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。

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