Apply Backend Database Patterns

Apply Backend Database Patterns

Purpose

Provide practical patterns for safe data access and persistence: transactional workflows, consistent querying, and predictable performance.

When to use

Use this skill when you are:

• Implementing repository methods or complex queries
• Adding transactional “all-or-nothing” workflows
• Debugging performance issues (slow queries, N+1)
• Introducing pagination, filtering, or sorting
• Designing idempotent writes for retries
• Implementing soft delete or archival behavior

Inputs

• Data model (entities/relations) and invariants
• Expected read/write volume and latency requirements
• Failure modes that must be handled (conflicts, duplicates, timeouts)
• The query layer used by the codebase (ORM, query builder, raw SQL)

Outputs

• Repository method designs (inputs/outputs and invariants)
• A transaction strategy (where transactions start/end)
• Query patterns for pagination/filtering
• A verification checklist for correctness and performance

Core rules

• Persistence code MUST be isolated behind repositories (or a clearly defined data-access layer).
• Transactions MUST be used when partial failure would corrupt state.
• Writes SHOULD be idempotent when requests can be retried.
• Queries MUST be scoped and paginated for potentially large datasets.

Common patterns

Transactions

Use a transaction when:

• multiple writes must commit together
• you need consistency across reads and writes
• you need to prevent partial side effects

Avoid long-lived transactions:

• keep them as short as possible
• do not call external services inside a transaction unless the design explicitly accounts for it

Pagination

• Use cursor pagination when:
  • datasets are large
  • stable ordering is required
• Use offset pagination when:
  • datasets are small
  • simplicity matters more than absolute correctness under concurrent writes

Soft delete

Soft delete is appropriate when:

• you need auditability or recovery
• external systems may reference records

When using soft delete:

• queries MUST consistently filter out deleted rows unless explicitly requested
• unique constraints MAY need to include “deleted” flags

Avoid N+1 queries

• Fetch related entities via joins/includes when appropriate.
• Consider batching, caching, or explicit query composition.

Steps

1. Define data invariants (what MUST always be true).
2. Define repository contract:
   • inputs
   • outputs
   • error cases
3. Choose transaction boundaries for multi-step workflows.
4. Implement queries with:
   • explicit filters
   • explicit ordering
   • pagination for large lists
5. Validate performance assumptions:
   • expected query count
   • indexes required
   • hot paths
6. Add verification:
   • one correctness test
   • one performance sanity check (where practical)

Verification

• Repository methods return expected results for valid inputs
• Transactions roll back completely on failure (no partial commits)
• Pagination returns correct page sizes and cursor/offset handling
• Soft delete queries correctly exclude deleted records by default
• Query performance meets latency targets (check query plans if needed)
• N+1 queries are not introduced (verify query count in tests)

Boundaries

• MUST NOT call external services inside database transactions
• MUST NOT expose ORM-specific types to service layer (unless intentionally standardized)
• MUST NOT skip transaction boundaries for multi-write operations
• MUST NOT use unbounded queries on large datasets
• SHOULD NOT use offset pagination for large or frequently-updated datasets
• SHOULD NOT hold transactions open longer than necessary

Included assets

• Templates: ./templates/ includes pagination and transaction scaffolds.
• Examples: ./examples/ includes transactional workflow patterns.

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