2Skywalking 使用(Windows) · SpringCloud微服务实战 · 看云
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本次演示我们直接本地演示,Docker 版的部署放在容器化章节,感兴趣的可以部署尝试下。
本节代码地址
GitHub:https://github.com/xuyisu/fw-sping-cloud/tree/master/fw-cloud-register/fw-register-eureka
GitHub:https://github.com/xuyisu/fw-sping-cloud/tree/master/fw-cloud-ribbon/fw-cloud-ribbon-server
GitHub:https://github.com/xuyisu/fw-sping-cloud/tree/master/fw-cloud-gateways/fw-cloud-gateways-gateway-simple
1. 下载部署包
2. 运行Skywalking
解压安装包,不需要修改配置文件,在bin目录下执行startup.bat
执行startup.bat之后会启动如下两个服务:
- 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。
- Skywalking-Webapp:管理平台页面 默认端口 8080
浏览器输入http://localhost:8080/可以看到没有链路内容
3. Skywalking 配置信息
配置文件在config目录里面的application.yml
3.1 收集器相关配置
支持 http/gRPC收集
core:
default:
# Mixed: Receive agent data, Level 1 aggregate, Level 2 aggregate
# Receiver: Receive agent data, Level 1 aggregate
# Aggregator: Level 2 aggregate
role: ${SW_CORE_ROLE:Mixed} # Mixed/Receiver/Aggregator
restHost: ${SW_CORE_REST_HOST:0.0.0.0}
restPort: ${SW_CORE_REST_PORT:12800}
restContextPath: ${SW_CORE_REST_CONTEXT_PATH:/}
gRPCHost: ${SW_CORE_GRPC_HOST:0.0.0.0}
gRPCPort: ${SW_CORE_GRPC_PORT:11800}
downsampling:
- Hour
- Day
- Month
# Set a timeout on metrics data. After the timeout has expired, the metrics data will automatically be deleted.
enableDataKeeperExecutor: ${SW_CORE_ENABLE_DATA_KEEPER_EXECUTOR:true} # Turn it off then automatically metrics data delete will be close.
dataKeeperExecutePeriod: ${SW_CORE_DATA_KEEPER_EXECUTE_PERIOD:5} # How often the data keeper executor runs periodically, unit is minute
recordDataTTL: ${SW_CORE_RECORD_DATA_TTL:90} # Unit is minute
minuteMetricsDataTTL: ${SW_CORE_MINUTE_METRIC_DATA_TTL:90} # Unit is minute
hourMetricsDataTTL: ${SW_CORE_HOUR_METRIC_DATA_TTL:36} # Unit is hour
dayMetricsDataTTL: ${SW_CORE_DAY_METRIC_DATA_TTL:45} # Unit is day
monthMetricsDataTTL: ${SW_CORE_MONTH_METRIC_DATA_TTL:18} # Unit is month
# Cache metric data for 1 minute to reduce database queries, and if the OAP cluster changes within that minute,
# the metrics may not be accurate within that minute.
enableDatabaseSession: ${SW_CORE_ENABLE_DATABASE_SESSION:true}
3.2 收集信息存储
默认使用h2存储,同时给出了mysql、elasticsearchde 的配置样例,推荐生产使用elasticsearchde
storage:
# elasticsearch:
# 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}
# metadataQueryMaxSize: ${SW_STORAGE_MYSQL_QUERY_MAX_SIZE:5000}
3.3 可采集信息
支持 jvm运行相关信息,zipkin追踪日志等
receiver-sharing-server:
default:
receiver-register:
default:
receiver-trace:
default:
bufferPath: ${SW_RECEIVER_BUFFER_PATH:../trace-buffer/} # Path to trace buffer files, suggest to use absolute path
bufferOffsetMaxFileSize: ${SW_RECEIVER_BUFFER_OFFSET_MAX_FILE_SIZE:100} # Unit is MB
bufferDataMaxFileSize: ${SW_RECEIVER_BUFFER_DATA_MAX_FILE_SIZE:500} # Unit is MB
bufferFileCleanWhenRestart: ${SW_RECEIVER_BUFFER_FILE_CLEAN_WHEN_RESTART:false}
sampleRate: ${SW_TRACE_SAMPLE_RATE:10000} # The sample rate precision is 1/10000. 10000 means 100% sample in default.
slowDBAccessThreshold: ${SW_SLOW_DB_THRESHOLD:default:200,mongodb:100} # The slow database access thresholds. Unit ms.
receiver-jvm:
default:
receiver-clr:
default:
service-mesh:
default:
bufferPath: ${SW_SERVICE_MESH_BUFFER_PATH:../mesh-buffer/} # Path to trace buffer files, suggest to use absolute path
bufferOffsetMaxFileSize: ${SW_SERVICE_MESH_OFFSET_MAX_FILE_SIZE:100} # Unit is MB
bufferDataMaxFileSize: ${SW_SERVICE_MESH_BUFFER_DATA_MAX_FILE_SIZE:500} # Unit is MB
bufferFileCleanWhenRestart: ${SW_SERVICE_MESH_BUFFER_FILE_CLEAN_WHEN_RESTART:false}
4. Agent 使用
Skywalking 采用 Java 探针技术,对客户端应用程序没有任何代码侵入,使用起来简单方便,当然其具体实现就是需要针对不同的框架及服务提供探针插件
下面我们对以下3个系统进行改造演示
- fw-register-eureka
- fw-cloud-ribbon-server
- fw-gateways-gateway
配置启动的命令
将agent目录下的具体地址skywalking-agent.jar写到启动参数里面
具体内容如下
-javaagent:D:\soft\skywalking\windows\apache-skywalking-apm-bin\agent\skywalking-agent.jar
-Dskywalking.agent.service_name=fw-register-eureka
-Dskywalking.collector.backend_service=localhost:11800
分别是
- -javaagent:配置探针的地址
- -Dskywalking.agent.service_name: 配置在Skywalking 链路中的服务名
- -Dskywalking.collector.backend_service: 配置Skywalking 服务的地址
一次改造完之后,启动项目
5. 链路演示
项目启动完之后,我们在Postman 中输入localhost:8699/user/2可以看到具体的链路调用
刷新http://localhost:8080/
可以看到仪表盘显示的内容
拓扑图的显示
链路追踪的显示
指标对比显示
到此,Skywalking 和 Spring Cloud 应用的整合结束
6. 总结
本节主要介绍了Skywalking 的使用,从入门介绍到Skywalking 的安装使用,可以发现其实主要功能是跟Zinkin 链路跟踪和拓扑图等主要功能是类似的,但是Skywalking 更细致、更酷炫一点,通过和Spring Cloud 的整合,我们知道了Skywalking是使用探针的方式交互,相信对Skywalking 的运用已经掌握了









