### Benchmark Results Comparison in Go

Source: https://lo.samber.dev/llms.txt

Presents example benchmark results comparing the `lo.Uniq` function with a manual implementation. This highlights performance differences in terms of operations per second, memory allocation, and operations per allocation, guiding optimization choices.

```go
// Example benchmark results for lo.Uniq vs manual implementation
// BenchmarkLoUniq-8          1000000    1200 ns/op    800 B/op   4 allocs/op
// BenchmarkManualUniq-8       5000000     240 ns/op     0 B/op   0 allocs/op  (when in-place)
```

--------------------------------

### Basic Collection Operations in Go

Source: https://lo.samber.dev/llms.txt

Demonstrates fundamental collection manipulation using the `lo` library, including removing duplicates from slices and performing combined filtering and transformation operations. These functions are useful for common data cleaning and preparation tasks.

```go
import "github.com/samber/lo"
import "fmt"

// Remove duplicates from a slice
names := lo.Uniq([]string{"Samuel", "John", "Samuel"})
// []string{"Samuel", "John"}

// Filter and transform in one operation
numbers := lo.FilterMap([]int{1, 2, 3, 4, 5}, func(item int, index int) (string, bool) {
    if item%2 == 0 {
        return fmt.Sprintf("even-%d", item), true
    }
    return "", false
})
// []string{"even-2", "even-4"}
```

--------------------------------

### Concurrency and Async Operations in Go with Lo

Source: https://lo.samber.dev/llms.txt

Showcases how to leverage the `lo` library for parallel processing, debouncing operations, and safe error handling. These patterns are crucial for building responsive and resilient applications.

```go
import (
    "github.com/samber/lo"
    "time"
)

// Placeholder types and functions for demonstration
var items []Item
var query string

type Item struct {}
type Result struct {}

func processItem(item Item) Result { return Result{} }
func searchAPI(q string) []Result { return nil }
func updateUI(results []Result) {}
func dangerousOperation() (int, error) { return 0, nil }

// Parallel processing of large datasets
results := lo.Map(items, func(item Item) Result {
    return processItem(item) // Executed in parallel
})

// Debounce API calls to avoid rate limiting
debouncedSearch := lo.NewDebounce(300*time.Millisecond, func() {
    results := searchAPI(query)
    updateUI(results)
})

// Safe error handling with fallbacks
result, ok := lo.TryOr(func() (int, error) {
    return dangerousOperation()
}, 0)
```

--------------------------------

### Functional Control Flow in Go using Lo

Source: https://lo.samber.dev/llms.txt

Demonstrates elegant conditional logic and robust retry mechanisms provided by the `lo` library. This includes using `lo.If`, `ElseIfF`, `Else` for conditional expressions and `lo.AttemptWithDelay` for retrying operations with backoff.

```go
import (
    "context"
    "errors"
    "time"

    "github.com/samber/lo"
)

// Placeholder types and functions for demonstration
var user any
var isGuest bool
var items []string

type Order struct {}

func checkInventory(items []string) struct{ Available bool } { return struct{ Available bool }{true} }
func createOrder(user any, items []string) (Order, error) { return Order{}, nil }
func callExternalAPI(ctx context.Context) error { return nil }

// Elegant conditional logic
message := lo.If(user != nil, "Welcome back!").
    ElseIfF(isGuest, func() string { return "Welcome guest!" }).
    Else("Please login")

// Retry logic with exponential backoff
result, err := lo.AttemptWithDelay(3, time.Second, func(i int, d time.Duration) error {
    return callExternalAPI(context.Background())
})

// Transaction management
tx := lo.NewTransaction[Order]()
def tx.Rollback()

order, err := tx.Run(func() (Order, error) {
    inventory := checkInventory(items)
    if !inventory.Available {
        return Order{}, errors.New("insufficient inventory")
    }
    return createOrder(user, items)
})
```

--------------------------------

### Advanced Data Processing with Lo in Go

Source: https://lo.samber.dev/llms.txt

Illustrates complex data transformations and safe data access patterns using the `lo` library. This includes creating data processing pipelines with `lo.Pipe` and safely accessing nested map values with `lo.ValueOr` and `lo.CoalesceMap`.

```go
import "github.com/samber/lo"

type User struct { ID int; Name string; Active bool }

// Complex data transformation pipeline
users := []User{{ID: 1, Name: "Alice", Active: true}, {ID: 2, Name: "Bob", Active: false}}

// Group active users by name length, then extract IDs
activeUserIDs := lo.Pipe(
    users,
    lo.Filter(func(u User) bool { return u.Active }),
    lo.GroupBy(func(u User) int { return len(u.Name) }),
    lo.MapValues(func(users []User) []int {
        return lo.Map(users, func(u User) int { return u.ID })
    }),
)
// map[int][]int{5: []int{1}}

// Safe nested data access
config := map[string]any{
    "database": map[string]any{
        "host": "localhost",
        "port": 5432,
    },
}

host := lo.ValueOr(lo.CoalesceMap(
    lo.MapValues(config, func(v any) map[string]any {
        if m, ok := v.(map[string]any); ok {
            return m
        }
        return nil
    }),
), "localhost")
```

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