Spring Cloud Sleuth使用ELK收集&分析日志
TIPS
本文基于Spring Cloud Greenwich SR2,理论兼容Spring Cloud所有版本。
应用整合
-
加依赖:
<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>6.1</version> </dependency>
注意,
logstash-logback-encoder
的版本务必和Logback兼容,否则会导致应用启动不起来,而且不会打印任何日志!可前往https://github.com/logstash/logstash-logback-encoder
查看和Logback的兼容性。 -
在
resources
目录下创建配置文件:logback-spring.xml
,内容如下:<?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="/Users/reno/Desktop/未命名文件夹/elk/logs/${springAppName}"/> <!-- You can override this to have a custom pattern --> <property name="CONSOLE_LOG_PATTERN" value="%clr(%d{yyyy-MM-dd HH:mm:ss.SSS}){faint} %clr(${LOG_LEVEL_PATTERN:-%5p}) %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"/> <!-- uncomment this to have also JSON logs --> <appender-ref ref="logstash"/> <!--<appender-ref ref="flatfile"/>--> </root> </configuration>
-
新建
bootstrap.yml
,并将application.yml
中的以下属性移到bootstrap.yml
中。spring: application: name: user-center
由于上面的
logback-spring.xml
含有变量(例如springAppName
),故而spring.application.name
属性必须设置在bootstrap.yml
文件中,否则,logback-spring.xml
将无法正确读取属性。
测试
-
启动应用
-
日志会打印到
/Users/reno/Desktop/未命名文件夹/elk/logs/目录中
,并且文件名称为user-center.json
,内容类似如下:{"@timestamp":"2019-08-29T02:38:42.468Z","severity":"DEBUG","service":"microservice-provider-user","trace":"5cf9479e966fb5ec","span":"5cf9479e966fb5ec","parent":"","exportable":"false","pid":"13144","thread":"http-nio-8000-exec-1","class":"o.s.w.s.m.m.a.RequestResponseBodyMethodProcessor","rest":"Using 'application/json;q=0.8', given [text/html, application/xhtml+xml, image/webp, image/apng, application/signed-exchange;v=b3, application/xml;q=0.9, */*;q=0.8] and supported [application/json, application/*+json, application/json, application/*+json]"} {"@timestamp":"2019-08-29T02:38:42.469Z","severity":"DEBUG","service":"microservice-provider-user","trace":"5cf9479e966fb5ec","span":"5cf9479e966fb5ec","parent":"","exportable":"false","pid":"13144","thread":"http-nio-8000-exec-1","class":"o.s.w.s.m.m.a.RequestResponseBodyMethodProcessor","rest":"Writing [Optional[User(id=1, username=account1, name=张三, age=20, balance=100.00)]]"} {"@timestamp":"2019-08-29T02:38:42.491Z","severity":"DEBUG","service":"microservice-provider-user","trace":"5cf9479e966fb5ec","span":"5cf9479e966fb5ec","parent":"","exportable":"false","pid":"13144","thread":"http-nio-8000-exec-1","class":"o.s.o.j.s.OpenEntityManagerInViewInterceptor","rest":"Closing JPA EntityManager in OpenEntityManagerInViewInterceptor"} {"@timestamp":"2019-08-29T02:38:42.492Z","severity":"DEBUG","service":"microservice-provider-user","trace":"5cf9479e966fb5ec","span":"5cf9479e966fb5ec","parent":"","exportable":"false","pid":"13144","thread":"http-nio-8000-exec-1","class":"o.s.web.servlet.DispatcherServlet","rest":"Completed 200 OK"} {"@timestamp":"2019-08-29T02:38:58.141Z","severity":"ERROR","service":"microservice-provider-user","trace":"","span":"","parent":"","exportable":"","pid":"13144","thread":"ThreadPoolTaskScheduler-1","class":"o.s.c.alibaba.nacos.discovery.NacosWatch","rest":"Error watching Nacos Service change"}
下面,只需要让Logstash收集到这个JSON文件,就可以在Kibana上检索日志啦!
ELK搭建
简单起见,本文使用Docker搭建ELK;其他搭建方式,请看官自行百度,比较简单,但很耗时。
-
创建
docker-compose.yml
文件,内容如下:version: '3' services: elasticsearch: image: elasticsearch:7.3.1 environment: discovery.type: single-node ports: - "9200:9200" - "9300:9300" logstash: image: logstash:7.3.1 command: logstash -f /etc/logstash/conf.d/logstash.conf volumes: # 挂载logstash配置文件 - ./config:/etc/logstash/conf.d - /Users/reno/Desktop/未命名文件夹/elk/logs/:/opt/build/ ports: - "5000:5000" kibana: image: kibana:7.3.1 environment: - ELASTICSEARCH_URL=http://elasticsearch:9200 ports: - "5601:5601"
需要注意,上面的
/Users/reno/Desktop/未命名文件夹/elk/logs/
需要改成你应用的打印路径。 -
在docker-compose.yml文件所在目录创建
config/logstash.conf
,内容如下:input { file { codec => json path => "/opt/build/*.json" # 改成你项目打印的json日志文件。 } } filter { grok { match => { "message" => "%{TIMESTAMP_ISO8601:timestamp}\s+%{LOGLEVEL:severity}\s+\[%{DATA:service},%{DATA:trace},%{DATA:span},%{DATA:exportable}\]\s+%{DATA:pid}\s+---\s+\[%{DATA:thread}\]\s+%{DATA:class}\s+:\s+%{GREEDYDATA:rest}" } } } output { elasticsearch { hosts => "elasticsearch:9200" # 改成你的Elasticsearch地址 } }
-
启动ELK
docker-compose up
测试Sleuth & ELK
-
访问你微服务的API,让它生成一些日志(如果产生日志比较少,可将
org.springframework
包的日志级别设为debug
) -
访问
http://localhost:5601
(Kibana地址),可看到类似如下的界面,按照如图配置Kibana。 -
输入条件,即可分析日志:
原理分析
原理比较简单:
- 让Sleuth打印JSON格式的日志;
- 然后在Logstash的配置文件中,配置grok语法,解析并收集JSON格式的日志,并存储到Elasticsearch中去;
- Kibana可视化分析日志。
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