Spring Boot integrates with ELK to implement log analysis and monitoring

With the continuous development of Internet technology and big data technology, the complexity of application systems has become higher and higher. At the same time, log management has also become an important topic. Traditional manual viewing of log files can no longer meet the needs of system administrators. In order to better manage system logs, an efficient solution is to use the ELK technology stack.

ELK technology stack is a set of open source software, including Elasticsearch, Logstash and Kibana. Elasticsearch is a distributed, RESTful, open source search engine that can store, search, and analyze large data sets in near real-time; Logstash is an open source server-side data processing pipeline that can collect data from multiple sources and analyze the data. Transform and transfer; Kibana is an open source data visualization platform that can interactively display data in the Elasticsearch index and conduct search, analysis and interactive operations.

Aiming at the log management issues of application systems, this article will introduce how to implement log analysis and monitoring through the integration of Spring Boot and ELK.

1. Spring Boot log collection

Spring Boot is a rapid development framework that has been used by more and more developers. In the implementation of Spring Boot, printing logs is a commonly used debugging and error troubleshooting tool. Spring Boot integrates logback as a logging framework by default, which can be managed uniformly through configuration files.

Sample code:

@Slf4j
@RestController
public class HelloController {

    @RequestMapping("/hello")
    public String hello() {
        log.info("hello world");
        return "Hello world";
    }
}

In the above sample code, we define the log object by annotating @Slf4j and print the log in the method. In the actual development process, we can define the log level, output location, file name and other information in the Spring Boot configuration file.

2. Integration of ELK

After understanding Spring Boot’s log collection, we next consider how to implement ELK integration.

1. Installation and configuration of Elasticsearch

Elasticsearch is the core component of the ELK technology stack and needs to be installed and configured before proceeding to the next step.

Official website download address: https://www.elastic.co/cn/downloads/elasticsearch

After the installation is completed, you can verify the installation and installation of Elasticsearch through http://localhost:9200 Operation status.

2. Installation and configuration of Logstash

Logstash is a component used for log collection, aggregation and transmission, and needs to be used together with Elasticsearch. Logstash also needs to be installed and configured first.

Official website download address: https://www.elastic.co/cn/downloads/logstash

Configure input, filter and output in Logstash, where input is obtained from the Spring Boot log Information, filter is used for data processing, and output is output to Elasticsearch.

Sample configuration file:

input {
  tcp {
    port => 9500
    codec => "json_lines"
  }
}

filter {
  if [type] == "springboot" {
    mutate {
      add_field => {
        "ip" => "%{host}"
      }
    }
  }
}

output {
  elasticsearch {
    hosts => ["http://localhost:9200"]
    index => "springboot-%{+YYYY.MM.dd}"
    document_id => "%{fingerprint}"
  }
}

In the configuration file, we specify Logstash to listen for log information on port 9500, process the logs input from springboot, and output them to a file named springboot-yyyy In the index in .mm.dd format.

3. Kibana installation and configuration

Kibana is an open source data visualization platform used to display data in Elasticsearch. You also need to install and configure Kibana first.

Official website download address: https://www.elastic.co/cn/downloads/kibana

In Kibana, you can create data visualization charts, search, filter and use dashboards, etc. method to analyze and monitor the collected Spring Boot application logs.

3. Log analysis and monitoring

With the support of the ELK technology stack, we can quickly and efficiently implement log analysis and monitoring of Spring Boot applications. Through Kibana's dashboard, we can view the health and abnormality of the application system in real time, and use data visualization charts to understand the running status of the system more intuitively.

At the same time, we can also conduct more in-depth analysis and research based on the data in Kibana. For large-scale clusters and multi-dimensional log data, we can process and analyze it more efficiently. This is incomparable to traditional manual viewing of log files.

Conclusion

This article introduces in detail how to implement log analysis and monitoring through the integration of Spring Boot and ELK. Among them, Spring Boot collects log information, Logstash performs data processing and transmission, and Elasticsearch performs data storage and Search, and Kibana provides data visualization and interactive operations.

For enterprise application developers and system administrators, application logs are very important monitoring and analysis objects. The ELK technology stack provides efficient, flexible, and scalable solutions, making the log management of application systems simpler, more efficient, and more visual.

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