The practice of using cache to improve real-time stream computing of big data in Golang.

With the advent of the big data era, real-time processing of data has become more and more important. In real-time streaming computing, performance is a key factor. For the Golang language, caching can be used to improve the performance of big data real-time stream computing.

In this article, we will explore how to use caching in Golang to improve the performance of big data real-time streaming computing. We will first introduce what caching is and its advantages, then introduce how to implement caching in Golang, and use examples to illustrate how to use caching in big data real-time stream computing.

What is caching and its advantages

Cache is a data storage technology used to improve the access speed of data. Caching usually uses high-speed storage devices to store the most recent or most frequently used data to avoid repeated calculations or IO operations. The main advantage of using caching is improved program performance and responsiveness.

In real-time stream computing, a large amount of data needs to be analyzed and calculated. Storing data in cache can greatly improve the performance and responsiveness of your program. In a cache, frequently used data can be stored in high-speed memory, thereby avoiding the overhead of retrieving data from disk or network for each access. At the same time, using cache can also reduce network and IO burdens, thereby improving the performance and response speed of the entire system.

The main risk of using cache is the inconsistency of cached data. Because data in the cache may be modified or deleted, this can lead to cache inconsistency. To avoid this situation, developers need to use some techniques and strategies to ensure the consistency of cached data.

Implementing caching in Golang

In Golang, caching can be implemented using the built-in caching mechanism in the standard library. The standard library provides two common cache implementations: map and sync.Pool.

map is an unordered collection of key-value pairs that can access values ​​through keys. In Golang, you can use map to implement caching. Use map to quickly store and retrieve data, while also providing easy access to data. The following is a sample code that uses map to implement caching:

package main

import (
    "fmt"
    "sync"
    "time"
)

var cache = make(map[string]string)
var mu sync.Mutex

func main() {
    go dataReader()
    go dataReader()

    time.Sleep(2 * time.Second)
}

func dataReader() {
    for {
        getData("key")
        time.Sleep(100 * time.Millisecond)
    }
}

func getData(key string) string {
    mu.Lock()
    defer mu.Unlock()

    if val, ok := cache[key]; ok {
        fmt.Println("Cached: ", val)
        return val
    }

    time.Sleep(500 * time.Millisecond)
    data := "Data " + time.Now().Format(time.StampMilli)
    fmt.Println("Loaded: ", data)
    cache[key] = data
    return data
}

In this example, we use map to implement a simple caching function. We use sync.Mutex to protect the reading and writing of the map, and determine whether the data has been cached in the map in the getData method. If it exists, the data is obtained directly from the map; if it does not exist, the data is read from the data source. After getting the data, we store it in the map so that it can be quickly retrieved the next time it is accessed.

Another common cache implementation is sync.Pool. Pool is an object pool that can be used to store and reuse temporary objects to avoid frequent creation and destruction of objects. Using Pool can improve the performance and responsiveness of your program. The following is a sample code that uses sync.Pool to implement caching:

package main

import (
    "bytes"
    "fmt"
    "sync"
)

var bufPool = sync.Pool{
    New: func() interface{} {
        return bytes.NewBuffer([]byte{})
    },
}

func main() {
    var wg sync.WaitGroup
    for i := 0; i < 10; i++ {
        wg.Add(1)
        go func() {
            defer wg.Done()
            b := bufPool.Get().(*bytes.Buffer)
            defer bufPool.Put(b)
            b.WriteString("Hello World!")
            fmt.Println(b.String())
        }()
    }
    wg.Wait()
}

In this example, we use sync.Pool to implement a cache pool for storing and reusing temporary byte buffers. We define a function to create a new byte buffer and use the Put and Get methods to store and get the byte buffer. After using the byte buffer, we put it back into the cache pool for next use.

Examples of using cache to improve big data real-time stream computing performance

In actual applications, it is very common to use cache to improve the performance of big data real-time stream computing. The following is a sample code that uses caching to improve the performance of big data real-time stream computing:

package main

import (
    "fmt"
    "math/rand"
    "sync"
    "time"
)

type Data struct {
    Key   string
    Value int
    Time  time.Time
}

var cache = make(map[string]*Data)
var mu sync.Mutex

func main() {
    go producer()
    go consumer()

    time.Sleep(10 * time.Second)
}

func producer() {
    for {
        key := randString(10)
        value := rand.Intn(100)
        data := &Data{Key: key, Value: value, Time: time.Now()}
        mu.Lock()
        cache[key] = data
        mu.Unlock()
        time.Sleep(500 * time.Millisecond)
    }
}

func consumer() {
    for {
        mu.Lock()
        for key, data := range cache {
            if time.Since(data.Time) >= 2*time.Second {
                delete(cache, key)
            } else {
                fmt.Println(data.Key, data.Value)
            }
        }
        mu.Unlock()
        time.Sleep(100 * time.Millisecond)
    }
}

func randString(length int) string {
    const charset = "abcdefghijklmnopqrstuvwxyz0123456789"
    b := make([]byte, length)
    for i := range b {
        b[i] = charset[rand.Intn(len(charset))]
    }
    return string(b)
}

In this example, we use map to implement caching, and protect the concurrent reading and writing of map by locking (mutex) . We use the producer function to generate a random string of length 10 as the key every 500ms, randomly generate a value between 0 and 100, and the current time as the value. We store the generated data in a map. In the consumer function, we traverse the data in the map every 100ms and check their timestamps. If the timestamp of the data has exceeded 2s, it is deleted from the map. Otherwise, we output the key and value of the data.

Using cache can significantly improve the performance and response speed of the program. In the example above, we can see that the program is constantly producing data and writing it to the cache, while another thread is constantly reading data from the cache. If caching is not used, the performance and responsiveness of the program will be greatly affected.

Conclusion

In this article, we introduced what caching is and its advantages. We also introduced how to use the standard library to implement caching in Golang, and used an example to illustrate how to use caching in big data real-time stream computing. Using cache can greatly improve the performance and response speed of the program, and reduce the burden on the network and IO. In actual applications, we should consider using caching to optimize program performance and response speed.

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