目录
一、什么是SkyWalking
二、SkyWalking的搭建
三、整合springboot
四、报警系统
一、什么是SkyWalking
SkyWalking是一个开源的观测平台,用于从服务和云原生等基础设施中收集、分析、聚合以及可视化数据。SkyWalking 提供了一种简便的方式来清晰地观测分布式系统。相比较zipkin而言,skywalking利用agent字节码增强技术实现代码无侵入,通信方式采用GRPC,性能较好,实现方式是java探针,支持告警,支持JVM监控,支持全局调用统计,UI界面更加强大等优点。
二、SkyWalking的搭建
skywalking使用Agent代理技术,相当于JVM层面的AOP 技术,在执行我们项目的时候,skywalking经过代理,然后收集我们的系统数据,然后经过可视化界面展示。
安装skywalking
从8.8.0开始,Java Agent从原始的主存储库中分离出来。所以我们需要下载两个文件
Apache Downloads 主程序
Apache Downloads agent代理
在apache-skywalking-apm-9.0.0\apache-skywalking-apm-bin\bin路径下启动,在webapp中的webapp.yml中可以修改启动端口,默认是8080
然后访问 ip:8080
agent模块解压后有skywalking-agent.jar
三、整合springboot
1、整合skywalking
由于skywalking是无代码侵入式的,启动项目时需要配置jvm参数
#skywalking-agent.jar的路径
-javaagent:E:\java-tools\skywalking\skywalking-agent\skywalking-agent.jar
#启动服务的名称
-Dskywalking.agent.service_name=xiaojie-sso
#连接到skywalking的地址
-Dskywalking.collector.backend_service=127.0.0.1:11800
2、初识skywalking界面
安装好之后可以随意点点,提供了CPU、JVM、垃圾回收,数据库、线程池、日志、拓扑图等等很多信息。
3、上报日志
maven依赖
<dependency>
<groupId>org.apache.skywalking</groupId>
<artifactId>apm-toolkit-logback-1.x</artifactId>
<version>8.10.0</version>
</dependency>
logback.yml
<?xml version="1.0" encoding="utf-8" ?>
<!---scan这个属性是用来查看配置信息的,scanPeriod的值是固定多长时间扫描一次,周期内新生成的文件会覆盖旧文件-->
<configuration>
<property name="LOG_FILE_LOCATION" value="./log/"/>
<property name="CONSOLE_LOG_PATTERN"
value="%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %highlight(%-5level) %cyan(%logger{50}) - %highlight(%msg) %n"/>
<property name="FILE_LOG_PATTERN"
value="%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{50} - %msg%n"/>
<appender name="consoleLog" class="ch.qos.logback.core.ConsoleAppender">
<layout class="ch.qos.logback.classic.PatternLayout">
<pattern>${CONSOLE_LOG_PATTERN}</pattern>
</layout>
</appender>
<appender name="fileInfoLog" class="ch.qos.logback.core.rolling.RollingFileAppender">
<filter class="ch.qos.logback.classic.filter.LevelFilter">
<level>ERROR</level>
<!--匹配就舍去-->
<onMatch>DENY</onMatch>
<onMismatch>ACCEPT</onMismatch>
</filter>
<file>${LOG_FILE_LOCATION}/info.log</file>
<encoder>
<pattern>
${FILE_LOG_PATTERN}
</pattern>
</encoder>
<!--滚动策略-->
<rollingPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedRollingPolicy">
<!--日志文件输出的文件名-->
<FileNamePattern>${LOG_FILE_LOCATION}/bak/info.%d{yyyy-MM-dd}.%i.log.gz</FileNamePattern>
<!--日志文件保留天数-->
<MaxHistory>30</MaxHistory>
<MaxFileSize>10MB</MaxFileSize>
</rollingPolicy>
</appender>
<appender name="fileErrorLog" class="ch.qos.logback.core.rolling.RollingFileAppender">
<filter class="ch.qos.logback.classic.filter.ThresholdFilter">
<level>ERROR</level>
</filter>
<file>${LOG_FILE_LOCATION}/error.log</file>
<encoder>
<pattern>
<pattern>${FILE_LOG_PATTERN}
</pattern>
</pattern>
</encoder>
<!--滚动策略-->
<rollingPolicy class="ch.qos.logback.core.rolling.SizeAndTimeBasedRollingPolicy">
<!--日志文件输出的文件名-->
<FileNamePattern>${LOG_FILE_LOCATION}/bak/error.%d{yyyy-MM-dd}.%i.log.gz</FileNamePattern>
<!--日志文件保留天数-->
<MaxHistory>30</MaxHistory>
<MaxFileSize>10MB</MaxFileSize>
</rollingPolicy>
</appender>
<!--打印tranceid-->
<appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
<encoder class="ch.qos.logback.core.encoder.LayoutWrappingEncoder">
<layout class="org.apache.skywalking.apm.toolkit.log.logback.v1.x.mdc.TraceIdMDCPatternLogbackLayout">
<Pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%X{tid}] [%thread] %-5level %logger{36} -%msg%n</Pattern>
</layout>
</encoder>
</appender>
<appender name="ASYNC" class="ch.qos.logback.classic.AsyncAppender">
<discardingThreshold>0</discardingThreshold>
<queueSize>1024</queueSize>
<neverBlock>true</neverBlock>
<appender-ref ref="STDOUT"/>
</appender>
<!--上报日志-->
<appender name="grpc-log" class="org.apache.skywalking.apm.toolkit.log.logback.v1.x.log.GRPCLogClientAppender">
<encoder class="ch.qos.logback.core.encoder.LayoutWrappingEncoder">
<layout class="org.apache.skywalking.apm.toolkit.log.logback.v1.x.mdc.TraceIdMDCPatternLogbackLayout">
<Pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%X{tid}] [%thread] %-5level %logger{36} -%msg%n</Pattern>
</layout>
</encoder>
</appender>
<root level="INFO">
<appender-ref ref="grpc-log"/>
<appender-ref ref="ASYNC"/>
<appender-ref ref="consoleLog"/>
<appender-ref ref="fileInfoLog"/>
<appender-ref ref="fileErrorLog"/>
</root>
</configuration>
4、持久化配置
修改apache-skywalking-apm-9.0.0\apache-skywalking-apm-bin\config路径下的application.yml
第一处、修改storage为mysql,我这里以mysql为例,支持多种修改
第二处、修改mysql的连接
需要手动在数据库创建swtest的数据库,然后启动skywalking,表自动创建。
四、报警系统
apache-skywalking-apm-9.0.0\apache-skywalking-apm-bin\config路径下的alarm-settings.yml配置了告警策略
rules:
# Rule unique name, must be ended with `_rule`.
service_resp_time_rule:
metrics-name: service_resp_time
op: ">"
threshold: 1000
period: 10
count: 3
silence-period: 5
#过去3分钟内服务平均响应时间超过1秒
message: Response time of service {name} is more than 1000ms in 3 minutes of last 10 minutes.
service_sla_rule:
# Metrics value need to be long, double or int
metrics-name: service_sla
op: "<"
threshold: 8000
# The length of time to evaluate the metrics
period: 10
# How many times after the metrics match the condition, will trigger alarm
count: 2
# How many times of checks, the alarm keeps silence after alarm triggered, default as same as period.
silence-period: 3
#服务成功率在过去2分钟内低于80%
message: Successful rate of service {name} is lower than 80% in 2 minutes of last 10 minutes
service_resp_time_percentile_rule:
# Metrics value need to be long, double or int
metrics-name: service_percentile
op: ">"
threshold: 1000,1000,1000,1000,1000
period: 10
count: 3
silence-period: 5
#再过去的3分钟内有超过50%,75%,90%,95%,99%响应时间大于1000毫秒
message: Percentile response time of service {name} alarm in 3 minutes of last 10 minutes, due to more than one condition of p50 > 1000, p75 > 1000, p90 > 1000, p95 > 1000, p99 > 1000
service_instance_resp_time_rule:
metrics-name: service_instance_resp_time
op: ">"
threshold: 1000
period: 10
count: 2
silence-period: 5
#最近2分钟内服务实例的平均响应时间超过1秒
message: Response time of service instance {name} is more than 1000ms in 2 minutes of last 10 minutes
database_access_resp_time_rule:
metrics-name: database_access_resp_time
threshold: 1000
op: ">"
period: 10
count: 2
#最近2分钟内数据库的平均响应时间超过1秒
message: Response time of database access {name} is more than 1000ms in 2 minutes of last 10 minutes
endpoint_relation_resp_time_rule:
metrics-name: endpoint_relation_resp_time
threshold: 1000
op: ">"
period: 10
count: 2
#端点平均响应时间过去2分钟超过1秒,断点可以理解为某个路径
message: Response time of endpoint relation {name} is more than 1000ms in 2 minutes of last 10 minutes
# Active endpoint related metrics alarm will cost more memory than service and service instance metrics alarm.
# Because the number of endpoint is much more than service and instance.
#
# endpoint_resp_time_rule:
# metrics-name: endpoint_resp_time
# op: ">"
# threshold: 1000
# period: 10
# count: 2
# silence-period: 5
# message: Response time of endpoint {name} is more than 1000ms in 2 minutes of last 10 minutes
webhooks:
- http://127.0.0.1:8090/notify/ #告警路径,需要我们自定义接口实现,post接口
# - http://127.0.0.1/go-wechat/
属性参照如下
定义告警实体类
@Data
public class AlarmMessageDto {
private String scopeId;
private String name;
private String id0;
private String id1;
private String alarmMessage;
private long startTime;
}
定义接口
@Override
public void send(List<AlarmMessageDto> alarmMessageList) {
//实际生产中应当单独建立一个监控系统的服务,使用mq 发送短信,邮件或者微信公众号模板方式解决,此处只是演示
for (AlarmMessageDto alarm: alarmMessageList) {
log.info("报警信息为>>>>>>>>{}",alarm.getAlarmMessage());
}
}
完整代码参考 spring-boot: Springboot整合redis、消息中间件等相关代码