SoFunction
Updated on 2025-03-05

Develop a high-concurrency system using Go language

What is a high concurrency system

A high concurrency system refers to a system that can support many user requests at the same time and handle a large number of parallel computing. This system is characterized by its ability to handle multiple tasks at the same time to ensure efficient completion of each user's operations. In the Internet field, such as online shopping, booking systems, search engines, online videos and other applications, high concurrency systems are required to handle real-time requests from a large number of users.

There are mainly the following technical methods for dealing with high concurrency systems:

  • Load balancing: Through load balancing technology, load can be distributed among multiple servers, reducing the pressure of a single server, and improving system availability and concurrent processing capabilities.
  • Caching technology: Caching technology can store frequently queried data or results, and directly read the cached data when querying again, such as Redis, avoiding frequent database operations.
  • Database optimization: includes database design, index optimization, query optimization, database division and table division, etc. to improve the processing capabilities of the database.
  • Asynchronous processing: Some non-critical, time-consuming processing work can be performed asynchronously to reduce user waiting time and server pressure.
  • Horizontal scaling of ordinary hardware: When the server load is too high, the system's processing power can be expanded by adding more servers.
  • Use high concurrency programming models: such as event-driven models, Reactor models, distributed computing, etc.

How to develop a high concurrency system using Go

  • Understand the concurrency characteristics of Go: Go language goroutine and channel are the core of Go concurrent programming. A Goroutine can be regarded as a lightweight thread that the Go language will schedule, and channel is the way of communication between goroutines. Understanding these two concepts is crucial for concurrent programming.
  • Use of coroutines: Coroutines are lighter than threads. Go supports coroutines at the language level. Related scheduling and management are managed by Go runtime. For developers, starting a coroutine is very simple, and you only need to use the go keyword.
  • Data Sharing with Channel: Channel is a channel between coroutines that can be used for data sharing. You should try to avoid using shared memory as it can lead to various complex problems. Channel makes data sharing simple and secure.
  • Use the Select:Select statement to handle send/receive operations of one or more channels. If multiple cases are ready at the same time, Select will randomly select one execution.
  • Use locks and condition variables in sync packages: In some cases, resources need to be protected by mutexes and read-write locks (RWMutex).
  • Use context to control the end of concurrency: context can pass request domain data across API boundaries, and also contains signals such as the operation or end of Go processes.
  • Testing concurrent programs: The testing of concurrent programs is usually complex, and it is necessary to use pressure testing, simulate high concurrent requests and other methods to discover and locate concurrency problems.
  • Optimization and debugging: Use pprof for performance analysis, use GODEBUG to locate problems, etc.

The above are just some basic steps and concepts for using Go to develop a high-concurrency system. In actual development, it is also necessary to combine the actual business needs of the system. It may be necessary to use message queues, distributed databases, microservices and other technologies for horizontal scaling to improve the system's concurrency processing capabilities.

Go language code example

Let me demonstrate that a Go language can implement a simple crawler system

package main

import (
   "fmt"
   "net/http"
   "sync"

   "/x/net/html"
)

func main() {
   urls := []string{
      "/",
      "/",
      "/",
   }

   fetchAll(urls)
}

func fetchAll(urls []string) {
   var wg 

   for _, url := range urls {
      (1)
      go fetch(&wg, url)
   }

   ()
}

func fetch(wg *, url string) {
   defer ()

   res, err := (url)
   if err != nil {
      ("Error fetching: %s\n", url)
      return
   }
   defer ()

   doc, err := ()
   if err != nil {
      ("Error parsing: %s\n", url)
   }

   title := extractTitle(doc)
   ("Title of %s: %s\n", url, title)
}
// Here we only do simple analytical titles, which can operate according to actual application scenariosfunc extractTitle(doc *) string {
   var title string

   traverseNodes := func(n *) {
      if  ==  &&  == "title" {
         title = 
      }
      for c := ; c != nil; c =  {
         traverseNodes(c)
      }
   }

   traverseNodes(doc)

   return title
}

In this example, crawling and parsing of each URL is done in a separate goroutine, so it is possible to parse another web page while waiting for one web page to download, greatly improving efficiency. WaitGroup is used to wait for all crawling tasks to be completed.

But in reality, it is impossible for us to open as many goroutines as many URLs as we have. So how can we improve our code so that this crawler can crawl in a limited goroutine?

// Import packages
package main

import (
   "fmt"
   "sync"
   "net/http"
   "io/ioutil"
   "regexp"
   "time"
)

// Maximum number of working goroutines
const MaxWorkNum = 10

// URL channel
var UrlChannel = make(chan string, MaxWorkNum)
// Results channel
var ResultsChannel = make(chan string, MaxWorkNum)

func GenerateUrlProducer(seedUrl string) {
   go func() {
      UrlChannel <- seedUrl
   }()
}

func GenerateWorkers() {
   var wg 
   // Limit the number of working goroutines
   for i := 0; i < MaxWorkNum; i++ {
      (1)
      go func(i int) {
         defer ()
         for {
            url, ok := <- UrlChannel
            if !ok {
               return
            }
            newUrls, err := fetch(url)
            if err != nil {
               ("Worker %d: %v\n", i, err)
               return
            }
            for _, newUrl := range newUrls {
               UrlChannel <- newUrl
            }
            ResultsChannel <- url
         }
      }(i)
   }
   ()
}

func fetch(url string) ([]string, error) {
   resp, err := (url)
   if err != nil {
      return nil, err
   }
   body, _ := ()
   urlRegexp := (`http[s]?://[^"'\s]+`)
   newUrls := (string(body), -1)
   ()
   () // to prevent IP being blocked
   return newUrls, nil
}

func ResultsConsumer() {
   for {
      url, ok := <- ResultsChannel
      if !ok {
         return
      }
      ("Fetched:", url)
   }
}

func main() {
   go GenerateUrlProducer("")
   go GenerateWorkers()
   ResultsConsumer()
}

In the above example, we generate two working pools, one working pool is used to process the URL and a result consumer, and then pass it in to chan from the seed address, and then crawl the new URLs continuously and repeatedly. The above code snippets do not deal with problems such as loop crawling the same URL and deduplication of URLs. These need to be paid attention to in actual development. For the sake of simplicity of the code and no good handling of errors, these are all areas that need improvement. This is just a very basic concurrent crawler, hoping to help you understand how to build a simple high concurrent crawler through goroutine and channel.

This is the end of this article about developing a high-concurrency system using Go language. For more relevant content on Go high-concurrency system, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!