> ## 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.
# column_anomalies
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`elementary.column_anomalies`
Executes column level monitors and anomaly detection on the column.
Specific monitors are detailed in the table below and can be configured using the `columns_anomalies` configuration.
The test checks the data type of the column and only executes monitors that are relevant to it.
**Default monitors by 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 |
### Test configuration
No mandatory configuration, however it is highly recommended to configure a `timestamp_column`.
data\_tests:
-- elementary.column\_anomalies:
arguments:
column\_anomalies: column monitors list
dimensions: sql expression
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
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]
```yml Models theme={null}
models:
- name: < model name >
config:
elementary:
timestamp_column: < timestamp column >
columns:
- name: < column name >
data_tests:
- elementary.column_anomalies:
arguments:
column_anomalies: < specific monitors, all if null >
where_expression: < sql expression >
time_bucket: # Daily by default
period: < time period >
count: < number of periods >
- name: < model name >
## if no timestamp is configured, elementary will monitor without time filtering
columns:
- name: < column name >
data_tests:
- elementary.column_anomalies:
arguments:
column_anomalies: < specific monitors, all if null >
where_expression: < sql expression >
```
```yml Models example theme={null}
models:
- name: login_events
config:
elementary:
timestamp_column: 'loaded_at'
columns:
- name: user_name
data_tests:
- elementary.column_anomalies:
arguments:
column_anomalies:
- missing_count
- min_length
where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'"
time_bucket:
period: day
count: 1
config:
tags: ['elementary']
- name: users
## if no timestamp is configured, elementary will monitor without time filtering
data_tests:
elementary.volume_anomalies
config:
tags: ['elementary']
columns:
- name: user_id
data_tests:
- elementary.column_anomalies:
config:
tags: ['elementary']
arguments:
timestamp_column: 'updated_at'
where_expression: "event_type in ('event_1', 'event_2') and country_name != 'unwanted country'"
time_bucket:
period: < time period >
count: < number of periods >
- name: user_name
data_tests:
- elementary.column_anomalies:
arguments:
column_anomalies:
- missing_count
- min_length
config:
tags: ['elementary']
```