# Event freshness anomalies

The `elementary.event_freshness_anomalies` test monitors the freshness of event data over time, measuring the expected time it takes each event to load. This is calculated as the time between when the event actually occurs (the `event timestamp`) and when it is loaded to the database (the `update timestamp`).

This test complements the `freshness_anomalies` test and is primarily intended for data that is updated in a continuous or streaming fashion.

### How it works

The test can operate in two modes:

1. If only an `event_timestamp_column` is supplied:
   * The test measures the difference between the current timestamp ("now") and the most recent event timestamp.
2. If both an `event_timestamp_column` and an `update_timestamp_column` are provided:
   * The test measures the difference between these two columns.

{% tabs %}
{% tab title="Models" %}

```yml
models:
  - name: < model name >
    tests:
      - elementary.event_freshness_anomalies:
          event_timestamp_column: < timestamp column > # Mandatory
          update_timestamp_column: < timestamp column > # Optional
          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
    tests:
      - elementary.event_freshness_anomalies:
          event_timestamp_column: "occurred_at"
          update_timestamp_column: "updated_at"
          # optional - use tags to run elementary tests on a dedicated run
          tags: ["elementary"]
          config:
            # optional - change severity
            severity: warn
```

{% endtab %}
{% endtabs %}

### Test configuration <a href="#test-configuration" id="test-configuration"></a>

```yaml
tests:
  — elementary.event_freshness_anomalies:
    event_timestamp_column: column name
    update_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
    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

* **Required configuration**: `event_timestamp_column` is mandatory.
* **Default configuration**: `anomaly_direction: spike` to alert only on delays.
  {% endhint %}