Documentation Index
Fetch the complete documentation index at: https://openmetadata-feat-feat-2mbfixtestexui.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Column Tests - UI Config
Tests applied on top of Column metrics. For table-level tests, see the Tests UI Overview.
Here is the list of all column tests:
Column Values to Be Unique
Ensures each value in a column appears only once.
Dimension
Uniqueness
When to Use
- Primary keys or natural identifiers
- Fields like email, username, or ID
Behavior
| Condition | Status |
|---|
| All values are unique | ✅ |
| Any duplicate value found | ❌ |
Column Values to Be Not Null
Ensures there are no NULL entries in the column.
Dimension
Completeness
When to Use
- Mandatory fields such as
email, amount, created_at
- Required keys or business-critical columns
Behavior
| Condition | Status |
|---|
| No NULLs present | ✅ |
| Any NULL value present | ❌ |
Column Values to Match Regex
This test allows us to specify how many values in a column we expect that will match a certain regex expression. Please note that for certain databases we will fall back to SQL LIKE expression. The databases supporting regex pattern as of 0.13.2 are:
- redshift
- postgres
- oracle
- mysql
- mariaDB
- sqlite
- clickhouse
- snowflake
Ensures all values match a specified regular expression pattern.
Dimension
Validity
When to Use
- Emails, zip codes, IDs, structured formats
Behavior
| Condition | Status |
|---|
| All values match regex | ✅ |
| Any value does not match | ❌ |
Column Values to not Match Regex
This test allows us to specify values in a column we expect that will not match a certain regex expression. If the test find values matching the forbiddenRegex the test will fail. Please note that for certain databases we will fall back to SQL LIKE expression. The databases supporting regex pattern as of 0.13.2 are:
- redshift
- postgres
- oracle
- mysql
- mariaDB
- sqlite
- clickhouse
- snowflake
The other databases will fall back to the LIKE expression
Ensures values do not match a restricted regex pattern.
Dimension
Validity
When to Use
- Prevent forbidden values, test strings, or patterns
Behavior
| Condition | Status |
|---|
| No value matches forbidden pattern | ✅ |
| Any value matches the pattern | ❌ |
Column Values to Be in Set
Ensures values are within a predefined whitelist.
Dimension
Validity
When to Use
- Enum values:
status, currency, country_code
Behavior
| Condition | Status |
|---|
All values in set (if matchEnum = true) | ✅ |
Any value not in set (if matchEnum = true) | ❌ |
Any value from set exists (if matchEnum = false) | ✅ |
No values from set found (if matchEnum = false) | ❌ |
Column Values to Be Not In Set
Ensures values are not in a specified blacklist.
Dimension
Validity
When to Use
- Block invalid values like
"NA", "Unknown", -1
Behavior
| Condition | Status |
|---|
| No values from forbidden set | ✅ |
| Any value from forbidden set found | ❌ |
Column Values to Be Between
Validates numeric values of a column are within a given range.
Dimension
Accuracy
When to Use
- Username length, field input length validation
Behavior
| Condition | Status |
|---|
Length within [min, max] | ✅ |
| Length < min or > max | ❌ |
Column Values Missing Count to Be Equal
Ensures total missing values (NULL + defined “missing” strings) match a target count.
Dimension
Completeness
When to Use
- Auditing known missing values
- Accounting for
"NA", "N/A", "null"
Behavior
| Condition | Status |
|---|
| Missing count = expected value | ✅ |
| Missing count ≠ expected value | ❌ |
Column Values Lengths to Be Between
Ensures that the length of each string value in the column is within a defined character range.
Dimension
Accuracy
When to Use
- To validate field length constraints like
name, address, or description
- To catch too-short or too-long values that may break UI or downstream logic
Behavior
| Condition | Status |
|---|
All values have length within [min, max] | ✅ |
| Any value length < min or > max | ❌ |
Column Value Max to Be Between
Validates the maximum value of a column lies within a range.
Dimension
Accuracy
When to Use
- Cap validation for
score, amount, age
Behavior
| Condition | Status |
|---|
Max value in range [min, max] | ✅ |
| Max < min or Max > max | ❌ |
Column Value Min to Be Between
Validates the minimum value of a column lies within a range.
Dimension
Accuracy
When to Use
- Threshold validation for
discount, price, etc.
Behavior
| Condition | Status |
|---|
Min value in range [min, max] | ✅ |
| Min < min or Min > max | ❌ |
Column Value Mean to Be Between
Validates that the mean (average) value is in the expected range.
Dimension
Accuracy
When to Use
- Check dataset drift or pipeline behavior
Behavior
| Condition | Status |
|---|
Mean value in [min, max] | ✅ |
| Mean < min or Mean > max | ❌ |
Validates the median value is in the expected range.
Dimension
Accuracy
When to Use
- Median income, score, latency checks
Behavior
| Condition | Status |
|---|
Median in range [min, max] | ✅ |
| Median < min or Median > max | ❌ |
Column Values Sum to Be Between
Validates the total sum of values in a column is within a defined range.
Dimension
Accuracy
When to Use
- Revenue, units sold, total scores, etc.
Behavior
| Condition | Status |
|---|
Sum in range [min, max] | ✅ |
| Sum < min or Sum > max | ❌ |
Column Values Standard Deviation to Be Between
Validates the standard deviation (spread) of values is acceptable.
Dimension
Accuracy
When to Use
- Monitoring variance in numeric datasets
Behavior
| Condition | Status |
|---|
Std Dev in [min, max] | ✅ |
| Std Dev < min or > max | ❌ |
Column Values To Be At Expected Location
Validates latitude/longitude values are within a defined area.
Dimension
Accuracy
When to Use
- Verifying address coordinates
- Mapping regional data
Behavior
| Condition | Status |
|---|
| Coordinates within buffer of expected location | ✅ |
| Any record outside allowed radius | ❌ |
