Introduction to the Distinct Count Check
The Distinct Count check asserts that the number of distinct values in a field satisfies a comparison against an expected number. Use it when the cardinality of a column is itself the rule: a lookup table with a known number of entries, a dimension that should not gain categories overnight.
This section is the complete guide to the check. The Deep Dive covers the definition, properties, and full evaluation semantics, explains how anomalies are reported, walks through production examples, and collects best practices; the How-tos are step-by-step tutorials for creating, editing, and deleting a check; the API page documents the payload for programmatic use; and the FAQ answers the most frequent questions.
Next Steps
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How It Works
The complete reference: definition, field scope, the comparison operators, approximation, NULL handling, filter behavior, and why coverage does not apply.
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Anomaly Reporting
The anomaly message the check produces, what the counts mean, and why per-row reporting does not apply.
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Examples
Three production scenarios with sample data, anomaly messages, and the SQL equivalent of what the check evaluates.
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Best Practices
Guidelines for choosing the operator, allowing for approximation, and keeping the signal clean.
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Permissions
The team permission each action needs: view, create, edit, archive, restore, and delete.
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Create a Check
Step-by-step tutorial for creating a Distinct Count check with a comparison and a value.
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Edit a Check
Step-by-step tutorial for changing the operator or the expected count, or adjusting the filter.
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Delete a Check
Step-by-step tutorial for archiving a check and deleting it permanently.
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Troubleshooting
Common problems with approximation, NULLs, operator choice, and anomaly reporting, and how to resolve them.
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API
Payload shape and field notes for creating a Distinct Count check programmatically.
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FAQ
Short answers to questions about approximation, operators, NULLs, and anomaly reporting.