ia-postgresql

PostgreSQL schema design, query optimization, indexing, and administration. Use when working with PostgreSQL, JSONB, partitioning, RLS, CTEs, window functions, or EXPLAIN ANALYZE.

PostgreSQL

Data Type Defaults

Need	Use	Avoid
Primary key	BIGINT GENERATED ALWAYS AS IDENTITY	SERIAL, BIGSERIAL
Timestamps	TIMESTAMPTZ	TIMESTAMP (loses timezone)
Text	TEXT	VARCHAR(n) unless constraint needed
Money	NUMERIC(precision, scale)	MONEY, FLOAT
Boolean	BOOLEAN with NOT NULL DEFAULT	nullable booleans
JSON	JSONB	JSON (no indexing), text JSON
UUID	gen_random_uuid() (PG13+)	uuid-ossp extension
IP addresses	INET / CIDR	text
Ranges	TSTZRANGE, INT4RANGE, etc.	pair of columns

Schema Rules

• Every FK column gets an index (PG does NOT auto-create these)
• NOT NULL on every column unless NULL has business meaning
• CHECK constraints for domain rules at DB level
• EXCLUDE constraints for range overlaps: EXCLUDE USING gist (room WITH =, during WITH &&)
• Default created_at TIMESTAMPTZ NOT NULL DEFAULT now()
• Separate updated_at with trigger, never trust app layer alone
• Use BIGINT PKs -- cheaper JOINs than UUID, better index locality
• Safe migrations: CREATE INDEX CONCURRENTLY, add columns with DEFAULT (instant add). Never ALTER TYPE on large tables in-place.
• NULLS NOT DISTINCT on unique indexes (PG15+) -- treats NULLs as equal for uniqueness
• Revoke default public schema access: REVOKE ALL ON SCHEMA public FROM public

Migration Safety

Core rules:

• Every schema change is a migration. No ad-hoc DDL in production.
• Migrations are immutable once deployed -- never edit a migration that has run in any shared environment.
• Schema migrations and data migrations are separate files. Schema changes are fast and transactional; data backfills are slow and may need batching.
• Forward-only in production. Rollback = a new forward migration that reverses the change.

Expand-contract pattern for zero-downtime renames and removals:

1. Expand: add the new column/table, backfill data, update writes to populate both old and new
2. Migrate: switch reads to the new column/table, verify in production
3. Contract: remove the old column/table in a later deploy

Never rename or remove a column in a single migration -- callers reading the old name will break between deploy and code rollout.

Dangerous operations:

• NOT NULL without a DEFAULT on an existing table locks and rewrites every row. Add the column nullable first, backfill, then add the constraint.
• CREATE INDEX (without CONCURRENTLY) locks writes for the duration. Always use CONCURRENTLY, which cannot run inside a transaction block -- keep it in its own migration.
• Large data backfills: batch with FOR UPDATE SKIP LOCKED to avoid locking the entire table:

UPDATE target SET new_col = compute(old_col)
WHERE id IN (
  SELECT id FROM target
  WHERE new_col IS NULL
  LIMIT 1000
  FOR UPDATE SKIP LOCKED
);

Run in a loop until zero rows affected.

Full-replace clobber on read-modify-write loops. A migration that loops SELECT col → mutate in app → UPDATE SET col = new_full_value WHERE id = ? silently drops concurrent writes that landed between SELECT and UPDATE. Any column written by live traffic is exposed: jsonb documents, comma-separated tag fields, denormalized counters, JSON-encoded attribute blobs. Mitigations, in order of preference:

• In-place atomic update when the edit is expressible as SQL: UPDATE t SET col = jsonb_set(col, '{path}', :value) WHERE ..., or UPDATE t SET tags = array_append(tags, :tag) WHERE ... — no read-modify-write window.
• Row-level lock during the loop: wrap each iteration in a transaction, SELECT ... WHERE id = ? FOR UPDATE, then mutate and write. Cheaper to author, accepts more lock contention.
• Compare-and-swap retry: include the original snapshot in WHERE col = :original_value, check the affected-row count; on 0, re-read and retry. Robust under contention, requires explicit retry-loop handling.

Default chunked decode-encode loops are only safe during a maintenance window with writes blocked. ORM "chunkById + load + mutate + save" patterns hit this same trap.

Index Strategy

Type	Use When
B-tree (default)	Equality, range, sorting, LIKE 'prefix%'
GIN	JSONB (@>, ?, ?&), arrays, full-text (tsvector)
GiST	Geometry, ranges, full-text (smaller but slower than GIN)
BRIN	Large tables with natural ordering (timestamps, serial IDs)

Index rules:

• Composite: most selective column first, max 3-4 columns
• Partial: WHERE status = 'active' -- smaller, faster
• Covering: INCLUDE (col) -- avoids heap lookup
• Expression: ON (lower(email)) -- for function-based WHERE
• fillfactor = 70-90 on write-heavy tables -- reserves space for HOT updates, reducing index bloat
• Drop unused indexes (only after one full business cycle since last restart -- check pg_stat_database.stats_reset first, otherwise you may drop a primary key on a freshly restarted DB or read replica): SELECT * FROM pg_stat_user_indexes WHERE idx_scan = 0

Detect unindexed foreign keys:

SELECT conrelid::regclass, a.attname
FROM pg_constraint c
JOIN pg_attribute a ON a.attrelid = c.conrelid AND a.attnum = ANY(c.conkey)
WHERE c.contype = 'f'
  AND NOT EXISTS (
    SELECT 1 FROM pg_index i
    WHERE i.indrelid = c.conrelid AND a.attnum = ANY(i.indkey)
  );

JSONB Patterns

-- GIN index for containment queries
CREATE INDEX ON items USING gin (metadata);
SELECT * FROM items WHERE metadata @> '{"status": "active"}';

-- Expression index for specific key access
CREATE INDEX ON items ((metadata->>'category'));
SELECT * FROM items WHERE metadata->>'category' = 'electronics';

Prefer typed columns over JSONB for frequently queried, well-structured data. Use JSONB for truly dynamic/variable attributes.

Use jsonb_path_ops operator class for containment-only (@>) queries -- 2-3x smaller index. Use default jsonb_ops when key-existence (?, ?|) is needed.

Delete operators:

Operator	Operand	Behavior	Example
-	text	remove top-level key from object	'{"a":1,"b":2}'::jsonb - 'a' → {"b":2}
-	text[]	remove multiple top-level keys	'{"a":1,"b":2}'::jsonb - ARRAY['a','b'] → {}
-	integer	remove array element by index	'[1,2,3]'::jsonb - 1 → [1,3]
#-	text[]	remove value at nested path	'{"a":{"b":1}}'::jsonb #- '{a,b}' → {"a":{}}

Common mistakes:

• col - 'a,b' treats 'a,b' as a single key name (no-op against a normally-structured document — the comma isn't a path separator).
• col - 'a' - 'b' first removes the entire a subtree before attempting - 'b' on the result (data loss of a.*, then a no-op).
• jsonb_set(col, '{a,b}', 'null'::jsonb) sets the value to JSON null rather than removing the key — strict "key absent" checks downstream then fail. Worse: jsonb_set(col, '{a,b}', NULL) with a bare SQL NULL makes the STRICT function return SQL NULL, clobbering the entire column on update. To delete the key, use #-; to set it explicitly to JSON null, use 'null'::jsonb (and know that's distinct from absence).

For nested deletes, use #- with a text-array path. Verify with one round-tripped row of the worst-case shape before committing the migration: SELECT col #- '{a,b}' FROM t WHERE id = ? LIMIT 1, then confirm the key is gone (not present-as-null, no sibling data loss).

Row-Level Security (RLS)

ALTER TABLE orders ENABLE ROW LEVEL SECURITY;
ALTER TABLE orders FORCE ROW LEVEL SECURITY;  -- applies to table owner too

-- Set session context (generic, no extensions needed)
SET app.current_user_id = '123';

CREATE POLICY orders_user_policy ON orders
  FOR ALL
  USING (user_id = current_setting('app.current_user_id')::bigint);

Performance: Policy expressions evaluate per row. Wrap function calls in a scalar subquery so PG evaluates once and caches:

-- BAD: called per row
USING (get_current_user() = user_id)
-- GOOD: evaluated once, cached
USING ((SELECT get_current_user()) = user_id)

Always index columns referenced in RLS policies. For complex multi-table checks, use SECURITY DEFINER helper functions.

Query Optimization

• Always EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) before optimizing
• Use pg_stat_statements for slow-query detection and pg_stat_user_tables for bloat (see Detection queries below for the full SQL)
• Sequential scan on large table -> add index or check WHERE for function wrapping
• High rows removed by filter -> index doesn't match predicate
• CTEs are inlined by default; use MATERIALIZED/NOT MATERIALIZED hints to control optimization
• Prefer EXISTS over IN for correlated subqueries
• Use LATERAL JOIN when subquery needs outer row reference
• Cursor pagination (WHERE id > $last ORDER BY id LIMIT $n) over OFFSET
• Approximate row counts: SELECT reltuples FROM pg_class WHERE relname = 'table' -- avoids full count(*) on large tables
• Materialized views for expensive aggregations: REFRESH MATERIALIZED VIEW CONCURRENTLY (needs unique index). Schedule refresh, not per-query.

Concurrency Patterns

See concurrency-patterns.md for UPSERT, deadlock prevention, N+1 elimination, batch inserts, and queue processing with SKIP LOCKED.

Partitioning

Use when table exceeds ~100M rows or needs TTL purge:

• RANGE -- time-series (by month/year), most common
• LIST -- categorical (by region, tenant)
• HASH -- even distribution when no natural key

Partition key must be in every unique/PK constraint. Create indexes on partitions, not parent.

Transactions & Locking

• Keep transactions short -- long txns block vacuum and bloat tables
• Advisory locks for application-level mutual exclusion: pg_advisory_xact_lock(key)
• Non-blocking alternative: pg_try_advisory_lock(key) -- returns false instead of waiting
• Check blocked queries: SELECT * FROM pg_stat_activity WHERE wait_event_type = 'Lock'
• Monitor deadlocks: SELECT deadlocks FROM pg_stat_database WHERE datname = current_database()

Full-Text Search

See full-text-search.md for weighted tsvector setup, query syntax, highlighting, and when to use PG full-text vs external search.

Connection Pooling

Always pool in production. Direct connections cost ~10MB each.

• PgBouncer in transaction mode for most workloads
• statement mode if no session-level features (prepared statements, temp tables, advisory locks)

Prepared statement caveat: Named prepared statements are bound to a specific connection. In transaction-mode pooling, the next request may hit a different connection. Use unnamed/extended-query-protocol statements (most ORMs default to this), or deallocate immediately after use.

Operations

See operations.md for performance tuning, maintenance/monitoring, WAL, replication, and backup/recovery.

Vector Search (pgvector)

CREATE EXTENSION vector;
ALTER TABLE items ADD COLUMN embedding vector(1536);  -- match your model's output dimensions

-- HNSW: better recall, higher memory. Default choice.
CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops);

-- IVFFlat: lower memory for large datasets. Set lists = sqrt(row_count).
CREATE INDEX ON items USING ivfflat (embedding vector_cosine_ops) WITH (lists = 1000);

Always filter BEFORE vector search (use partial indexes or CTEs with pre-filtered rows). Distance operators: <=> cosine, <-> L2, <#> inner product.

Anti-Patterns

Anti-Pattern	Fix
SELECT *	List needed columns
N+1 queries in application loop	Use JOIN, IN, or batch fetch
OFFSET for pagination on large tables	Cursor pagination: WHERE id > $last ORDER BY id LIMIT $n
count(*) on large tables	Approximate: SELECT reltuples FROM pg_class WHERE relname = 'table'
Nullable booleans	NOT NULL DEFAULT false -- three-valued logic causes subtle bugs
Missing FK indexes	See detection query in Index Strategy above
ORDER BY RANDOM()	Use TABLESAMPLE or application-side shuffle

Detection queries:

-- Slow queries (requires pg_stat_statements)
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
WHERE mean_exec_time > 100
ORDER BY mean_exec_time DESC LIMIT 20;

-- Table bloat (dead tuples awaiting vacuum)
SELECT relname, n_dead_tup, last_vacuum, last_autovacuum
FROM pg_stat_user_tables
WHERE n_dead_tup > 10000
ORDER BY n_dead_tup DESC;

-- Unused indexes (candidates for removal)
SELECT schemaname, relname, indexrelname, idx_scan
FROM pg_stat_user_indexes
WHERE idx_scan = 0 AND indexrelname NOT LIKE '%_pkey'
ORDER BY pg_relation_size(indexrelid) DESC;

Verify

Run EXPLAIN (ANALYZE, BUFFERS) on changed queries. Confirm no sequential scans on large tables and no unindexed FK columns before declaring done.