> ## 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.

# Freshness Anomalies

The `elementary.freshness_anomalies` test monitors the freshness of your table over time, measuring the expected time between data updates.<br />Monitors the freshness of your table over time, as the expected time between data updates.

### How it works

1. Data is split into time buckets (daily by default, configurable with the `time_bucket` field).
2. The maximum freshness value is computed per bucket for the last `training_period` (14 days by default).
3. The test compares the freshness of each bucket within the detection period (last 2 days by default, controlled by the `detection_period` var) to the freshness of previous time buckets.
4. If any anomalies are detected during the detection period, the test will fail.

<Tabs>
  <Tab title="Models">
    ```yml theme={"system"}
    models:
      - name: < model name >
        tests:
          - elementary.freshness_anomalies:
              timestamp_column: < timestamp column > # Mandatory
              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
        tests:
          - elementary.freshness_anomalies:
              timestamp_column: "updated_at"
              # optional - use tags to run elementary tests on a dedicated run
              tags: ["elementary"]
              config:
                # optional - change severity
                severity: warn
    ```
  </Tab>
</Tabs>

## Test configuration

```yaml theme={"system"}
tests:
  — elementary.freshness_anomalies:
    timestamp_column: column name
    where_expression: sql expression
    anomaly_sensitivity: int
    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
    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]
```

<Warning>
  **Notes:**

  * **Required Configuration**: `timestamp_column`
  * **Default configuration**\*:\* `anomaly_direction: spike` to alert only on delays.
</Warning>


## Related topics

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