Skywalking简单入门使用

缺乏、安全感 2024-04-27 15:40 94阅读 0赞

?今日学习目标：

?Skywalking简单入门使用  
✅创作者：林在闪闪发光  
⏰预计时间：50分钟  
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## **目录** ##

简介

Skywalking架构

部署安装

UI界面说明

追踪

日志

告警

日志对接

2、添加配置文件

3、接入探针agent

--------------------

## 简介 ##

**Skywalking**是一个国产的开源框架，2015年有吴晟个人开源，2017年加入Apache孵化器，**国人**开源的产品，主要开发人员来自于华为，2019年4月17日Apache董事会批准SkyWalking成为顶级项目，支持Java、.Net、NodeJs等探针，数据存储支持Mysql、Elasticsearch等，跟Pinpoint一样采用字节码注入的方式实现代码的无侵入，探针采集数据粒度粗，但性能表现优秀，且对云原生支持，目前增长势头强劲，社区活跃。  
**Skywalking是分布式系统的应用程序性能监视工具，专为微服务，云原生架构和基于容器（Docker，K8S,Mesos）架构而设计，它是一款优秀的APM（Application Performance Management）工具，包括了分布式追踪，性能指标分析和服务依赖分析等。**

官网：https://skywalking.apache.org/

## Skywalking架构 ##

SkyWalking 逻辑上分为四部分: 探针, 平台后端, 存储和用户界面。

**探针** 基于不同的来源可能是不一样的, 但作用都是收集数据, 将数据格式化为 SkyWalking 适用的格式.  
**平台后端**, 支持数据聚合, 数据分析以及驱动数据流从探针到用户界面的流程。分析包括 Skywalking 原生追踪和性能指标以及第三方来源，包括 Istio 及 Envoy telemetry , Zipkin 追踪格式化等。  
**存储** 通过开放的插件化的接口存放 SkyWalking 数据. 你可以选择一个既有的存储系统, 如 ElasticSearch, H2 或 MySQL 集群(Sharding-Sphere 管理),也可以选择自己实现一个存储系统. 当然, 我们非常欢迎你贡献新的存储系统实现。  
**UI** 一个基于接口高度定制化的Web系统，用户可以可视化查看和管理 SkyWalking 数据。

## 部署安装 ##

多种方式，只给出docker部署的docker-compose例子，其他的自行在官网查找。

存储使用的是es7，同时将oap注册到了nacos

> version: '2'  
> services:  
> elasticsearch:  
> image: elasticsearch:7.14.2  
> container\_name: elasticsearch  
> restart: always  
> ports:  
> \- 9200:9200  
> environment:  
> \- "TAKE\_FILE\_OWNERSHIP=true" \# volumes 挂载权限 如果不想要挂载es文件改配置可以删除  
> \- "discovery.type=single-node" \#单机模式启动  
> \- "TZ=Asia/Shanghai" \# 设置时区  
> \- "ES\_JAVA\_OPTS=-Xms512m -Xmx512m" \# 设置jvm内存大小  
> volumes:  
> \- /data/skywalking/elasticsearch/logs:/usr/share/elasticsearch/logs  
> \- /data/skywalking/elasticsearch/data:/usr/share/elasticsearch/data  
> ulimits:  
> memlock:  
> soft: -1  
> hard: -1  
> skywalking-oap-server:  
> image: apache/skywalking-oap-server:8.9.1  
> container\_name: skywalking-oap-server  
> depends\_on:  
> \- elasticsearch  
> links:  
> \- elasticsearch  
> restart: always  
> ports:  
> \- 11800:11800  
> \- 12800:12800  
> environment:  
> \#此处的参数为容器里/skywalking/config/application.yml的配置  
> SW\_STORAGE: elasticsearch \# 指定ES版本  
> SW\_STORAGE\_ES\_CLUSTER\_NODES: elasticsearch:9200  
> SW\_CLUSTER: nacos  
> SW\_SERVICE\_NAME: skywalking-oap-server  
> SW\_CLUSTER\_NACOS\_HOST\_PORT: 192.168.0.200  
> SW\_CLUSTER\_NACOS\_NAMESPACE: dev  
> TZ: Asia/Shanghai  
> skywalking-ui:  
> image: apache/skywalking-ui:8.9.1  
> container\_name: skywalking-ui  
> depends\_on:  
> \- skywalking-oap-server  
> links:  
> \- skywalking-oap-server  
> restart: always  
> ports:  
> \- 8080:8080  
> environment:  
> SW\_OAP\_ADDRESS: http://skywalking-oap-server:12800  
> TZ: Asia/Shanghai

#### UI界面说明 ####

*  仪表盘：查看被监控服务的运行状态
 *  拓扑图：以拓扑图的方式展现服务直接的关系，并以此为入口查看相关信息
 *  追踪：以接口列表的方式展现，追踪接口内部调用过程
 *  性能剖析：单独端点进行采样分析，并可查看堆栈信息
 *  告警：触发告警的告警列表，包括实例，请求超时等。
 *  事件：
 *  调试：

**仪表盘**

仪表盘分为：全局(Global)、服务(Service)、实例(Instance)、端点(Endpoint)四个维度

*  **Global全局维度**

![bfc845a79ef7458daf7c90ec721c999d.png][]

> Services load：服务每分钟请求数  
> Slow Services：慢响应服务，单位ms  
> Un-Health services(Apdex):Apdex性能指标，1为满分。  
> Global Response Latency：百分比响应延时，不同百分比的延时时间，单位ms  
> Global Heatmap：服务响应时间热力分布图，根据时间段内不同响应时间的数量显示颜色深度

*  **Service服务维度**

![f20b0dbcc52a4a8dbb9909abd349c2ff.png][]

> Service Apdex（数字）:当前服务的评分  
> Service Apdex（折线图）：不同时间的Apdex评分  
> Successful Rate（数字）：请求成功率  
> Successful Rate（折线图）：不同时间的请求成功率  
> Servce Load（数字）：每分钟请求数  
> Servce Load（折线图）：不同时间的每分钟请求数  
> Service Avg Response Times：平均响应延时，单位ms  
> Global Response Time Percentile：百分比响应延时  
> Servce Instances Load：每个服务实例的每分钟请求数  
> Show Service Instance：每个服务实例的最大延时  
> Service Instance Successful Rate：每个服务实例的请求成功率

*  **Instance实例维度**

![d0e99c5f21a24dbeabdd137887bdb2e8.png][]

> Service Instance Load：当前实例的每分钟请求数  
> Service Instance Successful Rate：当前实例的请求成功率  
> Service Instance Latency：当前实例的响应延时  
> JVM CPU:jvm占用CPU的百分比  
> JVM Memory：JVM内存占用大小，单位m  
> JVM GC Time：JVM垃圾回收时间，包含YGC和OGC  
> JVM GC Count：JVM垃圾回收次数，包含YGC和OGC  
> CLR XX：类似JVM虚拟机，这里用不上就不做解释了

*  **Endpoint端点维度**

![91abf85a2b3c4290b5fc266b5f3687d9.png][]

> Endpoint Load in Current Service：每个端点的每分钟请求数  
> Slow Endpoints in Current Service：每个端点的最慢请求时间，单位ms  
> Successful Rate in Current Service：每个端点的请求成功率  
> Endpoint Load：当前端点每个时间段的请求数据  
> Endpoint Avg Response Time：当前端点每个时间段的请求行响应时间  
> Endpoint Response Time Percentile：当前端点每个时间段的响应时间占比  
> Endpoint Successful Rate：当前端点每个时间段的请求成功率

界面上的指标可以做一些自定义的调整(更换图表、统计数量等)，也可以增加自定义指标(应该是要二开)

![28df82dc3e184c299a018530d4b2c0ae.png][]

**拓扑图**

![3e19e5a722bf49c4ab0d45dd385590a9.png][]

点击具体的服务，右侧将显示服务的状态，服务周边出现触点；点击连线，将会显示服务之间的连接情况

![a8d6d932b730408eaf02edcc7963d4ff.png][]

## **追踪**  ##

![39610dc6a899428796df5053452c0d00.png][]

*  左侧：api接口列表，红色-异常请求，蓝色-正常请求
 *  右侧：api追踪列表，api请求连接各端点的先后顺序和时间

## 日志 ##

![e7e7ec30cb234761823b6ce7268773c8.png][]

点击可查看具体的日志，同一追踪id表明是同一个请求

![287c195adc714dbe998d13bbeb4a5366.png][]

## 告警 ##

对于服务的异常信息，比如接口有较长延迟，skywalking也做出了告警功能，如下图：

![b0ade37a69554900af3b50bfc7d80797.png][]

skywalking中有一些默认的告警规则，如下：

*  最近3分钟内服务的平均响应时间超过1秒
 *  最近2分钟服务成功率低于80%
 *  最近3分钟90%服务响应时间超过1秒
 *  最近2分钟内服务实例的平均响应时间超过1秒

**当然除了以上四种，随着Skywalking不断迭代也会新增其他规则，这些规则的配置在**`config/alarm-settings.yml`**配置文件中**

> \# Licensed to the Apache Software Foundation (ASF) under one  
> \# or more contributor license agreements. See the NOTICE file  
> \# distributed with this work for additional information  
> \# regarding copyright ownership. The ASF licenses this file  
> \# to you under the Apache License, Version 2.0 (the  
> \# "License"); you may not use this file except in compliance  
> \# with the License. You may obtain a copy of the License at  
> \#  
> \# http://www.apache.org/licenses/LICENSE-2.0  
> \#  
> \# Unless required by applicable law or agreed to in writing, software  
> \# distributed under the License is distributed on an "AS IS" BASIS,  
> \# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
> \# See the License for the specific language governing permissions and  
> \# limitations under the License.  
> \# Sample alarm rules.  
> 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  
> 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  
> 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  
> 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  
> 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  
> 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  
> 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\_avg\_rule:  
> \# metrics-name: endpoint\_avg  
> \# 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/notify/  
> \# - http://127.0.0.1/go-wechat/

规则中的参数属性解释  
Rule name。在告警信息中显示的唯一名称。必须以\_rule结尾。规则命名用户自定义

![f55f0583481d4ec4b58e636601c9280b.png][]

metrics-name是oal脚本中的度量名。详细参数可见**\\config\\oal\\core.oal文**件：

> /\*  
> \* Licensed to the Apache Software Foundation (ASF) under one or more  
> \* contributor license agreements. See the NOTICE file distributed with  
> \* this work for additional information regarding copyright ownership.  
> \* The ASF licenses this file to You under the Apache License, Version 2.0  
> \* (the "License"); you may not use this file except in compliance with  
> \* the License. You may obtain a copy of the License at  
> \*  
> \* http://www.apache.org/licenses/LICENSE-2.0  
> \*  
> \* Unless required by applicable law or agreed to in writing, software  
> \* distributed under the License is distributed on an "AS IS" BASIS,  
> \* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
> \* See the License for the specific language governing permissions and  
> \* limitations under the License.  
> \*  
> \*/
> 
> // For services using protocols HTTP 1/2, gRPC, RPC, etc., the cpm metrics means "calls per minute",  
> // for services that are built on top of TCP, the cpm means "packages per minute".
> 
> // All scope metrics  
> all\_percentile = from(All.latency).percentile(10); // Multiple values including p50, p75, p90, p95, p99  
> all\_heatmap = from(All.latency).histogram(100, 20);
> 
> // Service scope metrics  
> service\_resp\_time = from(Service.latency).longAvg();  
> service\_sla = from(Service.\*).percent(status == true);  
> service\_cpm = from(Service.\*).cpm();  
> service\_percentile = from(Service.latency).percentile(10); // Multiple values including p50, p75, p90, p95, p99  
> service\_apdex = from(Service.latency).apdex(name, status);  
> service\_mq\_consume\_count = from(Service.\*).filter(type == RequestType.MQ).count();  
> service\_mq\_consume\_latency = from((str->long)Service.tag\["transmission.latency"\]).filter(type == RequestType.MQ).filter(tag\["transmission.latency"\] != null).longAvg();
> 
> // Service relation scope metrics for topology  
> service\_relation\_client\_cpm = from(ServiceRelation.\*).filter(detectPoint == DetectPoint.CLIENT).cpm();  
> service\_relation\_server\_cpm = from(ServiceRelation.\*).filter(detectPoint == DetectPoint.SERVER).cpm();  
> service\_relation\_client\_call\_sla = from(ServiceRelation.\*).filter(detectPoint == DetectPoint.CLIENT).percent(status == true);  
> service\_relation\_server\_call\_sla = from(ServiceRelation.\*).filter(detectPoint == DetectPoint.SERVER).percent(status == true);  
> service\_relation\_client\_resp\_time = from(ServiceRelation.latency).filter(detectPoint == DetectPoint.CLIENT).longAvg();  
> service\_relation\_server\_resp\_time = from(ServiceRelation.latency).filter(detectPoint == DetectPoint.SERVER).longAvg();  
> service\_relation\_client\_percentile = from(ServiceRelation.latency).filter(detectPoint == DetectPoint.CLIENT).percentile(10); // Multiple values including p50, p75, p90, p95, p99  
> service\_relation\_server\_percentile = from(ServiceRelation.latency).filter(detectPoint == DetectPoint.SERVER).percentile(10); // Multiple values including p50, p75, p90, p95, p99
> 
> // Service Instance relation scope metrics for topology  
> service\_instance\_relation\_client\_cpm = from(ServiceInstanceRelation.\*).filter(detectPoint == DetectPoint.CLIENT).cpm();  
> service\_instance\_relation\_server\_cpm = from(ServiceInstanceRelation.\*).filter(detectPoint == DetectPoint.SERVER).cpm();  
> service\_instance\_relation\_client\_call\_sla = from(ServiceInstanceRelation.\*).filter(detectPoint == DetectPoint.CLIENT).percent(status == true);  
> service\_instance\_relation\_server\_call\_sla = from(ServiceInstanceRelation.\*).filter(detectPoint == DetectPoint.SERVER).percent(status == true);  
> service\_instance\_relation\_client\_resp\_time = from(ServiceInstanceRelation.latency).filter(detectPoint == DetectPoint.CLIENT).longAvg();  
> service\_instance\_relation\_server\_resp\_time = from(ServiceInstanceRelation.latency).filter(detectPoint == DetectPoint.SERVER).longAvg();  
> service\_instance\_relation\_client\_percentile = from(ServiceInstanceRelation.latency).filter(detectPoint == DetectPoint.CLIENT).percentile(10); // Multiple values including p50, p75, p90, p95, p99  
> service\_instance\_relation\_server\_percentile = from(ServiceInstanceRelation.latency).filter(detectPoint == DetectPoint.SERVER).percentile(10); // Multiple values including p50, p75, p90, p95, p99
> 
> // Service Instance Scope metrics  
> service\_instance\_sla = from(ServiceInstance.\*).percent(status == true);  
> service\_instance\_resp\_time= from(ServiceInstance.latency).longAvg();  
> service\_instance\_cpm = from(ServiceInstance.\*).cpm();
> 
> // Endpoint scope metrics  
> endpoint\_cpm = from(Endpoint.\*).cpm();  
> endpoint\_avg = from(Endpoint.latency).longAvg();  
> endpoint\_sla = from(Endpoint.\*).percent(status == true);  
> endpoint\_percentile = from(Endpoint.latency).percentile(10); // Multiple values including p50, p75, p90, p95, p99  
> endpoint\_mq\_consume\_count = from(Endpoint.\*).filter(type == RequestType.MQ).count();  
> endpoint\_mq\_consume\_latency = from((str->long)Endpoint.tag\["transmission.latency"\]).filter(type == RequestType.MQ).filter(tag\["transmission.latency"\] != null).longAvg();
> 
> // Endpoint relation scope metrics  
> endpoint\_relation\_cpm = from(EndpointRelation.\*).filter(detectPoint == DetectPoint.SERVER).cpm();  
> endpoint\_relation\_resp\_time = from(EndpointRelation.rpcLatency).filter(detectPoint == DetectPoint.SERVER).longAvg();  
> endpoint\_relation\_sla = from(EndpointRelation.\*).filter(detectPoint == DetectPoint.SERVER).percent(status == true);  
> endpoint\_relation\_percentile = from(EndpointRelation.rpcLatency).filter(detectPoint == DetectPoint.SERVER).percentile(10); // Multiple values including p50, p75, p90, p95, p99
> 
> database\_access\_resp\_time = from(DatabaseAccess.latency).longAvg();  
> database\_access\_sla = from(DatabaseAccess.\*).percent(status == true);  
> database\_access\_cpm = from(DatabaseAccess.\*).cpm();  
> database\_access\_percentile = from(DatabaseAccess.latency).percentile(10);

如果想要调整默认的规则，比如监控返回的信息，监控的参数等等，只需要改动上述配置文件中的参数即可。

当然除了以上默认的几种规则，skywalking还适配了一些钩子**（webhooks）**。其实就是相当于一个回调，一旦触发了上述规则告警，skywalking则会调用配置的webhook，这样开发者就可以定制一些处理方法，比如发送**邮件、微信、钉钉**通知运维人员处理。

当然这个钩子也是有些规则的，如下：

> POST请求  
> application/json 接收数据  
> 接收的参数必须是AlarmMessage中指定的参数。

注意：AlarmMessage这个类随着skywalking版本的迭代可能出现不同，一定要到对应版本源码中去找到这个类，拷贝其中的属性。这个类在源码的路径：**org.apache.skywalking.oap.server.core.alarm**，如下图：  
![ebd1bb39f9bf4ff484fdb2e7ed1f353b.png][]

## 日志对接 ##

在skywalking的UI端有一个日志的模块，用于收集客户端的日志，默认是没有数据的，那么需要如何将日志数据传输到skywalking中呢？

日志框架的种类很多，比较出名的有log4j，logback，log4j2，以springboot默认的logback为例子介绍一下如何配置，官方文档如下：

log4j：java-agent/application-toolkit-log4j-1.x/  
log4j2：java-agent/application-toolkit-log4j-2.x/  
logback：java-agent/application-toolkit-logback-1.x/

**1、添加依赖**

根据官方文档，需要先添加依赖，如下：

<dependency>
       <groupId>org.apache.skywalking</groupId>
       <artifactId>apm-toolkit-logback-1.x</artifactId>
       <version>8.10.0</version>
    </dependency>

## 2、添加配置文件 ##

新建一个`logback-spring.xml`放在resource目录下，配置如下：

<?xml version="1.0" encoding="UTF-8"?>
    <configuration scan="true" scanPeriod=" 5 seconds">
        <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>
    
    
        <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"/>
        </root>
    </configuration>

## **3、接入探针agent** ##

1）下载探针

https://archive.apache.org/dist/skywalking/java-agent/

根据Skywalking版本进行下载

2）idea使用探针

*  配置VM options

![66085fdf8544403d9139933e67238aaf.png][]

> \-javaagent:D:\\Java\\plugin\\apache-skywalking-java-agent-8.9.0\\skywalking-agent\\skywalking-agent.jar -Dskywalking.agent.service\_name=log-demo-service2 -Dskywalking.collector.backend\_service=192.168.0.203:11800

![35b004614ef64212a6793fbbc0d5e8bb.png][]

**启动程序**

**会发现Skywalking里已经有了日志，与idea控制台的日志一致**

![75adc21f9ac6409a818a18c1a78a22dd.png][]

![eaa9212687904277896a66398554e3cd.png][]

**调用接口**

![f8554089c5b64072ab0868ac25167b22.png][]

![c1c34ec520ef48b98985bbbe671d743a.png][]

**3）jar包使用探针**

与idea一样，启动时配置VM options即可

> java -javaagent:D:\\Java\\plugin\\apache-skywalking-java-agent-8.9.0\\skywalking-agent\\skywalking-agent.jar -Dskywalking.agent.service\_name=log-demo-service2 -Dskywalking.collector.backend\_service=192.168.0.203:11800 -jar app.jar

**4）docker使用探针**  
其实原理都一样，配置探针包的地址、配置skywalking服务端地址、配置服务名称。

有几种方式：

1、将外部的agent包，挂载到docker容器中，然后指定vm参数

2、构建docker时，把agent包打进docker镜像里，dockerfile修改enrtypoint，添加-javaagent参数

3、将agent包打到基础镜像里，dockerfile直接引用这个基础镜像

我采用第三种方式，而且这个基础镜像其实官方是有发布的，直接引用即可，但是只有几个版本，如果你的程序对jdk有要求，还是要采用其他方案，或者自己构建一个基础镜像。

在docker仓库里选择对应版本即可，镜像地址：https://hub.docker.com/r/apache/skywalking-java-agent，这个仓库是apache自己的，但是概述里有说明，这个不是ASF( Apache 软件基金会Apache Software Foundation)的官方版本  
![7e7921eaeb3941ddacc5dc59175a783b.png][]

把这个替换到dockerfile中的FROM即可

> FROM apache/skywalking-java-agent:8.9.0-java8  
> ..

这个镜像自带探针包，无需指定探针包地址，那么其他两个参数怎么配置呢？

我是在**docker-compose**文件中配置了环境变量**SW\_AGENT\_NAME**与**SW\_AGENT\_COLLECTOR\_BACKEND\_SERVICES**

> version: '2'  
> services:  
> log-demo-service1:  
> image: log-service1:0.0.1-SKYWALKING  
> container\_name: log-demo-service-skywalking1  
> restart: always  
> ports:  
> \- "8090:8090"  
> environment:  
> TZ: Asia/Shanghai  
> SW\_AGENT\_NAME: log-demo-service1  
> SW\_AGENT\_COLLECTOR\_BACKEND\_SERVICES: 192.168.0.203:11800  
> log-demo-service2:  
> image: log-service2:0.0.1-SKYWALKING  
> container\_name: log-demo-service-skywalking2  
> restart: always  
> ports:  
> \- "8091:8091"  
> environment:  
> TZ: Asia/Shanghai  
> SW\_AGENT\_NAME: log-demo-service2  
> SW\_AGENT\_COLLECTOR\_BACKEND\_SERVICES: 192.168.0.203:1180

这两个变量是取自探针的配置文件**/agent/config/agent.config**

> agent.service\_name=$\{SW\_AGENT\_NAME:Your\_ApplicationName\}  
> collector.backend\_service=$\{SW\_AGENT\_COLLECTOR\_BACKEND\_SERVICES:127.0.0.1:11800\}

其实就对应着2）和3）中配置的**VM options**的-**Dskywalking.agent.service\_name**和-**Dskywalking.collector.backend\_service**，所以2）和3）中配置环境变量也是可以的。

启动**docker-compose**，会发现比idea中多了一行，就是这个基础镜像自带的配置，定义了一个**JAVA\_TOOL\_OPTIONS**，在这个变量里指定了agent包的位置。

![47ac4a2477c24c77a67f7b85e01655af.png][]

![2d48292c673d4329a122707b7e9fa01c.png][]

[Link 1]: https://blog.csdn.net/weixin_68829137?type=blog
[Link 2]: https://bbs.csdn.net/forums/947a8b4d170b4a8f8d6bbfbf524c1a1e?spm=1001.2014.3001.6682
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