Introduction to the Field Count Check
The Field Count check asserts that a container holds exactly the number of fields you expect. It is a structural rule: it looks at the shape of the table or file rather than at any value inside it, which makes it the cheapest way to detect a column being added or dropped upstream.
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, container scope, the expected count, how the shape is evaluated, and why coverage and filters do not apply.
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Anomaly Reporting
What the Shape Anomaly reports, how to read it, and why per-row reporting does not apply.
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Examples
Three production scenarios showing a stable schema, a dropped column, and an added column.
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Best Practices
Guidelines for pairing with Expected Schema, keeping the count current, and avoiding false alarms.
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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 Field Count check on a container.
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Edit a Check
Step-by-step tutorial for changing the expected number of fields or the organizational properties.
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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 schema changes, the missing filter and coverage options, and anomaly reporting.
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API
Payload shape and field notes for creating a Field Count check programmatically.
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FAQ
Short answers to questions about the expected count, schema drift, and anomaly reporting.