SpringBoot教程（三十二） | SpringBoot集成Skywalking链路跟踪
- 一、Skywalking是什么？
- 二、Skywalking与JDK版本的对应关系
- 三、Skywalking下载
- 四、Skywalking 数据存储
- 五、Skywalking 的启动
- 六、部署探针
- 前提： Agents 8.9.0 放入 项目工程
- 方式一：IDEA 部署探针
- 方式二：Java 命令行启动方式
- 方式三：编写sh脚本启动（linux环境）
- 七、Springboot 的启动
- IDEA 部署探针方式启动
- Skywalking 进行日志配置
- 实现入参、返参都可查看
- 方式一：通过 Agent 配置实现 （有缺点）
- 方式二：通过 trace 和 Filter 实现
- 方式三：通过 trace 和 Aop 去实现
一、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。
二、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。
三、Skywalking下载
Skywalking 官网下载地址 https://skywalking.apache.org/downloads/
-
其他的版本的 APM 地址
https://archive.apache.org/dist/skywalking/ -
其他的java 版本的 Agents 地址
https://archive.apache.org/dist/skywalking/java-agent/
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
四、Skywalking 数据存储
Skywalking 存在多种数据存储
- h2（默认的存储方式，重启后数据会丢失）
- Elasticsearch （最常用的数据存储方式）
- MySQL
- TiDB
- …
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
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 indexesindexReplicasNumber: ${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 0superDatasetIndexShardsFactor: ${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 templatebulkActions: ${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 requestsresultWindowMaxSize: ${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 configurationurl: ${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 collectduration: ${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 requestconnectionResponseFormat: ${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 indexesindexReplicasNumber: ${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 0superDatasetIndexShardsFactor: ${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 requestsresultWindowMaxSize: ${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: ${SW_STORAGE_IOTDB_FETCH_TASK_LOG_MAX_SIZE:1000} # the max number of fetch task log in a request五、Skywalking 的启动
进入 D:\apache-skywalking-apm-8.9.1\apache-skywalking-apm-bin\bin ，双击运行 startup.bat（用管理员方式启动），会开启两个命令行窗口。
- （1）Skywalking-Collector：追踪信息收集器，通过 gRPC/Http 收集客户端的采集信息 。Http默认端口 12800，gRPC默认端口 11800。（如需要修改，可前往 apache-skywalking-apm-bin\config\applicaiton.yml 进行修改）
- （2）Skywalking-Webapp：管理平台页面 默认端口 8080 （如需要修改，可前往 apache-skywalking-apm-bin\webapp\webapp.yml 进行修改）
启动图如下：
接着浏览器Skywalking访问：http://localhost:8080/
这个右边有个自动刷新的按钮，一定要启动起来
不然到时候，springboot工程启动以后，你以为没有连接成功（F5刷新页面是没有用的）
六、部署探针
前提： Agents 8.9.0 放入 项目工程
也不说放其他位置不好，不过放到项目里面更好一点，后面你就能感受到便利了
方式一：IDEA 部署探针
修改启动类的 VM options（虚拟机选项）配置
配置的jvm参数如下：
-javaagent:D:\ideaObject\reactBoot\springboot-full\src\main\skywalking-agent\skywalking-agent.jar
-Dskywalking.agent.service_name=woqu-ndy
-Dskywalking.collector.backend_service=127.0.0.1:11800
- javaagent: 表示 skywalking‐agent.jar的本地磁盘的路径
（我这边是放到项目里面了）
-Dskywalking.agent.service_name：表示在skywalking上显示的服务名
-Dskywalking.collector.backend_service：表示skywalking的collector服务的IP及端口- 注意：-Dskywalking.collector.backend_service 可以指定远程地址， 但是 javaagent 必须绑定你本机物理路径的 skywalking-agent.jar
方式二：Java 命令行启动方式
java -javaagent:D:\ideaObject\reactBoot\springboot-full\src\main\skywalking-agent\skywalking-agent.jar=-Dskywalking.agent.service_name=service-myapp,-Dskywalking.collector.backend_service=localhost:11800 -jar service-myapp.jar
方式三：编写sh脚本启动（linux环境）
#!/bin/bash # 设置 SkyWalking Agent 的路径
AGENT_PATH="/home/yourusername/Desktop/apache-skywalking-apm-6.6.0/apache-skywalking-apm-bin/agent" # 设置 Java 应用的 JAR 文件路径
JAR_PATH="/path/to/your/service-myapp.jar" # 设置 SkyWalking 服务名称和 Collector 后端服务地址
SERVICE_NAME="service-myapp"
COLLECTOR_BACKEND_SERVICE="localhost:11800" # 构造 Java Agent 参数
JAVA_AGENT="-javaagent:$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 $JAR_PATH
七、Springboot 的启动
IDEA 部署探针方式启动
启动后，控制台日志输出开头出现了以下的记录，就表示连接上Skywalking了
再看 Skywalking（http://localhost:8080/） 页面那边，你就会发现有个这个图（表示连接上了）
我们再请求一下 Controller 的接口，就会发现捕获了相关接口记录
（但是目前，还是没有接口具体详细的日志入参或者出参的）
Skywalking 进行日志配置
为log日志增加 skywalking的 traceId（追踪ID）。便于排查
首先引入maven依赖
<!-- SkyWalking 的日志工具包 -->
<dependency><groupId>org.apache.skywalking</groupId><artifactId>apm-toolkit-logback-1.x</artifactId><version>9.0.0</version>
</dependency>
接着在 resources文件夹下创建 logback-spring.xml文件
<?xml version="1.0" encoding="UTF-8"?>
<configuration debug="false"><!--定义日志文件的存储地址 勿在 LogBack 的配置中使用相对路径--><property name="LOG_HOME" value="D:/logs/" ></property><!-- 彩色日志 --><conversionRule conversionWord="clr" converterClass="org.springframework.boot.logging.logback.ColorConverter" /><!--控制台日志， 控制台输出 --><appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender"><encoder class="ch.qos.logback.core.encoder.LayoutWrappingEncoder"><layout class="org.apache.skywalking.apm.toolkit.log.logback.v1.x.mdc.TraceIdMDCPatternLogbackLayout"><!--格式化输出：%d表示日期，%thread表示线程名，%-5level：级别从左显示5个字符宽度%msg：日志消息，%n是换行符--><pattern>%clr(%d{yyyy-MM-dd HH:mm:ss.SSS}){faint} [%X{tid}] %clr([%-10.10thread]){faint} %clr(%-5level) %clr(%-50.50logger{50}:%-3L){cyan} %clr(-){faint} %msg%n</pattern></layout></encoder></appender><!--文件日志， 按照每天生成日志文件 （只能是 由 Logger 或者 LoggerFactory 记录的日志消息哦）--><!--以下关于 日志文件的pattern 需要去掉颜色，防止出现 ANSI转义序列--><appender name="FILE" class="ch.qos.logback.core.rolling.RollingFileAppender"><rollingPolicy class="ch.qos.logback.core.rolling.TimeBasedRollingPolicy"><!--日志文件输出的文件名--><FileNamePattern>${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
实现入参、返参都可查看
方式一：通过 Agent 配置实现 （有缺点）
首先，你需要确认SkyWalking的Agent配置。
SkyWalking的Agent在启动时会读取配置文件，通常是agent.config。
默认情况下，请求参数的采集是关闭的，你需要手动开启。
具体步骤如下：
在你的SkyWalking Agent配置文件agent.config中，找到plugin部分，确保以下配置项设置为true：
plugin.tomcat.collect_http_params=${SW_PLUGIN_TOMCAT_COLLECT_HTTP_PARAMS:true}
plugin.springmvc.collect_http_params=${SW_PLUGIN_SPRINGMVC_COLLECT_HTTP_PARAMS:true}
plugin.httpclient.collect_http_params=${SW_PLUGIN_HTTPCLIENT_COLLECT_HTTP_PARAMS:true}
缺点：可是以上设置，只能开启get请求的入参采集，post无法获取到，这个方式不怎么好
方式二：通过 trace 和 Filter 实现
一、引入追踪工具包
<!-- SkyWalking 追踪工具包 -->
<dependency><groupId>org.apache.skywalking</groupId><artifactId>apm-toolkit-trace</artifactId><version>9.0.0</version>
</dependency>
二、使用 HttpFilter 和 ContentCachingRequestWrapper
知识小贴士：为什么不用HttpServletRequest？
如果直接把HttpServletRequest中的InputStream读取后输出日志，会导致后续业务逻辑读取不到InputStream中的内容，因为流只能读取一次。
package com.example.springbootfull.quartztest.Filter;import lombok.extern.slf4j.Slf4j;
import org.apache.skywalking.apm.toolkit.trace.ActiveSpan;
import org.springframework.stereotype.Component;
import org.springframework.util.StringUtils;
import org.springframework.web.util.ContentCachingRequestWrapper;
import org.springframework.web.util.ContentCachingResponseWrapper;import javax.servlet.FilterChain;
import javax.servlet.ServletException;
import javax.servlet.http.HttpFilter;
import javax.servlet.http.HttpServletRequest;
import javax.servlet.http.HttpServletResponse;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.util.Enumeration;
import java.util.HashSet;
import java.util.Set;
import java.util.stream.Collectors;@Slf4j
@Component
public class ApmHttpInfo extends HttpFilter {//被忽略的头部信息 private static final Set<String> IGNORED_HEADERS;static {Set<String> ignoredHeaders = new HashSet<>();ignoredHeaders.addAll(java.util.Arrays.asList("Content-Type","User-Agent","Accept","Cache-Control","Postman-Token","Host","Accept-Encoding","Connection","Content-Length").stream().map(String::toUpperCase).collect(Collectors.toList()));IGNORED_HEADERS = ignoredHeaders;}@Overridepublic void doFilter(HttpServletRequest request, HttpServletResponse response, FilterChain filterChain) throws IOException, ServletException {ContentCachingRequestWrapper requestWrapper = new ContentCachingRequestWrapper(request);ContentCachingResponseWrapper responseWrapper = new ContentCachingResponseWrapper(response);try {filterChain.doFilter(requestWrapper, responseWrapper);} finally {try {//构造请求信息: 比如 curl -X GET http://localhost:18080/getPerson?id=1 -H 'token: me-token' -d '{ "name": "hello" }'//构造请求的方法&URL&参数StringBuilder sb = new StringBuilder("curl").append(" -X ").append(request.getMethod()).append(" ").append(request.getRequestURL().toString());if (StringUtils.hasLength(request.getQueryString())) {sb.append("?").append(request.getQueryString());}//构造headerEnumeration<String> headerNames = request.getHeaderNames();while (headerNames.hasMoreElements()) {String headerName = headerNames.nextElement();if (!IGNORED_HEADERS.contains(headerName.toUpperCase())) {sb.append(" -H '").append(headerName).append(": ").append(request.getHeader(headerName)).append("'");}}//获取bodyString body = new String(requestWrapper.getContentAsByteArray(), StandardCharsets.UTF_8);if (StringUtils.hasLength(body)) {sb.append(" -d '").append(body).append("'");}//输出到inputActiveSpan.tag("input", sb.toString());//获取返回值bodyString responseBody = new String(responseWrapper.getContentAsByteArray(), StandardCharsets.UTF_8);//输出到outputActiveSpan.tag("output", responseBody);} catch (Exception e) {log.warn("fail to build http log", e);} finally {//这一行必须添加，否则就一直不返回responseWrapper.copyBodyToResponse();}}}
}
效果如下（get请求）：
效果如下（post请求）：
方式三：通过 trace 和 Aop 去实现
在此就不细说了，这个也是一种方案
参考文章
【1】skywalking环境搭建（windows）
【2】windows下安装skywalking 9.2
【3】skywalking9.1结合logback配置日志收集
【4】SpringBoot集成Skywalking日志收集
【5】skywalking展示http请求和响应
