postgresql

Expert guidance for PostgreSQL database design, optimization, and advanced features including pgvector for AI embeddings.

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Install skill "postgresql" with this command: npx skills add housegarofalo/claude-code-base/housegarofalo-claude-code-base-postgresql

PostgreSQL Skill

Expert guidance for PostgreSQL database design, optimization, and advanced features including pgvector for AI embeddings.

Triggers

Use this skill when:

  • Designing PostgreSQL database schemas or normalization

  • Creating indexes (B-tree, GIN, GiST, BRIN)

  • Working with pgvector for AI embeddings and similarity search

  • Implementing JSON/JSONB operations

  • Building full-text search functionality

  • Writing window functions, CTEs, or recursive queries

  • Tuning PostgreSQL performance

  • Implementing table partitioning

  • Setting up backup and restore procedures

  • Keywords: postgresql, postgres, pgvector, jsonb, full-text search, database indexing, sql optimization, partitioning, cte

Table of Contents

  • Database Design & Normalization

  • Index Types & Strategies

  • pgvector for AI Embeddings

  • JSON/JSONB Operations

  • Full-Text Search

  • Window Functions

  • CTEs & Recursive Queries

  • Performance Tuning

  • Table Partitioning

  • Backup & Restore

Database Design & Normalization

Normalization Levels

-- 1NF: Atomic values, no repeating groups CREATE TABLE users ( id SERIAL PRIMARY KEY, email VARCHAR(255) UNIQUE NOT NULL, first_name VARCHAR(100), last_name VARCHAR(100), created_at TIMESTAMPTZ DEFAULT NOW() );

-- 2NF: No partial dependencies (all non-key columns depend on entire PK) CREATE TABLE order_items ( order_id INTEGER REFERENCES orders(id), product_id INTEGER REFERENCES products(id), quantity INTEGER NOT NULL, unit_price DECIMAL(10,2) NOT NULL, -- Price at time of order PRIMARY KEY (order_id, product_id) );

-- 3NF: No transitive dependencies CREATE TABLE addresses ( id SERIAL PRIMARY KEY, user_id INTEGER REFERENCES users(id), street VARCHAR(255), city VARCHAR(100), state VARCHAR(50), postal_code VARCHAR(20), country VARCHAR(100) );

Strategic Denormalization

-- Denormalized for read-heavy analytics CREATE TABLE order_analytics ( id SERIAL PRIMARY KEY, order_id INTEGER, user_id INTEGER, user_email VARCHAR(255), -- Denormalized user_name VARCHAR(200), -- Denormalized product_count INTEGER, -- Pre-computed total_amount DECIMAL(12,2), -- Pre-computed order_date DATE, created_at TIMESTAMPTZ DEFAULT NOW() );

-- Materialized view for complex aggregations CREATE MATERIALIZED VIEW monthly_sales AS SELECT DATE_TRUNC('month', order_date) AS month, COUNT(*) AS order_count, SUM(total_amount) AS revenue, AVG(total_amount) AS avg_order_value FROM orders GROUP BY DATE_TRUNC('month', order_date) WITH DATA;

-- Refresh strategy REFRESH MATERIALIZED VIEW CONCURRENTLY monthly_sales;

Essential Constraints

-- Comprehensive table with constraints CREATE TABLE products ( id SERIAL PRIMARY KEY, sku VARCHAR(50) UNIQUE NOT NULL, name VARCHAR(255) NOT NULL, price DECIMAL(10,2) NOT NULL CHECK (price >= 0), stock_quantity INTEGER DEFAULT 0 CHECK (stock_quantity >= 0), category_id INTEGER REFERENCES categories(id) ON DELETE SET NULL, status VARCHAR(20) DEFAULT 'active' CHECK (status IN ('active', 'inactive', 'discontinued')), metadata JSONB DEFAULT '{}', created_at TIMESTAMPTZ DEFAULT NOW(), updated_at TIMESTAMPTZ DEFAULT NOW() );

-- Trigger for updated_at CREATE OR REPLACE FUNCTION update_updated_at() RETURNS TRIGGER AS $$ BEGIN NEW.updated_at = NOW(); RETURN NEW; END; $$ LANGUAGE plpgsql;

CREATE TRIGGER products_updated_at BEFORE UPDATE ON products FOR EACH ROW EXECUTE FUNCTION update_updated_at();

Index Types & Strategies

B-tree Indexes (Default)

-- Standard B-tree for equality and range queries CREATE INDEX idx_users_email ON users(email); CREATE INDEX idx_orders_created ON orders(created_at DESC);

-- Composite index (column order matters!) CREATE INDEX idx_orders_user_date ON orders(user_id, created_at DESC);

-- Partial index for filtered queries CREATE INDEX idx_active_orders ON orders(created_at) WHERE status = 'active';

-- Expression index CREATE INDEX idx_users_email_lower ON users(LOWER(email));

-- Unique index CREATE UNIQUE INDEX idx_users_email_unique ON users(email);

GIN Indexes (Generalized Inverted)

-- For JSONB containment queries CREATE INDEX idx_products_metadata ON products USING GIN(metadata);

-- Query using GIN index SELECT * FROM products WHERE metadata @> '{"featured": true}'; SELECT * FROM products WHERE metadata ? 'discount';

-- For array columns CREATE TABLE articles ( id SERIAL PRIMARY KEY, title VARCHAR(255), tags TEXT[] ); CREATE INDEX idx_articles_tags ON articles USING GIN(tags);

-- Query arrays SELECT * FROM articles WHERE tags @> ARRAY['postgresql', 'database']; SELECT * FROM articles WHERE 'sql' = ANY(tags);

-- For full-text search CREATE INDEX idx_articles_fts ON articles USING GIN(to_tsvector('english', title));

GiST Indexes (Generalized Search Tree)

-- For geometric/spatial data CREATE EXTENSION IF NOT EXISTS postgis;

CREATE TABLE locations ( id SERIAL PRIMARY KEY, name VARCHAR(255), coordinates GEOMETRY(Point, 4326) );

CREATE INDEX idx_locations_geo ON locations USING GIST(coordinates);

-- Range types CREATE TABLE reservations ( id SERIAL PRIMARY KEY, room_id INTEGER, during TSTZRANGE NOT NULL, EXCLUDE USING GIST (room_id WITH =, during WITH &&) );

-- Query spatial data SELECT * FROM locations WHERE ST_DWithin( coordinates, ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326), 1000 -- meters );

BRIN Indexes (Block Range)

-- Excellent for naturally ordered data (time-series) CREATE TABLE sensor_readings ( id BIGSERIAL PRIMARY KEY, sensor_id INTEGER, reading_value DECIMAL(10,4), recorded_at TIMESTAMPTZ DEFAULT NOW() );

-- BRIN is much smaller than B-tree for ordered data CREATE INDEX idx_readings_time ON sensor_readings USING BRIN(recorded_at);

-- Best for append-only tables with natural ordering

Index Maintenance

-- Check index usage SELECT indexrelname AS index_name, idx_scan AS times_used, idx_tup_read AS tuples_read, idx_tup_fetch AS tuples_fetched, pg_size_pretty(pg_relation_size(indexrelid)) AS size FROM pg_stat_user_indexes ORDER BY idx_scan DESC;

-- Find unused indexes SELECT indexrelname, idx_scan FROM pg_stat_user_indexes WHERE idx_scan = 0 AND indexrelname NOT LIKE '%_pkey';

-- Rebuild bloated indexes REINDEX INDEX CONCURRENTLY idx_users_email;

pgvector for AI Embeddings

Setup

-- Install extension CREATE EXTENSION IF NOT EXISTS vector;

-- Create table with vector column CREATE TABLE documents ( id SERIAL PRIMARY KEY, title VARCHAR(255) NOT NULL, content TEXT, embedding vector(1536), -- OpenAI ada-002 dimensions metadata JSONB DEFAULT '{}', created_at TIMESTAMPTZ DEFAULT NOW() );

Vector Indexes

-- IVFFlat index (faster build, good for < 1M vectors) CREATE INDEX idx_documents_embedding_ivf ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

-- HNSW index (slower build, better recall, good for > 1M vectors) CREATE INDEX idx_documents_embedding_hnsw ON documents USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 64);

-- Distance operators: -- vector_cosine_ops - Cosine distance (<=>) -- vector_l2_ops - Euclidean distance (<->) -- vector_ip_ops - Inner product (<#>)

Similarity Search

-- Find similar documents using cosine similarity SELECT id, title, 1 - (embedding <=> $1) AS similarity FROM documents ORDER BY embedding <=> $1 -- $1 is query embedding LIMIT 10;

-- With metadata filter SELECT id, title, 1 - (embedding <=> $1) AS similarity FROM documents WHERE metadata->>'category' = 'technical' ORDER BY embedding <=> $1 LIMIT 10;

-- Hybrid search: vector + keyword SELECT d.id, d.title, 1 - (d.embedding <=> $1) AS vector_score, ts_rank(to_tsvector('english', d.content), plainto_tsquery($2)) AS text_score FROM documents d WHERE to_tsvector('english', d.content) @@ plainto_tsquery($2) ORDER BY (1 - (d.embedding <=> $1)) * 0.7 + ts_rank(to_tsvector('english', d.content), plainto_tsquery($2)) * 0.3 DESC LIMIT 10;

RAG Pattern

-- Function for semantic search CREATE OR REPLACE FUNCTION search_documents( query_embedding vector(1536), match_count INTEGER DEFAULT 5, similarity_threshold FLOAT DEFAULT 0.7 ) RETURNS TABLE ( id INTEGER, title VARCHAR, content TEXT, similarity FLOAT ) AS $$ BEGIN RETURN QUERY SELECT d.id, d.title, d.content, 1 - (d.embedding <=> query_embedding) AS similarity FROM documents d WHERE 1 - (d.embedding <=> query_embedding) > similarity_threshold ORDER BY d.embedding <=> query_embedding LIMIT match_count; END; $$ LANGUAGE plpgsql;

JSON/JSONB Operations

Basic Operations

-- Insert JSONB data INSERT INTO products (name, metadata) VALUES ('Laptop', '{"brand": "Dell", "specs": {"ram": 16, "storage": 512}}');

-- Access nested values SELECT name, metadata->>'brand' AS brand, -- Text metadata->'specs'->>'ram' AS ram, -- Nested text (metadata->'specs'->'ram')::INTEGER AS ram_int -- Cast to integer FROM products;

-- Path extraction SELECT metadata #>> '{specs, storage}' AS storage FROM products;

JSONB Operators

-- Containment (@>) SELECT * FROM products WHERE metadata @> '{"brand": "Dell"}';

-- Key existence (?) SELECT * FROM products WHERE metadata ? 'discount'; SELECT * FROM products WHERE metadata ?| ARRAY['discount', 'sale']; -- Any key SELECT * FROM products WHERE metadata ?& ARRAY['brand', 'specs']; -- All keys

-- Update JSONB UPDATE products SET metadata = metadata || '{"featured": true}' WHERE id = 1;

-- Set nested value UPDATE products SET metadata = jsonb_set(metadata, '{specs,ram}', '32') WHERE id = 1;

-- Remove key UPDATE products SET metadata = metadata - 'discount';

JSONB Aggregation

-- Build JSON from rows SELECT jsonb_agg( jsonb_build_object( 'id', id, 'name', name, 'price', price ) ) AS products FROM products WHERE category_id = 1;

-- Object aggregation SELECT jsonb_object_agg(sku, price) AS price_map FROM products;

-- Expand JSONB to rows SELECT p.id, elem->>'key' AS setting_key, elem->>'value' AS setting_value FROM products p, LATERAL jsonb_array_elements(p.metadata->'settings') AS elem;

Full-Text Search

Setup

-- Add tsvector column ALTER TABLE articles ADD COLUMN search_vector tsvector;

-- Populate search vector UPDATE articles SET search_vector = setweight(to_tsvector('english', COALESCE(title, '')), 'A') || setweight(to_tsvector('english', COALESCE(content, '')), 'B');

-- Create GIN index CREATE INDEX idx_articles_search ON articles USING GIN(search_vector);

-- Auto-update trigger CREATE OR REPLACE FUNCTION articles_search_trigger() RETURNS TRIGGER AS $$ BEGIN NEW.search_vector := setweight(to_tsvector('english', COALESCE(NEW.title, '')), 'A') || setweight(to_tsvector('english', COALESCE(NEW.content, '')), 'B'); RETURN NEW; END; $$ LANGUAGE plpgsql;

CREATE TRIGGER articles_search_update BEFORE INSERT OR UPDATE ON articles FOR EACH ROW EXECUTE FUNCTION articles_search_trigger();

Search Queries

-- Basic search SELECT * FROM articles WHERE search_vector @@ plainto_tsquery('english', 'postgresql database');

-- Phrase search SELECT * FROM articles WHERE search_vector @@ phraseto_tsquery('english', 'full text search');

-- Advanced query syntax SELECT * FROM articles WHERE search_vector @@ to_tsquery('english', 'postgres & (performance | optimization)');

-- Ranked results with highlights SELECT id, title, ts_rank(search_vector, query) AS rank, ts_headline('english', content, query, 'StartSel=<mark>, StopSel=</mark>, MaxWords=50') AS snippet FROM articles, plainto_tsquery('english', 'postgresql optimization') query WHERE search_vector @@ query ORDER BY rank DESC LIMIT 10;

Window Functions

Ranking Functions

-- ROW_NUMBER, RANK, DENSE_RANK SELECT name, category, price, ROW_NUMBER() OVER (ORDER BY price DESC) AS row_num, RANK() OVER (ORDER BY price DESC) AS rank, DENSE_RANK() OVER (ORDER BY price DESC) AS dense_rank FROM products;

-- Partition by category SELECT name, category, price, ROW_NUMBER() OVER (PARTITION BY category ORDER BY price DESC) AS category_rank FROM products;

-- NTILE for percentiles SELECT name, price, NTILE(4) OVER (ORDER BY price) AS price_quartile FROM products;

Aggregate Windows

-- Running totals SELECT order_date, amount, SUM(amount) OVER (ORDER BY order_date) AS running_total, AVG(amount) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg_7d FROM orders;

-- Comparison with previous/next SELECT order_date, amount, LAG(amount, 1) OVER (ORDER BY order_date) AS prev_amount, LEAD(amount, 1) OVER (ORDER BY order_date) AS next_amount, amount - LAG(amount, 1) OVER (ORDER BY order_date) AS change FROM orders;

-- First/Last in partition SELECT DISTINCT category, FIRST_VALUE(name) OVER (PARTITION BY category ORDER BY price DESC) AS most_expensive, LAST_VALUE(name) OVER ( PARTITION BY category ORDER BY price DESC RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING ) AS cheapest FROM products;

CTEs & Recursive Queries

Standard CTEs

-- Multiple CTEs for complex queries WITH active_users AS ( SELECT id, email, created_at FROM users WHERE status = 'active' ), user_orders AS ( SELECT user_id, COUNT(*) AS order_count, SUM(total) AS total_spent FROM orders WHERE created_at > NOW() - INTERVAL '1 year' GROUP BY user_id ) SELECT au.email, COALESCE(uo.order_count, 0) AS orders, COALESCE(uo.total_spent, 0) AS spent FROM active_users au LEFT JOIN user_orders uo ON uo.user_id = au.id ORDER BY spent DESC;

Recursive CTEs

-- Hierarchical data (org chart, categories) CREATE TABLE categories ( id SERIAL PRIMARY KEY, name VARCHAR(100), parent_id INTEGER REFERENCES categories(id) );

-- Get all descendants WITH RECURSIVE category_tree AS ( -- Base case SELECT id, name, parent_id, 0 AS depth, ARRAY[id] AS path FROM categories WHERE parent_id IS NULL

UNION ALL

-- Recursive case
SELECT c.id, c.name, c.parent_id, ct.depth + 1, ct.path || c.id
FROM categories c
INNER JOIN category_tree ct ON c.parent_id = ct.id

) SELECT id, REPEAT(' ', depth) || name AS name, depth, path FROM category_tree ORDER BY path;

-- Get all ancestors WITH RECURSIVE ancestors AS ( SELECT id, name, parent_id FROM categories WHERE id = 42 -- Starting category

UNION ALL

SELECT c.id, c.name, c.parent_id
FROM categories c
INNER JOIN ancestors a ON c.id = a.parent_id

) SELECT * FROM ancestors;

Performance Tuning

Query Analysis

-- Detailed execution plan EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) SELECT u.email, COUNT(o.id) AS order_count FROM users u LEFT JOIN orders o ON o.user_id = u.id WHERE u.created_at > '2024-01-01' GROUP BY u.id HAVING COUNT(o.id) > 5 ORDER BY order_count DESC LIMIT 10;

-- Key metrics to watch: -- - Seq Scan vs Index Scan -- - Rows estimated vs actual -- - Buffers hit vs read -- - Sort method (quicksort vs external merge)

Configuration Tuning

-- Memory settings (adjust based on available RAM) ALTER SYSTEM SET shared_buffers = '4GB'; -- 25% of RAM ALTER SYSTEM SET effective_cache_size = '12GB'; -- 75% of RAM ALTER SYSTEM SET work_mem = '256MB'; -- Per-operation memory ALTER SYSTEM SET maintenance_work_mem = '1GB'; -- For VACUUM, INDEX

-- Query planner ALTER SYSTEM SET random_page_cost = 1.1; -- For SSDs (default 4.0) ALTER SYSTEM SET effective_io_concurrency = 200; -- For SSDs

-- Parallel queries ALTER SYSTEM SET max_parallel_workers_per_gather = 4; ALTER SYSTEM SET max_parallel_workers = 8;

-- Apply changes SELECT pg_reload_conf();

Common Optimizations

-- SLOW: Function on indexed column SELECT * FROM users WHERE LOWER(email) = 'user@example.com';

-- FAST: Expression index or store lowercase CREATE INDEX idx_users_email_lower ON users(LOWER(email));

-- SLOW: OR conditions SELECT * FROM orders WHERE status = 'pending' OR status = 'processing';

-- FAST: Use IN or UNION ALL SELECT * FROM orders WHERE status IN ('pending', 'processing');

-- SLOW: SELECT * SELECT * FROM orders WHERE user_id = 1;

-- FAST: Select only needed columns SELECT id, status, total FROM orders WHERE user_id = 1;

-- SLOW: OFFSET for pagination SELECT * FROM products ORDER BY id LIMIT 10 OFFSET 10000;

-- FAST: Keyset pagination SELECT * FROM products WHERE id > 10000 ORDER BY id LIMIT 10;

Monitoring Queries

-- Slow query log ALTER SYSTEM SET log_min_duration_statement = 1000; -- Log queries > 1s

-- Active queries SELECT pid, NOW() - query_start AS duration, state, LEFT(query, 100) AS query_preview FROM pg_stat_activity WHERE state != 'idle' ORDER BY duration DESC;

-- Table statistics SELECT relname AS table, n_live_tup AS live_rows, n_dead_tup AS dead_rows, last_vacuum, last_autovacuum, last_analyze FROM pg_stat_user_tables ORDER BY n_dead_tup DESC;

Table Partitioning

Range Partitioning (Time-based)

-- Parent table CREATE TABLE events ( id BIGSERIAL, event_type VARCHAR(50), payload JSONB, created_at TIMESTAMPTZ NOT NULL, PRIMARY KEY (id, created_at) ) PARTITION BY RANGE (created_at);

-- Create partitions CREATE TABLE events_2024_q1 PARTITION OF events FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');

CREATE TABLE events_2024_q2 PARTITION OF events FOR VALUES FROM ('2024-04-01') TO ('2024-07-01');

-- Default partition for unmatched values CREATE TABLE events_default PARTITION OF events DEFAULT;

-- Auto-create partitions (function) CREATE OR REPLACE FUNCTION create_monthly_partition(table_name TEXT, partition_date DATE) RETURNS VOID AS $$ DECLARE partition_name TEXT; start_date DATE; end_date DATE; BEGIN partition_name := table_name || '_' || TO_CHAR(partition_date, 'YYYY_MM'); start_date := DATE_TRUNC('month', partition_date); end_date := start_date + INTERVAL '1 month';

EXECUTE FORMAT(
    'CREATE TABLE IF NOT EXISTS %I PARTITION OF %I FOR VALUES FROM (%L) TO (%L)',
    partition_name, table_name, start_date, end_date
);

END; $$ LANGUAGE plpgsql;

List Partitioning

-- Partition by region CREATE TABLE sales ( id SERIAL, region VARCHAR(20) NOT NULL, amount DECIMAL(12,2), sale_date DATE, PRIMARY KEY (id, region) ) PARTITION BY LIST (region);

CREATE TABLE sales_americas PARTITION OF sales FOR VALUES IN ('US', 'CA', 'MX', 'BR');

CREATE TABLE sales_europe PARTITION OF sales FOR VALUES IN ('UK', 'DE', 'FR', 'IT');

CREATE TABLE sales_apac PARTITION OF sales FOR VALUES IN ('JP', 'CN', 'AU', 'IN');

Hash Partitioning

-- Distribute by user_id CREATE TABLE user_activity ( id BIGSERIAL, user_id INTEGER NOT NULL, action VARCHAR(50), created_at TIMESTAMPTZ, PRIMARY KEY (id, user_id) ) PARTITION BY HASH (user_id);

CREATE TABLE user_activity_0 PARTITION OF user_activity FOR VALUES WITH (MODULUS 4, REMAINDER 0); CREATE TABLE user_activity_1 PARTITION OF user_activity FOR VALUES WITH (MODULUS 4, REMAINDER 1); CREATE TABLE user_activity_2 PARTITION OF user_activity FOR VALUES WITH (MODULUS 4, REMAINDER 2); CREATE TABLE user_activity_3 PARTITION OF user_activity FOR VALUES WITH (MODULUS 4, REMAINDER 3);

Backup & Restore

pg_dump

Full database backup

pg_dump -h localhost -U postgres -d mydb -F c -f backup.dump

Schema only

pg_dump -h localhost -U postgres -d mydb --schema-only -f schema.sql

Data only

pg_dump -h localhost -U postgres -d mydb --data-only -f data.sql

Specific tables

pg_dump -h localhost -U postgres -d mydb -t users -t orders -F c -f tables.dump

Exclude tables

pg_dump -h localhost -U postgres -d mydb --exclude-table='logs_*' -F c -f backup.dump

Parallel dump (faster for large databases)

pg_dump -h localhost -U postgres -d mydb -F d -j 4 -f backup_dir/

pg_restore

Restore full database

pg_restore -h localhost -U postgres -d mydb -c backup.dump

Restore specific tables

pg_restore -h localhost -U postgres -d mydb -t users backup.dump

Parallel restore

pg_restore -h localhost -U postgres -d mydb -j 4 backup_dir/

List contents of dump

pg_restore -l backup.dump

Automated Backup Script

#!/bin/bash

backup.sh - Automated PostgreSQL backup

DB_NAME="mydb" BACKUP_DIR="/backups" RETENTION_DAYS=30 DATE=$(date +%Y%m%d_%H%M%S)

Create backup

pg_dump -h localhost -U postgres -d $DB_NAME -F c
-f "$BACKUP_DIR/${DB_NAME}_${DATE}.dump"

Verify backup

if pg_restore -l "$BACKUP_DIR/${DB_NAME}${DATE}.dump" > /dev/null 2>&1; then echo "Backup successful: ${DB_NAME}${DATE}.dump" else echo "Backup verification failed!" exit 1 fi

Cleanup old backups

find $BACKUP_DIR -name "*.dump" -mtime +$RETENTION_DAYS -delete

Quick Reference

Common Data Types

Type Description Example

SERIAL

Auto-increment integer id SERIAL PRIMARY KEY

UUID

Universal unique identifier id UUID DEFAULT gen_random_uuid()

VARCHAR(n)

Variable-length string name VARCHAR(255)

TEXT

Unlimited text content TEXT

INTEGER

4-byte integer quantity INTEGER

BIGINT

8-byte integer views BIGINT

DECIMAL(p,s)

Exact numeric price DECIMAL(10,2)

TIMESTAMPTZ

Timestamp with timezone created_at TIMESTAMPTZ

JSONB

Binary JSON metadata JSONB

vector(n)

pgvector embedding embedding vector(1536)

Essential Extensions

-- UUID generation CREATE EXTENSION IF NOT EXISTS "uuid-ossp"; -- or use gen_random_uuid() (built-in in PG13+)

-- Cryptographic functions CREATE EXTENSION IF NOT EXISTS pgcrypto;

-- Vector similarity search CREATE EXTENSION IF NOT EXISTS vector;

-- Full-text search dictionaries CREATE EXTENSION IF NOT EXISTS unaccent;

-- Statistical functions CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- Spatial data CREATE EXTENSION IF NOT EXISTS postgis;

Performance Checklist

  • Index foreign keys

  • Use partial indexes for filtered queries

  • Configure shared_buffers (25% RAM)

  • Set random_page_cost = 1.1 for SSDs

  • Enable pg_stat_statements

  • Regular VACUUM ANALYZE

  • Use EXPLAIN ANALYZE for slow queries

  • Consider partitioning for tables > 100M rows

  • Use connection pooling (PgBouncer)

  • Monitor with pg_stat_activity

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