anchor.diff:  + health (`API_Health`, optional)
   + request (`API_Request__Request`, fixed-type) - Request description.
   + disclose_status: false (boolean, optional) - If `true` API status will be publically available in API Status endpoint.
  ++ matching_priority: 1 (number, optional) - Priority in which APIs are matched agains consumer request path. Default value is 1.
   
   ### `API_Request__Request`
   + methods (array[`HTTP_Verb`], required) - HTTP request method.
  
positive.code:     + Attributes (Response_OK)
          + data (`Metrics`)
  
  ## Cluster Status [/annon_status]
  
  ### Get Cluster Status [GET]
  
  
negative.code: 
  To monitor services status we will use DogStatsD integration, that will receive following metrics:
  
  - `request.count` (counter) - [API](#reference/apis) hit rate.
  - `request.size` (gauge) - HTTP `Content-Length ` request size.
  - `responses.count` (counter) - same as `request.count` but sent after request dispatch and additionally tagged with `http.status`.
  - `latencies.{client,gateway,upstream}` (gauge) - total request latency for a API consumer, additionally tagged with `http.status`.
  
  All metrics have tags: `http.host`, `http.port`, `http.method`, `api.name` and `api.id` (if matches any), `request.id`. This allows to set different aggregated views on counter data.
  
  We recommend you to try [DataDog](https://www.datadoghq.com/) for collecting and displaying performance metrics. But this is not a hard constraint, instead you can use any StatsD collector.
  
  