Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
PostgreSQL Table Design
Core Rules
Define a PRIMARY KEY for reference tables (users, orders, etc.). Not always needed for time-series/event/log data. When used, prefer BIGINT GENERATED ALWAYS AS IDENTITY; use UUID only when global uniqueness/opacity is needed.
Normalize first (to 3NF) to eliminate data redundancy and update anomalies; denormalize only for measured, high-ROI reads where join performance is proven problematic. Premature denormalization creates maintenance burden.
Add NOT NULL everywhere it’s semantically required; use DEFAULTs for common values.
Create indexes for access paths you actually query: PK/unique (auto), FK columns (manual!), frequent filters/sorts, and join keys.
Prefer TIMESTAMPTZ for event time; NUMERIC for money; TEXT for strings; BIGINT for integer values, DOUBLE PRECISION for floats (or NUMERIC for exact decimal arithmetic).
PostgreSQL “Gotchas”
Identifiers: unquoted → lowercased. Avoid quoted/mixed-case names. Convention: use snake_case for table/column names.
Unique + NULLs: UNIQUE allows multiple NULLs. Use UNIQUE (...) NULLS NOT DISTINCT (PG15+) to restrict to one NULL.
FK indexes: PostgreSQL does not auto-index FK columns. Add them.
No silent coercions: length/precision overflows error out (no truncation). Example: inserting 999 into NUMERIC(2,0) fails with error, unlike some databases that silently truncate or round.
Sequences/identity have gaps (normal; don't "fix"). Rollbacks, crashes, and concurrent transactions create gaps in ID sequences (1, 2, 5, 6...). This is expected behavior—don't try to make IDs consecutive.
Heap storage: no clustered PK by default (unlike SQL Server/MySQL InnoDB); CLUSTER is one-off reorganization, not maintained on subsequent inserts. Row order on disk is insertion order unless explicitly clustered.
MVCC: updates/deletes leave dead tuples; vacuum handles them—design to avoid hot wide-row churn.
Data Types
IDs: BIGINT GENERATED ALWAYS AS IDENTITY preferred (GENERATED BY DEFAULT also fine); UUID when merging/federating/used in a distributed system or for opaque IDs. Generate with uuidv7() (preferred if using PG18+) or gen_random_uuid() (if using an older PG version).
Integers: prefer BIGINT unless storage space is critical; INTEGER for smaller ranges; avoid SMALLINT unless constrained.
Floats: prefer DOUBLE PRECISION over REAL unless storage space is critical. Use NUMERIC for exact decimal arithmetic.
Strings: prefer TEXT; if length limits needed, use CHECK (LENGTH(col) <= n) instead of VARCHAR(n); avoid CHAR(n). Use BYTEA for binary data. Large strings/binary (>2KB default threshold) automatically stored in TOAST with compression. TOAST storage: PLAIN (no TOAST), EXTENDED (compress + out-of-line), EXTERNAL (out-of-line, no compress), MAIN (compress, keep in-line if possible). Default EXTENDED usually optimal. Control with ALTER TABLE tbl ALTER COLUMN col SET STORAGE strategy and ALTER TABLE tbl SET (toast_tuple_target = 4096) for threshold. Case-insensitive: for locale/accent handling use non-deterministic collations; for plain ASCII use expression indexes on LOWER(col) (preferred unless column needs case-insensitive PK/FK/UNIQUE) or CITEXT.
Money: NUMERIC(p,s) (never float).
Time: TIMESTAMPTZ for timestamps; DATE for date-only; INTERVAL for durations. Avoid TIMESTAMP (without timezone). Use now() for transaction start time, clock_timestamp() for current wall-clock time.
Booleans: BOOLEAN with NOT NULL constraint unless tri-state values are required.
Enums: CREATE TYPE ... AS ENUM for small, stable sets (e.g. US states, days of week). For business-logic-driven and evolving values (e.g. order statuses) → use TEXT (or INT) + CHECK or lookup table.
Do not use the following data types
DO NOT use timestamp (without time zone); DO use timestamptz instead.
DO NOT use char(n) or varchar(n); DO use text instead.
DO NOT use money type; DO use numeric instead.
DO NOT use timetz type; DO use timestamptz instead.
DO NOT use timestamptz(0) or any other precision specification; DO use timestamptz instead
DO NOT use serial type; DO use generated always as identity instead.
Table Types
Regular: default; fully durable, logged.
TEMPORARY: session-scoped, auto-dropped, not logged. Faster for scratch work.
UNLOGGED: persistent but not crash-safe. Faster writes; good for caches/staging.
Row-Level Security
Enable with ALTER TABLE tbl ENABLE ROW LEVEL SECURITY. Create policies: CREATE POLICY user_access ON orders FOR SELECT TO app_users USING (user_id = current_user_id()). Built-in user-based access control at the row level.
Constraints
PK: implicit UNIQUE + NOT NULL; creates a B-tree index.
FK: specify ON DELETE/UPDATE action (CASCADE, RESTRICT, SET NULL, SET DEFAULT). Add explicit index on referencing column—speeds up joins and prevents locking issues on parent deletes/updates. Use DEFERRABLE INITIALLY DEFERRED for circular FK dependencies checked at transaction end.
UNIQUE: creates a B-tree index; allows multiple NULLs unless NULLS NOT DISTINCT (PG15+). Standard behavior: (1, NULL) and (1, NULL) are allowed. With NULLS NOT DISTINCT: only one (1, NULL) allowed. Prefer NULLS NOT DISTINCT unless you specifically need duplicate NULLs.
CHECK: row-local constraints; NULL values pass the check (three-valued logic). Example: CHECK (price > 0) allows NULL prices. Combine with NOT NULL to enforce: price NUMERIC NOT NULL CHECK (price > 0).
EXCLUDE: prevents overlapping values using operators. EXCLUDE USING gist (room_id WITH =, booking_period WITH &&) prevents double-booking rooms. Requires appropriate index type (often GiST).
Indexing
B-tree: default for equality/range queries (=, <, >, BETWEEN, ORDER BY)
Composite: order matters—index used if equality on leftmost prefix (WHERE a = ? AND b > ? uses index on (a,b), but WHERE b = ? does not). Put most selective/frequently filtered columns first.
Covering: CREATE INDEX ON tbl (id) INCLUDE (name, email) - includes non-key columns for index-only scans without visiting table.
Partial: for hot subsets (WHERE status = 'active' → CREATE INDEX ON tbl (user_id) WHERE status = 'active'). Any query with status = 'active' can use this index.
Expression: for computed search keys (CREATE INDEX ON tbl (LOWER(email))). Expression must match exactly in WHERE clause: WHERE LOWER(email) = 'user@example.com'.
BRIN: very large, naturally ordered data (time-series)—minimal storage overhead. Effective when row order on disk correlates with indexed column (insertion order or after CLUSTER).
Partitioning
Use for very large tables (>100M rows) where queries consistently filter on partition key (often time/date).
Alternate use: use for tables where data maintenance tasks dictates e.g. data pruned or bulk replaced periodically
RANGE: common for time-series (PARTITION BY RANGE (created_at)). Create partitions: CREATE TABLE logs_2024_01 PARTITION OF logs FOR VALUES FROM ('2024-01-01') TO ('2024-02-01'). TimescaleDB automates time-based or ID-based partitioning with retention policies and compression.
LIST: for discrete values (PARTITION BY LIST (region)). Example: FOR VALUES IN ('us-east', 'us-west').
HASH: for even distribution when no natural key (PARTITION BY HASH (user_id)). Creates N partitions with modulus.
Constraint exclusion: requires CHECK constraints on partitions for query planner to prune. Auto-created for declarative partitioning (PG10+).
Prefer declarative partitioning or hypertables. Do NOT use table inheritance.
Limitations: no global UNIQUE constraints—include partition key in PK/UNIQUE. FKs from partitioned tables not supported; use triggers.
Special Considerations
Update-Heavy Tables
Separate hot/cold columns—put frequently updated columns in separate table to minimize bloat.
Use fillfactor=90 to leave space for HOT updates that avoid index maintenance.
Avoid updating indexed columns—prevents beneficial HOT updates.
Partition by update patterns—separate frequently updated rows in a different partition from stable data.
Insert-Heavy Workloads
Minimize indexes—only create what you query; every index slows inserts.
Use COPY or multi-row INSERT instead of single-row inserts.
UNLOGGED tables for rebuildable staging data—much faster writes.
Defer index creation for bulk loads—>drop index, load data, recreate indexes.
Partition by time/hash to distribute load. TimescaleDB automates partitioning and compression of insert-heavy data.
Use a natural key for primary key such as a (timestamp, device_id) if enforcing global uniqueness is important many insert-heavy tables don't need a primary key at all.
If you do need a surrogate key, Prefer BIGINT GENERATED ALWAYS AS IDENTITY over UUID.
Upsert-Friendly Design
Requires UNIQUE index on conflict target columns—ON CONFLICT (col1, col2) needs exact matching unique index (partial indexes don't work).
Use EXCLUDED.column to reference would-be-inserted values; only update columns that actually changed to reduce write overhead.
DO NOTHING faster than DO UPDATE when no actual update needed.
Safe Schema Evolution
Transactional DDL: most DDL operations can run in transactions and be rolled back—BEGIN; ALTER TABLE...; ROLLBACK; for safe testing.
Concurrent index creation: CREATE INDEX CONCURRENTLY avoids blocking writes but can't run in transactions.
Volatile defaults cause rewrites: adding NOT NULL columns with volatile defaults (e.g., now(), gen_random_uuid()) rewrites entire table. Non-volatile defaults are fast.
Drop constraints before columns: ALTER TABLE DROP CONSTRAINT then DROP COLUMN to avoid dependency issues.
Function signature changes: CREATE OR REPLACE with different arguments creates overloads, not replacements. DROP old version if no overload desired.
Generated Columns
... GENERATED ALWAYS AS (<expr>) STORED for computed, indexable fields. PG18+ adds VIRTUAL columns (computed on read, not stored).
Extensions
pgcrypto: crypt() for password hashing.
uuid-ossp: alternative UUID functions; prefer pgcrypto for new projects.
pg_trgm: fuzzy text search with % operator, similarity() function. Index with GIN for LIKE '%pattern%' acceleration.
citext: case-insensitive text type. Prefer expression indexes on LOWER(col) unless you need case-insensitive constraints.
btree_gin/btree_gist: enable mixed-type indexes (e.g., GIN index on both JSONB and text columns).
hstore: key-value pairs; mostly superseded by JSONB but useful for simple string mappings.
timescaledb: essential for time-series—automated partitioning, retention, compression, continuous aggregates.
postgis: comprehensive geospatial support beyond basic geometric types—essential for location-based applications.
pgvector: vector similarity search for embeddings.
pgaudit: audit logging for all database activity.
JSONB Guidance
Prefer JSONB with GIN index.
Default: CREATE INDEX ON tbl USING GIN (jsonb_col); → accelerates:
Heavy @> workloads: consider opclass jsonb_path_ops for smaller/faster containment-only indexes:
CREATE INDEX ON tbl USING GIN (jsonb_col jsonb_path_ops);
Trade-off: loses support for key existence (?, ?|, ?&) queries—only supports containment (@>)
Equality/range on a specific scalar field: extract and index with B-tree (generated column or expression):
ALTER TABLE tbl ADD COLUMN price INT GENERATED ALWAYS AS ((jsonb_col->>'price')::INT) STORED;
CREATE INDEX ON tbl (price);
Prefer queries like WHERE price BETWEEN 100 AND 500 (uses B-tree) over WHERE (jsonb_col->>'price')::INT BETWEEN 100 AND 500 without index.
Arrays inside JSONB: use GIN + @> for containment (e.g., tags). Consider jsonb_path_ops if only doing containment.
Keep core relations in tables; use JSONB for optional/variable attributes.
Use constraints to limit allowed JSONB values in a column e.g. config JSONB NOT NULL CHECK(jsonb_typeof(config) = 'object')
Examples
Users
CREATE TABLE users (
user_id BIGINT GENERATED ALWAYS ASIDENTITYPRIMARY KEY,
email TEXT NOT NULLUNIQUE,
name TEXT NOT NULL,
created_at TIMESTAMPTZ NOT NULLDEFAULT now()
);
CREATEUNIQUE INDEX ON users (LOWER(email));
CREATE INDEX ON users (created_at);
Orders
CREATE TABLE orders (
order_id BIGINT GENERATED ALWAYS ASIDENTITYPRIMARY KEY,
user_id BIGINTNOT NULLREFERENCES users(user_id),
status TEXT NOT NULLDEFAULT'PENDING'CHECK (status IN ('PENDING','PAID','CANCELED')),
total NUMERIC(10,2) NOT NULLCHECK (total >0),
created_at TIMESTAMPTZ NOT NULLDEFAULT now()
);
CREATE INDEX ON orders (user_id);
CREATE INDEX ON orders (created_at);
JSONB
CREATE TABLE profiles (
user_id BIGINTPRIMARY KEYREFERENCES users(user_id),
attrs JSONB NOT NULLDEFAULT'{}',
theme TEXT GENERATED ALWAYS AS (attrs->>'theme') STORED
);
CREATE INDEX profiles_attrs_gin ON profiles USING GIN (attrs);
Arrays: TEXT[], INTEGER[], etc. Use for ordered lists where you query elements. Index with GIN for containment (@>, <@) and overlap (&&) queries. Access: arr[1] (1-indexed), arr[1:3] (slicing). Good for tags, categories; avoid for relations—use junction tables instead. Literal syntax: '{val1,val2}' or ARRAY[val1,val2].
Range types: daterange, numrange, tstzrange for intervals. Support overlap (&&), containment (@>), operators. Index with GiST. Good for scheduling, versioning, numeric ranges. Pick a bounds scheme and use it consistently; prefer [) (inclusive/exclusive) by default.
Network types: INET for IP addresses, CIDR for network ranges, MACADDR for MAC addresses. Support network operators (<<, >>, &&).
Geometric types: POINT, LINE, POLYGON, CIRCLE for 2D spatial data. Index with GiST. Consider PostGIS for advanced spatial features.
Text search: TSVECTOR for full-text search documents, TSQUERY for search queries. Index tsvector with GIN. Always specify language: to_tsvector('english', col) and to_tsquery('english', 'query'). Never use single-argument versions. This applies to both index expressions and queries.
Domain types: CREATE DOMAIN email AS TEXT CHECK (VALUE ~ '^[^@]+@[^@]+$') for reusable custom types with validation. Enforces constraints across tables.
Composite types: CREATE TYPE address AS (street TEXT, city TEXT, zip TEXT) for structured data within columns. Access with (col).field syntax.
JSONB: preferred over JSON; index with GIN. Use only for optional/semi-structured attrs. ONLY use JSON if the original ordering of the contents MUST be preserved.
Vector types: vector type by pgvector for vector similarity search for embeddings.
