Composite key constraints involve multiple columns and require careful consideration of column order, indexing strategy, and impact on related foreign keys. Consider performance implications and ensure all components are necessary.
Different constraints handle NULL values differently: PRIMARY KEY doesn't allow NULL, UNIQUE typically allows one NULL, CHECK constraints need explicit NULL handling, and FOREIGN KEY allows NULL unless explicitly prohibited.
Implement soft delete using filtered foreign keys, check constraints on active/inactive status, or triggers. Consider impact on queries, indexes, and maintenance operations when choosing an approach.
A PRIMARY KEY constraint enforces uniqueness and doesn't allow NULL values, while a UNIQUE constraint allows one NULL value (in most databases) and multiple columns can have UNIQUE constraints. PRIMARY KEY also implicitly creates a clustered index by default.
Referential actions include: CASCADE (propagate changes), SET NULL (set to NULL), SET DEFAULT (set to default value), and NO ACTION/RESTRICT (prevent changes). These actions determine how child records are handled when parent records are updated or deleted.
Handle constraint violations through error catching, appropriate error messages, transaction management, and retry logic where appropriate. Consider using TRY-CATCH blocks and implementing specific handling for different constraint violation types.
The main types of constraints in SQL are: PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, NOT NULL, and DEFAULT constraints. Each type enforces different rules to maintain data integrity and relationships between tables.
CHECK constraints enforce domain integrity by limiting the values that can be entered into a column based on a logical expression. They can validate data against specific rules, ranges, or patterns before allowing inserts or updates.
Constraints impact INSERT, UPDATE, and DELETE performance due to validation overhead. Foreign keys can affect join performance, while CHECK constraints may impact DML operations. Proper indexing and constraint design are crucial for optimal performance.
DRI uses database constraints to enforce data integrity rules automatically. It's important because it ensures consistent enforcement of rules, reduces application code complexity, and maintains data quality at the database level.
Constraints can affect bulk loading, index rebuilds, and partition maintenance. Consider disabling/re-enabling constraints for large operations, verify constraint integrity after maintenance, and plan for appropriate maintenance windows.
Use consistent, descriptive naming conventions that identify constraint type, affected tables/columns, and purpose. Consider including prefixes for constraint types and ensure names are unique within the database.
Complex business rules can be implemented using combinations of CHECK constraints, computed columns, and trigger-based validation. Consider performance impact, maintainability, and the balance between constraint and application-level validation.
Temporal constraints enforce rules based on date/time values, such as valid periods or sequential relationships. They can be implemented using CHECK constraints, triggers, or temporal tables with system-versioning.
Cross-table constraints can be implemented using foreign keys, CHECK constraints with subqueries (where supported), or triggers. Consider performance impact and maintenance implications of different approaches.
Consider partition alignment, constraint checking overhead, and maintenance operations. Ensure constraints work effectively across partitions and understand impact on partition switching operations.
Conditional constraints can be implemented using CHECK constraints with CASE expressions, filtered indexes, or triggers for more complex conditions. Consider performance and maintainability trade-offs.
Dynamic defaults can be implemented using computed columns, triggers, or application logic. Consider performance impact, maintainability, and whether logic belongs at database or application level.
Create clear, actionable error messages that help identify the specific violation. Consider using custom error messages with CHECK constraints and appropriate error handling in applications.
Consider lock contention, deadlock potential, and validation performance. Choose appropriate constraint types and implement proper indexing to support constraint checking efficiently.
Document constraint purposes, business rules implemented, maintenance procedures, and testing requirements. Include information about dependencies, performance implications, and modification procedures.
Plan constraint deployment carefully, script modifications idempotently, verify constraint state after migration, and consider impact on existing data. Include rollback procedures in migration plans.
Hierarchical constraints can use self-referencing foreign keys, CHECK constraints for level limitations, or specialized solutions like closure tables. Consider query performance and maintenance complexity.
Consider constraint checking on both primary and secondary servers, impact on replication performance, and handling of constraint violations during replication. Ensure consistent constraint definition across servers.
Use row-level security, filtered indexes, or tenant-specific schemas. Implement appropriate constraints for tenant isolation and consider performance impact of different approaches.
Use CHECK constraints for basic validation, triggers for complex rules, and consider using computed columns for derived values. Implement appropriate error handling and validation reporting.
In data warehouses, constraints help ensure data quality, maintain relationships between fact and dimension tables, and support slowly changing dimensions. Balance constraint enforcement with load performance requirements.
Test constraints with boundary values, NULL cases, and complex scenarios. Verify constraint behavior during concurrent operations, test referential actions, and ensure proper error handling.
Plan constraint modifications carefully, use appropriate transaction isolation, consider impact on existing data, and implement proper validation before and after changes. Maintain backup constraints where necessary.
