Data preprocessing techniques for Golang function performance optimization

In Golang function performance optimization, data preprocessing skills are crucial, including: caching commonly used data and avoiding I/O operations and calculations. Precompute derived values ​​to save repeated calculations. Use slices to extend the length and avoid multiple allocations and copies.

Golang function performance optimization data preprocessing skills

To optimize function performance in Golang, data preprocessing skills are crucial . By preprocessing data, unnecessary overhead during function execution can be reduced, thereby improving execution efficiency.

1. Cache commonly used data

For frequently accessed data (such as configuration values, constants), caching it in memory can avoid frequent I/O operations and calculations. For example:

var cachedConfig *Config
func GetConfig() *Config {
    if cachedConfig == nil {
        cachedConfig, err := LoadConfigFromFile("config.json")
        if err != nil {
            // 处理错误
        }
    }
    return cachedConfig
}

2. Precompute derived values

You can save repeated calculations in functions by precomputing derived values ​​(such as hashes, converted values). For example:

var hashedPassword string
func CheckPassword(password string, hashedPassword string) bool {
    if hashedPassword == "" {
        hashedPassword = Hash(password)
    }
    return hashedPassword == Hash(password)
}

3. Use slices to extend the length

When it is predicted that the slice will continue to expand, use append(slice, ...) = nil Extending the length of a slice avoids multiple allocations and copies. For example:

func AppendToSlice(slice []int, values ...int) {
    slice = append(slice, values...) // 扩展切片长度
    _ = slice[:cap(slice)]          // 清除未分配的元素
}

Practical case

The following is an actual optimization example of a function call:

// 不优化
func ProcessData(data [][]int) {
    for _, row := range data {
        for _, col := range row {
            // 对 col 进行计算
        }
    }
}

// 优化
func ProcessData(data [][]int) {
    // 将 data 转换为 map，以列为键
    cols := make(map[int][]int)
    for _, row := range data {
        for i, col := range row {
            cols[i] = append(cols[i], col)
        }
    }

    // 遍历列并进行计算
    for col, values := range cols {
        // 对 values 进行计算
    }
}

After optimization, function By pre-fetching columns into a map, performance is improved by reducing the number of iterations over the original data.

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