Spring Cloud Sleuth与ELK配合使用

一新建项目microservice-simple-provider-user-trace-elk

二为项目添加以下依赖

  <dependencies>
    <dependency>
      <groupId>org.springframework.boot</groupId>
      <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
      <groupId>org.springframework.boot</groupId>
      <artifactId>spring-boot-starter-data-jpa</artifactId>
    </dependency>
    <dependency>
      <groupId>com.h2database</groupId>
      <artifactId>h2</artifactId>
    </dependency>
    <dependency>
      <groupId>org.springframework.boot</groupId>
      <artifactId>spring-boot-starter-actuator</artifactId>
    </dependency>
    <dependency>
      <groupId>org.springframework.cloud</groupId>
      <artifactId>spring-cloud-starter-sleuth</artifactId>
    </dependency>
    <dependency>
      <groupId>net.logstash.logback</groupId>
      <artifactId>logstash-logback-encoder</artifactId>
      <version>4.6</version>
    </dependency>
  </dependencies>

三新建logback-spring.xml文件

<?xml version="1.0" encoding="UTF-8"?>
<configuration>
  <include resource="org/springframework/boot/logging/logback/defaults.xml" />
 
  <springProperty scope="context" name="springAppName" source="spring.application.name" />
  <!-- Example for logging into the build folder of your project -->
  <property name="LOG_FILE" value="${BUILD_FOLDER:-build}/${springAppName}" />
 
  <property name="CONSOLE_LOG_PATTERN"
    value="%clr(%d{yyyy-MM-dd HH:mm:ss.SSS}){faint} %clr(${LOG_LEVEL_PATTERN:-%5p}) %clr([${springAppName:-},%X{X-B3-TraceId:-},%X{X-B3-SpanId:-},%X{X-B3-ParentSpanId:-},%X{X-Span-Export:-}]){yellow} %clr(${PID:- }){magenta} %clr(---){faint} %clr([%15.15t]){faint} %clr(%-40.40logger{39}){cyan} %clr(:){faint} %m%n${LOG_EXCEPTION_CONVERSION_WORD:-%wEx}" />
  <!-- Appender to log to console -->
  <appender name="console" class="ch.qos.logback.core.ConsoleAppender">
    <filter class="ch.qos.logback.classic.filter.ThresholdFilter">
      <!-- Minimum logging level to be presented in the console logs -->
      <level>DEBUG</level>
    </filter>
    <encoder>
      <pattern>${CONSOLE_LOG_PATTERN}</pattern>
      <charset>utf8</charset>
    </encoder>
  </appender>
  <!-- Appender to log to file -->
  <appender name="flatfile" class="ch.qos.logback.core.rolling.RollingFileAppender">
    <file>${LOG_FILE}</file>
    <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
      <fileNamePattern>${LOG_FILE}.%d{yyyy-MM-dd}.gz</fileNamePattern>
      <maxHistory>7</maxHistory>
    </rollingPolicy>
    <encoder>
      <pattern>${CONSOLE_LOG_PATTERN}</pattern>
      <charset>utf8</charset>
    </encoder>
  </appender>
 
  <!-- Appender to log to file in a JSON format -->
  <appender name="logstash" class="ch.qos.logback.core.rolling.RollingFileAppender">
    <file>${LOG_FILE}.json</file>
    <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
      <fileNamePattern>${LOG_FILE}.json.%d{yyyy-MM-dd}.gz</fileNamePattern>
      <maxHistory>7</maxHistory>
    </rollingPolicy>
    <encoder class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
      <providers>
        <timestamp>
          <timeZone>UTC</timeZone>
        </timestamp>
        <pattern>
          <pattern>
            {
              "severity": "%level",
              "service": "${springAppName:-}",
              "trace": "%X{X-B3-TraceId:-}",
              "span": "%X{X-B3-SpanId:-}",
              "parent": "%X{X-B3-ParentSpanId:-}",
              "exportable": "%X{X-Span-Export:-}",
              "pid": "${PID:-}",
              "thread": "%thread",
              "class": "%logger{40}",
              "rest": "%message"
            }
          </pattern>
        </pattern>
      </providers>
    </encoder>
  </appender>
 
  <root level="INFO">
    <appender-ref ref="console" />
    <appender-ref ref="logstash" />
    <!--<appender-ref ref="flatfile"/> -->
  </root>
</configuration>

四编写application.yml

server:
  port: 8000
spring:
  jpa:
    generate-ddl: false
    show-sql: true
    hibernate:
      ddl-auto: none
  datasource:                           # 指定数据源
    platform: h2                        # 指定数据源类型
    schema: classpath:schema.sql        # 指定h2数据库的建表脚本
    data: classpath:data.sql            # 指定h2数据库的数据脚本
logging:
  level:
    root: INFO
    org.springframework.cloud.sleuth: DEBUG
    # org.springframework.web.servlet.DispatcherServlet: DEBUG

五编写bootstrap.yml

spring:
  application:
    name: microservice-provider-user
# 注意：本例中的spring.application.name只能放在bootstrap.*文件中，不能放在application.*文件中，因为我们使用了自定义的logback-spring.xml。
# 如果放在application.*文件中，自定义的logback文件将无法正确读取属性。

六编写Logstash配置文件，命名为logstash.conf,内容如下

input {
  file {
    codec => json
    path => "F:/springcloud/temp/microservice-simple-provider-user-trace-elk/build/*.json"
  }
}
filter {
  grok {
    match => { "message" => "%{TIMESTAMP_ISO8601:timestamp}\s+%{LOGLEVEL:severity}\s+\[%{DATA:service},%{DATA:trace},%{DATA:span},%{DATA:exportable}\]\s+%{DATA:pid}---\s+\[%{DATA:thread}\]\s+%{DATA:class}\s+:\s+%{GREEDYDATA:rest}" }
  }
}
output {
  elasticsearch {
    hosts => "localhost:9200"
  }
}

七测试

1 启动ELK

1.1 ElasticSearch启动方法：在服务中启动。

1.2 Logstash启动方法

D:\Logstash\logstash-6.2.2\bin>logstash -f D:/Logstash/logstash-6.2.2/conf/logstash.conf

1.3 kinana启动方法

D:\kinana\kibana-6.2.2\bin>kibana.bat

2 启动项目microservice-simple-provider-user-trace-elk

3 多次访问http://localhost:8000/1,产生一些日志

4 访问http://localhost:5601,可以看到Kibana的首页，

然后在点击Discover，有了数据，如下图

测试成功！

八 ELK架构图

Spring Cloud Sleuth与ELK配合使用

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