> ## Documentation Index
> Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt
> Use this file to discover all available pages before exploring further.
# volume_anomalies
Let our Slack chatbot create the anomaly test you need.
`elementary.volume_anomalies`
Monitors the row count of your table over time per time bucket (if configured without `timestamp_column`, will count table total rows).
Upon running the test, your data is split into time buckets (daily by default, configurable with the `time bucket` field),
and then we compute the row count per bucket for the last [`training_period`](/data-tests/anomaly-detection-configuration/training-period) days (by default 14).
The test then compares the row count of each bucket within the detection period (last 2 days by default, configured as [`detection_period`](/data-tests/anomaly-detection-configuration/detection-period)),
and compares it to the row count of the previous time buckets.
**The test will only run on completed time buckets**, so if you run it with daily buckets in the middle of today, the test would only count yesterday as a complete bucket.
If there were any anomalies during the detection period, the test will fail.
### Test configuration
No mandatory configuration, however it is highly recommended to configure a `timestamp_column`.
data\_tests:
- elementary.volume\_anomalies:
arguments:
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]
```yml Models theme={null}
models:
- name: < model name >
data_tests:
- elementary.volume_anomalies:
arguments:
timestamp_column: < timestamp column >
where_expression: < sql expression >
time_bucket: # Daily by default
period: < time period >
count: < number of periods >
```
```yml Models example theme={null}
models:
- name: login_events
config:
elementary:
timestamp_column: "loaded_at"
data_tests:
- elementary.volume_anomalies:
arguments:
where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'"
time_bucket:
period: day
count: 1
config:
# optional - use tags to run elementary tests on a dedicated run
tags: ["elementary"]
# optional - change severity
severity: warn
- name: users
# if no timestamp is configured, elementary will monitor without time filtering
data_tests:
- elementary.volume_anomalies:
config:
tags: ["elementary"]
```