db-sculptor

# DB Sculptor Design performant database schemas. Model for access patterns first, normalize later. Based on PostgreSQL internals, Prisma/Drizzle best practices, and production patterns from PlanetScale, Neon, and pganalyze. ## Sub-Commands | Command | Description | |---------|-------------| | `design` | Design a schema from access patterns and data volume estimates | | `index` | Analyze queries and recommend/create optimal indexes | | `optimize` | Diagnose slow queries with EXPLAIN ANALYZE and fix them | | `migrate` | Create a safe, zero-downtime migration (expand/contract) | | `audit` | Audit existing schema against best practices and anti-patterns | ## Workflow ### Step 1: Model for access patterns | Question | Determine | |----------|-----------| | Read/write ratio? | How many indexes can the table support | | Data volume? | Current rows, growth rate/month | | Consistency requirements? | ACID vs eventual, read replicas OK? | | Latency budget? | p50 < 5ms, p95 < 50ms, p99 < 200ms | **Decision tree:** - High write volume, simple reads → Normalize (3NF). Minimum indexes. - High read volume, complex joins → Denormalize strategically. Add composite + covering indexes. - Time-series data → Partition by time (monthly). BRIN indexes on timestamp. - Full-text search needed → GIN index with `tsvector` + `tsquery`. - JSON queries → GIN index on JSONB column. ### Step 2: Primary key strategy | PK type | Pros | Cons | When | |---------|------|------|------| | UUIDv7 | Time-so...