Advanced Example: Abnormal Dips in User Traffic using Filtersedit

Again, building on the previous example we can use the match_all property to query only GoogleAds click through rates from the social media measurement.

One or more match_all conditions can be added and will automatically change the queries to your data sources with the right filters.

An updated user_traffic.json file (Or .yml file if YAML format is used) will become:

{
  "name": "traffic-model",
  "seasonality": {
      "daytime": true,
      "weekday": true
  },
  "bucket_interval": "1m",
  "default_datasource": "my-datasource",
  "features": {
    "io": [{
      "default": 0,
      "metric": "count",
      "field": "requests",
      "measurement": "traffic",
      "name": "count_all_requests",
      "anomaly_type": "low"
    }],
    "i": [
      {
      "default": 0,
      "metric": "max",
      "field": "active_users",
      "measurement": "traffic",
      "name": "max_users"
      },
      {
      "default": 0,
      "metric": "mean",
      "field": "click_through_rate",
      "measurement": "social",
      "match_all": [
        {"tag": "channel", "value": "GoogleAds"}
      ],
      "name": "avg_ctr_googleads"
      }
    ]
  },
  "interval": 60,
  "max_evals": 10,
  "offset": 30,
  "forecast": 5,
  "span": 20,
  "max_threshold": 90,
  "min_threshold": 50,
  "type": "timeseries"
}