Seamless integration and data analysis of Spring Boot and Elastic Stack

With the rapid growth of data volume, the demand for data analysis is also becoming stronger and stronger. During the development process, it is often necessary to centralize and store the log data generated by the application, and analyze and visually display the data. To solve this problem, Elastic Stack came into being. As a framework for quickly building enterprise-level applications, the seamless integration of Spring Boot and Elastic Stack has also become a major choice for developers.

This article will introduce the integration method of Spring Boot and Elastic Stack, and how to use Elastic Stack to perform data analysis and visual display of logs generated by business systems.

1. Integration method of Spring Boot and Elastic Stack

In Spring Boot, we can use log frameworks such as log4j2 or logback to collect and record application log data. Writing these log data to the Elastic Stack requires the use of logstash. Therefore, we need to configure the pipeline for communication between logstash and Spring Boot applications to achieve data transmission.

The following is a basic configuration example combining Spring Boot and Elastic Stack:

1. Configure logstash:

input {
    tcp {
        port => 5000
        codec => json
    }
}

output {
    elasticsearch {
        hosts => "localhost:9200"
        index => "logs-%{+YYYY.MM.dd}"
    }
}

Here, logstash will listen to 5000 Port that receives log data from Spring Boot applications in JSON format and stores the data into the logs-yyyy.mm.dd index in Elasticsearch.

2. Introduce logback into the Spring Boot application to configure log output:

<?xml version="1.0" encoding="UTF-8"?>
<configuration>

    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
        <encoder>
            <pattern>%d{ISO8601} [%thread] %-5level %logger{36} - %msg%n</pattern>
        </encoder>
    </appender>

    <appender name="LOGSTASH" class="net.logstash.logback.appender.LogstashTcpSocketAppender">
        <destination>localhost:5000</destination>
        <encoder class="net.logstash.logback.encoder.LogstashEncoder" />
    </appender>

    <root level="info">
        <appender-ref ref="STDOUT" />
        <appender-ref ref="LOGSTASH" />
    </root>

</configuration>

In this logback configuration file, we configure two appenders: STDOUT and LOGSTASH . Among them, STDOUT outputs the log to the console, while LOGSTASH outputs the log to the 5000 port we defined in the logstash configuration file.

Through the above configuration, we can send the logs generated by the Spring Boot application to the Elastic Stack for storage and analysis.

2. Data analysis and visual display

After storing log data in Elasticsearch, we can use Kibana to query, analyze and visually display the data.

1. Querying and analyzing log data

In Kibana, we can use Search and Discover to query and analyze log data. Among them, Search provides more advanced query syntax and allows us to perform operations such as aggregation, filtering, and sorting. Discover, on the other hand, focuses more on simple browsing and filtering of data.

2. Visual display of log data

In addition to query and analysis of log data, Kibana also provides tools such as Dashboard, Visualization and Canvas for visual display of data .

Dashboard provides a way to combine multiple visualizations to build customized dashboards. Visualization allows us to display data through charts, tables, etc. Finally, Canvas provides a more flexible way to create more dynamic and interactive visualizations.

Through the above data analysis and visual display tools, we can convert the log data generated by the application into more valuable information, providing more support for the optimization and improvement of business systems.

Conclusion

This article introduces the seamless integration of Spring Boot and Elastic Stack, and how to use Elastic Stack to perform data analysis and visual display of logs generated by business systems. In modern application development, data analysis and visualization have become an indispensable task, and the Elastic Stack provides us with a set of efficient, flexible and scalable solutions.

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