分布式应用监控: SkyWalking 快速接入实践
分布式应用，会存在各种问题。而要解决这些难题，除了要应用自己做一些监控埋点外，还应该有一些外围的系统进行主动探测，主动发现。
APM工具就是干这活的，SkyWalking 是国人开源的一款优秀的APM应用，已成为apache的顶级项目。
今天我们就来实践下 SkyWalking 下吧。
实践目标： 达到监控现有的几个系统，清楚各调用关系，可以找到出性能问题点。
实践步骤：
1. SkyWalking 服务端安装运行；
2. 应用端的接入；
3. 后台查看效果；
4. 分析排查问题；
5. 深入了解（如有心情）；
1. SkyWalking 服务端安装
下载应用包:
# 主下载页 http://skywalking.apache.org/downloads/ # 点开具体下载地址后进行下载，如： wget http://mirrors.tuna.tsinghua.edu.cn/apache/skywalking/6.5.0/apache-skywalking-apm-6.5.0.tar.gz
解压安装包:
tar -xzvf apache-skywalking-apm-6.5.0.tar.gz
使用默认配置端口，默认存储方式 h2, 直接启动服务：
./bin/startup.sh
好产品就是这么简单！
现在服务端就启起来了，可以打开后台地址查看(默认是8080端口): http://localhost:8080 界面如下：
当然，上面是已存在应用的页面。现在你是看不到任何应用的，因为你还没有接入嘛。
2. 应用端的接入
我们只以java应用接入方式实践。
直接使用 javaagent 进行启动即可：
java -javaagent:/root/skywalking/agent/skywalking-agent.jar -Dskywalking.agent.service_name=app1 -Dskywalking.collector.backend_service=localhost:11800 -jar myapp.jar
参数说明：
# 参数解释 skywalking.agent.service_name: 本应用在skywalking中的名称 skywalking.collector.backend_service: skywalking 服务端地址，grpc上报地址，默认端口是 11800 # 上面两个参数也可以使用另外的表现形式 SW_AGENT_COLLECTOR_BACKEND_SERVICES: 与 skywalking.collector.backend_service 含义相同 SW_AGENT_NAME: 与 skywalking.agent.service_name 含义相同
随便访问几个接口或页面，使监控抓取到数据。
再回管理页面，已经看到有节点了。截图如上。
现在我们还可以查看各应用之间的关系了!
关系清晰吧！一目了然，代码再复杂也不怕了。
我们还可以追踪具体链路：
只要知道问题发生的时间点，即可以很快定位到发生问题的接口、系统，快速解决。
3. SkyWalking 配置文件
如上，我们并没有改任何配置文件，就让系统跑起来了。幸运的同时，我们应该要知道更多！至少配置得知道。
config/application.yml : 收集器服务端配置
webapp/webapp.yml : 配置 Web 的端口及获取数据的 OAP(Collector)的IP和端口
agent/config/agent.config : 配置 Agent 信息，如 Skywalking OAP(Collector)的地址和名称
下面是 skywalking 的默认配置，我们可以不用更改就能跑起来一个样例！更改以生产化配置！
config/application.yml
cluster: standalone: # Please check your ZooKeeper is 3.5+, However, it is also compatible with ZooKeeper 3.4.x. Replace the ZooKeeper 3.5+ # library the oap-libs folder with your ZooKeeper 3.4.x library. # zookeeper: # nameSpace: ${SW_NAMESPACE:""} # hostPort: ${SW_CLUSTER_ZK_HOST_PORT:localhost:2181} # #Retry Policy # baseSleepTimeMs: ${SW_CLUSTER_ZK_SLEEP_TIME:1000} # initial amount of time to wait between retries # maxRetries: ${SW_CLUSTER_ZK_MAX_RETRIES:3} # max number of times to retry # # Enable ACL # enableACL: ${SW_ZK_ENABLE_ACL:false} # disable ACL in default # schema: ${SW_ZK_SCHEMA:digest} # only support digest schema # expression: ${SW_ZK_EXPRESSION:skywalking:skywalking} # kubernetes: # watchTimeoutSeconds: ${SW_CLUSTER_K8S_WATCH_TIMEOUT:60} # namespace: ${SW_CLUSTER_K8S_NAMESPACE:default} # labelSelector: ${SW_CLUSTER_K8S_LABEL:app=collector,release=skywalking} # uidEnvName: ${SW_CLUSTER_K8S_UID:SKYWALKING_COLLECTOR_UID} # consul: # serviceName: ${SW_SERVICE_NAME:"SkyWalking_OAP_Cluster"} # Consul cluster nodes, example: 10.0.0.1:8500,10.0.0.2:8500,10.0.0.3:8500 # hostPort: ${SW_CLUSTER_CONSUL_HOST_PORT:localhost:8500} # nacos: # serviceName: ${SW_SERVICE_NAME:"SkyWalking_OAP_Cluster"} # hostPort: ${SW_CLUSTER_NACOS_HOST_PORT:localhost:8848} # # Nacos Configuration namespace # namespace: 'public' # etcd: # serviceName: ${SW_SERVICE_NAME:"SkyWalking_OAP_Cluster"} # etcd cluster nodes, example: 10.0.0.1:2379,10.0.0.2:2379,10.0.0.3:2379 # hostPort: ${SW_CLUSTER_ETCD_HOST_PORT:localhost:2379} 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} 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} 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} istio-telemetry: default: envoy-metric: default: # alsHTTPAnalysis: ${SW_ENVOY_METRIC_ALS_HTTP_ANALYSIS:k8s-mesh} #receiver_zipkin: # default: # host: ${SW_RECEIVER_ZIPKIN_HOST:0.0.0.0} # port: ${SW_RECEIVER_ZIPKIN_PORT:9411} # contextPath: ${SW_RECEIVER_ZIPKIN_CONTEXT_PATH:/} query: graphql: path: ${SW_QUERY_GRAPHQL_PATH:/graphql} alarm: default: telemetry: none: configuration: none: # apollo: # apolloMeta: http://106.12.25.204:8080 # apolloCluster: default # # apolloEnv: # defaults to null # appId: skywalking # period: 5 # nacos: # # Nacos Server Host # serverAddr: 127.0.0.1 # # Nacos Server Port # port: 8848 # # Nacos Configuration Group # group: 'skywalking' # # Nacos Configuration namespace # namespace: '' # # Unit seconds, sync period. Default fetch every 60 seconds. # period : 60 # # the name of current cluster, set the name if you want to upstream system known. # clusterName: "default" # zookeeper: # period : 60 # Unit seconds, sync period. Default fetch every 60 seconds. # nameSpace: /default # hostPort: localhost:2181 # #Retry Policy # baseSleepTimeMs: 1000 # initial amount of time to wait between retries # maxRetries: 3 # max number of times to retry # etcd: # period : 60 # Unit seconds, sync period. Default fetch every 60 seconds. # group : 'skywalking' # serverAddr: localhost:2379 # clusterName: "default" # consul: # # Consul host and ports, separated by comma, e.g. 1.2.3.4:8500,2.3.4.5:8500 # hostAndPorts: ${consul.address} # # Sync period in seconds. Defaults to 60 seconds. # period: 1 #exporter: # grpc: # targetHost: ${SW_EXPORTER_GRPC_HOST:127.0.0.1} # targetPort: ${SW_EXPORTER_GRPC_PORT:9870}
webapp/webapp.yml
server: port: 8080 collector: path: /graphql ribbon: ReadTimeout: 10000 # Point to all backend's restHost:restPort, split by , listOfServers: 127.0.0.1:12800
agent/config/agent.config
# The agent namespace # agent.namespace=${SW_AGENT_NAMESPACE:default-namespace} # The service name in UI agent.service_name=${SW_AGENT_NAME:Your_ApplicationName} # The number of sampled traces per 3 seconds # Negative number means sample traces as many as possible, most likely 100% # agent.sample_n_per_3_secs=${SW_AGENT_SAMPLE:-1} # Authentication active is based on backend setting, see application.yml for more details. # agent.authentication = ${SW_AGENT_AUTHENTICATION:xxxx} # The max amount of spans in a single segment. # Through this config item, skywalking keep your application memory cost estimated. # agent.span_limit_per_segment=${SW_AGENT_SPAN_LIMIT:300} # Ignore the segments if their operation names end with these suffix. # agent.ignore_suffix=${SW_AGENT_IGNORE_SUFFIX:.jpg,.jpeg,.js,.css,.png,.bmp,.gif,.ico,.mp3,.mp4,.html,.svg} # If true, skywalking agent will save all instrumented classes files in `/debugging` folder. # Skywalking team may ask for these files in order to resolve compatible problem. # agent.is_open_debugging_class = ${SW_AGENT_OPEN_DEBUG:true} # The operationName max length # agent.operation_name_threshold=${SW_AGENT_OPERATION_NAME_THRESHOLD:500} # Backend service addresses. collector.backend_service=${SW_AGENT_COLLECTOR_BACKEND_SERVICES:127.0.0.1:11800} # Logging file_name logging.file_name=${SW_LOGGING_FILE_NAME:skywalking-api.log} # Logging level logging.level=${SW_LOGGING_LEVEL:DEBUG} # Logging dir # logging.dir=${SW_LOGGING_DIR:""} # Logging max_file_size, default: 300 * 1024 * 1024 = 314572800 # logging.max_file_size=${SW_LOGGING_MAX_FILE_SIZE:314572800} # The max history log files. When rollover happened, if log files exceed this number, # then the oldest file will be delete. Negative or zero means off, by default. # logging.max_history_files=${SW_LOGGING_MAX_HISTORY_FILES:-1} # mysql plugin configuration # plugin.mysql.trace_sql_parameters=${SW_MYSQL_TRACE_SQL_PARAMETERS:false}
4. SkyWalking 架构
来自官网的图片，感受一下！无须细说，大概原理就是： 针对各种不同客户端实现不同的指标采集，统一通过grpc/http发送到apm服务端，然后经过分析引擎后存储到es/h2/mysql等等存储系统，最后由前端通过查询引擎进行展现。
5. 可以用来干啥
发现系统耗时或者说瓶颈在哪里。
发现各系统之间的调用关系。
监控服务异常。
排查系统故障。
6. 其他存储系统接入
h2只是一个内存存储系统，其目的是为了让你能够快速验证快速响应，它还没有强大到足以支撑线上系统运行。
所以，线上一定得选用某个更可靠存储。
一般地，ES会是个不错的选择，一来它以搜索速度著称而这正好符合后台查询的需求，二来es是分布式存储，可以避免一定的大数据量问题。
mysql: 一般地对普通开发同学友好，且单机mysql容易搭建。
tidb: 与mysql协议完全兼容，分布式存储。
配置方法如demo所示。。。
不要害怕今日的苦，你要相信明天，更苦！
