Using Flume for log collection and processing in Beego

As the scale and complexity of Internet applications continue to increase, log management and analysis have become a very important issue, and Flume, as a distributed, reliable, and highly available log collection and processing system, is particularly suitable Used in large-scale Internet applications.

This article mainly introduces how to use Flume in the Beego framework for log collection and processing. I hope it will be helpful to developers who need to manage logs.

1. What is Beego framework

Beego is a Web framework developed in Go language. It is fast, flexible, simple, and easy to expand. It adopts the MVC architecture, comes with common components such as ORM, Session, Cache, etc., and supports hot loading, which can greatly improve development efficiency.

2. What is Flume

Flume is a distributed system for data collection, aggregation and movement. Flume is mainly used to collect generated data, such as web server logs, transaction logs, etc., and then uniformly transmit the collected data to the Hadoop cluster for processing and analysis.

Flume provides a series of components for data collection, including Source, Channel and Sink. Source is used to obtain data from the data source, Channel mainly implements data caching and processing, and Sink is responsible for storing data into the target system.

3. Using Flume for log management in Beego

In Beego, we can implement log collection and transmission by introducing the recommended beego/toolbox library. The specific steps are as follows:

1. Install beego/toolbox

Enter the following command in the terminal to install beego/toolbox:

go get github.com/astaxie/beego/toolbox

2. Create Flume Related configuration files

Create a file named flume.conf on the local computer with the following content:

a1.sources = r1
a1.channels = c1
a1.sinks = k1

a1.sources.r1.type = exec
a1.sources.r1.command = tail -F /var/log/nginx/access.log

a1.channels.c1.type = memory

a1.sinks.k1.type = avro
a1.sinks.k1.hostname = localhost
a1.sinks.k1.port = 2004

a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

This configuration file defines three main components, namely Source , Channel and Sink. Among them:

• Source: Use the exec type to obtain Nginx access logs in real time through the tail command;
• Channel: Use the memory type to cache the data in memory;
• Sink: Use avro type to transmit data to the Flume Agent process through the network.

3. Write Beego’s log collection and transmission code

Add the following code to the main.go file of the Beego project:

package main

import (
  "github.com/astaxie/beego"
  "github.com/astaxie/beego/logs"
  "github.com/astaxie/beego/toolbox"
)

func main() {
  beego.SetLogger(logs.AdapterFile, `{"filename":"example.log","level":6,"maxlines":0,"maxsize":0,"daily":true,"maxdays":10}`)
  toolbox.AddTask("log", &toolbox.Task{
    TaskFunc: func() error {
      logs.GetBeeLogger().Flush()
      return nil
    },
    CronExpr: "0 0 */1 * * *",
   })
  toolbox.StartTask()
  defer toolbox.StopTask()
  beego.Run()
}

This In the code:

• Use the beego.SetLogger method to set the log output to a file and define some log-related configurations;
• Use the AddTask method in the toolbox library to define a name It is a scheduled task for "log";
• Use Cron expression to set the frequency of scheduled task execution;
• Use the toolbox.StartTask method to start the scheduled task, and stop it through the defer statement when the application ends Scheduled tasks.

4. Conclusion

Through the introduction of this article, we have learned about the method of using Flume for log collection and processing in the Beego framework. With the continuous development of Internet applications and the continuous maturity of big data technology, the importance of log processing has become increasingly prominent. By using distributed systems such as Flume, we can collect, transmit and process logs more efficiently, providing better management and performance optimization support for applications.

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