Use when writing or reviewing PostgreSQL/PL-pgSQL, designing table schemas, writing functions and procedures, building migrations, defining domains, or architecting a Postgres application database. Also use when writing RAISE EXCEPTION patterns, BEFORE/AFTER triggers for cross-table constraints, base/subtype hierarchies, composite key designs, row-level security policies, or idempotent DDL scripts. If you are touching Postgres for an application database, use this skill. PostgreSQL-specific — examples will not run on other engines.
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Use when writing or reviewing PostgreSQL/PL-pgSQL, designing table schemas, writing functions and procedures, building migrations, defining domains, or architecting a Postgres application database. Also use when writing RAISE EXCEPTION patterns, BEFORE/AFTER triggers for cross-table constraints, base/subtype hierarchies, composite key designs, row-level security policies, or idempotent DDL scripts. If you are touching Postgres for an application database, use this skill. PostgreSQL-specific — examples will not run on other engines.
Postgres Writing Guidelines
When to Use
Starting a new PostgreSQL application database from scratch
Adding tables, views, functions, procedures, or triggers to an existing schema following this methodology
Reviewing PL/pgSQL for type safety, RLS coverage, and structural enforcement
Writing migrations that must be idempotent and safe to rerun
When NOT to use: one-off ad-hoc queries, read-only reporting databases, or any non-Postgres engine. Examples and syntax throughout are PostgreSQL/PL-pgSQL.
snake_case Everywhere
Postgres folds unquoted identifiers to lowercase. Using PascalCase forces double-quoting ("PascalCase") on every reference, forever. Use snake_case for everything: tables, columns, views, functions, procedures, types, constraints, parameters.
Avoid SQL keywords as verbs — use add/modify/remove/find instead of create/update/delete/select. Avoid abbreviations unless universally understood (no for Number, id for external identifiers).
Never use bare built-in types (VARCHAR, INT, TIMESTAMPTZ, BOOLEAN) for columns. Define a catalog of named DOMAINs that form a consistent, semantic layer. Postgres DOMAINs support inline CHECK constraints.
CREATE DOMAIN email AS VARCHAR(100) NOT NULL
CHECK (VALUE LIKE '%_@_%.__%');
CREATE DOMAIN api_key AS VARCHAR(128) NOT NULL;
CREATE DOMAIN account_no AS BIGINT NOT NULL;
CREATE DOMAIN ts_now AS TIMESTAMPTZ NOT NULL DEFAULT clock_timestamp();
CREATE DOMAIN bool_false AS BOOLEAN NOT NULL DEFAULT FALSE;
Every column uses a named DOMAIN. email instead of VARCHAR(100). ts_now instead of TIMESTAMPTZ. bool_false instead of BOOLEAN.
Consistency. Change the domain definition once; every column using it updates.
Semantic inference.api_key tells you what the data is; VARCHAR(128) tells you nothing. Find every API key column by querying information_schema.columns for domain_name = 'api_key'.
NOT NULL by default. Define DOMAINs as NOT NULL. Nullable only with explicit business reason — optional is a deliberate design choice, not the default.
Organize by category (identity, web/auth, civic, financial, generic primitives) and maintain a YAML manifest as the source of truth. Domain names are unique per system — a property management app has party_no, entrata_id, lease_no; a financial app has account_no, transaction_no, routing_number.
Enums vs Reference Tables
Postgres has two ways to constrain a column to a fixed vocabulary: CREATE TYPE ... AS ENUM and a reference (lookup) table with a FK. Default to reference tables — they support extra columns (display name, sort order, deprecated flag), serve as FK targets, and accept runtime additions. Reach for ENUM only when the vocabulary is closed, stable, and value-only (audit actions, simple labels).
For decision rules, the migration path between them, and the hybrid pattern — read Enums vs Reference Tables.
Time & Timezones
Always use timestamptz, never timestamp (without timezone). Store UTC at rest; convert at the read boundary using AT TIME ZONE 'America/New_York'. Use clock_timestamp() for true wall-clock samples, transaction_timestamp() (or now()) for "as of the transaction start." Use date (not timestamptz at midnight) when time-of-day doesn't matter. For non-overlapping periods, use tstzrange + EXCLUDE constraints.
For bucketing, gap-filling with generate_series, range operators, and common gotchas — read Time & Timezones.
UUID Strategies
Pick UUIDs only when you need distributed ID generation, public-facing identifiers, or merge-friendly cross-system IDs. Default to bigint (or hierarchical composite keys) for internal data — they're smaller, faster, and index better. Common production pattern: bigint PK for internal joins + a uuidpublic_id column for the API boundary. If you do use a UUID PK on a high-write table, prefer UUID v7 (time-ordered) over v4 to keep B-tree index performance acceptable.
For storage costs, index fragmentation, v4 vs v7 tradeoffs — read UUID Strategies.
Generated Columns
For derived data that's queried often, use GENERATED ALWAYS AS (...) STORED instead of a trigger:
full_name text GENERATED ALWAYS AS (first_name || ' ' || last_name) STORED
Postgres keeps it in sync automatically, you can index it like any column, and the expression is type-checked at definition time. The expression must be deterministic (no clock_timestamp(), no cross-row data); use a trigger for anything else.
Split your DB into logical schemas — app (live tables), audit (history), ref (lookup), archive (cold storage), util (helpers). Schemas are namespaces inside one DB; cross-schema FKs and joins work transparently. Granting a role access requires both USAGE on the schema and SELECT/EXECUTE on the objects — forgetting the USAGE is a common permission bug.
For layout, search_path management, and migration tool configuration — read Schema Separation.
Two Access Rules in Postgres
Postgres's native row-level security puts the security boundary at the table layer. Views and procedures don't need to act as access gates to keep data safe — they earn their keep for other reasons.
The new posture:
Direct table access is fine when RLS + CHECK constraints + triggers cover the rules.
Use views when they add value: flattening multi-table joins, role-specific column projections, hiding schema evolution.
Use procedures when you need: multi-statement atomicity, multi-table coordination, business rule enforcement spanning entities.
Views and procedures become tools for API ergonomics and business logic, not security boundaries. Don't add a procedure where a constrained-and-policied table will do.
Each table holding user-scoped data enables RLS and defines policies. The filter happens transparently regardless of how the data is queried — SELECT * returns only rows the current role is allowed to see.
ALTER TABLE customer ENABLE ROW LEVEL SECURITY;
CREATE POLICY customer_owner_can_read ON customer
FOR SELECT
USING (owner_id = fn_current_app_user_id());
CREATE POLICY customer_owner_can_modify ON customer
FOR UPDATE
USING (owner_id = fn_current_app_user_id())
WITH CHECK (owner_id = fn_current_app_user_id());
CREATE POLICY admin_full_access ON customer
FOR ALL TO admin_role
USING (TRUE) WITH CHECK (TRUE);
fn_current_app_user_id() is a SECURITY DEFINER function that returns the application user's ID, typically derived from a session variable set at connection time (SET LOCAL app.user_id = ...).
For full RLS patterns — policies, USING vs WITH CHECK, FORCE RLS, multi-tenant designs, bypass roles, debugging — read Row-Level Security.
PROCEDUREs can COMMIT/ROLLBACK inside. They cannot be called from within an explicit transaction (Postgres errors).
FUNCTIONs always run inside a transaction (the caller's, or implicit). They cannot COMMIT/ROLLBACK.
No subtransactions without an explicit SAVEPOINT.
The one rule: no explicit nested transactions. Never wrap one BEGIN inside another. Use SAVEPOINT only for selective rollback within a single logical unit. Postgres prevents most accidental nesting at the engine layer; this rule covers the rest.
A small helper fn_assert_not_in_transaction() can be called at the top of any operation that assumes it owns its own transaction.
For full procedure/function templates, transaction patterns, savepoints, and the assertion helper — read Procedure Structure.
Concurrency & Locking
Postgres has four row-lock strengths: FOR KEY SHARE, FOR SHARE, FOR NO KEY UPDATE, and FOR UPDATE (strongest). Most app updates only need FOR NO KEY UPDATE — FOR UPDATE is overkill unless you're changing keys. Use SKIP LOCKED for queue claim, NOWAIT to fail fast instead of blocking. For high-throughput state changes where conflicts are rare, prefer optimistic concurrency (version columns) over pessimistic locking. Use pg_advisory_xact_lock(...) to serialize operations that don't map to a single row (e.g., max-plus-one ID generation per parent).
For lock modes, deadlock prevention (consistent ordering of lock acquisition), and isolation levels — read Concurrency & Locking.
Error Handling (EXCEPTION Blocks, SQLSTATE Codes)
PL/pgSQL has no GOTO. Errors flow through EXCEPTION WHEN ... THEN blocks, which automatically roll back the surrounding subtransaction. For client-parseable errors, use a SQLSTATE catalog:
SQLSTATE codes are 5 alphanumeric chars. User-defined codes use class P0 (P0001–P0999 is the conventional range). Maintain a constants document mapping codes to semantic names so client apps can match on the code:
Code
Meaning
P0001
INVALID_INPUT
P0002
NOT_FOUND
P0003
DUPLICATE_KEY
P0004
FORBIDDEN
P0005
STATE_CONFLICT
P0010
TYPE_DISCRIMINATOR_MISMATCH
P0011
TRANSACTION_REQUIRED
P0012
TRANSACTION_FORBIDDEN
Reserve EXCEPTION WHEN OTHERS for truly unexpected failures — don't use it as control flow. Catch specific conditions (unique_violation, foreign_key_violation, named SQLSTATEs) when you need to translate them.
For the full code catalog and EXCEPTION block patterns — read Error Handling.
Postgres CHECK constraints can't reliably reach across tables — functions in CHECK must be IMMUTABLE, but cross-table reads are not. Use BEFORE INSERT/UPDATE triggers that delegate to validation functions. Triggers stay thin; functions hold all logic.
-- The function holds the validation logic
CREATE OR REPLACE FUNCTION tg_savings_account_check_type()
RETURNS TRIGGER AS $$
BEGIN
IF NOT EXISTS (
SELECT 1 FROM account
WHERE account_no = NEW.account_no AND type = 'savings'
) THEN
RAISE EXCEPTION 'savings_account requires account.type = ''savings'''
USING ERRCODE = 'P0010';
END IF;
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
-- The trigger is a one-liner that calls the function
CREATE TRIGGER savings_account_must_be_savings_type
BEFORE INSERT OR UPDATE ON savings_account
FOR EACH ROW EXECUTE FUNCTION tg_savings_account_check_type();
This enforces at the schema level that a savings_account row can only reference an account with type = 'savings'. The database rejects invalid data.
Use triggers + functions for:
Type discriminator enforcement across base/subtype tables
When entities share common attributes but have specialized ones, use primary key inheritance instead of polymorphic columns. A base table holds shared attributes and a type discriminator. Each subtype table inherits the base PK as both its PK and FK, plus a trigger enforcing the discriminator.
CREATE TABLE savings_account (
account_no account_no PRIMARY KEY,
interest_rate growth_rate NOT NULL,
min_balance money_amount NOT NULL,
CONSTRAINT savings_account_is_account
FOREIGN KEY (account_no) REFERENCES account(account_no)
);
-- Trigger enforces type discriminator (see Cross-Table Constraints)
Avoid Postgres's INHERITS keyword — it has FK and uniqueness gotchas. Stick with the explicit FK + trigger pattern.
For full pattern — base table setup, type discriminator triggers, referencing base vs subtype, inserting subtypes — read Base/Subtype Inheritance.
Hierarchical Composite Keys
Tables in a parent-child hierarchy use composite primary keys that grow wider as the hierarchy deepens. Each child inherits the full PK of its parent and adds its own discriminator:
fk_rental_customer describes the mechanism. customer_rents_vehicle describes the meaning. Constraint violations in error logs immediately tell you which business rule was broken.
Role-Scoped Views
Views still earn their keep for flattening joins and projecting role-specific column sets. The vw_role_intent naming convention:
The difference: no in-view current_user filter — RLS handles row filtering at the table layer. Views become pure projections.
For view templates and projection patterns — read View Patterns.
Relational Queues (FOR UPDATE SKIP LOCKED)
Postgres queue tables use SELECT ... FOR UPDATE SKIP LOCKED for atomic concurrent claim. Optionally augment with LISTEN/NOTIFY for low-latency wake-up.
-- Worker claims and starts the next item atomically
WITH claimed AS (
SELECT job_id FROM job_queue
WHERE status = 'pending' AND scheduled_for <= clock_timestamp()
ORDER BY scheduled_for
LIMIT 1
FOR UPDATE SKIP LOCKED
)
UPDATE job_queue
SET status = 'in_progress',
started_at = clock_timestamp(),
attempt_num = attempt_num + 1
FROM claimed
WHERE job_queue.job_id = claimed.job_id
RETURNING job_queue.*;
For full queue patterns — table shapes, state machines, claim/report procedures, LISTEN/NOTIFY integration, max-attempts, queues as base/subtypes — read Relational Queues.
Soft Deletes
A deleted_at timestamptz column preserves the row in a "deleted" state so FK references still resolve and history queries still work. RLS policies filter WHERE deleted_at IS NULL for normal callers; admin/BYPASSRLS roles see all rows. Make all uniqueness constraints partial (WHERE deleted_at IS NULL) so the column can be reused after deletion. Use procedures (pr_remove_*, pr_restore_*) for the transitions so the timestamp and actor are captured consistently.
For FK cascade behavior, GDPR-driven hard delete, and integration with audit — read Soft Deletes.
Auditing & History Tables
Every audited table gets a parallel <table>_audit capturing INSERT/UPDATE/DELETE with before_data, after_data, and change_diff (only the changed keys) — all jsonb. An AFTER trigger calls tg_<table>_audit() to write the audit row, using to_jsonb(OLD) / to_jsonb(NEW) for snapshots. A GIN index on change_diff makes "every change that touched the email field" cheap.
For the diff helper, retention/archival, performance mitigations, and system-versioned alternatives — read Auditing & History Tables.
Indexing Strategy
Five access methods: B-tree (default), GIN (arrays, jsonb, full-text), GiST (ranges, geo), BRIN (huge append-only), Hash (rarely useful). For composite indexes, equality columns first, ranges last. Partial indexes (CREATE INDEX ... WHERE ...) are nearly mandatory for soft-deletable tables and queue tables — they cut index size by orders of magnitude. Covering indexes (INCLUDE (col1, col2)) enable index-only scans for hot lookups. Default to "no index" — add when measurement justifies it.
For each access method, partial/expression/covering patterns, and maintenance — read Indexing Strategy.
Idempotent Migrations (Mostly Native)
Postgres has native IF NOT EXISTS for most DDL, dramatically reducing the meta-function library:
CREATE TABLE IF NOT EXISTS customer (...);
ALTER TABLE customer ADD COLUMN IF NOT EXISTS is_verified bool_false;
CREATE INDEX IF NOT EXISTS customer_email_idx ON customer(email);
DROP TABLE IF EXISTS old_customer;
CREATE OR REPLACE FUNCTION ...;
CREATE OR REPLACE PROCEDURE ...;
A small meta-function library is still useful for things Postgres doesn't natively guard:
fn_constraint_exists(table_name, constraint_name) — for adding/dropping named constraints
fn_column_data_type(table_name, column_name) — for type-change migrations
fn_policy_exists(table_name, policy_name) — for RLS policy migrations
fn_trigger_exists(table_name, trigger_name) — for trigger migrations
For migration templates and the slim meta-function library — read Migration Patterns.
Reference Tables
When creating a reference (lookup) table, immediately seed it in the same DDL script using ON CONFLICT DO NOTHING for idempotency:
CREATE TABLE account_type (
type type_name PRIMARY KEY
);
INSERT INTO account_type(type) VALUES
('savings'),
('checking'),
('money_market'),
('certificate_of_deposit')
ON CONFLICT DO NOTHING;
This ensures FK constraints referencing the table are immediately enforceable. A subtype table with FOREIGN KEY (type) REFERENCES account_type(type) won't accept any inserts until the reference data exists.
Application Settings
A centralized app_settings table for runtime configuration — max retry attempts, feature flags, service endpoints, batch sizes:
CREATE TABLE app_settings (
param param_name PRIMARY KEY,
val_bool BOOLEAN NOT NULL DEFAULT FALSE,
val_int BIGINT NOT NULL DEFAULT 0,
val_float DOUBLE PRECISION NOT NULL DEFAULT 0,
val_str TEXT NOT NULL DEFAULT ''
);
Each row is a named parameter using dot-separated namespaces (notification.max_attempts, smtp.host, feature.email_enabled). Functions read from the typed column wrapped in COALESCE with sane defaults so the system works even if a setting hasn't been configured.
Optionally augment with Postgres GUC custom params (SET app.max_attempts = 3) for session-scoped overrides.
For each extension's purpose and basic usage — read Useful Extensions.
Connection Pooling Caveats
Most production apps run transaction-mode pooling (pgbouncer, PgCat, RDS Proxy). In this mode the physical connection rotates between transactions, so:
Session SET is lost — use SET LOCAL inside the transaction (including for RLS identity)
LISTEN/NOTIFY doesn't work — needs session pooling
Session-scoped advisory locks lost — use pg_advisory_xact_lock
Test against real Postgres — never mock the DB. Use transactional isolation: BEGIN before each test, ROLLBACK after, runs thousands per second. For RLS tests, SET LOCAL ROLE to a non-privileged role and assert what they see. For trigger tests, do the DML and assert the side effects. Match exceptions on SQLSTATE, not message text. Use pgTAP for schema and SQL-only unit tests; use your app's test framework for everything that crosses the boundary.
For fixture strategies, RLS test setup, procedure testing constraints, and CI configuration — read Testing Patterns.
Normal Form Violations
Engine-agnostic theory; same patterns apply. The most damaging violation in practice is the Relational Breach: a child table with a BIGSERIAL/IDENTITY surrogate PK instead of a composite key including the parent's PK. Every table that does this severs itself from its ancestry — joins that should be direct require traversing every intermediate table.
For each violation: what it looks like in Postgres, why it's wrong, and the fix pattern — read Normal Form Violations. For the theoretical foundation, see relational-db-design.
Query Patterns
SARGable WHERE clauses matter. Window functions use standard SQL syntax. Postgres-specific patterns:
LATERAL joins for per-row subqueries
DISTINCT ON replaces ROW_NUMBER() = 1 for "first row per group"
Array operators (ANY, ALL, @>, &&) for set operations
JSONB operators (->, ->>, @>) for semi-structured data
INSERT ... ON CONFLICT for upserts
Parameter sniffing is rarely a concern; trust the planner
For the full Postgres-specific catalog — read Query Patterns.
Common Mistakes
Mistake
Fix
Using bare VARCHAR, INT, TIMESTAMPTZ for columns
Always use a named DOMAIN — email, account_no, ts_now
Using PascalCase for identifiers
Use snake_case — Postgres folds unquoted identifiers to lowercase
Naming constraints fk_table_othertable
Use predicates: customer_rents_vehicle
Using BIGSERIAL/IDENTITY for child table keys
Use composite PKs that include the parent's PK
Forgetting ON CONFLICT DO NOTHING on reference seeds
