SpringBoot教程（三十二） | SpringBoot集成Skywalking链路跟踪

Slow菜鸟2024-10-15 18:15

SpringBoot教程（三十二） | SpringBoot集成Skywalking链路跟踪

Skywalking是什么？

SkyWalking是一个开源的、用于观测分布式系统（特别是微服务、云原生和容器化应用）的平台。

它提供了对分布式系统的追踪、监控和诊断能力。

Skywalking与JDK版本的对应关系

SkyWalking 8.x版本要求Java版本至少为8（即JDK 1.8），

SkyWalking 9.x版本则要求Java版本至少为11（即JDK 11）

所以选择的时候需要注意一下JDK版本。

Skywalking下载

Skywalking 官网下载地址 https://skywalking.apache.org/downloads/

注意点：

7.x及以下版本 APM 包里面有包括 Agents，但是8.x的就发现被分开了，所以8.x的及以上的 就需要 Agents 也得下载

目前该文选择 下载 APM 8.9.1 和 Agents 8.9.0 后解压

接着把 Agents 文件放到 APM 文件中

Skywalking 数据存储

Skywalking 存在多种数据存储

  1. h2（默认的存储方式，重启后数据会丢失）
  2. Elasticsearch （最常用的数据存储方式）
  3. MySQL
  4. TiDB
  5. ...

相关文件OAP 配置文件（config/application.yml）

我只截取了关于设置存储方式的部分

yaml 复制代码 
storage:
  selector: ${SW_STORAGE:h2}
  elasticsearch:
    namespace: ${SW_NAMESPACE:""}
    clusterNodes: ${SW_STORAGE_ES_CLUSTER_NODES:localhost:9200}
    protocol: ${SW_STORAGE_ES_HTTP_PROTOCOL:"http"}
    connectTimeout: ${SW_STORAGE_ES_CONNECT_TIMEOUT:500}
    socketTimeout: ${SW_STORAGE_ES_SOCKET_TIMEOUT:30000}
    numHttpClientThread: ${SW_STORAGE_ES_NUM_HTTP_CLIENT_THREAD:0}
    user: ${SW_ES_USER:""}
    password: ${SW_ES_PASSWORD:""}
    trustStorePath: ${SW_STORAGE_ES_SSL_JKS_PATH:""}
    trustStorePass: ${SW_STORAGE_ES_SSL_JKS_PASS:""}
    secretsManagementFile: ${SW_ES_SECRETS_MANAGEMENT_FILE:""} # Secrets management file in the properties format includes the username, password, which are managed by 3rd party tool.
    dayStep: ${SW_STORAGE_DAY_STEP:1} # Represent the number of days in the one minute/hour/day index.
    indexShardsNumber: ${SW_STORAGE_ES_INDEX_SHARDS_NUMBER:1} # Shard number of new indexes
    indexReplicasNumber: ${SW_STORAGE_ES_INDEX_REPLICAS_NUMBER:1} # Replicas number of new indexes
    # Super data set has been defined in the codes, such as trace segments.The following 3 config would be improve es performance when storage super size data in es.
    superDatasetDayStep: ${SW_SUPERDATASET_STORAGE_DAY_STEP:-1} # Represent the number of days in the super size dataset record index, the default value is the same as dayStep when the value is less than 0
    superDatasetIndexShardsFactor: ${SW_STORAGE_ES_SUPER_DATASET_INDEX_SHARDS_FACTOR:5} #  This factor provides more shards for the super data set, shards number = indexShardsNumber * superDatasetIndexShardsFactor. Also, this factor effects Zipkin and Jaeger traces.
    superDatasetIndexReplicasNumber: ${SW_STORAGE_ES_SUPER_DATASET_INDEX_REPLICAS_NUMBER:0} # Represent the replicas number in the super size dataset record index, the default value is 0.
    indexTemplateOrder: ${SW_STORAGE_ES_INDEX_TEMPLATE_ORDER:0} # the order of index template
    bulkActions: ${SW_STORAGE_ES_BULK_ACTIONS:5000} # Execute the async bulk record data every ${SW_STORAGE_ES_BULK_ACTIONS} requests
    # flush the bulk every 10 seconds whatever the number of requests
    # INT(flushInterval * 2/3) would be used for index refresh period.
    flushInterval: ${SW_STORAGE_ES_FLUSH_INTERVAL:15}
    concurrentRequests: ${SW_STORAGE_ES_CONCURRENT_REQUESTS:2} # the number of concurrent requests
    resultWindowMaxSize: ${SW_STORAGE_ES_QUERY_MAX_WINDOW_SIZE:10000}
    metadataQueryMaxSize: ${SW_STORAGE_ES_QUERY_MAX_SIZE:5000}
    segmentQueryMaxSize: ${SW_STORAGE_ES_QUERY_SEGMENT_SIZE:200}
    profileTaskQueryMaxSize: ${SW_STORAGE_ES_QUERY_PROFILE_TASK_SIZE:200}
    oapAnalyzer: ${SW_STORAGE_ES_OAP_ANALYZER:"{\"analyzer\":{\"oap_analyzer\":{\"type\":\"stop\"}}}"} # the oap analyzer.
    oapLogAnalyzer: ${SW_STORAGE_ES_OAP_LOG_ANALYZER:"{\"analyzer\":{\"oap_log_analyzer\":{\"type\":\"standard\"}}}"} # the oap log analyzer. It could be customized by the ES analyzer configuration to support more language log formats, such as Chinese log, Japanese log and etc.
    advanced: ${SW_STORAGE_ES_ADVANCED:""}
  h2:
    driver: ${SW_STORAGE_H2_DRIVER:org.h2.jdbcx.JdbcDataSource}
    url: ${SW_STORAGE_H2_URL:jdbc:h2:mem:skywalking-oap-db;DB_CLOSE_DELAY=-1}
    user: ${SW_STORAGE_H2_USER:sa}
    metadataQueryMaxSize: ${SW_STORAGE_H2_QUERY_MAX_SIZE:5000}
    maxSizeOfArrayColumn: ${SW_STORAGE_MAX_SIZE_OF_ARRAY_COLUMN:20}
    numOfSearchableValuesPerTag: ${SW_STORAGE_NUM_OF_SEARCHABLE_VALUES_PER_TAG:2}
    maxSizeOfBatchSql: ${SW_STORAGE_MAX_SIZE_OF_BATCH_SQL:100}
    asyncBatchPersistentPoolSize: ${SW_STORAGE_ASYNC_BATCH_PERSISTENT_POOL_SIZE:1}
  mysql:
    properties:
      jdbcUrl: ${SW_JDBC_URL:"jdbc:mysql://localhost:3306/swtest?rewriteBatchedStatements=true"}
      dataSource.user: ${SW_DATA_SOURCE_USER:root}
      dataSource.password: ${SW_DATA_SOURCE_PASSWORD:root@1234}
      dataSource.cachePrepStmts: ${SW_DATA_SOURCE_CACHE_PREP_STMTS:true}
      dataSource.prepStmtCacheSize: ${SW_DATA_SOURCE_PREP_STMT_CACHE_SQL_SIZE:250}
      dataSource.prepStmtCacheSqlLimit: ${SW_DATA_SOURCE_PREP_STMT_CACHE_SQL_LIMIT:2048}
      dataSource.useServerPrepStmts: ${SW_DATA_SOURCE_USE_SERVER_PREP_STMTS:true}
    metadataQueryMaxSize: ${SW_STORAGE_MYSQL_QUERY_MAX_SIZE:5000}
    maxSizeOfArrayColumn: ${SW_STORAGE_MAX_SIZE_OF_ARRAY_COLUMN:20}
    numOfSearchableValuesPerTag: ${SW_STORAGE_NUM_OF_SEARCHABLE_VALUES_PER_TAG:2}
    maxSizeOfBatchSql: ${SW_STORAGE_MAX_SIZE_OF_BATCH_SQL:2000}
    asyncBatchPersistentPoolSize: ${SW_STORAGE_ASYNC_BATCH_PERSISTENT_POOL_SIZE:4}
  tidb:
    properties:
      jdbcUrl: ${SW_JDBC_URL:"jdbc:mysql://localhost:4000/tidbswtest?rewriteBatchedStatements=true"}
      dataSource.user: ${SW_DATA_SOURCE_USER:root}
      dataSource.password: ${SW_DATA_SOURCE_PASSWORD:""}
      dataSource.cachePrepStmts: ${SW_DATA_SOURCE_CACHE_PREP_STMTS:true}
      dataSource.prepStmtCacheSize: ${SW_DATA_SOURCE_PREP_STMT_CACHE_SQL_SIZE:250}
      dataSource.prepStmtCacheSqlLimit: ${SW_DATA_SOURCE_PREP_STMT_CACHE_SQL_LIMIT:2048}
      dataSource.useServerPrepStmts: ${SW_DATA_SOURCE_USE_SERVER_PREP_STMTS:true}
      dataSource.useAffectedRows: ${SW_DATA_SOURCE_USE_AFFECTED_ROWS:true}
    metadataQueryMaxSize: ${SW_STORAGE_MYSQL_QUERY_MAX_SIZE:5000}
    maxSizeOfArrayColumn: ${SW_STORAGE_MAX_SIZE_OF_ARRAY_COLUMN:20}
    numOfSearchableValuesPerTag: ${SW_STORAGE_NUM_OF_SEARCHABLE_VALUES_PER_TAG:2}
    maxSizeOfBatchSql: ${SW_STORAGE_MAX_SIZE_OF_BATCH_SQL:2000}
    asyncBatchPersistentPoolSize: ${SW_STORAGE_ASYNC_BATCH_PERSISTENT_POOL_SIZE:4}
  influxdb:
    # InfluxDB configuration
    url: ${SW_STORAGE_INFLUXDB_URL:http://localhost:8086}
    user: ${SW_STORAGE_INFLUXDB_USER:root}
    password: ${SW_STORAGE_INFLUXDB_PASSWORD:}
    database: ${SW_STORAGE_INFLUXDB_DATABASE:skywalking}
    actions: ${SW_STORAGE_INFLUXDB_ACTIONS:1000} # the number of actions to collect
    duration: ${SW_STORAGE_INFLUXDB_DURATION:1000} # the time to wait at most (milliseconds)
    batchEnabled: ${SW_STORAGE_INFLUXDB_BATCH_ENABLED:true}
    fetchTaskLogMaxSize: ${SW_STORAGE_INFLUXDB_FETCH_TASK_LOG_MAX_SIZE:5000} # the max number of fetch task log in a request
    connectionResponseFormat: ${SW_STORAGE_INFLUXDB_CONNECTION_RESPONSE_FORMAT:MSGPACK} # the response format of connection to influxDB, cannot be anything but MSGPACK or JSON.
  postgresql:
    properties:
      jdbcUrl: ${SW_JDBC_URL:"jdbc:postgresql://localhost:5432/skywalking"}
      dataSource.user: ${SW_DATA_SOURCE_USER:postgres}
      dataSource.password: ${SW_DATA_SOURCE_PASSWORD:123456}
      dataSource.cachePrepStmts: ${SW_DATA_SOURCE_CACHE_PREP_STMTS:true}
      dataSource.prepStmtCacheSize: ${SW_DATA_SOURCE_PREP_STMT_CACHE_SQL_SIZE:250}
      dataSource.prepStmtCacheSqlLimit: ${SW_DATA_SOURCE_PREP_STMT_CACHE_SQL_LIMIT:2048}
      dataSource.useServerPrepStmts: ${SW_DATA_SOURCE_USE_SERVER_PREP_STMTS:true}
    metadataQueryMaxSize: ${SW_STORAGE_MYSQL_QUERY_MAX_SIZE:5000}
    maxSizeOfArrayColumn: ${SW_STORAGE_MAX_SIZE_OF_ARRAY_COLUMN:20}
    numOfSearchableValuesPerTag: ${SW_STORAGE_NUM_OF_SEARCHABLE_VALUES_PER_TAG:2}
    maxSizeOfBatchSql: ${SW_STORAGE_MAX_SIZE_OF_BATCH_SQL:2000}
    asyncBatchPersistentPoolSize: ${SW_STORAGE_ASYNC_BATCH_PERSISTENT_POOL_SIZE:4}
  zipkin-elasticsearch:
    namespace: ${SW_NAMESPACE:""}
    clusterNodes: ${SW_STORAGE_ES_CLUSTER_NODES:localhost:9200}
    protocol: ${SW_STORAGE_ES_HTTP_PROTOCOL:"http"}
    trustStorePath: ${SW_STORAGE_ES_SSL_JKS_PATH:""}
    trustStorePass: ${SW_STORAGE_ES_SSL_JKS_PASS:""}
    dayStep: ${SW_STORAGE_DAY_STEP:1} # Represent the number of days in the one minute/hour/day index.
    indexShardsNumber: ${SW_STORAGE_ES_INDEX_SHARDS_NUMBER:1} # Shard number of new indexes
    indexReplicasNumber: ${SW_STORAGE_ES_INDEX_REPLICAS_NUMBER:1} # Replicas number of new indexes
    # Super data set has been defined in the codes, such as trace segments.The following 3 config would be improve es performance when storage super size data in es.
    superDatasetDayStep: ${SW_SUPERDATASET_STORAGE_DAY_STEP:-1} # Represent the number of days in the super size dataset record index, the default value is the same as dayStep when the value is less than 0
    superDatasetIndexShardsFactor: ${SW_STORAGE_ES_SUPER_DATASET_INDEX_SHARDS_FACTOR:5} #  This factor provides more shards for the super data set, shards number = indexShardsNumber * superDatasetIndexShardsFactor. Also, this factor effects Zipkin and Jaeger traces.
    superDatasetIndexReplicasNumber: ${SW_STORAGE_ES_SUPER_DATASET_INDEX_REPLICAS_NUMBER:0} # Represent the replicas number in the super size dataset record index, the default value is 0.
    user: ${SW_ES_USER:""}
    password: ${SW_ES_PASSWORD:""}
    secretsManagementFile: ${SW_ES_SECRETS_MANAGEMENT_FILE:""} # Secrets management file in the properties format includes the username, password, which are managed by 3rd party tool.
    bulkActions: ${SW_STORAGE_ES_BULK_ACTIONS:5000} # Execute the async bulk record data every ${SW_STORAGE_ES_BULK_ACTIONS} requests
    # flush the bulk every 10 seconds whatever the number of requests
    # INT(flushInterval * 2/3) would be used for index refresh period.
    flushInterval: ${SW_STORAGE_ES_FLUSH_INTERVAL:15}
    concurrentRequests: ${SW_STORAGE_ES_CONCURRENT_REQUESTS:2} # the number of concurrent requests
    resultWindowMaxSize: ${SW_STORAGE_ES_QUERY_MAX_WINDOW_SIZE:10000}
    metadataQueryMaxSize: ${SW_STORAGE_ES_QUERY_MAX_SIZE:5000}
    segmentQueryMaxSize: ${SW_STORAGE_ES_QUERY_SEGMENT_SIZE:200}
    profileTaskQueryMaxSize: ${SW_STORAGE_ES_QUERY_PROFILE_TASK_SIZE:200}
    oapAnalyzer: ${SW_STORAGE_ES_OAP_ANALYZER:"{\"analyzer\":{\"oap_analyzer\":{\"type\":\"stop\"}}}"} # the oap analyzer.
    oapLogAnalyzer: ${SW_STORAGE_ES_OAP_LOG_ANALYZER:"{\"analyzer\":{\"oap_log_analyzer\":{\"type\":\"standard\"}}}"} # the oap log analyzer. It could be customized by the ES analyzer configuration to support more language log formats, such as Chinese log, Japanese log and etc.
    advanced: ${SW_STORAGE_ES_ADVANCED:""}
  iotdb:
    host: ${SW_STORAGE_IOTDB_HOST:127.0.0.1}
    rpcPort: ${SW_STORAGE_IOTDB_RPC_PORT:6667}
    username: ${SW_STORAGE_IOTDB_USERNAME:root}
    password: ${SW_STORAGE_IOTDB_PASSWORD:root}
    storageGroup: ${SW_STORAGE_IOTDB_STORAGE_GROUP:root.skywalking}
    sessionPoolSize: ${SW_STORAGE_IOTDB_SESSIONPOOL_SIZE:16}
    fetchTaskLogMaxSize: ${SW_STORAGE_IOTDB_FETCH_TASK_LOG_MAX_SIZE:1000} # the max number of fetch task log in a request

Skywalking 的启动

进入 D:\apache-skywalking-apm-8.9.1\apache-skywalking-apm-bin\bin ，双击运行 startup.bat（用管理员方式启动），会开启两个命令行窗口。

  • （1）Skywalking-Collector：追踪信息收集器，通过 gRPC/Http 收集客户端的采集信息 。Http默认端口 12800，gRPC默认端口 11800。（如需要修改，可前往 apache-skywalking-apm-bin\config\applicaiton.yml 进行修改）
  • （2）Skywalking-Webapp：管理平台页面 默认端口 8080 （如需要修改，可前往 apache-skywalking-apm-bin\webapp\webapp.yml 进行修改）

启动图如下：

接着浏览器Skywalking访问：http://localhost:8080/

这个右边有个自动刷新的按钮，一定要启动起来

不然到时候，springboot工程启动以后，你以为没有连接成功（F5刷新页面是没有用的）

部署探针

方式一：IDEA 部署探针

修改启动类的 VM options（虚拟机选项）配置

配置的jvm参数如下：

bash 复制代码 
-javaagent:D:\apache-skywalking-apm-8.9.1\apache-skywalking-apm-bin\skywalking-agent\skywalking-agent.jar
-DSW_AGENT_NAME=woqu-ndy
-DSW_AGENT_COLLECTOR_BACKEND_SERVICES=127.0.0.1:11800
  • javaagent: 表示 skywalking‐agent.jar的本地磁盘的路径
  • DSW_AGENT_NAME：表示在skywalking上显示的服务名
  • DSW_AGENT_COLLECTOR_BACKEND_SERVICES：表示skywalking的collector服务的IP及端口
  • 注意：DSW_AGENT_COLLECTOR_BACKEND_SERVICES 可以指定远程地址， 但是 javaagent 必须绑定你本机物理路径的skywalking-agent.jar

方式二：Java 命令行启动方式

bash 复制代码 
java -javaagent:C:\Users\ke\Desktop\apache-skywalking-apm-6.6.0\apache-skywalking-apm-bin\agent/skywalking-agent.jar=-Dskywalking.agent.service_name=service-myapp,-Dskywalking.collector.backend_service=localhost:11800 -jar service-myapp.jar

方式三：编写sh脚本启动（linux环境）

bash 复制代码 
#!/bin/bash  

# 设置 SkyWalking Agent 的路径  
AGENT_PATH="/home/yourusername/Desktop/apache-skywalking-apm-6.6.0/apache-skywalking-apm-bin/agent"  

# 设置 Java 应用的 JAR 文件路径  
JAR_PATH="/path/to/your/service-myapp.jar"  

# 设置 SkyWalking 服务名称和 Collector 后端服务地址  
SERVICE_NAME="service-myapp"  
COLLECTOR_BACKEND_SERVICE="localhost:11800"  

# 构造 Java Agent 参数  
JAVA_AGENT="-javaagent:$AGENT_PATH/skywalking-agent.jar \  
            -Dskywalking.agent.service_name=$SERVICE_NAME \  
            -Dskywalking.collector.backend_service=$COLLECTOR_BACKEND_SERVICE"  
  
# 启动 Java 应用  
java $JAVA_AGENT -jar $JAR_PATH

Springboot 的启动

IDEA 部署探针方式启动

启动后，控制台日志输出开头出现了以下的记录，就表示连接上Skywalking了

再看 Skywalking（http://localhost:8080/） 页面那边，你就会发现有个这个图（表示连接上了）

我们再请求一下 Controller 的接口，就会发现捕获了相关接口记录

（但是目前，还是没有接口具体详细的日志入参或者出参的）

Skywalking 进行日志配置

为log日志增加 skywalking的 traceId（追踪ID）。便于排查

首先引入maven依赖

xml 复制代码 
<!-- skywalking-logback skyWalking中的traceId记录到logback日志 ↓ -->
<dependency>
   <groupId>org.apache.skywalking</groupId>
   <artifactId>apm-toolkit-logback-1.x</artifactId>
   <version>9.0.0</version>
</dependency>

接着在 resources文件夹下创建 logback-spring.xml文件

xml 复制代码 
<?xml version="1.0" encoding="UTF-8"?>
<configuration debug="false">

    <!--定义日志文件的存储地址 勿在 LogBack 的配置中使用相对路径-->
    <property name="LOG_HOME" value="D:/logs/" ></property>

    <!-- 彩色日志 -->
    <conversionRule conversionWord="clr" converterClass="org.springframework.boot.logging.logback.ColorConverter" />

    <!--控制台日志， 控制台输出 -->
    <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">
                <!--格式化输出：%d表示日期，%thread表示线程名，%-5level：级别从左显示5个字符宽度%msg：日志消息，%n是换行符-->
                <pattern>%clr(%d{yyyy-MM-dd HH:mm:ss.SSS}){faint} [%X{tid}] %clr([%-10.10thread]){faint} %clr(%-5level) %clr(%-50.50logger{50}:%-3L){cyan} %clr(-){faint} %msg%n</pattern>
            </layout>
        </encoder>
    </appender>

    <!--文件日志， 按照每天生成日志文件 （只能是 由 Logger 或者 LoggerFactory 记录的日志消息哦）-->
    <!--以下关于 日志文件的pattern 需要去掉颜色，防止出现 ANSI转义序列-->
    <appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender">
        <rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
            <!--日志文件输出的文件名-->
            <FileNamePattern>${LOG_HOME}/%d{yyyy-MM-dd}/pro.log</FileNamePattern>
            <!--日志文件保留天数-->
            <MaxHistory>30</MaxHistory>
        </rollingPolicy>
        <encoder class="ch.qos.logback.core.encoder.LayoutWrappingEncoder">
            <layout class="org.apache.skywalking.apm.toolkit.log.logback.v1.x.mdc.TraceIdMDCPatternLogbackLayout">
                <!--格式化输出：%d表示日期，%thread表示线程名，%-5level：级别从左显示5个字符宽度%msg：日志消息，%n是换行符-->
                <!--            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{50} - %msg%n</pattern>-->
                <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%X{tid}] [%-10.10thread] %-5level %-50.50logger{50}:%-3L - %msg%n</pattern>
            </layout>
        </encoder>
        <!--日志文件最大的大小-->
        <triggeringPolicy class="ch.qos.logback.core.rolling.SizeBasedTriggeringPolicy">
            <MaxFileSize>10MB</MaxFileSize>
        </triggeringPolicy>
    </appender>

    <!--skywalking grpc 日志收集-->
    <appender name="grpc" 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>


    <!-- 日志输出级别 -->
    <root level="INFO">
        <appender-ref ref="STDOUT" ></appender-ref>
        <appender-ref ref="FILE" ></appender-ref>
        <appender-ref ref="grpc"/>
    </root>
</configuration>

请求接口就可以发现TID的输出

（在这里是882c67dc859046c398fbfc5725df9de0.109.17288962842340001）

然后把它放到 追踪 栏目的追踪id ，可以查到记录

然后把它放到 日志 栏目的追踪id ，可以查到记录

参考文章

【1】skywalking环境搭建（windows）

【2】windows下安装skywalking 9.2

【3】SpringBoot集成Skywalking日志收集

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