> ## 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.

# Volume Anomalies

The `elementary.volume_anomalies` test monitors the row count of your table over time per time bucket. If configured without a `timestamp_column`, it will count total table rows.

### How it works

1. Data is split into time buckets (daily by default, configurable with the `time_bucket` field).
2. Row count is computed per bucket for the last `training_period` days (14 days by default).
3. The test compares the row count of each bucket within the detection period (last 2 days by default, configured as `detection_period`) to the row count of previous time buckets.
4. The test only runs on completed time buckets. For example, with daily buckets, a test run in the middle of today would only count yesterday as a complete bucket.
5. If any anomalies are detected during the detection period, the test will fail.

### Configuration

<Tabs>
  <Tab title="Models">
    ```yml theme={"system"}
    models:
      - name: < model name >
        tests:
          - elementary.volume_anomalies:
              timestamp_column: < timestamp column >
              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.volume_anomalies:
              where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'"
              time_bucket:
                period: day
                count: 1
              # optional - use tags to run elementary tests on a dedicated run
              tags: ["elementary"]
              config:
                # optional - change severity
                severity: warn

      - name: users
        # if no timestamp is configured, elementary will monitor without time filtering
        tests:
          - elementary.volume_anomalies:
              tags: ["elementary"]
    ```
  </Tab>
</Tabs>

### Test configuration

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

```yaml theme={"system"}
tests:
  — elementary.volume_anomalies:
    timestamp_column: column name
    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
    fail_on_zero: [true | false]
    ignore_small_changes:
      spike_failure_percent_threshold: int
      drop_failure_percent_threshold: int
    detection_delay:
      period: [hour | day | week | month]
      count: int
    anomaly_exclude_metrics: [SQL expression]
```


## Related topics

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