Practical techniques for using cache to handle massive requests in Golang.

Practical techniques for using cache to handle massive requests in Golang

With the development of the Internet, massive requests have become an inevitable problem for modern Web applications. These requests need to be responded to efficiently, otherwise the user experience will be seriously affected. In Golang, we can use caching to improve request response speed to better cope with the challenge of massive requests.

This article will introduce the practical skills of using cache to handle massive requests in Golang, including cache data structure, cache generation method, cache update and deletion, cache capacity and concurrency security, etc.

Cache data structure

The cache data structure in Golang is generally implemented using map. This is because the map in Golang has very high search efficiency and can also support dynamic addition and deletion of elements.

For example, we can define a map to store user information:

type User struct {
    Name string
    Age int
}

var usersCache = make(map[int]*User)

Among them, usersCache is a map used to cache user information. The key value is the user ID and the value is the User structure. pointer.

Cache generation method

The cache generation method can be divided into two categories: static generation and dynamic generation.

Static generation refers to generating a cache when the application is started. This method is suitable for situations where cache data does not change frequently. We can read data from the database or other data sources during program initialization and cache it.

For example, we can read user information from the database when the program starts and cache it:

func init() {
    // 从数据库中读取用户信息
    rows, err := db.Query("SELECT * FROM users")
    if err != nil {
        log.Fatal(err)
    }
    defer rows.Close()

    // 将用户信息缓存起来
    for rows.Next() {
        var user User
        if err := rows.Scan(&user.ID, &user.Name, &user.Age); err != nil {
            log.Fatal(err)
        }
        usersCache[user.ID] = &user
    }
}

Dynamic generation means that when there is no data in the cache, the data is generated as needed. The cache is dynamically generated from the source.

For example, we can define a GetUser function to obtain user information, read data from the data source and generate cache as needed:

func GetUser(id int) (*User, error) {
    // 先从缓存中查找用户信息
    user, ok := usersCache[id]
    if ok {
        return user, nil
    }

    // 如果缓存中不存在，则从数据库中读取用户信息
    var user User
    err := db.QueryRow("SELECT * FROM users WHERE id=?", id).Scan(&user.ID, &user.Name, &user.Age)
    if err != nil {
        return nil, err
    }

    // 将用户信息缓存起来
    usersCache[id] = &user

    return &user, nil
}

Cache update and deletion

When the data in the data source changes, the data in the cache also needs to be updated and deleted accordingly.

For example, when user information changes, we need to update the user information in the cache:

func UpdateUser(id int, name string, age int) error {
    // 更新数据库中的用户信息
    _, err := db.Exec("UPDATE users SET name=?, age=? WHERE id=?", name, age, id)
    if err != nil {
        return err
    }

    // 更新缓存中的用户信息
    user, ok := usersCache[id]
    if ok {
        user.Name = name
        user.Age = age
    }

    return nil
}

When the user logs out, we need to delete the user information from the cache:

func DeleteUser(id int) error {
    // 从数据库中删除用户信息
    _, err := db.Exec("DELETE FROM users WHERE id=?", id)
    if err != nil {
        return err
    }

    // 从缓存中删除用户信息
    delete(usersCache, id)

    return nil
}

Cache capacity and concurrency security

Cache capacity is a very important issue. If the cache is not large enough, it may cause cached data to be frequently recycled and re-applied, affecting system performance. If the cache is too large, it may cause problems such as memory overflow and system crash. Therefore, cache capacity needs to be fully considered when designing caches.

In addition, since multiple goroutines may access the cache at the same time, the concurrency security of the cache is also an issue that requires attention. We can use Mutex or RWMutex provided by the sync package to ensure cache concurrency safety.

For example, we can use RWMutex to ensure the concurrency safety of the GetUser function:

type UsersCache struct {
    cache map[int]*User
    mu sync.RWMutex
}

var usersCache = UsersCache{cache: make(map[int]*User)}

func GetUser(id int) (*User, error) {
    usersCache.mu.RLock()
    user, ok := usersCache.cache[id]
    usersCache.mu.RUnlock()

    if ok {
        return user, nil
    }

    usersCache.mu.Lock()
    defer usersCache.mu.Unlock()

    // 二次检查
    user, ok = usersCache.cache[id]
    if !ok {
        // 如果缓存中不存在，则从数据库中读取用户信息
        var user User
        err := db.QueryRow("SELECT * FROM users WHERE id=?", id).Scan(&user.ID, &user.Name, &user.Age)
        if err != nil {
            return nil, err
        }

        // 将用户信息缓存起来
        usersCache.cache[id] = &user

        return &user, nil
    }

    return user, nil
}

In the above example, we use RWMutex to ensure the concurrency safety of the cache and use double locking technology To avoid duplicate creation of cache.

Summary

This article introduces the practical skills of using cache to handle massive requests in Golang, including cache data structure, cache generation method, cache update and deletion, cache capacity and concurrency Safety and other aspects. By flexibly applying caching, we can better cope with the challenge of massive requests and improve system performance and stability.

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