Schema Design

Schema Design

Purpose

Model the domain so the database — not application code — enforces integrity, and so reads are fast by design via the right index type for each access pattern.

Universal — normalization (3NF then justified denormalization), constraint-at-the-DB discipline, and index-type-by-data-shape apply to any relational DB; index type names and DDL differ.

Procedure

1. Normalize to 3NF first

   • One fact in one place; no transitive dependencies
   • Denormalize ONLY with a measured read-pattern justification (documented)

2. Enforce integrity at the database

   • NOT NULL, UNIQUE, CHECK, foreign keys with explicit ON DELETE
   • The DB is the last line of defense — app validation (see data-validation) is the first, not the only

3. Choose index type by data shape

   • B-tree — equality + range (default; most columns)
   • GIN — JSONB containment, array membership, full-text search
   • GiST — spatial / nearest-neighbor / range overlap
   • BRIN — very large append-only, naturally-ordered tables (time-series) — tiny index, range pruning
   • Hash — equality-only (rarely worth it over B-tree)

4. Index the access patterns, not the columns

   • Composite index column order = most-selective / equality columns first, range last
   • Covering index (INCLUDE) for index-only scans on hot queries
   • Always index foreign-key columns — Postgres does NOT auto-index FK child columns; missing index → slow cascade deletes and exclusive locks on the parent update (silent perf cliff)
   • Partial indexes (WHERE status = 'active') shrink the index and speed the dominant predicate — common 10x win on tables with a small active subset
   • Don't over-index writes — each index is write overhead

5. Design keys deliberately

   • Natural vs surrogate (UUID v7 / bigint) — UUID v7 for distributed-friendly time-ordered IDs
   • Avoid random UUID v4 as a clustered/primary key on huge tables (index fragmentation)
   • UUID v7 generation: Postgres 18+ has native uuidv7(); on earlier versions generate it app-side (no core/uuid-ossp function exists — uuid-ossp only provides v1/v3/v4/v5)

5b. Soft-delete vs hard-delete — decide deliberately

• Hard delete (DELETE): smaller tables, simpler queries, irreversible. Default unless a real requirement says otherwise
• Soft delete (deleted_at timestamp): audit / undo / cross-reference survival. Cost: every query must filter WHERE deleted_at IS NULL (a single forgotten filter leaks deleted data — automate via RLS or a view), and unique constraints need partial indexes to allow re-creation after deletion
• Archive table: move-on-delete to a sibling table. Keeps the hot table small and the history intact — a middle ground

6. Validate (validation loop)
   • For each planned hot query, run EXPLAIN ANALYZE; if Seq Scan on a large table or bad row estimates → adjust index and re-run (hand off to query-optimization)

Anti-patterns

Severity tiers

Completion Criteria

• Schema in 3NF (denormalizations documented with read justification)
• All integrity rules enforced by DB constraints
• Index type matches each hot query's data shape (verified via EXPLAIN)
• No orphan-allowing relationships (FKs with explicit ON DELETE)

Output

• Schema: Prisma schema (or DDL) with constraints + indexes
• Index rationale doc: which index, which query, which type and why
• Commit format: feat(schema): model <domain> / perf(schema): add GIN index for <jsonb query>

Implementation

TypeScript + Prisma + Postgres (default)

• Prisma schema: @@index, @@unique, @relation(onDelete:), @db.* types
• Index types Prisma can't express directly (GIN/GiST/BRIN) → raw SQL in a migration
• UUID v7: Postgres 18+ @default(dbgenerated("uuidv7()")); pre-18 generate app-side (e.g., uuidv7 npm package) — uuid_generate_v7() does NOT exist in core or uuid-ossp
• Verify: EXPLAIN ANALYZE in psql / Supabase dashboard

Other stacks

• Python: SQLAlchemy / Alembic — same Postgres index types via Index(..., postgresql_using='gin')
• Go: GORM or sqlc — index types in migration SQL
• Universal: 3NF, constraint-at-DB, and index-type-by-shape are relational-DB principles; MySQL has fewer index types (no BRIN/GiST) — note that difference

Related skills

• query-optimization — indexes are validated against actual query plans there
• migration-strategy — schema changes ship via safe migrations
• transaction-management — constraints + isolation enforce integrity together

Reference

• Key insight encoded: Match index type to data shape — B-tree for equality/range, GIN for jsonb/array/full-text, BRIN for large append-only ordered tables, GiST for spatial. Enforce integrity at the DB, not just the app.