SQL Server Identity Exhaustion Risk

Why this problem matters

When an identity column reaches its data type's upper or lower limit, new row inserts can fail. Exhaustion can arrive earlier than expected when growth rate is ignored, even if the remaining range looks large.

How to diagnose it

Inventory last_value, seed, increment, and base data type for every identity column. Account for negative increments, reseeding, and the fact that deleting rows does not reclaim identity values.

The query reads identity metadata and does not calculate a potentially misleading remaining percentage. Accurate capacity analysis requires type limits, increment direction, and consumption rate over time.

SELECT SCHEMA_NAME(t.schema_id) AS schema_name,
       t.name AS table_name,
       c.name AS column_name,
       ty.name AS data_type,
       ic.seed_value,
       ic.increment_value,
       ic.last_value
FROM sys.identity_columns AS ic
JOIN sys.tables AS t ON t.object_id = ic.object_id
JOIN sys.columns AS c
  ON c.object_id = ic.object_id
 AND c.column_id = ic.column_id
JOIN sys.types AS ty ON ty.user_type_id = c.user_type_id
ORDER BY schema_name, table_name;

A safe solution approach

If risk is confirmed, plan a controlled migration to a wider data type or a new key design. Test the schema change against dependent indexes, foreign keys, application types, and outage requirements.

Why continuous monitoring matters

Exhaustion is a capacity problem that requires both remaining range and consumption rate. moon can monitor the trend and route projected-risk alerts to the responsible team through Slack, PagerDuty, or webhooks.