How to implement process monitoring in golang

In development and operation and maintenance, we often need to monitor processes to ensure that they can run normally and that problems can be detected and dealt with in a timely manner when they occur. In this article, we will introduce how to use Go language to implement process monitoring, helping you better understand and apply monitoring technology.

1. Basic principles of process monitoring

Process monitoring mainly refers to a technology for monitoring, controlling and exception handling of the running status of applications. In layman's terms, it is to use certain monitoring methods to monitor the running status of the program in real time, while ensuring that the program executes normally and can handle errors in a timely manner.

In process monitoring, the most commonly used monitoring method is the heartbeat mechanism. The so-called heartbeat mechanism is to continuously send messages to the outside during the running of the program to indicate its normal operation status. And we determine whether the program is healthy by capturing and parsing these messages. Once a program abnormality is discovered, it will be processed accordingly to ensure the normal operation of the system.

2. Implementation ideas for process monitoring in golang

In the Go language, we can use some ready-made packages to implement process monitoring. The specific implementation steps are as follows:

1. Get all process information

We can use the process package in the psutil package to obtain relevant information about all processes in the system. First, we need to introduce the package:

import (
    "github.com/shirou/gopsutil/process"
)

Then, get all the processes in the system by calling the process.Processes() function:

processes, err := process.Processes()
if err != nil {
    log.Fatalf("failed to get all processes: %v", err)
}

This function returns a process slice, each of which Each element is a structure containing process information. For specific structures, please refer to the official documentation of psutil.

2. Filter the processes that need to be monitored

In order to monitor a specific process, we need to find the process we are interested in among all processes and record their pids. Here we can use some conditional filters to quickly filter out the processes that need to be monitored. For example, we can filter out the processes we need based on process name, process ID and other conditions. Taking the process name as an example, the code is as follows:

targetProcessName := "myApp"
targetPidList := make([]int32, 0)

for _, proc := range processes {
    name, err := proc.Name()
    if err != nil || name != targetProcessName {
        continue
    }
    pid := proc.Pid
    targetPidList = append(targetPidList, pid)
    log.Printf("find target process %s, pid = %d\n", name, pid)
}

3. Perform heartbeat detection on the process that needs to be monitored

For the process that needs to be monitored, we can determine the process by sending a heartbeat message to it. Whether it is running normally. Specifically, we can make a judgment by reading its CPU and memory usage. If you find abnormal CPU and memory usage, you can think that there is a problem with the process.

In order to implement heartbeat detection, we can use a goroutine to periodically read the status of the specified process and record it. The specific implementation code is as follows:

type ProcessState struct {
    CpuPercent    float64
    MemResident   uint64
    MemVirtual    uint64
    MemSwap       uint64
}

func checkProcessState(targetPid int32, stateChan chan<- ProcessState) {
    ticker := time.NewTicker(5 * time.Second)
    for {
        select {
        case <-ticker.C:
            proc, err := process.NewProcess(targetPid)
            if err != nil {
                log.Printf("failed to get process %d: %v", targetPid, err)
                continue
            }
            cpuPercent, err := proc.CPUPercent()
            if err != nil {
                log.Printf("failed to get cpu percent for process %d: %v", targetPid, err)
                continue
            }
            memInfo, err := proc.MemoryInfo()
            if err != nil {
                log.Printf("failed to get memory info for process %d: %v", targetPid, err)
                continue
            }
            state := ProcessState{
                CpuPercent:  cpuPercent,
                MemResident: memInfo.RSS,
                MemVirtual:  memInfo.VMS,
                MemSwap:     memInfo.Swap,
            }
            stateChan <- state
        }
    }
}

In the above code, we first use a ticker timer to check the status of the specified process every five seconds. During the detection process, we obtain the process handle of the specified pid through the process.NewProcess() function, and use the functions provided to obtain the CPU usage and memory usage. Store this information in a structure defined above and send it to the outside through stateChan.

3. Complete golang process monitoring code example

Based on the above implementation ideas and code, we can write a complete golang process monitoring code example. The complete code is as follows:

package main

import (
    "fmt"
    "github.com/shirou/gopsutil/process"
    "log"
    "time"
)

type ProcessState struct {
    CpuPercent    float64
    MemResident   uint64
    MemVirtual    uint64
    MemSwap       uint64
}

func main() {
    targetProcessName := "myApp"
    targetPidList := make([]int32, 0)
    processes, err := process.Processes()
    if err != nil {
        log.Fatalf("failed to get all processes: %v", err)
    }
    for _, proc := range processes {
        name, err := proc.Name()
        if err != nil || name != targetProcessName {
            continue
        }
        pid := proc.Pid
        targetPidList = append(targetPidList, pid)
        log.Printf("find target process %s, pid = %d\n", name, pid)
    }
    stateChan := make(chan ProcessState)
    for _, pid := range targetPidList {
        go checkProcessState(pid, stateChan)
    }
    for {
        select {
        case state := <-stateChan:
            if state.CpuPercent > 100.0 || state.MemSwap > 0 || state.MemVirtual > 2*state.MemResident {
                log.Printf("process is not healthy: %+v\n", state)
            } else {
                log.Printf("process is healthy: %+v\n", state)
            }
        }
    }
}

func checkProcessState(targetPid int32, stateChan chan<- ProcessState) {
    ticker := time.NewTicker(5 * time.Second)
    for {
        select {
        case <-ticker.C:
            proc, err := process.NewProcess(targetPid)
            if err != nil {
                log.Printf("failed to get process %d: %v", targetPid, err)
                continue
            }
            cpuPercent, err := proc.CPUPercent()
            if err != nil {
                log.Printf("failed to get cpu percent for process %d: %v", targetPid, err)
                continue
            }
            memInfo, err := proc.MemoryInfo()
            if err != nil {
                log.Printf("failed to get memory info for process %d: %v", targetPid, err)
                continue
            }
            state := ProcessState{
                CpuPercent:  cpuPercent,
                MemResident: memInfo.RSS,
                MemVirtual:  memInfo.VMS,
                MemSwap:     memInfo.Swap,
            }
            stateChan <- state
        }
    }
}

In this example, we first find the process we need to monitor, and start a goroutine for each process to regularly detect its status. The program continuously receives status messages from goroutine from chan, and determines whether the program is running normally based on the status. If a process abnormality is found, corresponding prompts will be provided through logs.

4. Summary

This article introduces how to use the Go language to implement process monitoring, mainly by calling the API provided in the psutil package and using goroutine for regular detection. Process monitoring allows us to better control the running status of the application and handle it in a timely manner when an exception occurs in the program, helping us to better implement system development and operation and maintenance.

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