Using Flume and Kafka in Beego for log collection and analysis

Beego is an efficient Go language web framework that supports rapid development and easy expansion. In practical applications, we often face how to collect and analyze a large amount of Web log data to obtain useful information and knowledge. In this article, we will introduce how to use Flume and Kafka to collect and analyze Beego Web log data.

Flume is a reliable, scalable distributed log collection, aggregation and transmission system that can support the collection, aggregation and transmission of large amounts of log data from various data sources and various streaming data pipelines. Kafka is a high-throughput, distributed, and durable message middleware system that can handle large amounts of real-time data streams and has simple horizontal scalability and elastic scalability. They are all open source projects supported and maintained by the Apache Foundation.

1. Install and configure Flume

First, we need to install and configure Flume. In this article, we will use Flume version 1.9.0 and test it in a local environment. Flume can be downloaded from the official website: http://flume.apache.org/download.html.

After installing Flume, we need to configure the Flume Agent configuration file. In this article, we will use Flume's simple configuration method. We need to create a configuration file named flume.conf in the Flume installation directory and define our Flume Agent in it.

In the flume.conf file, we need to define a Flume Agent with source, channel and sink, as shown below:

agent.sources = avro-source
agent.channels = memory-channel
agent.sinks = kafka-sink
 
# Define the source
agent.sources.avro-source.type = avro
agent.sources.avro-source.bind = localhost
agent.sources.avro-source.port = 10000
 
# Define the channel
agent.channels.memory-channel.type = memory
agent.channels.memory-channel.capacity = 10000
 
# Define the sink
agent.sinks.kafka-sink.type = org.apache.flume.sink.kafka.KafkaSink
agent.sinks.kafka-sink.kafka.bootstrap.servers = localhost:9092
agent.sinks.kafka-sink.kafka.topic = beego-log
agent.sinks.kafka-sink.batchSize = 20
agent.sinks.kafka-sink.requiredAcks = 1
 
# Bind the source and sink to the channel
agent.sources.avro-source.channels = memory-channel
agent.sinks.kafka-sink.channel = memory-channel

In the above configuration file, we define a name It is the source of avro-source. Its type is avro. It will listen to port 10000 on the localhost of the machine and accept Beego Web log data. We also define a channel named memory-channel, whose type is memory, which can store up to 10,000 events in memory, and provide a sink named kafka-sink, whose type is KafkaSink, which will Beego Web log data is sent to a topic named beego-log in Kafka. In this configuration, we also set some properties of KafkaSink, such as batchSize (the number of messages written to Kafka each time) and requiredAcks (the number of messages written to Kafka that need to be acknowledged).

2. Install and configure Kafka

Next, we need to install and configure Kafka. In this article, we will use Kafka version 2.2.0 and test it in a local environment. Kafka can be downloaded from the official website: http://kafka.apache.org/downloads.html.

After installing Kafka, we need to create a topic named beego-log. We can use Kafka's command line tool to create the topic, as shown below:

bin/kafka-topics.sh --zookeeper localhost:2181 --create --replication-factor 1 --partitions 1 --topic beego-log

In the above command , we use Kafka's command line tool kafka-topics.sh to create a topic named beego-log, specify the replication factor (replication-factor) as 1 and the partitions (partitions) as 1, and use the address of ZooKeeper as localhost:2181.

3. Application of Beego Web Framework

We use Beego Web framework to create a simple Web application and record Web log data in it. In this article, we will create an application with only one controller and one router as shown below:

package main
 
import (
    "github.com/astaxie/beego"
)
 
type MainController struct {
    beego.Controller
}
 
func (c *MainController) Get() {
    // do something
    c.Ctx.WriteString("Hello, World!")
}
 
func main() {
    beego.Router("/", &MainController{})
    beego.Run()
}

In the above application, we have created an application called MainController's controller, it has only one Get method. In the Get method, we implement some logic and then return a message to the client. We used Beego's routing function to map the root path "/" to the MainController's Get method.

We can enable the logging (log) function in Beego's configuration file and set the log level to Debug to record and track more details. We need to add the following content to Beego's configuration file app.conf:

appname = beego-log
httpport = 8080
runmode = dev
 
[log]
level = debug
 
[[Router]]
    Pattern = /
    HTTPMethod = get
    Controller = main.MainController:Get

In the above configuration file, we define the application name, HTTP port, operating mode and log level. We also specified a route named Router, defined a controller named MainController, and mapped the root path "/" to the Get method.

4. Using Flume and Kafka for log collection and analysis

Now that we have a simple Beego application and a Flume Agent, we can integrate them and use Kafka Carry out log collection and analysis.

We can start the Beego application and send some HTTP requests to it to produce some log data. We can use the curl command to send HTTP requests to Beego as follows:

$ curl http://localhost:8080/
Hello, World!

We can start the Flume Agent and use the following command to start it:

$ ./bin/flume-ng agent --conf ./conf --conf-file ./conf/flume.conf --name agent --foreground

In the above command, We use Flume's command line tool flume-ng to start a Flume Agent named agent, and specify the configuration file as ./conf/flume.conf.

Now, we can view the Beego Web log data in Kafka. We can use Kafka's command line tool kafka-console-consumer.sh to consume data from the beego-log topic, as shown below:

$ bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic beego-log --from-beginning

In the above command, we use Kafka's command line tool kafka- console-consumer.sh to start a consumer and consume data from the topic named beego-log. We use the --from-beginning option to start consuming from the oldest message.

When we request a Beego application, Flume will collect log events, store them into an in-memory channel, and then transfer them to a Kafka topic named beego-log. We can use command line tools or APIs in Kafka to consume and process these log data to obtain more valuable information and insights.

5. Summary

In this article, we introduce how to use Flume and Kafka to collect and analyze Beego Web log data. We first installed and configured Flume and Kafka, then created a simple Beego application and configured its logging functionality. Finally, we created a simple Flume Agent and integrated it with the Beego application, using Kafka for log collection and analysis.

In practical applications, we can flexibly configure and customize the parameters and properties of Flume and Kafka according to needs and scenarios, so as to better adapt to different data sources and processing tasks, and obtain more valuable information. and knowledge.

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