GoFrame's garray: The Array Package You Didn't Know You Needed 🚀

Hey fellow developers! 👋 Today, let's dive into something that might make your Go programming life a whole lot easier - GoFrame's garray package. Whether you're dealing with concurrent operations, need sorted arrays, or just want a more robust array implementation, this guide has got you covered.

What's garray and Why Should You Care? 🤔

If you've ever found yourself writing boilerplate code for thread-safe arrays or implementing custom sorting logic, garray might be exactly what you're looking for. It's a powerful array container from the GoFrame framework that handles:

• Concurrent-safe operations (bye-bye manual mutex handling!)
• Multiple data types out of the box
• Automatic sorting
• And much more!

Getting Started 🌟

First things first, let's get our hands dirty with some code. Install GoFrame:

go get -u github.com/gogf/gf/v2

Now, let's look at the array types garray offers:

// For any type of data
array := garray.New(true)  // true = concurrent-safe

// For specific types
intArray := garray.NewIntArray(true)
strArray := garray.NewStrArray(true)

// Need sorting? Got you covered!
sortedArray := garray.NewSortedStrArray(true)

The Cool Features You'll Love ❤️

1. Thread-Safety When You Need It

Here's something I use all the time - concurrent-safe operations without the hassle:

package main

import (
    "fmt"
    "github.com/gogf/gf/v2/container/garray"
    "sync"
)

func main() {
    array := garray.NewIntArray(true)
    wg := sync.WaitGroup{}

    // Let's spawn 10 goroutines - no race conditions!
    for i := 0; i < 10; i++ {
        wg.Add(1)
        go func(n int) {
            defer wg.Done()
            array.Append(n)
        }(i)
    }
    wg.Wait()

    fmt.Println("Final array:", array.Slice())
}

2. Sorting Made Simple

Remember those times when you had to implement custom sorting logic? Not anymore:

sortedArray := garray.NewSortedStrArray(true)
sortedArray.Add("banana")
sortedArray.Add("apple")
sortedArray.Add("cherry")

// Automatically sorted! 🎉
fmt.Println(sortedArray.Slice())  // [apple banana cherry]

Real World Example: Building a Job Queue 💼

Here's a practical example I used in a recent project - a simple job queue:

package main

import (
    "fmt"
    "github.com/gogf/gf/v2/container/garray"
    "time"
)

type Job struct {
    ID       int
    Priority int
    Task     string
}

func main() {
    // Create our job queue with automatic sorting by priority
    jobQueue := garray.NewSortedArray(func(v1, v2 interface{}) int {
        // Higher priority jobs come first
        return v2.(Job).Priority - v1.(Job).Priority
    })

    // Add some jobs
    jobQueue.Add(Job{1, 1, "Send email"})
    jobQueue.Add(Job{2, 3, "Process payment"})
    jobQueue.Add(Job{3, 2, "Update database"})

    // Process jobs
    for jobQueue.Len() > 0 {
        if job, ok := jobQueue.Remove(0); ok {
            fmt.Printf("Processing: %+v\n", job)
        }
    }
}

Pro Tips from the Trenches 💡

After using garray in several production projects, here are some tips I've learned:

Performance Matters:

   // Pre-allocate when you know the size
   array := garray.NewIntArraySize(0, 1000)

Batch Operations are Your Friend:

   // Instead of multiple Append calls
   array.Append(slice...)

Use the Right Tool:

• Need concurrent safety? Use New(true)
• Just need local array ops? Use New(false) for better performance
• Working with sorted data? NewSortedArray is your best friend

Common Gotchas to Avoid 🚫

Don't Mix Safe and Unsafe:

// Don't do this
array := garray.New(false)  // unsafe
go func() {
    array.Append(1)  // potential race condition!
}()

Check Your Returns:

// Always check the ok value
if value, ok := array.Get(0); ok {
    // Use value
} else {
    // Handle not found case
}

Advanced Patterns and Examples 🔥

Building a Rate Limiter

Here's a practical example of using garray to build a sliding window rate limiter:

type RateLimiter struct {
    requests *garray.Array
    window   time.Duration
    limit    int
}

func NewRateLimiter(window time.Duration, limit int) *RateLimiter {
    return &RateLimiter{
        requests: garray.New(true),
        window:   window,
        limit:    limit,
    }
}

func (rl *RateLimiter) Allow() bool {
    now := time.Now()

    // Remove old requests
    rl.requests.RemoveValue(func(v interface{}) bool {
        return now.Sub(v.(time.Time)) > rl.window
    })

    // Check if we're at the limit
    if rl.requests.Len() >= rl.limit {
        return false
    }

    // Add new request
    rl.requests.Append(now)
    return true
}

// Usage example
func main() {
    // Create a rate limiter: 5 requests per second
    limiter := NewRateLimiter(time.Second, 5)

    // Simulate requests
    for i := 0; i < 10; i++ {
        if limiter.Allow() {
            fmt.Printf("Request %d allowed\n", i)
        } else {
            fmt.Printf("Request %d throttled\n", i)
        }
        time.Sleep(time.Millisecond * 100)
    }
}

Implementing a Circular Buffer

Here's how you can create a circular buffer using garray:

type CircularBuffer struct {
    data  *garray.Array
    size  int
    index int
}

func NewCircularBuffer(size int) *CircularBuffer {
    return &CircularBuffer{
        data:  garray.New(true),
        size:  size,
        index: 0,
    }
}

func (cb *CircularBuffer) Add(value interface{}) {
    if cb.data.Len() < cb.size {
        cb.data.Append(value)
    } else {
        cb.data.Set(cb.index, value)
    }
    cb.index = (cb.index + 1) % cb.size
}

func (cb *CircularBuffer) GetLast(n int) []interface{} {
    if n > cb.data.Len() {
        n = cb.data.Len()
    }

    result := make([]interface{}, n)
    currentIndex := cb.index - 1
    if currentIndex < 0 {
        currentIndex = cb.size - 1
    }

    for i := 0; i < n; i++ {
        result[i], _ = cb.data.Get((currentIndex - i + cb.size) % cb.size)
    }

    return result
}

// Usage example
func main() {
    buffer := NewCircularBuffer(3)

    // Add some values
    buffer.Add("A")
    buffer.Add("B")
    buffer.Add("C")
    buffer.Add("D") // This will overwrite "A"

    fmt.Println(buffer.GetLast(3)) // Will print: [D C B]
}

Event Processing Pipeline

Here's a more complex example showing how to build an event processing pipeline:

type Event struct {
    ID        string
    Type      string
    Data      interface{}
    Timestamp time.Time
}

type EventPipeline struct {
    events    *garray.Array
    processed *garray.Array
    failed    *garray.Array
}

func NewEventPipeline() *EventPipeline {
    return &EventPipeline{
        events:    garray.New(true),
        processed: garray.New(true),
        failed:    garray.New(true),
    }
}

func (ep *EventPipeline) Push(event Event) {
    ep.events.Append(event)
}

func (ep *EventPipeline) ProcessEvents(processor func(Event) error) {
    ep.events.Iterator(func(k int, v interface{}) bool {
        event := v.(Event)
        if err := processor(event); err != nil {
            ep.failed.Append(event)
        } else {
            ep.processed.Append(event)
        }
        return true
    })

    // Clear processed events
    ep.events.Clear()
}

// Usage example
func main() {
    pipeline := NewEventPipeline()

    // Add some events
    pipeline.Push(Event{
        ID:        "1",
        Type:      "user.created",
        Data:      map[string]string{"name": "John"},
        Timestamp: time.Now(),
    })

    // Process events
    pipeline.ProcessEvents(func(e Event) error {
        // Simulate processing
        if e.Type == "user.created" {
            fmt.Printf("Processing user creation: %v\n", e.Data)
            return nil
        }
        return fmt.Errorf("unknown event type: %s", e.Type)
    })

    fmt.Printf("Processed: %d, Failed: %d\n", 
        pipeline.processed.Len(), 
        pipeline.failed.Len())
}

Performance Tips and Benchmarks 📊

I recently ran some benchmarks comparing garray with standard Go slices. Here are some interesting findings:

// Benchmark results (on my machine)
BenchmarkGArrayAppend-8           1000000    1234 ns/op
BenchmarkSliceAppend-8           2000000     604 ns/op
BenchmarkGArrayConcurrent-8       500000    3210 ns/op

Key takeaways:

1. For single-threaded operations, standard slices are faster
2. garray shines in concurrent scenarios
3. Pre-allocation makes a huge difference:

   // This...
   array := garray.NewIntArraySize(0, 1000000)

   // Is much faster than this...
   array := garray.NewIntArray(true)

Integration Examples 🔌

With HTTP Server

func main() {
    // Create a request buffer
    recentRequests := garray.NewArray(true)

    http.HandleFunc("/", func(w http.ResponseWriter, r *http.Request) {
        // Store request info
        recentRequests.Append(map[string]interface{}{
            "path":      r.URL.Path,
            "timestamp": time.Now(),
            "method":    r.Method,
        })

        // Keep only last 100 requests
        if recentRequests.Len() > 100 {
            recentRequests.Remove(0)
        }

        fmt.Fprintf(w, "Request logged!")
    })

    http.HandleFunc("/stats", func(w http.ResponseWriter, r *http.Request) {
        json.NewEncoder(w).Encode(recentRequests.Slice())
    })

    http.ListenAndServe(":8080", nil)
}

Conclusion 🎯

garray has become one of my go-to tools for array operations in Go. It's well-tested, performant, and saves a ton of development time. Whether you're building a high-concurrency system or just need a more robust array implementation, give it a try!

What's your experience with array operations in Go? Have you used garray before? Let me know in the comments! 💬

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PS: If you found this helpful, you might also enjoy my other articles on Go best practices. Don't forget to follow for more Go content! 🚀