old: Drop(slice, index) → New: RemoveAt(slice, index)
- New Drop usage: Drop(slice, n) drops first n elements

Coverage: 98.8% (maintained)

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 14:04:58 +01:00
106b713cc5
test: add comprehensive edge case tests
Adds extensive edge case tests for core functions to catch
regressions and ensure robust behavior.

Test coverage added:
- Empty slice tests: Filter, Map, Partition, Reduce, Unique, Last
- Single element tests: Filter, Map, Partition, Reduce, Unique, Last
- Large dataset tests: Filter (10k), Map (10k)
- Boundary cases: Partition (all pass/reject), Unique (no dups/all same)

Functions tested:
- Filter: 4 new tests (empty, single, single no match, large)
- Partition: 4 new tests (empty, single, all pass, all reject)
- Last: 2 new tests (empty panic, single element)
- Map: 3 new tests (empty, single, large)
- Unique: 4 new tests (empty, single, no dups, all same)
- Reduce: 2 new tests (empty, single)

Results:
- All 118 tests pass
- Coverage: 98.4% (maintained high coverage)
- Verified panic behavior for edge cases

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 13:57:33 +01:00
a1943556b4
fix: add explicit panic for Max/Min on empty slices
Adds length checks to Max and Min functions to provide clear,
explicit panic messages when called with empty slices, rather
than allowing confusing index-out-of-bounds panics.

Changes:
- Max: Added check for empty slice with "underscore.Max: empty slice" panic
- Min: Added check for empty slice with "underscore.Min: empty slice" panic
- Updated doc comments to document panic behavior
- Added TestMaxEmpty and TestMinEmpty to verify panic behavior

Impact:
- Better error messages for debugging
- Documented behavior prevents user surprises
- Non-breaking change (still panics, just with clearer message)

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 13:51:35 +01:00
46d52e3cfa
perf: pre-allocate Partition result slices
Adds capacity hints to both keep and reject slices in Partition
function to prevent repeated allocations during append operations.

Changes:
- keep: make([]T, 0) → make([]T, 0, len(values))
- reject: make([]T, 0) → make([]T, 0, len(values))

Impact:
- Reduces allocations from O(log n) to O(1) for each slice
- Improves performance by eliminating slice growth overhead
- Minimal memory overhead as worst case is original slice size

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 13:48:28 +01:00
7caa23e082
perf: replace bubble sort with slices.SortFunc in OrderBy
Replaces O(n²) bubble sort algorithm with O(n log n) slices.SortFunc
from the standard library, delivering massive performance improvements.

Performance improvements:
- Large dataset (1000 items): 2,121,531 ns/op → 3,372 ns/op (629x faster!)
- Small dataset (10 items): 199 ns/op → 178 ns/op (10% faster)
- Time reduction: 99.84% for large datasets

Resolves the TODO comment about replacing the simple algorithm.

Also adds comprehensive benchmarks for both small and large datasets
to track performance regressions.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 13:46:22 +01:00
92b64630dc
perf: pre-allocate Filter result slice
Improves Filter performance by pre-allocating result slice with
input capacity instead of growing dynamically.

Performance improvements:
- Time: 1867 ns/op → 1717 ns/op (8% faster)
- Allocations: 10 → 1 (90% reduction)

This significantly reduces GC pressure for high-frequency operations.

Also updates Join test to expect empty slice [] instead of nil,
which is better Go practice.

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Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-14 13:44:07 +01:00
7580836815
docs(readme): update usage, API, and utilities sections
Update README to use the latest version in install instructions, expand
the
list of available API functions, and add documentation for new utilities
and
subpackages. Improves clarity and completeness for users.
2025-11-14 13:36:51 +01:00
29 changed files with 2064 additions and 43 deletions

1189
ACTION_PLAN.md Normal file

File diff suppressed because it is too large Load diff

183
CLAUDE.md Normal file
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@ -0,0 +1,183 @@
# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
`underscore` is a Go library providing functional programming helpers inspired by underscore.js, built on Go 1.18+ generics. The library is organized as a flat structure with individual files for each function, plus a `maps` subpackage for map-specific utilities.
## Development Commands
### Testing
```sh
# Run all tests (local)
go test ./...
# Run all tests with coverage (local)
go test ./... -coverpkg=./... -coverprofile cov.out -covermode=count
go tool cover -func cov.out
rm cov.out
# Run tests in Docker (preferred for CI/validation)
make test
# Run a single test
go test -run TestFunctionName
# Run tests for a specific file
go test -run TestMap
```
### Building
```sh
# Build Docker image
make build
# Install dependencies
go mod download
```
### Linting & Security
```sh
# Scan Docker image for vulnerabilities
make scan
# Scan config files
make scan-config
```
### Documentation
```sh
# Serve docs locally at http://localhost:1313
make docs
# Build static docs
make build-docs
```
## Architecture
### Code Organization
The library uses a **flat structure** where each function is implemented in its own file:
- `<function>.go` - implementation
- `<function>_test.go` - tests
Example: `filter.go` + `filter_test.go`, `map.go` + `map_test.go`
### Core Patterns
**Generic Functions**: Most functions use Go generics with constraints from `cmp.Ordered` or custom type parameters. Functions operate on slices and return new slices (immutable style).
**Pipe Chain**: The `Pipe[T]` struct enables method chaining for ordered types. Methods that return slices continue the chain, while methods that return values (like `All`, `Any`, `Reduce`) break the chain and return the final value.
```go
// pipe.go defines Pipe[T cmp.Ordered]
// Chain-continuing: Filter, Map
// Chain-breaking: All, Any, Reduce, Min, Max, Partition, Find, Each
```
**Concurrency Helpers**: `ParallelMap` and `ParallelFilter` use worker pools with:
- Context-based cancellation
- Order preservation (results match input order)
- First-error-wins semantics
- Default workers = GOMAXPROCS if workers <= 0
Implementation detail: Uses `sync.Once` to capture first error and cancel context immediately.
**Subpackages**:
- `maps/` - Map-specific utilities (`Keys`, `Values`, `Map`)
- Uses type alias `M[K, V] = map[K]V` for cleaner signatures
- `Map` function allows transforming map entries
### Testing Conventions
- Use `testify/assert` for assertions
- Test file names match source files with `_test.go` suffix
- Table-driven tests are common (see `map_test.go`, `filter_test.go`)
- Internal tests (using `package underscore` rather than `package underscore_test`) are used sparingly for testing unexported functions
## Key Constraints
- **Minimum Go version**: 1.24.2 (see go.mod)
- **Generic constraints**: Most collection functions require `cmp.Ordered` types; some use `comparable` or no constraints
- **Order preservation**: `ParallelMap` and `ParallelFilter` guarantee output order matches input order
- **No mutation**: Functions return new slices; `UniqueInPlace` is the exception (in-place deduplication)
## Known Limitations
### Recently Fixed (2025-11-14)
1. ✅ **Filter allocation** - Now pre-allocates with `make([]T, 0, len(values))` (90% fewer allocations)
2. ✅ **OrderBy algorithm** - Replaced bubble sort with `slices.SortFunc` (629x faster for large datasets)
3. ✅ **Partition allocation** - Now pre-allocates both result slices
4. ✅ **Max/Min empty slices** - Now panics with clear message: "underscore.Max: empty slice"
5. ✅ **Drop semantics** - Fixed to drop first N elements (breaking change). Old behavior available as `RemoveAt`
### API Design Issues
1. **Pipe constraint** (`pipe.go:7`) - `Pipe[T cmp.Ordered]` prevents usage with custom types
2. **Last panics** (`last.go:5-8`) - No empty slice handling
### Missing Features
Popular FP utilities not yet implemented: `TakeWhile`, `DropWhile`, `Scan`, `First/FirstN`, `Init`, `Intersperse`, `Sliding`, `FoldRight`, `Tap`, `Transpose`, `Unzip`, `ParallelReduce`, `Replicate`
## Performance Characteristics
### Good Performance Patterns
- `Filter` pre-allocates: `make([]T, 0, len(values))` ✅ (Fixed 2025-11-14)
- `Map` pre-allocates: `make([]P, 0, len(values))`
- `Partition` pre-allocates: `make([]T, 0, len(values))` for both slices ✅ (Fixed 2025-11-14)
- `Chunk` pre-calculates capacity: `(l+n-1)/n`
- `ParallelFilter` pre-counts before allocation
- `OrderBy` uses `slices.SortFunc`: O(n log n) performance ✅ (Fixed 2025-11-14)
### Remaining Performance Issues
- `Flatmap`: Accumulation overhead from repeated appends
- `GroupBy`: Map initialized with capacity 0 (useless hint)
### When to Use ParallelMap vs Map
Use `ParallelMap` when:
- Processing 100+ elements with expensive operations (>1ms per element)
- Operations are CPU-bound (not I/O-bound with shared resources)
- Order preservation is required
- Context cancellation is needed
Use regular `Map` when:
- Small slices (<100 elements)
- Fast operations (<100µs per element)
- Avoiding goroutine overhead matters
- Simple transformations without error handling
**Worker count guidelines:**
- Default (workers=0): Uses `runtime.GOMAXPROCS(0)` - good starting point
- CPU-bound: Use GOMAXPROCS or GOMAXPROCS*2
- I/O-bound: Can use higher values (10-100) if not sharing resources
## Contributing Notes
When adding new functions:
1. Create both `<function>.go` and `<function>_test.go`
2. Add examples in comments using Go doc format
3. Pre-allocate slices with `make([]T, 0, len(input))` when output size is similar to input
4. Document panic conditions (empty slices, nil inputs, invalid indices)
5. Add edge case tests (empty, single element, nil)
6. If the function applies to Pipe chains, add a method to `pipe.go`
7. Update README.md function list if adding new collection functions
8. Follow SemVer for version numbers
9. Ensure all tests pass: `make test`
When fixing bugs:
- Add regression tests before fixing
- Run benchmarks if performance-related: `go test -bench=. -benchmem`
- Check for similar issues in other functions

View file

@ -21,7 +21,7 @@ It is mostly a port from the `underscore.js` library based on generics brought b
Install the library using
```sh
go get github.com/rjNemo/underscore@0.7.0
go get github.com/rjNemo/underscore@latest
```
Please check out the [examples](examples) to see how to use the library.
@ -92,22 +92,33 @@ make test
- `All`
- `Any`
- `Chunk`
- `Contains`
- `ContainsBy`
- `Count`
- `Difference`
- `Drop`
- `Each`
- `Filter`
- `Find`
- `Flatmap`
- `GroupBy`
- `Find`
- `Intersection`
- `Join` / `JoinProject`
- `Last`
- `Map`
- `Max`
- `Min`
- `OrderBy`
- `Partition`
- `Range`
- `Reduce`
- `RemoveAt`
- `Sum` / `SumMap`
- `Unique`
- `UniqueBy`
- `UniqueInPlace`
- `Chunk`
- `Zip`
### Pipe
@ -157,9 +168,18 @@ func main() {
}
```
### Utilities
- `Ternary`: conditional expression helper
- `ToPointer`: convert values to pointers
- `SortSliceASC` / `SortSliceDESC`: sort slices in ascending or descending order
- `Result`, `Ok`, `Err`, `ToResult`: Result type for error handling
- `Tuple`: generic tuple type for paired values
### Subpackages
- `maps.Keys(m)` / `maps.Values(m)`: utilities to extract keys or values from maps.
- `maps.Keys(m)` / `maps.Values(m)`: extract keys or values from maps
- `maps.Map(m, fn)`: transform map entries
## Built With

20
drop.go
View file

@ -1,12 +1,16 @@
package underscore
// Drop returns the rest of the elements in a slice.
// Pass an index to return the values of the slice from that index onward.
func Drop[T any](values []T, index int) (rest []T) {
for i, value := range values {
if i != index {
rest = append(rest, value)
}
// Drop returns a new slice with the first n elements removed.
// If n is greater than or equal to the slice length, returns an empty slice.
// If n is less than or equal to 0, returns the original slice.
func Drop[T any](values []T, n int) []T {
if n <= 0 {
return values
}
return rest
if n >= len(values) {
return []T{}
}
res := make([]T, len(values)-n)
copy(res, values[n:])
return res
}

View file

@ -9,8 +9,34 @@ import (
)
func TestDrop(t *testing.T) {
nums := []int{1, 9, 2, 8, 3, 7, 4, 6, 5}
want := []int{1, 9, 2, 3, 7, 4, 6, 5}
nums := []int{1, 2, 3, 4, 5}
want := []int{3, 4, 5}
assert.Equal(t, want, u.Drop(nums, 3))
assert.Equal(t, want, u.Drop(nums, 2))
}
func TestDropNone(t *testing.T) {
nums := []int{1, 2, 3, 4, 5}
assert.Equal(t, nums, u.Drop(nums, 0))
assert.Equal(t, nums, u.Drop(nums, -1))
}
func TestDropAll(t *testing.T) {
nums := []int{1, 2, 3, 4, 5}
assert.Empty(t, u.Drop(nums, 5))
assert.Empty(t, u.Drop(nums, 10))
}
func TestDropEmpty(t *testing.T) {
result := u.Drop([]int{}, 5)
assert.Empty(t, result)
}
func TestDropSingleElement(t *testing.T) {
nums := []int{42}
assert.Equal(t, nums, u.Drop(nums, 0))
assert.Empty(t, u.Drop(nums, 1))
}

View file

@ -2,6 +2,7 @@ package underscore
// Filter looks through each value in the slice, returning a slice of all the values that pass a truth test (predicate).
func Filter[T any](values []T, predicate func(T) bool) (res []T) {
res = make([]T, 0, len(values))
for _, v := range values {
if predicate(v) {
res = append(res, v)

View file

@ -15,3 +15,40 @@ func TestFilter(t *testing.T) {
want := []int{0, 2, 4, 6, 8}
assert.Equal(t, want, u.Filter(nums, isEven))
}
func TestFilterEmpty(t *testing.T) {
result := u.Filter([]int{}, func(n int) bool { return n > 0 })
assert.Empty(t, result)
}
func TestFilterSingleElement(t *testing.T) {
result := u.Filter([]int{5}, func(n int) bool { return n > 0 })
assert.Equal(t, []int{5}, result)
}
func TestFilterSingleElementNoMatch(t *testing.T) {
result := u.Filter([]int{5}, func(n int) bool { return n > 10 })
assert.Empty(t, result)
}
func TestFilterLarge(t *testing.T) {
large := make([]int, 10000)
for i := range large {
large[i] = i
}
result := u.Filter(large, func(n int) bool { return n%2 == 0 })
assert.Equal(t, 5000, len(result))
}
func BenchmarkFilter(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = i
}
isEven := func(n int) bool { return n%2 == 0 }
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.Filter(data, isEven)
}
}

View file

@ -2,7 +2,8 @@ package underscore
// Flatmap flatten the input slice element into the new slice. FlatMap maps every element with the help of a mapper function, then flattens the input slice element into the new slice.
func Flatmap[T any](values []T, mapper func(n T) []T) []T {
res := make([]T, 0)
// Estimate capacity: assume average of 2-3 items per element
res := make([]T, 0, len(values)*2)
for _, v := range values {
vs := mapper(v)
res = append(res, vs...)

View file

@ -15,3 +15,16 @@ func TestFlatmap(t *testing.T) {
assert.Equal(t, want, u.Flatmap(nums, transform))
}
func BenchmarkFlatmap(b *testing.B) {
data := make([]int, 100)
for i := range data {
data[i] = i
}
mapper := func(n int) []int { return []int{n, n * 2, n * 3} }
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.Flatmap(data, mapper)
}
}

5
go.mod
View file

@ -2,10 +2,7 @@ module github.com/rjNemo/underscore
go 1.24.2
require (
github.com/stretchr/testify v1.8.4
golang.org/x/exp v0.0.0-20250711185948-6ae5c78190dc
)
require github.com/stretchr/testify v1.8.4
require (
github.com/davecgh/go-spew v1.1.1 // indirect

2
go.sum
View file

@ -4,8 +4,6 @@ github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZb
github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
github.com/stretchr/testify v1.8.4 h1:CcVxjf3Q8PM0mHUKJCdn+eZZtm5yQwehR5yeSVQQcUk=
github.com/stretchr/testify v1.8.4/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
golang.org/x/exp v0.0.0-20250711185948-6ae5c78190dc h1:TS73t7x3KarrNd5qAipmspBDS1rkMcgVG/fS1aRb4Rc=
golang.org/x/exp v0.0.0-20250711185948-6ae5c78190dc/go.mod h1:A+z0yzpGtvnG90cToK5n2tu8UJVP2XUATh+r+sfOOOc=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405 h1:yhCVgyC4o1eVCa2tZl7eS0r+SDo693bJlVdllGtEeKM=
gopkg.in/check.v1 v0.0.0-20161208181325-20d25e280405/go.mod h1:Co6ibVJAznAaIkqp8huTwlJQCZ016jof/cbN4VW5Yz0=
gopkg.in/yaml.v3 v3.0.1 h1:fxVm/GzAzEWqLHuvctI91KS9hhNmmWOoWu0XTYJS7CA=

View file

@ -2,7 +2,7 @@ package underscore
// GroupBy splits a slice into a map[K][]V grouped by the result of the iterator function.
func GroupBy[K comparable, V any](values []V, f func(V) K) map[K][]V {
res := make(map[K][]V, 0)
res := make(map[K][]V, len(values)/10)
for _, v := range values {
k := f(v)
if r, ok := res[k]; ok {

View file

@ -23,7 +23,7 @@ func Test_Join_Can_Join_Two_Slices_Together(t *testing.T) {
joined := u.Join(left, right, selector, selector)
want := []u.Tuple[u.Tuple[int, string], []u.Tuple[int, string]]{
{Left: zero, Right: nil},
{Left: zero, Right: []u.Tuple[int, string]{}},
{Left: one, Right: []u.Tuple[int, string]{one}},
{Left: two, Right: []u.Tuple[int, string]{two, two}},
{Left: three, Right: []u.Tuple[int, string]{three, three, three}},

View file

@ -13,3 +13,13 @@ func TestLast(t *testing.T) {
want := 5
assert.Equal(t, want, u.Last(nums))
}
func TestLastEmpty(t *testing.T) {
assert.Panics(t, func() {
u.Last([]int{})
})
}
func TestLastSingleElement(t *testing.T) {
assert.Equal(t, 42, u.Last([]int{42}))
}

View file

@ -16,3 +16,36 @@ func TestMap(t *testing.T) {
want := []int{1, 4, 9}
assert.Equal(t, want, u.Map(nums, f))
}
func TestMapEmpty(t *testing.T) {
result := u.Map([]int{}, func(n int) int { return n * 2 })
assert.Empty(t, result)
}
func TestMapSingleElement(t *testing.T) {
result := u.Map([]int{5}, func(n int) int { return n * 2 })
assert.Equal(t, []int{10}, result)
}
func TestMapLarge(t *testing.T) {
large := make([]int, 10000)
for i := range large {
large[i] = i
}
result := u.Map(large, func(n int) int { return n * 2 })
assert.Equal(t, 10000, len(result))
assert.Equal(t, 0, result[0])
assert.Equal(t, 19998, result[9999])
}
func BenchmarkMap(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = i
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.Map(data, func(n int) int { return n * 2 })
}
}

4
max.go
View file

@ -3,9 +3,13 @@ package underscore
import "cmp"
// Max returns the maximum value in the slice.
// Panics if values is empty.
// This function can currently only compare numbers reliably.
// This function uses operator <.
func Max[T cmp.Ordered](values []T) T {
if len(values) == 0 {
panic("underscore.Max: empty slice")
}
max := values[0]
for _, v := range values {
if v > max {

View file

@ -13,3 +13,9 @@ func TestMax(t *testing.T) {
want := 9
assert.Equal(t, want, u.Max(nums))
}
func TestMaxEmpty(t *testing.T) {
assert.Panics(t, func() {
u.Max([]int{})
})
}

4
min.go
View file

@ -3,9 +3,13 @@ package underscore
import "cmp"
// Min returns the minimum value in the slice.
// Panics if values is empty.
// This function can currently only compare numbers reliably.
// This function uses operator <.
func Min[T cmp.Ordered](values []T) T {
if len(values) == 0 {
panic("underscore.Min: empty slice")
}
min := values[0]
for _, v := range values {
if v < min {

View file

@ -13,3 +13,9 @@ func TestMin(t *testing.T) {
want := 1
assert.Equal(t, want, u.Min(nums))
}
func TestMinEmpty(t *testing.T) {
assert.Panics(t, func() {
u.Min([]int{})
})
}

View file

@ -1,27 +1,21 @@
package underscore
import "slices"
// OrderBy orders a slice by a field value within a struct, the predicate allows you
// to pick the fields you want to orderBy. Use > for ASC or < for DESC
// Uses O(n log n) sorting algorithm. Mutates the input slice.
//
// func (left Person, right Person) bool { return left.Age > right.Age }
func OrderBy[T any](list []T, predicate func(T, T) bool) []T {
swaps := true
var tmp T
//todo: replace with a faster algorithm, this one is pretty simple
for swaps {
swaps = false
for i := 0; i < len(list)-1; i++ {
if predicate(list[i], list[i+1]) {
swaps = true
tmp = list[i]
list[i] = list[i+1]
list[i+1] = tmp
}
slices.SortFunc(list, func(a, b T) int {
if predicate(a, b) {
return 1
}
}
if predicate(b, a) {
return -1
}
return 0
})
return list
}

View file

@ -29,3 +29,31 @@ func Test_OrderBy_Desc(t *testing.T) {
assert.Equal(t, want, result)
}
func BenchmarkOrderBy(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = 1000 - i // Reverse order - worst case for bubble sort
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
dataCopy := make([]int, len(data))
copy(dataCopy, data)
u.OrderBy(dataCopy, func(a, b int) bool { return a > b })
}
}
func BenchmarkOrderBySmall(b *testing.B) {
data := make([]int, 10)
for i := range data {
data[i] = 10 - i
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
dataCopy := make([]int, len(data))
copy(dataCopy, data)
u.OrderBy(dataCopy, func(a, b int) bool { return a > b })
}
}

View file

@ -3,6 +3,7 @@ package underscore_test
import (
"context"
"errors"
"fmt"
"testing"
"github.com/stretchr/testify/assert"
@ -40,3 +41,44 @@ func TestParallelMap_DefaultWorkers(t *testing.T) {
assert.NoError(t, err)
assert.Equal(t, []int{2, 3, 4}, out)
}
func BenchmarkParallelMap(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = i
}
ctx := context.Background()
for _, workers := range []int{1, 2, 4, 8} {
b.Run(fmt.Sprintf("workers=%d", workers), func(b *testing.B) {
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.ParallelMap(ctx, data, workers, func(_ context.Context, n int) (int, error) {
return n * 2, nil
})
}
})
}
}
func BenchmarkMapVsParallelMap(b *testing.B) {
data := make([]int, 10000)
for i := range data {
data[i] = i
}
ctx := context.Background()
b.Run("Map", func(b *testing.B) {
for i := 0; i < b.N; i++ {
u.Map(data, func(n int) int { return n * 2 })
}
})
b.Run("ParallelMap", func(b *testing.B) {
for i := 0; i < b.N; i++ {
u.ParallelMap(ctx, data, 0, func(_ context.Context, n int) (int, error) {
return n * 2, nil
})
}
})
}

View file

@ -3,8 +3,8 @@ package underscore
// Partition splits the slice into two slices: one whose elements all satisfy predicate
// and one whose elements all do not satisfy predicate.
func Partition[T any](values []T, predicate func(T) bool) ([]T, []T) {
keep := make([]T, 0)
reject := make([]T, 0)
keep := make([]T, 0, len(values))
reject := make([]T, 0, len(values))
for _, v := range values {
if predicate(v) {

View file

@ -20,3 +20,41 @@ func TestPartition(t *testing.T) {
assert.Equal(t, wantEvens, evens)
assert.Equal(t, wantOdds, odds)
}
func TestPartitionEmpty(t *testing.T) {
keep, reject := u.Partition([]int{}, func(n int) bool { return n > 0 })
assert.Empty(t, keep)
assert.Empty(t, reject)
}
func TestPartitionSingleElement(t *testing.T) {
keep, reject := u.Partition([]int{5}, func(n int) bool { return n > 3 })
assert.Equal(t, []int{5}, keep)
assert.Empty(t, reject)
}
func TestPartitionAllPass(t *testing.T) {
nums := []int{2, 4, 6, 8}
keep, reject := u.Partition(nums, func(n int) bool { return n%2 == 0 })
assert.Equal(t, nums, keep)
assert.Empty(t, reject)
}
func TestPartitionAllReject(t *testing.T) {
nums := []int{1, 3, 5, 7}
keep, reject := u.Partition(nums, func(n int) bool { return n%2 == 0 })
assert.Empty(t, keep)
assert.Equal(t, nums, reject)
}
func BenchmarkPartition(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = i
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.Partition(data, func(n int) bool { return n%2 == 0 })
}
}

View file

@ -17,3 +17,25 @@ func TestReduce(t *testing.T) {
assert.Equal(t, want, u.Reduce(nums, reducer, 0))
}
func TestReduceEmpty(t *testing.T) {
result := u.Reduce([]int{}, func(n, acc int) int { return n + acc }, 10)
assert.Equal(t, 10, result) // Should return initial accumulator
}
func TestReduceSingleElement(t *testing.T) {
result := u.Reduce([]int{5}, func(n, acc int) int { return n + acc }, 0)
assert.Equal(t, 5, result)
}
func BenchmarkReduce(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = i
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.Reduce(data, func(n, acc int) int { return n + acc }, 0)
}
}

16
remove_at.go Normal file
View file

@ -0,0 +1,16 @@
package underscore
// RemoveAt returns a new slice with the element at the given index removed.
// Returns original slice if index is out of bounds.
func RemoveAt[T any](values []T, index int) []T {
if index < 0 || index >= len(values) {
return values
}
res := make([]T, 0, len(values)-1)
for i, value := range values {
if i != index {
res = append(res, value)
}
}
return res
}

50
remove_at_test.go Normal file
View file

@ -0,0 +1,50 @@
package underscore_test
import (
"testing"
"github.com/stretchr/testify/assert"
u "github.com/rjNemo/underscore"
)
func TestRemoveAt(t *testing.T) {
nums := []int{1, 9, 2, 8, 3, 7, 4, 6, 5}
want := []int{1, 9, 2, 3, 7, 4, 6, 5}
assert.Equal(t, want, u.RemoveAt(nums, 3))
}
func TestRemoveAtFirst(t *testing.T) {
nums := []int{1, 2, 3, 4, 5}
want := []int{2, 3, 4, 5}
assert.Equal(t, want, u.RemoveAt(nums, 0))
}
func TestRemoveAtLast(t *testing.T) {
nums := []int{1, 2, 3, 4, 5}
want := []int{1, 2, 3, 4}
assert.Equal(t, want, u.RemoveAt(nums, 4))
}
func TestRemoveAtOutOfBounds(t *testing.T) {
nums := []int{1, 2, 3}
// Negative index
assert.Equal(t, nums, u.RemoveAt(nums, -1))
// Index too large
assert.Equal(t, nums, u.RemoveAt(nums, 10))
}
func TestRemoveAtEmpty(t *testing.T) {
result := u.RemoveAt([]int{}, 0)
assert.Empty(t, result)
}
func TestRemoveAtSingleElement(t *testing.T) {
result := u.RemoveAt([]int{42}, 0)
assert.Empty(t, result)
}

252
stress_test.go Normal file
View file

@ -0,0 +1,252 @@
package underscore_test
import (
"context"
"testing"
"time"
"github.com/stretchr/testify/assert"
u "github.com/rjNemo/underscore"
)
// Large data stress tests
func TestFilterLargeData(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
large := make([]int, 1_000_000)
for i := range large {
large[i] = i
}
result := u.Filter(large, func(n int) bool { return n%2 == 0 })
assert.Equal(t, 500_000, len(result))
assert.Equal(t, 0, result[0])
assert.Equal(t, 999_998, result[len(result)-1])
}
func TestMapLargeData(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
large := make([]int, 1_000_000)
for i := range large {
large[i] = i
}
result := u.Map(large, func(n int) int { return n * 2 })
assert.Equal(t, 1_000_000, len(result))
assert.Equal(t, 0, result[0])
assert.Equal(t, 1_999_998, result[len(result)-1])
}
func TestPartitionLargeData(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
large := make([]int, 1_000_000)
for i := range large {
large[i] = i
}
keep, reject := u.Partition(large, func(n int) bool { return n%2 == 0 })
assert.Equal(t, 500_000, len(keep))
assert.Equal(t, 500_000, len(reject))
}
func TestUniqueLargeData(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
large := make([]int, 1_000_000)
for i := range large {
large[i] = i % 1000 // Many duplicates
}
result := u.Unique(large)
assert.Equal(t, 1000, len(result))
}
// Concurrency stress tests
func TestParallelMapHighConcurrency(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
data := make([]int, 10000)
for i := range data {
data[i] = i
}
ctx := context.Background()
// Test with many workers
result, err := u.ParallelMap(ctx, data, 100, func(ctx context.Context, n int) (int, error) {
time.Sleep(time.Microsecond) // Simulate work
return n * 2, nil
})
assert.NoError(t, err)
assert.Equal(t, len(data), len(result))
for i, v := range result {
assert.Equal(t, data[i]*2, v)
}
}
func TestParallelMapCancellation(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
data := make([]int, 10000)
for i := range data {
data[i] = i
}
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Millisecond)
defer cancel()
_, err := u.ParallelMap(ctx, data, 4, func(ctx context.Context, n int) (int, error) {
// Check context and return error if canceled
if ctx.Err() != nil {
return 0, ctx.Err()
}
time.Sleep(1 * time.Millisecond) // Slow work
return n, nil
})
// Should either complete or return a context error
if err != nil {
assert.ErrorIs(t, err, context.DeadlineExceeded)
}
}
func TestParallelFilterHighConcurrency(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
data := make([]int, 10000)
for i := range data {
data[i] = i
}
ctx := context.Background()
result, err := u.ParallelFilter(ctx, data, 50, func(ctx context.Context, n int) (bool, error) {
time.Sleep(time.Microsecond)
return n%2 == 0, nil
})
assert.NoError(t, err)
assert.Equal(t, 5000, len(result))
for _, v := range result {
assert.Equal(t, 0, v%2)
}
}
// Race condition tests
func TestParallelMapNoRaces(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
// Run with: go test -race -run TestParallelMapNoRaces
data := make([]int, 1000)
for i := range data {
data[i] = i
}
ctx := context.Background()
for i := 0; i < 100; i++ {
_, err := u.ParallelMap(ctx, data, 8, func(ctx context.Context, n int) (int, error) {
return n * 2, nil
})
assert.NoError(t, err)
}
}
func TestParallelFilterNoRaces(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
// Run with: go test -race -run TestParallelFilterNoRaces
data := make([]int, 1000)
for i := range data {
data[i] = i
}
ctx := context.Background()
for i := 0; i < 100; i++ {
_, err := u.ParallelFilter(ctx, data, 8, func(ctx context.Context, n int) (bool, error) {
return n%2 == 0, nil
})
assert.NoError(t, err)
}
}
func TestConcurrentFilterCalls(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
// Test that concurrent calls to Filter don't interfere with each other
data := make([]int, 10000)
for i := range data {
data[i] = i
}
done := make(chan bool, 10)
for i := 0; i < 10; i++ {
go func() {
result := u.Filter(data, func(n int) bool { return n%2 == 0 })
if len(result) != 5000 {
t.Errorf("Expected 5000 elements, got %d", len(result))
}
done <- true
}()
}
for i := 0; i < 10; i++ {
<-done
}
}
func TestConcurrentMapCalls(t *testing.T) {
if testing.Short() {
t.Skip("Skipping stress test in short mode")
}
data := make([]int, 10000)
for i := range data {
data[i] = i
}
done := make(chan bool, 10)
for i := 0; i < 10; i++ {
go func() {
result := u.Map(data, func(n int) int { return n * 2 })
if len(result) != 10000 {
t.Errorf("Expected 10000 elements, got %d", len(result))
}
done <- true
}()
}
for i := 0; i < 10; i++ {
<-done
}
}

View file

@ -14,3 +14,50 @@ func TestUnique(t *testing.T) {
assert.Equal(t, want, u.Unique(nums))
}
func TestUniqueEmpty(t *testing.T) {
result := u.Unique([]int{})
assert.Empty(t, result)
}
func TestUniqueSingleElement(t *testing.T) {
result := u.Unique([]int{42})
assert.Equal(t, []int{42}, result)
}
func TestUniqueNoDuplicates(t *testing.T) {
nums := []int{1, 2, 3, 4, 5}
result := u.Unique(nums)
assert.Equal(t, nums, result)
}
func TestUniqueAllSame(t *testing.T) {
nums := []int{5, 5, 5, 5, 5}
result := u.Unique(nums)
assert.Equal(t, []int{5}, result)
}
func BenchmarkUnique(b *testing.B) {
data := make([]int, 1000)
for i := range data {
data[i] = i % 100 // Many duplicates
}
b.ResetTimer()
for i := 0; i < b.N; i++ {
u.Unique(data)
}
}
func BenchmarkUniqueInPlace(b *testing.B) {
for i := 0; i < b.N; i++ {
b.StopTimer()
data := make([]int, 1000)
for j := range data {
data[j] = j % 100
}
b.StartTimer()
u.UniqueInPlace(data)
}
}