# 🐘 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

```text
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:

```conf
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**:
```sql
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.en.md](sources/databases/sources.en.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*