> ## Documentation Index > Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt > Use this file to discover all available pages before exploring further. # all_columns_anomalies Let our Slack chatbot create the anomaly test you need. `elementary.all_columns_anomalies` Executes column level monitors and anomaly detection on all the columns of the table. Specific monitors are detailed in the table below and can be configured using the `columns_anomalies` configuration. The test checks the data type of each column and only executes monitors that are relevant to it. You can use `column_anomalies` param to override the default monitors, and `exclude_prefix` / `exclude_regexp` to exclude columns from the test. **Default monitors by type:** | Data quality metric | Column Type | | -------------------- | ----------- | | `null_count` | any | | `null_percent` | any | | `min_length` | string | | `max_length` | string | | `average_length` | string | | `missing_count` | string | | `missing_percent` | string | | `min` | numeric | | `max` | numeric | | `average` | numeric | | `zero_count` | numeric | | `zero_percent` | numeric | | `standard_deviation` | numeric | | `variance` | numeric | **Opt-in monitors by type:** | Data quality metric | Column Type | | ------------------- | ----------- | | `sum` | numeric | ### Test configuration No mandatory configuration, however it is highly recommended to configure a `timestamp_column`. 
  
    data\_tests:
      -- elementary.all\_columns\_anomalies:
        arguments:
          timestamp\_column: column name
          column\_anomalies: column monitors list
          dimensions: sql expression
          exclude\_prefix: string
          exclude\_regexp: regex
          where\_expression: sql expression
          anomaly\_sensitivity: int
          anomaly\_direction: \[both | spike | drop]
          detection\_period:
            period: \[hour | day | week | month]
            count: int
          training\_period:
            period: \[hour | day | week | month]
            count: int
          time\_bucket:
            period: \[hour | day | week | month]
            count: int
          seasonality: day\_of\_week
          detection\_delay:
            period: \[hour | day | week | month]
            count: int
          ignore\_small\_changes:
            spike\_failure\_percent\_threshold: int
            drop\_failure\_percent\_threshold: int
          anomaly\_exclude\_metrics: \[SQL expression]
```yml Models theme={null} models: - name: < model name > config: elementary: timestamp_column: < timestamp column > data_tests: - elementary.all_columns_anomalies: arguments: column_anomalies: < specific monitors, all if null > where_expression: < sql expression > time_bucket: # Daily by default period: < time period > count: < number of periods > ``` ```yml Models example theme={null} models: - name: login_events config: elementary: timestamp_column: "loaded_at" data_tests: - elementary.all_columns_anomalies: config: tags: ["elementary"] arguments: where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'" time_bucket: period: day count: 1 # optional - change global sensitivity anomaly_sensitivity: 3.5 ```