博客

背景

在大型的微服务中，一个请求可能会经过多个服务，而且服务分布在成百上千的服务器中。一旦出问题或者需要做请求响应时间的优化，我们到底如何快速定位和分析问题出现在哪个服务呢？

单个请求到响应都会形成一条服务调用链路。

随着服务的越来越多，对调用链的分析会越来越复杂。它们之间的调用关系也许如下：

我们需要一些工具帮我们分析链路的行为和性能问题，这些工具就是APM（应用性能管理）工具。目前主流的微服务APM工具有Zipkin、Pinpoint、SkyWalking。

• Zipkin是Twitter开源的调用链分析工具，目前基于Spring Cloud Sleuth得到了广泛的使用，特点是轻量，使用部署简单。缺点是对代码有侵入性。

• Pinpoint是韩国人开源的基于字节码注入的调用链分析，以及应用监控分析工具。特点是支持多种插件，UI功能强大，接入端无代码侵入。

• SkyWalking是国内开源的基于字节码注入的调用链分析以及应用监控分析工具。特点是支持多种插件，UI功能较强，接入端无代码侵入。目前使用厂商最多，版本更新较快，已成为 Apache 基金会顶级项目。

  优缺点及性能分析请参考：
  
  分布式服务监控zipkin、Pinpoint、SkyWalking分析
  

实现方案

1. SkyWalking作为项目中的APM工具

参考

分布式服务监控zipkin、Pinpoint、SkyWalking分析

的分析，从性能、功能、界面、活跃度来看，我选择SkyWalking作为我项目中的APM工具。

1.1 SkyWalking服务端搭建

• 首先需要本地有ElasticSearch环境，因为SkyWalking官方推荐使用ES作为存储方案。具体ES搭建我使用docker搭建，大家可以根据自己的需要，参考网上资料搭建。

  docker-compose.yml

  version: '3.3'
  services:
    elasticsearch:
      image: wutang/elasticsearch-shanghai-zone:6.3.2
      container_name: elasticsearch
      restart: always
      ports:
        - 9200:9200
        - 9300:9300
      environment:
        cluster.name: elasticsearch
  

• 运行SkyWalking服务端两种方式
  
  （1）Linux直接安装SkyWalking服务端

  • 在Linux安装SkyWalking需要jdk的运行环境，官方下载地址http://skywalking.apache.org/downloads/，因为我们的ES版本是6的，所以选择SkyWalking的6.6.0版本的linux版本下载
    
    
  • 修改配置及启动
  • 下载完成后解压缩，进入 apache-skywalking-apm-bin/config 目录并修改 application.yml 配置文件

storage:
  elasticsearch:
    nameSpace: ${SW_NAMESPACE:""}
    clusterNodes: ${SW_STORAGE_ES_CLUSTER_NODES:192.168.160.105:9200}
    protocol: ${SW_STORAGE_ES_HTTP_PROTOCOL:"http"}
#    trustStorePath: ${SW_SW_STORAGE_ES_SSL_JKS_PATH:"../es_keystore.jks"}
#    trustStorePass: ${SW_SW_STORAGE_ES_SSL_JKS_PASS:""}
    user: ${SW_ES_USER:""}
    password: ${SW_ES_PASSWORD:""}
    indexShardsNumber: ${SW_STORAGE_ES_INDEX_SHARDS_NUMBER:2}
    indexReplicasNumber: ${SW_STORAGE_ES_INDEX_REPLICAS_NUMBER:0}
#    # Those data TTL settings will override the same settings in core module.
    recordDataTTL: ${SW_STORAGE_ES_RECORD_DATA_TTL:7} # Unit is day
    otherMetricsDataTTL: ${SW_STORAGE_ES_OTHER_METRIC_DATA_TTL:45} # Unit is day
    monthMetricsDataTTL: ${SW_STORAGE_ES_MONTH_METRIC_DATA_TTL:18} # Unit is month
#    # Batch process setting, refer to https://www.elastic.co/guide/en/elasticsearch/client/java-api/5.5/java-docs-bulk-processor.html
    bulkActions: ${SW_STORAGE_ES_BULK_ACTIONS:1000} # Execute the bulk every 1000 requests
    flushInterval: ${SW_STORAGE_ES_FLUSH_INTERVAL:10} # flush the bulk every 10 seconds whatever the number of requests
    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}
#  elasticsearch7:
#    nameSpace: ${SW_NAMESPACE:""}
#    clusterNodes: ${SW_STORAGE_ES_CLUSTER_NODES:localhost:9200}
#    protocol: ${SW_STORAGE_ES_HTTP_PROTOCOL:"http"}
#    trustStorePath: ${SW_SW_STORAGE_ES_SSL_JKS_PATH:"../es_keystore.jks"}
#    trustStorePass: ${SW_SW_STORAGE_ES_SSL_JKS_PASS:""}
#    user: ${SW_ES_USER:""}
#    password: ${SW_ES_PASSWORD:""}
#    indexShardsNumber: ${SW_STORAGE_ES_INDEX_SHARDS_NUMBER:2}
#    indexReplicasNumber: ${SW_STORAGE_ES_INDEX_REPLICAS_NUMBER:0}
#    # Those data TTL settings will override the same settings in core module.
#    recordDataTTL: ${SW_STORAGE_ES_RECORD_DATA_TTL:7} # Unit is day
#    otherMetricsDataTTL: ${SW_STORAGE_ES_OTHER_METRIC_DATA_TTL:45} # Unit is day
#    monthMetricsDataTTL: ${SW_STORAGE_ES_MONTH_METRIC_DATA_TTL:18} # Unit is month
#    # Batch process setting, refer to https://www.elastic.co/guide/en/elasticsearch/client/java-api/5.5/java-docs-bulk-processor.html
#    bulkActions: ${SW_STORAGE_ES_BULK_ACTIONS:1000} # Execute the bulk every 1000 requests
#    flushInterval: ${SW_STORAGE_ES_FLUSH_INTERVAL:10} # flush the bulk every 10 seconds whatever the number of requests
#    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}
#  h2:
#    driver: ${SW_STORAGE_H2_DRIVER:org.h2.jdbcx.JdbcDataSource}
#    url: ${SW_STORAGE_H2_URL:jdbc:h2:mem:skywalking-oap-db}
#    user: ${SW_STORAGE_H2_USER:sa}
#    metadataQueryMaxSize: ${SW_STORAGE_H2_QUERY_MAX_SIZE:5000}
#  mysql:
#    properties:
#      jdbcUrl: ${SW_JDBC_URL:"jdbc:mysql://localhost:3306/swtest"}
#      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}

• 启动/apache-skywalking-apm-bin/bin/startup.sh

（2）docker安装SkyWalking服务端（比较方便，推荐）

docker-compose.yml如下

version: '3.3'
services:
  oap:
    image: apache/skywalking-oap-server:6.6.0-es6
    container_name: skywalking-oap
    restart: always
    ports:
      - 11800:11800
      - 12800:12800
    environment:
      - SW_STORAGE=elasticsearch #Es的存储
      - SW_STORAGE_ES_CLUSTER_NODES=192.168.160.105:9200 # Es的节点地址，配置es的地址
      - TZ=Asia/Shanghai #设定东八区的城市，防止Oap记录数据时用0时区
  ui:
    image: apache/skywalking-ui:6.6.0
    container_name: skywalking-ui
    depends_on:
      - oap
    links:
      - oap
    restart: always
    ports:
      - 8080:8080 #默认8080端口，这里设置18080映射到宿主机，可修改
    environment:
      collector.ribbon.listOfServers: oap:12800

1.2项目中Skywalking客户端使用

• Java Agent 探针,参考官网
  
  Setup java agent
  
• （1）解压之前下载的SkyWalking包apache-skywalking-apm-bin，在agent目录下找到skywalking-agent.jar。复制全路径，我本地为E:\apache-skywalking-apm-bin\agent\skywalking-agent.jar
• （2）在idea中配置VM options:

-javaagent:E:\apache-skywalking-apm-bin\agent\skywalking-agent.jar
-Dskywalking.agent.service_name=light-demo-center
-Dskywalking.collector.backend_service=192.168.160.105:11800

• （3）启动项目后，多次发出项目请求，到SkyWalking服务端UI可以看到如下效果：

具体的SkyWalking使用可以参考

SkyWalking官网

下一篇，我们讲解如何在日志中查找同一条链路的日志吧，敬请期待。