knowledge-base/POSTGRESQL.en.md

🐘 PostgreSQL

Overview

PostgreSQL is the most advanced open-source relational database with emphasis on extensibility, SQL standards, and reliability. Development since 1996, strong community, active release cycle (major version every year).

Architecture

Process model

Postmaster (supervisor)
    ├── Backend process (1 per connection)
    ├── WAL writer
    ├── Checkpointer
    ├── Autovacuum launcher
    ├── Stats collector
    ├── Logical replication launcher
    └── Archiver (WAL archiving)

Each connection = its own OS process (not thread). Advantage: isolation, stability. Disadvantage: higher memory footprint with thousands of connections → connection pooler required (PgBouncer).

MVCC (Multi-Version Concurrency Control)

Each transaction sees a snapshot of data from the moment it started. Old row versions (tuples) remain in the table:

• INSERT creates a new tuple with xmin = current_xid
• DELETE marks tuple with xmax = current_xid (doesn't disappear immediately)
• UPDATE = DELETE old + INSERT new
• VACUUM physically deletes tuples older than the oldest active snapshot

VACUUM and autovacuum

Parameter	Description	Default
autovacuum_vacuum_threshold	Min. dead rows to trigger vacuum	50
autovacuum_vacuum_scale_factor	% of table as threshold	0.2 (20%)
autovacuum_analyze_threshold	Min. changed rows for ANALYZE	50
autovacuum_vacuum_cost_limit	Limits I/O of vacuum (prevents load)	200
autovacuum_naptime	Interval between checks	1 min
deadlock_timeout	Deadlock detection	1 s

Signs of insufficient vacuum: table growth (bloat), degraded index scan performance, XID wraparound hazard.

WAL (Write-Ahead Log)

Append-only log of all changes for crash recovery and replication:

wal_level = replica                 # or logical
archive_mode = on
archive_command = 'aws s3 cp %p s3://backups/pg-wal/%f'

PITR (Point-In-Time Recovery):

1. Restore base backup (pg_basebackup)
2. Replay WAL archives up to target time
3. recovery_target_time = '2026-06-03 10:30:00 UTC'

Replication slots

• Physical — guarantees WAL is not deleted by master until replica consumes it
• Logical — for logical replication (selective tables, data transformation)
• Risk: if replica fails, WAL grows on disk (disk full)
• Monitoring: pg_replication_slots, pg_stat_replication

Configuration

Main files (per Obe & Hsu):

• postgresql.conf — memory, network, logging, storage
• pg_hba.conf — access privileges
• pg_ident.conf — OS user to PostgreSQL role mapping

AI-Ready PostgreSQL 18

(Kumar, Linster, 2026) — PostgreSQL 18 as a unified platform for transactions, analytics, and AI:

Area	Technique
Vectors	pgvector — embeddings directly in table rows
Hybrid pattern	Semantic recall → SQL filtering
LLM integration	PostgreSQL + MCP (Model Context Protocol)
Embedding pipeline	Batch and stream embedding generation

Hybrid query:

SELECT p.*, pm.name
FROM products p
JOIN product_embeddings pe ON p.id = pe.product_id
WHERE pe.embedding <-> '[0.1, 0.3, ...]' < 0.8
  AND p.in_stock = true
  AND p.price < 100.00
ORDER BY pe.embedding <-> '[0.1, 0.3, ...]'
LIMIT 10;

Extensions

Extension	Purpose
pgvector	Vector search for AI/embeddings
PostGIS	Geographic data, spatial queries
pg_stat_statements	Query performance monitoring
pg_duckdb	Analytical queries (DuckDB engine inside PG)
pg_search	Full-text and hybrid search
pg_cron	DB job scheduling
citus	Horizontal scaling (sharding)
timescaledb	Time-series optimization
pgaudit	Audit logging

Connection pooling

Pooler	Type	Protocol
PgBouncer	Proxy (transaction/session)	PostgreSQL wire
Odyssey	Proxy (multithreaded)	PostgreSQL wire
pgpool-II	Proxy (replication, load balancing)	PostgreSQL wire
RDS Proxy	Managed proxy (AWS)	PostgreSQL wire

PgBouncer modes:

• Session pooling — connection held for entire application session → overhead
• Transaction pooling — connection returned after transaction completes → more efficient (requires statelessness)

Recommendations — where PostgreSQL is better

Area	PostgreSQL	Competition	Why PG
Extensibility	Extensions, custom types, operators, index methods	MySQL limited	Can add anything from vectors to full-text in DB
SQL standard	Closest to ANSI SQL	MySQL deviations (GROUP BY, ALTER TABLE)	Portability, fewer surprises
Geospatial data	PostGIS (gold standard GIS)	MySQL GIS (limited)	Only real open-source choice for GIS
Consistency	SSI serializable, foreign keys, CHECK, exclusions	MySQL MyISAM no FK, InnoDB only RC	Suitable for financial and critical systems
Concurrent read/write	MVCC without reader/writer blocking	MySQL InnoDB reader blocks writer (and vice versa) in older versions	Better read scalability
AI/vectors	pgvector natively in DB	Separate vector DB (increased latency)	Hybrid queries in single SQL
License	PostgreSQL license (MIT-like)	MySQL dual license (Oracle)	No vendor lock-in

When to use PostgreSQL

• Enterprise applications — require ACID, referential integrity, complex transactions
• Geographic systems — GIS, map applications, location services
• Financial systems — accounting, banking, compliance (audit logging, SSI)
• AI / RAG applications — hybrid vector + relational queries in one DB
• Analytics on relational data — pg_duckdb, materialized views, window functions
• Multi-tenant applications — row-level security, schemas per tenant

PostgreSQL licensing

Variant	License	Price	Restrictions
PostgreSQL	PostgreSQL license (MIT-like)	$0	None — can use, modify, distribute in commercial products. No "commercial license" needed
Amazon Aurora PostgreSQL	Proprietary (AWS)	~$0.10-1.00/hour	AWS managed, PostgreSQL compatible. AWS may use PG code thanks to PostgreSQL license
YugabyteDB	Apache 2.0	$0 (core)	PostgreSQL compatible distributed SQL, built on PG query layer
TimescaleDB	Apache 2.0 (community) / Timescale License (enterprise)	$0 (community)	Time-series extensions for PostgreSQL. Enterprise: tiered storage, compression, multi-node

Key point: The PostgreSQL license is one of the most liberal — it allows cloud providers (AWS, GCP, Azure) to offer PostgreSQL as a managed service without restrictions. This is different from MongoDB (SSPL) and Redis (RSALv2). Thanks to this, PostgreSQL has the broadest cloud support of any database.

Impact on choice: No license risk, no vendor lock-in, no hidden costs. PostgreSQL is a safe choice for any project.

When to use something else

• Simple web / blog → SQLite (lighter in embedded scenarios)
• High-throughput key-value → Redis (order of magnitude lower latency)
• Time-series at massive scale → TimescaleDB, InfluxDB
• Globally distributed data → CockroachDB, Spanner
• Full-text search primarily → Elasticsearch

Sources

References, books, and standards: sources/databases/sources.md

Recommended reading

Book	Authors	ISBN	Description
PostgreSQL: Up and Running (3rd ed.)	Regina Obe, Leo Hsu	978-1491962935	Practical guide to administration, configuration, and extensions
AI-Ready PostgreSQL 18	Kumar, Linster	—	PostgreSQL as unified platform for AI workloads

Last revision: 2026-06-03