1. Spring Cloud Sleuth和相关ID
通过将Spring Cloud Sleuth添加到Spring Microservices中,可以
- 如果不存在,则在服务调用中透明地创建并注入相关ID
- 管理相关ID到出站服务调用的传播,以便事务的相关ID自动添加到出站调用
- 将相关信息添加到Spring的MDC日志记录中,以便生成Spring Boots默认SL4J和Logback会自动记录相关ID 实现
- (可选)在服务调用中将跟踪信息发布到Zipkin分布式跟踪平台
将Spring Cloud Sleuth添加到licensing和organization服务中
添加依赖
<dependency>
<groupId>org.springframework.cloud</groupId>
<artifactId>spring-cloud-starter-sleuth</artifactId>
</dependency>
Spring Cloud Sleuth痕迹的剖析
Spring Cloud Sleuth将为每个日志条目添加四条信息
2. 日志聚合和Spring Cloud Sleuth
用于Spring Boot的日志聚合解决方案的选项
Spring Cloud Sleuth/ELK实现日志聚合
添加logback相关依赖
<dependency>
<groupId>net.logstash.logback</groupId>
<artifactId>logstash-logback-encoder</artifactId>
<version>4.9</version>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<version>1.2.3</version>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-core</artifactId>
<version>1.2.3</version>
</dependency>
创建logback-spring.xml日志配置文件
<?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"/>
<!-- 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>
<!-- 为logstash输出的JSON格式的Appender -->
<appender name="logstash-tcp"
class="net.logstash.logback.appender.LogstashTcpSocketAppender">
<destination>logstash:9601</destination>
<!-- 日志输出编码 -->
<encoder
class="net.logstash.logback.encoder.LoggingEventCompositeJsonEncoder">
<providers>
<timestamp>
<timeZone>UTC</timeZone>
</timestamp>
<pattern>
<pattern>
{
"severity": "%level",
"service": "${springAppName:-}",
"trace": "%X{X-B3-TraceId:-}",
"span": "%X{X-B3-SpanId:-}",
"exportable": "%X{X-Span-Export:-}",
"pid": "${PID:-}",
"thread": "%thread",
"class": "%logger{40}",
"rest": "%message"
}
</pattern>
</pattern>
</providers>
</encoder>
</appender>
<root level="INFO">
<appender-ref ref="console"/>
<appender-ref ref="logstash-tcp"/>
</root>
</configuration>
ELK日志收集界面(ELK通过docker部署,部署文件可查看源码)
3. 使用Open Zipkin进行分布式跟踪
在服务中添加Zipkin依赖
<dependency>
<groupId>org.springframework.cloud</groupId>
<artifactId>spring-cloud-starter-zipkin</artifactId>
</dependency>
配置服务以指向Zipkin
spring:
zipkin:
base-url: http://zipkin:9411/
sender:
type: rabbit
sleuth:
sampler:
probability: 1.0
安装和配置Zipkin服务器,使用docker部署
version: '3'
services:
zipkin:
image: openzipkin/zipkin
restart: always
ports:
- "9411:9411"
environment:
RABBIT_ADDRESSES: "mq-dev"
Zipkin跟踪结果
转载于:https://my.oschina.net/u/869718/blog/2250373