> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Column values match

> Learn how to use the column values match test

## Definition

The column values match test checks if, for a given column, the values are the same between the current dataset
and the reference dataset.

* In **development projects**, the training set is used as the reference and the validation set as the current
  dataset.
* In **monitoring projects**, the reference dataset is [uploaded by the user](/monitoring/uploading-reference-dataset)
  and the production data is the current dataset.

## Taxonomy

* **Task types**: LLM, tabular classification, tabular regression, text classification.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>.

## Why it matters

* The column values match test can help verifying that key attributes or features in your dataset remain consistent over time or across different datasets.

## Test configuration examples

If you are writing a `tests.json`, here are a few valid configurations for the character length test:

<CodeGroup>
  ```json Development theme={null}
  [
    {
      "name": "Values in `output` and `target` match",
      "description": "Make sure that rows in your two datasets have the same values for target_column_name where reference_column_name is also the same",
      "type": "consistency",
      "subtype": "columnValuesMatch",
      "thresholds": [
        {
          "insightName": "columnValuesMatch",
          "insightParameters": [
            { "name": "reference_column_name", "value": "output" }, // Selects the column `output` as the reference column
            { "name": "target_column_name", "value": "target" } // Selects the column `target` as the target column
          ],
          "measurement": "failingRowPercentage", // Must be one of `failingRowPercentage` or `failingRowCount`
          "operator": "<=",
          "value": 0.0
        }
      ],
      "subpopulationFilters": null,
      "mode": "development",
      "usesValidationDataset": true,
      "usesTrainingDataset": true,
      "usesMlModel": false,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689" // Some unique id
    }
  ]
  ```

  ```json Monitoring theme={null}
  [
    {
      "name": "Values in `output` and `target` match",
      "description": "Make sure that rows in your two datasets have the same values for target_column_name where reference_column_name is also the same",
      "type": "consistency",
      "subtype": "columnValuesMatch",
      "thresholds": [
        {
          "insightName": "columnValuesMatch",
          "insightParameters": [
            { "name": "reference_column_name", "value": "output" }, // Selects the column `output` as the reference column
            { "name": "target_column_name", "value": "target" } // Selects the column `target` as the target column
          ],
          "measurement": "failingRowPercentage", // Must be one of `failingRowPercentage` or `failingRowCount`
          "operator": "<=",
          "value": 0.0
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 3600, // 1 hour
      "delayWindow": 0,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689" // Some unique id
    }
  ]
  ```
</CodeGroup>