Performance Optimization

Strategies and techniques for optimizing JSON processing performance.

Processor Reuse

Reuse Processor Instances

// ✅ Package-level functions automatically reuse the global Processor
for _, item := range dataList {
    val := json.GetString(item, "name")
}

// ✅ Or explicitly reuse instances (suitable for custom configuration)
processor, err := json.New()
if err != nil {
    panic(err)
}
defer processor.Close()
for _, item := range dataList {
    val := processor.GetString(item, "name")
}

Memory Optimization

Reduce Allocations

// ✅ Use Marshal to return a byte slice
bytes, _ := json.Marshal(data)

// ✅ Use EncodeWithConfig to return a string (Encode is deprecated)
s, _ := json.EncodeWithConfig(data)

Pre-allocate Buffers

// Pre-allocate when processing large amounts of data
buf := make([]byte, 0, 1024*1024)

File Processing

Use Structured Iteration for Large Files

// ❌ Load everything at once
data, _ := os.ReadFile("large.json")
parsed, _ := json.ParseAny(string(data))

// ✅ Structured iteration (note: still loads the full file into memory)
processor, err := json.New()
if err != nil {
    panic(err)
}
defer processor.Close()
processor.ForeachFile("large.json", func(key any, item *json.IterableValue) error {
    processItem(item)
    return nil
})

NDJSON Processing

// Use StreamLinesInto for stream processing
file, _ := os.Open("data.jsonl")
defer file.Close()
entries, err := json.StreamLinesInto[LogEntry](file, func(lineNum int, entry LogEntry) error {
    // Process each JSON line
    return nil
})

Concurrent Processing

Parallel Array Processing

items := json.GetArray(data, "items")

var wg sync.WaitGroup
sem := make(chan struct{}, runtime.NumCPU())

for _, item := range items {
    wg.Add(1)
    go func(item any) {
        defer wg.Done()
        sem <- struct{}{}
        defer func() { <-sem }()

        processItem(item)
    }(item)
}
wg.Wait()

Using a Worker Pool

items := json.GetArray(data, "items")
jobs := make(chan any, len(items))

// Start a fixed number of workers, reusing goroutines to avoid frequent creation/destruction
var wg sync.WaitGroup
workers := runtime.NumCPU()
for w := 0; w < workers; w++ {
    wg.Add(1)
    go func() {
        defer wg.Done()
        for item := range jobs {
            processItem(item)
        }
    }()
}

// Close the channel after dispatching tasks to notify workers to exit
for _, item := range items {
    jobs <- item
}
close(jobs)
wg.Wait()

Configuration Optimization

Adjust Configuration Based on Scenario

// Small data: relaxed configuration
smallCfg := json.DefaultConfig()
smallCfg.MaxNestingDepthSecurity = 200 // Maximum allowed value (validation range 10-200)

// Untrusted input: security configuration
safeCfg := json.SecurityConfig()
safeCfg.MaxJSONSize = 1024 * 1024

Disable Unnecessary Features

// If you don't need Hooks, don't configure them
cfg := json.DefaultConfig() // Minimal configuration

Caching Strategies

Cache Parse Results

var cache sync.Map

func getOrParse(key string, data []byte) (any, error) {
    if val, ok := cache.Load(key); ok {
        return val, nil
    }

    result, err := json.ParseAny(string(data))
    if err != nil {
        return nil, err
    }

    cache.Store(key, result)
    return result, nil
}

Cache Path Queries

// Pre-compile commonly used paths (using Processor)
p, err := json.New()
if err != nil {
    panic(err)
}
defer p.Close()
path1, _ := p.CompilePath("user.name")
path2, _ := p.CompilePath("user.email")
path3, _ := p.CompilePath("items[*].id")

Benchmarking

Performance Testing Example

func BenchmarkParse(b *testing.B) {
    data := []byte(`{"name": "test", "items": [1, 2, 3]}`)

    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        _, _ = json.ParseAny(string(data))
    }
}

func BenchmarkGetString(b *testing.B) {
    data := `{"user": {"name": "CyberGo", "email": "[email protected]"}}`

    b.ResetTimer()
    for i := 0; i < b.N; i++ {
        json.GetString(data, "user.name")
    }
}

Memory Analysis

func TestMemoryUsage(t *testing.T) {
    var m runtime.MemStats
    runtime.ReadMemStats(&m)
    before := m.Alloc

    // Execute operation
    data := generateLargeJSON()
    _, _ = json.ParseAny(data)

    runtime.ReadMemStats(&m)
    after := m.Alloc

    fmt.Printf("Memory usage: %d bytes\n", after-before)
}

Performance Comparison

Operation	Small Data (<1KB)	Medium Data (1MB)	Large Data (>10MB)
Parse	Recommended	Recommended	Not recommended
ForeachFile	Unnecessary	Optional	Recommended

See Also

• Large File Processing
• Error Handling