> ## Documentation Index
> Fetch the complete documentation index at: https://openmetadata-docs-release-watchers.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Tests - UI Config | OpenMetadata Quality Config Guide
> Define UI tests to validate data quality during ingestion and enforce expectations at column or table level.
# Tests in the OpenMetadata UI
A **Test Definition** is a generic definition of a test. This Test Definition then gets specified in a Test Case. This Test Case is where the parameter(s) of a Test Definition are specified.
In this section, you will learn what tests we currently support and how to configure them in the OpenMetadata UI.
* [Table Tests](#table-tests)
* [Column Tests](/v2.0.x/how-to-guides/data-quality-observability/quality/tests-ui/column-tests)
## Table Tests
Table tests validate the structure and shape of a table as a whole, rather than the values inside any single column. Use them to catch problems like an unexpected change in row or column count, a missing or renamed column, or two tables falling out of sync after a migration or replication job.
### Table Row Count to Equal
This test checks that a table always has exactly the number of rows you expect, no more and no fewer. It's useful for tables that should stay a fixed size, such as reference or lookup tables, where any change in row count usually signals a problem upstream.
* **When to Use**
* To monitor tables where row count is expected to remain fixed (for example, dimension tables).
* To catch over- or under-loading issues after ETL processes.
* To verify baseline data volumes for test/staging/prod comparisons.
* **Test Summary**
| Property | Description |
| ------------------ | -------------------------------------------------- |
| **Expected Value** | The exact number of rows the table should contain. |
* **Test Logic**
| Condition | Status |
| --------------------------------- | --------- |
| Actual row count = expected value | ✅ Success |
| Actual row count ≠ expected value | ❌ Failed |
### Table Row Count to be Between
This test checks that a table's row count stays within a healthy range you define, rather than requiring an exact match. It helps you catch unusually large or small tables, for example after a failed load or an unexpected spike in incoming data.
* **When to Use**
* To monitor for abnormal growth or shrinkage in table size.
* To catch failed inserts, unintended truncations, or unexpected data surges.
* To set alerts based on historical data volume expectations.
* **Test Summary**
| Property | Description |
| ------------- | -------------------------------------------- |
| **Min Value** | Minimum expected number of rows (`minValue`) |
| **Max Value** | Maximum allowed number of rows (`maxValue`) |
At least one of these values is required to run the test.
* **Test Logic**
| Condition | Status |
| ---------------------------------------------- | --------- |
| Row count is between `minValue` and `maxValue` | ✅ Success |
| Row count is outside the defined range | ❌ Failed |
### Table Column Count to Equal
This test checks that a table has exactly the number of columns you expect. It helps you catch accidental schema changes, such as a column being added or removed upstream without your knowledge.
* **When to Use**
* To detect unapproved schema changes (for example, columns being added or dropped).
* To enforce data contracts between teams or systems.
* To ensure structural consistency across environments.
* **Test Summary**
| Property | Description |
| ------------------ | -------------------------------------------- |
| **Expected Count** | Exact number of columns the table must have. |
* **Test Logic**
| Condition | Status |
| ------------------------------------ | --------- |
| Actual column count = expected count | ✅ Success |
| Actual column count ≠ expected count | ❌ Failed |
### Table Column Count to be Between
This test checks that a table's column count stays within a minimum and maximum range you define. It's useful when some schema variation is expected, but you still want to catch major structural drift.
* **When to Use**
* To detect schema drift or changes in table structure.
* To ensure a table has a predictable number of columns across environments (for example, staging vs. production).
* **Test Summary**
| Property | Description |
| --------------- | -------------------------------------------------- |
| **Min Columns** | Minimum number of expected columns (`minColValue`) |
| **Max Columns** | Maximum number of allowed columns (`maxColValue`) |
* **Test Logic**
| Condition | Status |
| ------------------------------------------------ | --------- |
| Actual column count is within the defined range | ✅ Success |
| Actual column count is outside the defined range | ❌ Failed |
### Table Column Name to Exist
This test checks that a specific, named column is present in the table. It's useful for making sure critical fields, such as an ID or timestamp column that downstream processes depend on, are never accidentally dropped.
* **When to Use**
* To validate that required schema fields exist (for example, `order_id`, `customer_id`).
* To monitor schema changes that might break downstream processes.
* To enforce critical column presence in governed datasets.
* **Test Summary**
| Property | Description |
| --------------- | ------------------------------------------------ |
| **Column Name** | Name of the column that must exist in the table. |
* **Test Logic**
| Condition | Status |
| --------------------------------------- | --------- |
| `columnName` exists in the table schema | ✅ Success |
| `columnName` is missing from the table | ❌ Failed |
### Table Column to Match Set
This test checks that a table's column names match an exact, predefined list, optionally requiring the columns to appear in a specific order. It's useful when you need to guarantee the full structure of a table hasn't changed.
* **When to Use**
* To ensure schema alignment across different environments or pipeline stages.
* To detect unexpected column additions, deletions, or reordering.
* To enforce table contracts where the exact structure is critical.
* **Test Summary**
| Property | Description |
| ---------------- | ------------------------------------------------------------------------------- |
| **Column Names** | Comma-separated list of expected column names (for example, `col1, col2, col3`) |
| **Ordered** | Boolean flag (`true` or `false`), whether the order of columns must match. |
* **Test Logic**
| Ordered | Condition | Status |
| ------- | ------------------------------------------------ | --------- |
| `false` | All expected column names exist (any order) | ✅ Success |
| `true` | Column names match and appear in the exact order | ✅ Success |
| `false` | Some columns are missing or extra | ❌ Failed |
| `true` | Columns are present but order is incorrect | ❌ Failed |
### Table Custom SQL Test
This test lets you write your own SQL query to check for anything the built-in tests don't cover, such as business-specific rules, duplicate records, or unusual outliers. OpenMetadata runs your query and compares the result against a threshold you configure to decide whether the test passes or fails.
* **When to Use**
* To implement logic beyond predefined test definitions.
* To detect outliers, nulls, duplicates, or business-specific data anomalies.
* When you need full flexibility using SQL syntax.
* **Test Summary**
| Property | Description |
| ------------------ | --------------------------------------------------------------------------------------------- |
| **SQL Expression** | The SQL query used to evaluate the test. |
| **Strategy** | Defines how to interpret the result. Options: `ROWS` *(default)* or `COUNT`. |
| **Operator** | Comparison used against the threshold. Options: `==`, `>`, `>=`, `<`, `<=` *(default)*, `!=`. |
| **Threshold** | The value compared against the query result using `operator`. Default is `0`. |
* **Test Logic**
| Strategy | Condition | Status |
| -------- | ------------------------------------------------------------- | --------- |
| ROWS | Number of returned rows satisfies `operator threshold` | ✅ Success |
| ROWS | Number of returned rows does not satisfy `operator threshold` | ❌ Failed |
| COUNT | Count result satisfies `operator threshold` | ✅ Success |
| COUNT | Count result does not satisfy `operator threshold` | ❌ Failed |
### Table Row Inserted Count To Be Between
This test looks at how many new rows were added to a table within a recent time window, such as the last hour or day, and checks that count falls within an expected range. It's a good way to catch pipelines that silently stop loading data, or that suddenly load far more or fewer rows than usual.
* **When to Use**
* To detect whether recent data ingestion volumes are within acceptable limits.
* To monitor time-partitioned tables for daily/hourly/monthly data drops or spikes.
* To validate pipeline freshness and completeness over time.
* **Test Summary**
| Property | Description |
| ------------------ | ------------------------------------------------------------------ |
| **Min Row Count** | Minimum number of inserted rows expected in the given range. |
| **Max Row Count** | Maximum number of inserted rows allowed in the given range. |
| **Column Name** | Timestamp column used to filter the inserted rows. |
| **Range Type** | Time granularity: `HOUR`, `DAY`, `MONTH`, or `YEAR`. |
| **Range Interval** | Number of units (for example, last `1 DAY`, `2 HOURS`, and so on). |
* **Test Logic**
| Condition | Status |
| ------------------------------------------------- | --------- |
| Row count within `min` and `max` for the interval | ✅ Success |
| Row count outside of the expected range | ❌ Failed |
### Compare 2 Tables for Differences
This test compares two tables row by row, even if they live on different database platforms, and reports how many rows differ between them. It's commonly used to confirm that data was replicated or migrated correctly, for example after moving a table from one warehouse to another.
* **When to Use**
* After data replication or migration (for example, Snowflake to Redshift).
* To validate data integrity between source and target systems.
* **Test Summary**
| Property | Description |
| -------------------------- | --------------------------------------------------------------------------------------------------- |
| **Key Columns** | Columns used as the row-matching key. Defaults to the table's primary key if not specified. |
| **Columns to Compare** | Subset of columns used for comparison. If not provided, all columns will be compared. |
| **Second Table** | Fully qualified name of the second table (for example, `redshift_dbt.dev.dbt_jaffle.boolean_test`). |
| **Threshold** | Maximum number of mismatched rows allowed. Default is `0` (strict equality). |
| **Filter Condition** | *(Optional)* A `WHERE` clause (for example, `id != 999`) to limit rows involved in the comparison. |
| **Case-Sensitive Columns** | Set to `true` if column name case must match exactly (default is `false`). |
* **Test Logic**
| Condition | Status |
| ------------------------------------ | --------- |
| Number of differing rows ≤ threshold | ✅ Success |
| Number of differing rows > threshold | ❌ Failed |
* **Supported Data Sources**
* Snowflake
* BigQuery
* Athena
* Redshift
* Postgres
* MySQL
* MSSQL
* Oracle
* Trino
* SAP Hana
* Databricks
* Unity Catalog
* AzureSQL
For column-level test configurations in the UI, see [Column Tests - UI Config](/v2.0.x/how-to-guides/data-quality-observability/quality/tests-ui/column-tests).