# 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
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 >
```

{% endtab %}

{% tab title="Models example" %}

```yml
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
```

{% endtab %}
{% endtabs %}

### [​](https://docs.elementary-data.com/data-tests/anomaly-detection-tests/all-columns-anomalies#test-configuration)Test configuration <a href="#test-configuration" id="test-configuration"></a>

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

```yaml
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]
```

{% hint style="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.
  {% endhint %}


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