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](#四、Skywalking 数据存储)
- [五、Skywalking 的启动](#五、Skywalking 的启动)
- 六、部署探针
-
- [前提： Agents 8.9.0 放入 项目工程](#前提： Agents 8.9.0 放入 项目工程)
- [方式一：IDEA 部署探针](#方式一：IDEA 部署探针)
- [方式二：Java 命令行启动方式](#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](#七、Springboot 的启动)
-
- [IDEA 部署探针方式启动](#IDEA 部署探针方式启动)
- [Skywalking 进行日志配置](#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）](#方式一：通过 Agent 配置实现 （有缺点）)
- [方式二：通过 trace 和 Filter 实现](#方式二：通过 trace 和 Filter 实现)
- [方式三：通过 trace 和 Aop 去实现](#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
-
其他的java 版本的 Agents 地址
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 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: ${SW_STORAGE_IOTDB_FETCH_TASK_LOG_MAX_SIZE:1000} # the max number of fetch task log in a request五、Skywalking 的启动
进入 D:apache-skywalking-apm-8.9.1apache-skywalking-apm-binin ，双击运行 startup.bat（用管理员方式启动），会开启两个命令行窗口。
- （1）Skywalking-Collector：追踪信息收集器，通过 gRPC/Http 收集客户端的采集信息 。Http默认端口 12800，gRPC默认端口 11800。（如需要修改，可前往 apache-skywalking-apm-binconfigapplicaiton.yml 进行修改）
- （2）Skywalking-Webapp：管理平台页面 默认端口 8080 （如需要修改，可前往 apache-skywalking-apm-binwebappwebapp.yml 进行修改）
启动图如下：
接着浏览器Skywalking访问：http://localhost:8080/
这个右边有个自动刷新的按钮，一定要启动起来
不然到时候，springboot工程启动以后，你以为没有连接成功（F5刷新页面是没有用的）
六、部署探针
前提： Agents 8.9.0 放入 项目工程
也不说放其他位置不好，不过放到项目里面更好一点，后面你就能感受到便利了
方式一：IDEA 部署探针
修改启动类的 VM options（虚拟机选项）配置
配置的jvm参数如下：
-javaagent:D:ideaObjectreactBootspringboot-fullsrcmainskywalking-agentskywalking-agent.jar
-Dskywalking.agent.service_name=woqu-ndy
-Dskywalking.collector.backend_service=127.0.0.1:11800
- javaagent: 表示 skywalking‐agent.jar的本地磁盘的路径
（我这边是放到项目里面了）
-Dskywalking.agent.service_name：表示在skywalking上显示的服务名
-Dskywalking.collector.backend_service：表示skywalking的collector服务的IP及端口- 注意：-Dskywalking.collector.backend_service 可以指定远程地址， 但是 javaagent 必须绑定你本机物理路径的 skywalking-agent.jar
方式二：Java 命令行启动方式
java -javaagent:D:ideaObjectreactBootspringboot-fullsrcmainskywalking-agentskywalking-agent.jar=-Dskywalking.agent.service_name=service-myapp,-Dskywalking.collector.backend_service=localhost:11800 -jar service-myapp.jar方式三：编写sh脚本启动（linux环境）
#!/bin/bash
# 设置 SkyWalking Agent 的路径
AGENT_PATH="/home/yourusername/Desktop/apache-skywalking-apm-6.6.0/apache-skywalking-apm-bin/agent"
# 设置 Java 应用的 JAR 文件路径
JAR_PATH="/path/to/your/service-myapp.jar"
# 设置 SkyWalking 服务名称和 Collector 后端服务地址
SERVICE_NAME="service-myapp"
COLLECTOR_BACKEND_SERVICE="localhost:11800"
# 构造 Java Agent 参数
JAVA_AGENT="-javaagent:$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;
}
@Override
public 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header
Enumeration<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body
String body = new String(requestWrapper.getContentAsByteArray(), StandardCharsets.UTF_8);
if (StringUtils.hasLength(body)) {
sb.append(" -d '").append(body).append("'");
}
//输出到input
ActiveSpan.tag("input", sb.toString());
//获取返回值body
String responseBody = new String(responseWrapper.getContentAsByteArray(), StandardCharsets.UTF_8);
//输出到output
ActiveSpan.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