Golang API caching strategy and optimization

The caching strategies in Golang API can improve performance and reduce server load. Commonly used strategies are: LRU, LFU, FIFO and TTL. Optimization techniques include selecting appropriate cache storage, hierarchical caching, invalidation management, and monitoring and tuning. In the practical case, LRU cache is used to optimize the API for obtaining user information from the database, and the data can be quickly retrieved from the cache. Otherwise, the cache is updated after being obtained from the database.

Golang API caching strategy and optimization

Cache strategy

Cache is to store recently acquired data so that A technique for quickly responding to subsequent requests. In Golang API, caching strategies can significantly improve performance, reduce latency and reduce server load. Some common strategies include:

LRU (Least Recently Used) : Removes the least recently used items to make room for new data.
LFU (Least Recently Used) : Delete the least frequently used items.
FIFO (First In, First Out) : Delete the first item added to the cache.
TTL (Time to Live): Set a time limit after which the project will be automatically deleted.

Optimization Tips

In addition to choosing an appropriate caching strategy, the following tips can further optimize cache performance in Golang API:

• Choose the appropriate cache storage: According to different usage scenarios, choose the appropriate storage backend, such as Redis, Memcached or local memory.
• Hiered Caching: Create multiple caching tiers, storing hot data in tiers closer to the client and cold data in tiers closer to the source.
• Invalidation Management: When the source data changes, obsolete items are cleared from the cache in a timely manner.
• Monitoring and Tuning: Regularly monitor the hit rate, error rate, and size of the cache, and adjust policies and configurations as needed.

Practical Case

Consider a simple Golang API that gets user information from the database:

package api

import (
    "context"
    "database/sql"
    "fmt"
)

// User represents a user in the system.
type User struct {
    ID   int64
    Name string
}

// GetUserInfo retrieves user information from the database.
func GetUserInfo(ctx context.Context, db *sql.DB, userID int64) (*User, error) {
    row := db.QueryRowContext(ctx, "SELECT id, name FROM users WHERE id = ?", userID)
    var user User
    if err := row.Scan(&user.ID, &user.Name); err != nil {
        return nil, fmt.Errorf("failed to scan user: %w", err)
    }
    return &user, nil
}

We can use LRU cache To optimize this API:

package api

import (
    "context"
    "database/sql"
    "fmt"
    "sync"
    "time"

    "github.com/golang/lru"
)

// Cache holds a LRU cache for user information.
type Cache struct {
    mu    sync.RWMutex
    cache *lru.Cache
}

// NewCache creates a new LRU cache with a maximum size of 100 entries.
func NewCache() (*Cache, error) {
    cache, err := lru.New(100)
    if err != nil {
        return nil, fmt.Errorf("failed to create LRU cache: %w", err)
    }
    return &Cache{cache: cache}, nil
}

// GetUserInfo retrieves user information from the database or cache.
func (c *Cache) GetUserInfo(ctx context.Context, db *sql.DB, userID int64) (*User, error) {
    c.mu.RLock()
    user, ok := c.cache.Get(userID)
    c.mu.RUnlock()

    if ok {
        return user.(*User), nil
    }

    c.mu.Lock()
    defer c.mu.Unlock()

    user, err := GetUserInfo(ctx, db, userID)
    if err != nil {
        return nil, err
    }

    c.cache.Add(userID, user)
    return user, nil
}

The cached GetUserInfo method first checks whether there is data in the cache. If there is, it returns the cached data immediately. If not, it fetches the data from the database, adds it to the cache, and returns it.

The above is the detailed content of Golang API caching strategy and optimization. For more information, please follow other related articles on the PHP Chinese website!