> ## Documentation Index
> Fetch the complete documentation index at: https://docs.paradime.io/llms.txt
> Use this file to discover all available pages before exploring further.

# All Columns Anomalies

The `elementary.all_columns_anomalies` test executes column-level monitors and anomaly detection on all columns of the table. It checks the data type of each column and only executes monitors that are relevant to it.

### How it works

* The test analyzes all columns in the table.
* Based on the data type of each column, it applies relevant monitors.
* You can override default monitors using the `column_anomalies` parameter.
* Columns can be excluded using `exclude_prefix` or `exclude_regexp` parameters.

### Default Monitors by Data 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     |

<Tabs>
  <Tab title="Models">
    ```yml theme={"system"}
    models:
      - name: < model name >
        config:
          elementary:
            timestamp_column: < timestamp column >
        tests:
          - elementary.all_columns_anomalies:
              column_anomalies: < specific monitors, all if null >
              where_expression: < sql expression >
              time_bucket: # Daily by default
                period: < time period >
                count: < number of periods >
    ```
  </Tab>

  <Tab title="Models example">
    ```yml theme={"system"}
    models:
      - name: login_events
        config:
          elementary:
            timestamp_column: "loaded_at"
        tests:
          - elementary.all_columns_anomalies:
              where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'"
              time_bucket:
                period: day
                count: 1
              tags: ["elementary"]
              # optional - change global sensitivity
              anomaly_sensitivity: 3.5
    ```
  </Tab>
</Tabs>

### [​](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/all-columns-anomalies#test-configuration)Test configuration

No mandatory configuration, however it is highly recommended to configure a `timestamp_column`.

```yaml theme={"system"}
tests:
  — elementary.all_columns_anomalies:
    timestamp_column: column name
    column_anomalies: column monitors list
    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]
```

<Info>
  **Important Notes**

  * No mandatory configuration, however, it is highly recommended to configure a `timestamp_column`.
  * Use `column_anomalies` to specify which monitors to run (if not specified, all default monitors will run).
  * `exclude_prefix` and `exclude_regexp` can be used to exclude specific columns from the test.
  * The `where_expression` can be used to filter the data being tested.
  * Global sensitivity can be adjusted using the `anomaly_sensitivity` parameter.
  * Tags can be used to run elementary tests on a dedicated run.
</Info>


## Related topics

- [Column Anomalies](/integrations/elementary-data/anomaly-detection-tests/column-anomalies.md)
- [Anomaly Tests Parameters](/integrations/elementary-data/anomaly-detection-tests/anomaly-tests-parameters.md)
- [Dimension Anomalies](/integrations/elementary-data/anomaly-detection-tests/dimension-anomalies.md)
- [Volume Anomalies](/integrations/elementary-data/anomaly-detection-tests/volume-anomalies.md)
- [Freshness Anomalies](/integrations/elementary-data/anomaly-detection-tests/freshness-anomalies.md)