Following data-modeling best practices ensures scalable, performant, and maintainable PostgreSQL schemas.
Strong data models reduce bugs, speed query performance, and simplify future feature work. By standardizing naming, data types, and constraints, teams avoid ambiguity and keep Galaxy’s AI copilot context accurate.
Use lowercase, snake_case, and singular nouns (products, order_items). Prefix primary keys with id and foreign keys with referenced table (customer_id).Consistency helps autocompletion and team onboarding.
Pick one style and stick to it. Singular is common in PostgreSQL; plural can feel more natural. Galaxy’s autocomplete benefits most from predictable patterns.
Match the business meaning: use INTEGER for ids, NUMERIC(10,2) for money, TIMESTAMPTZ for time zones, and BOOLEAN for flags.Avoid generic TEXT when length is predictable.
Normalize to 3NF for OLTP workloads: separate Customers, Orders, and OrderItems. Denormalize selectively for analytics tables or read-heavy endpoints. Always document trade-offs.
Add FOREIGN KEY constraints with ON DELETE and ON UPDATE actions. Index foreign keys to speed joins.Use DEFERRABLE constraints for bulk loads.
Create B-tree indexes on foreign keys and frequently filtered columns (order_date, total_amount). Use expression indexes for case-insensitive searches (LOWER(email)).
Implement Type 2 history tables with valid_from/valid_to columns and composite primary keys. Use CHECK (valid_from < valid_to) constraints.
Store comments in the catalog with COMMENT ON.Galaxy can surface these inline, helping developers understand columns without leaving the editor.
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No for OLTP workloads; indexed joins are fast. Denormalize only when profiling shows bottlenecks.
UUIDs aid sharding and hide row counts. SERIAL is smaller and faster. Choose based on scaling needs.
Use version-controlled migration files plus COMMENT ON statements. Galaxy surfaces comments in the UI.