🐹 🗄️ Advanced Caching Patterns with TuskLang & Go

Go Documentation

🗄️ Advanced Caching Patterns with TuskLang & Go

Introduction

Caching is the secret weapon of high-performance systems. TuskLang and Go let you implement sophisticated caching patterns with config-driven strategies, multi-level caches, intelligent invalidation, and distributed coordination.

Key Features

- Multi-level caching (L1, L2, L3) - Cache invalidation strategies - Cache warming and preloading - Distributed caching with Redis/Memcached - Cache-aside, write-through, write-behind patterns - Cache compression and optimization

Example: Multi-Level Cache Config

[cache]
l1_backend: memory
l1_size: @env("L1_CACHE_SIZE", "100MB")
l2_backend: redis
l2_uri: @env.secure("REDIS_URI")
l3_backend: database
l3_query: @query("SELECT * FROM cache_store WHERE key = ?")
compression: @env("CACHE_COMPRESSION", true)
metrics: @metrics("cache_hit_rate", 0)

Go: Multi-Level Cache Implementation

package cache

import ( "context" "encoding/json" "sync" "time" "github.com/go-redis/redis/v8" "github.com/klauspost/compress/gzip" )

type MultiLevelCache struct { l1 *sync.Map l2 *redis.Client l3 Database stats *CacheStats }

type CacheStats struct { l1Hits int64 l2Hits int64 l3Hits int64 misses int64 mu sync.RWMutex }

func (c *MultiLevelCache) Get(ctx context.Context, key string) (interface{}, error) { // Try L1 cache first if val, ok := c.l1.Load(key); ok { atomic.AddInt64(&c.stats.l1Hits, 1) return val, nil } // Try L2 cache (Redis) val, err := c.l2.Get(ctx, key).Result() if err == nil { // Store in L1 for next time c.l1.Store(key, val) atomic.AddInt64(&c.stats.l2Hits, 1) return val, nil } // Try L3 cache (Database) if val, err := c.l3.Get(ctx, key); err == nil { // Store in L2 and L1 c.l2.Set(ctx, key, val, time.Hour) c.l1.Store(key, val) atomic.AddInt64(&c.stats.l3Hits, 1) return val, nil } atomic.AddInt64(&c.stats.misses, 1) return nil, ErrCacheMiss }

Cache Invalidation Strategies

type InvalidationStrategy interface {
    Invalidate(ctx context.Context, pattern string) error
}

type TimeBasedInvalidation struct { ttl time.Duration }

func (t *TimeBasedInvalidation) Invalidate(ctx context.Context, pattern string) error { // Set TTL for matching keys keys, err := redis.Keys(ctx, pattern).Result() if err != nil { return err } for _, key := range keys { redis.Expire(ctx, key, t.ttl) } return nil }

type EventBasedInvalidation struct { eventBus EventBus }

func (e *EventBasedInvalidation) Invalidate(ctx context.Context, event string) error { // Publish invalidation event return e.eventBus.Publish(ctx, "cache:invalidate", event) }

Cache Warming

func (c *MultiLevelCache) WarmCache(ctx context.Context, patterns []string) error {
    for _, pattern := range patterns {
        // Preload frequently accessed data
        data, err := c.l3.GetPattern(ctx, pattern)
        if err != nil {
            continue
        }
        
        for key, value := range data {
            c.l2.Set(ctx, key, value, time.Hour)
            c.l1.Store(key, value)
        }
    }
    return nil
}

Cache-Aside Pattern

func (c *MultiLevelCache) GetOrSet(ctx context.Context, key string, loader func() (interface{}, error)) (interface{}, error) {
    // Try cache first
    if val, err := c.Get(ctx, key); err == nil {
        return val, nil
    }
    
    // Load from source
    val, err := loader()
    if err != nil {
        return nil, err
    }
    
    // Store in cache
    c.Set(ctx, key, val)
    return val, nil
}

Write-Through Pattern

func (c *MultiLevelCache) WriteThrough(ctx context.Context, key string, value interface{}) error {
    // Write to all cache levels and source
    if err := c.l3.Set(ctx, key, value); err != nil {
        return err
    }
    
    c.l2.Set(ctx, key, value, time.Hour)
    c.l1.Store(key, value)
    return nil
}

Write-Behind Pattern

type WriteBehindCache struct {
    cache    *MultiLevelCache
    queue    chan WriteOperation
    workers  int
}

type WriteOperation struct { Key string Value interface{} }

func (w *WriteBehindCache) Set(ctx context.Context, key string, value interface{}) error { // Write to cache immediately w.cache.Set(ctx, key, value) // Queue for background write to source select { case w.queue <- WriteOperation{Key: key, Value: value}: return nil default: return ErrQueueFull } }

func (w *WriteBehindCache) StartWorkers() { for i := 0; i < w.workers; i++ { go w.worker() } }

func (w *WriteBehindCache) worker() { for op := range w.queue { // Write to source in background w.cache.l3.Set(context.Background(), op.Key, op.Value) } }

Cache Compression

func CompressCache(data []byte) ([]byte, error) {
    var buf bytes.Buffer
    gz := gzip.NewWriter(&buf)
    
    if _, err := gz.Write(data); err != nil {
        return nil, err
    }
    
    if err := gz.Close(); err != nil {
        return nil, err
    }
    
    return buf.Bytes(), nil
}

func DecompressCache(data []byte) ([]byte, error) { gz, err := gzip.NewReader(bytes.NewReader(data)) if err != nil { return nil, err } defer gz.Close() return ioutil.ReadAll(gz) }

Best Practices

- Use appropriate cache levels for different data types - Implement intelligent invalidation strategies - Monitor cache hit rates and adjust TTLs - Use compression for large objects - Implement cache warming for critical data - Handle cache failures gracefully

Performance Optimization

- Use connection pooling for Redis - Implement batch operations for cache updates - Use efficient serialization (MessagePack, Protocol Buffers) - Monitor memory usage and implement LRU eviction

Security Considerations

- Validate cache keys to prevent injection - Use secure connections for distributed caches - Implement cache poisoning protection - Encrypt sensitive cached data

Troubleshooting

- Monitor cache hit rates and adjust strategies - Check for memory leaks in local caches - Verify Redis connectivity and performance - Monitor cache invalidation patterns

Conclusion

TuskLang + Go = caching that's intelligent, distributed, and blazing fast. Cache everything, cache smart.