PostgreSQL or MySQL ??which is better?

Both are production-grade databases used at massive scale. PostgreSQL is more feature-rich (better JSON, full-text search, custom types, window functions, CTEs) and stricter about SQL standards. MySQL has faster reads for simple queries, simpler replication setup, and broader hosting support. For greenfield projects, PostgreSQL is usually the better default.

Which is faster, PostgreSQL or MySQL?

It depends on the workload. MySQL (InnoDB) is generally faster for simple read-heavy workloads with straightforward queries. PostgreSQL is faster for complex queries with JOINs, aggregations, and analytical workloads. For most web applications, both perform similarly ??indexing strategy and query design matter far more than the engine choice.

Can I switch from MySQL to PostgreSQL later?

Yes, but it requires migration work. SQL dialects differ (AUTO_INCREMENT vs SERIAL/IDENTITY, LIMIT vs FETCH FIRST, string functions). Tools like pgloader can automate data migration but schema translation is manual. Plan the migration carefully ??feature differences in JSON, full-text search, and data types often require application changes.

Which databases do major platforms support?

Both are widely supported. AWS: RDS for both, Aurora (MySQL+PostgreSQL compatible). Google Cloud: Cloud SQL for both. Azure: Azure Database for both. Heroku, Railway, Render, Supabase: PostgreSQL. PlanetScale: MySQL-compatible. Neon, Xata, CockroachDB: PostgreSQL-compatible.

PostgreSQL vs MySQL | Deep Comparison | When to Use Each

2026년 4월 7일 · 18분 읽기 · 수정 2026년 4월 7일 intermediate tutorial

이 글의 핵심

PostgreSQL and MySQL are both excellent relational databases, but they excel in different scenarios. This guide covers concrete differences in types, JSON, full-text search, locking, and replication ??so you can make the right choice for your project.

Quick Decision Matrix

Use PostgreSQL when:
  ??Complex queries with JOINs and aggregations
  ??JSON/JSONB document storage alongside relational data
  ??Full-text search without external engine
  ??Custom data types, enums, arrays
  ??Geospatial data (PostGIS extension)
  ??Strict SQL standard compliance matters
  ??Strong ACID requirements with complex transactions

Use MySQL when:
  ??Simple read-heavy workloads
  ??Existing MySQL infrastructure
  ??PlanetScale (MySQL-compatible serverless DB)
  ??WordPress, Drupal, or other MySQL-native CMS
  ??Team expertise is MySQL-based
  ??Simpler replication/cluster setup is a priority

Executive summary

If you are choosing a relational engine for a new system, the honest answer is that both PostgreSQL and MySQL are credible choices at scale, but they optimize for different pain points. PostgreSQL tends to win when the database must behave like a programmable analytical engine inside your stack: rich types, standards?�oriented SQL, strong indexing for semi?�structured data, and extensibility. MySQL (with InnoDB) tends to win when the workload is dominated by simple, predictable primary?�key and indexed lookups on a stack where operational familiarity and host ecosystem breadth matter as much as raw SQL feature depth. The sections below are written to be comparative, not competitive: pick the tool whose failure modes and operational model match your team and product.

Dimension	PostgreSQL (typical strengths)	MySQL 8 + InnoDB (typical strengths)
SQL & analytics	Window functions, CTEs, `FILTER`, `LATERAL`, rich planner	Solid core SQL; fewer niceties; optimizer improves steadily
Semi?�structured data	`JSONB`, GIN/GiST, powerful containment	`JSON` type + generated columns; path indexes; less flexible
Full?�text	Tsearch2 / FTS with dictionaries, weights, rank	`FULLTEXT` indexes; good for ?�good enough??search
Extensibility	Extensions (PostGIS, `pg_trgm`, etc.)	Server plugins exist; different culture (fewer in?�DB extensions)
Ops culture	Patroni, logical replication, strong HA patterns	Mature async replication, Group Replication, wide hosting

Performance: OLTP vs OLAP and what benchmarks actually show

Workload shape matters more than the logo on the box. In practice, OLTP systems are dominated by short transactions, B?�tree point lookups, and a stable working set in buffer cache. Both engines perform well when indexes match access paths and the application avoids ?�chatty??SQL. OLAP and reporting?�large scans, complex joins, windowed aggregates?�tend to favor PostgreSQL?�s query planner and feature set, though either database can be tuned for read replicas and separate reporting schemas.

Public benchmarks (for example, TPC?�C-style and sysbench tests you will find in vendor blogs) are useful only as a sanity check because they are sensitive to hardware, buffer pool size, fsync policy, and tuning. A representative pattern in published comparisons is that MySQL can lead on narrow, read?�heavy, single?�row microbenchmarks when the dataset is small and hot in memory, while PostgreSQL more often leads on query shapes that require sophisticated joins, sorting, and aggregation?�but your indexes and statistics will dominate either result.

Scenario	What to measure	Fair interpretation
Key?�value?�like reads	Point selects by PK, p95/p99 latency	Often similar; verify connection pooling and client batching
Write throughput	Sustained inserts/updates, checkpoint stalls	Both need tuning (`checkpoint_timeout`, `innodb_flush_*`, autovacuum)
Analytical SQL	Multi?�way joins, large sorts, CTEs	PostgreSQL frequently easier to express and tune; consider DuckDB/ClickHouse for heavy OLAP
Mixed JSON + SQL	Filter + sort on JSON paths	PostgreSQL `JSONB` + GIN is usually less painful at scale

Rule of thumb for fairness: if performance is a gate, build a benchmark clone of your top five queries and your top two write patterns on identical hardware, then compare p95 and disk bytes written, not a single ?�QPS??headline number.

Native arrays, constraints, and modeling flexibility

PostgreSQL ships first?�class array types and rich constraints (including EXCLUDE for non?�overlapping ranges) that are awkward or impossible in stock MySQL. MySQL 8.0+ closes many gaps (check constraints, functional indexes, better JSON functions), but array semantics and exclusion constraints still push people toward PostgreSQL for complex domain modeling (scheduling, tagging, and multi?�valued attributes without join tables). Neither choice is ?�wrong??if you can model the domain cleanly; the cost is where it shows up: join complexity in MySQL vs. more PostgreSQL?�specific knowledge on the team.

Feature	PostgreSQL	MySQL 8 (InnoDB)
Arrays	Native `text[]`, `int[]`, operators, GIN	Workarounds: JSON, separate tables, or strings
Range / exclusion	`tstzrange`, `EXCLUDE USING gist`	Manual checks or triggers
Generated columns	`GENERATED` columns (stored)	`GENERATED` columns + functional indexes (good parity)
Check constraints	Long?�standing, widely used	Supported (historically uneven adoption in apps)

Data Types Comparison

-- Auto-increment primary keys
-- MySQL
CREATE TABLE users (
  id INT AUTO_INCREMENT PRIMARY KEY,
  ...
);

-- PostgreSQL (modern way)
CREATE TABLE users (
  id INT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  ...
);

-- PostgreSQL (traditional, still works)
CREATE TABLE users (
  id SERIAL PRIMARY KEY,  -- shorthand for INTEGER + sequence
  ...
);

-- String types
-- MySQL
VARCHAR(255)    -- variable length, up to 255 chars
TEXT            -- up to 65KB
MEDIUMTEXT      -- up to 16MB
LONGTEXT        -- up to 4GB

-- PostgreSQL
VARCHAR(255)    -- variable length (rarely needed ??use TEXT)
TEXT            -- unlimited length (no performance difference from VARCHAR)
-- PostgreSQL has NO size limit on TEXT ??use it everywhere

-- PostgreSQL extra types MySQL lacks:
CITEXT          -- case-insensitive text (extension)
UUID            -- native UUID type (MySQL stores as VARCHAR)
INET            -- IP address
MACADDR         -- MAC address
MONEY           -- monetary amounts
BYTEA           -- binary data (MySQL: BLOB)

-- Temporal types
-- MySQL
DATETIME        -- YYYY-MM-DD HH:MM:SS (no timezone)
TIMESTAMP       -- stored as UTC, displayed in server timezone
DATE, TIME

-- PostgreSQL
TIMESTAMP WITH TIME ZONE (TIMESTAMPTZ)  -- stores UTC, shows with offset
TIMESTAMP WITHOUT TIME ZONE             -- no timezone info
DATE, TIME, INTERVAL                    -- interval: '2 hours 30 minutes'

-- PostgreSQL is stricter: use TIMESTAMPTZ for any production timestamp
-- MySQL TIMESTAMP has 2038 problem (32-bit Unix timestamp)

JSON Support

PostgreSQL’s JSONB is a significant advantage for mixed relational/document storage.

-- PostgreSQL JSONB
CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  name TEXT NOT NULL,
  attributes JSONB            -- Binary JSON ??indexed, queryable
);

-- Insert
INSERT INTO products (name, attributes) VALUES
  ('Laptop', '{"brand": "Dell", "specs": {"ram": 16, "storage": 512}, "tags": ["gaming", "work"]}');

-- Query JSONB fields
SELECT name, attributes->>'brand' AS brand
FROM products
WHERE attributes->>'brand' = 'Dell';

-- Deep path access
SELECT name, attributes->'specs'->>'ram' AS ram_gb
FROM products
WHERE (attributes->'specs'->>'ram')::int > 8;

-- Array containment
SELECT * FROM products
WHERE attributes->'tags' ? 'gaming';      -- contains key/element

SELECT * FROM products
WHERE attributes @> '{"specs": {"ram": 16}}';  -- contains subset

-- Index on JSONB ??critical for performance
CREATE INDEX idx_products_brand ON products USING GIN (attributes);
-- Or for specific field:
CREATE INDEX idx_products_brand_btree ON products ((attributes->>'brand'));

-- Update specific field (without replacing entire JSON)
UPDATE products
SET attributes = jsonb_set(attributes, '{specs, ram}', '32')
WHERE id = 1;

-- MySQL JSON (added in 5.7, improved in 8.0)
CREATE TABLE products (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(255),
  attributes JSON
);

-- Query
SELECT name, JSON_EXTRACT(attributes, '$.brand') AS brand
FROM products
WHERE JSON_EXTRACT(attributes, '$.brand') = '"Dell"';

-- Shorthand
SELECT name, attributes->>'$.brand' AS brand
FROM products;

-- MySQL lacks JSONB ??JSON stored as text, parsed on every access
-- No GIN index support ??only functional indexes on specific paths
CREATE INDEX idx_brand ON products ((attributes->>'$.brand'));

Winner: PostgreSQL ??JSONB is stored binary (faster reads), supports GIN indexes (fast containment queries), and has richer operators.

Full-Text Search

-- PostgreSQL built-in FTS
CREATE TABLE articles (
  id SERIAL PRIMARY KEY,
  title TEXT,
  body TEXT,
  search_vector TSVECTOR GENERATED ALWAYS AS (
    setweight(to_tsvector('english', COALESCE(title, '')), 'A') ||
    setweight(to_tsvector('english', COALESCE(body, '')), 'B')
  ) STORED
);

CREATE INDEX idx_articles_search ON articles USING GIN (search_vector);

-- Search
SELECT id, title,
  ts_rank(search_vector, query) AS rank
FROM articles,
  to_tsquery('english', 'javascript & (tutorial | guide)') AS query
WHERE search_vector @@ query
ORDER BY rank DESC;

-- Highlight matching terms
SELECT title,
  ts_headline('english', body, to_tsquery('javascript'), 'MaxWords=20') AS excerpt
FROM articles
WHERE search_vector @@ to_tsquery('english', 'javascript');

-- MySQL FULLTEXT (simpler, less powerful)
CREATE TABLE articles (
  id INT AUTO_INCREMENT PRIMARY KEY,
  title VARCHAR(255),
  body TEXT,
  FULLTEXT INDEX idx_search (title, body)
);

-- Boolean mode search
SELECT id, title,
  MATCH(title, body) AGAINST('javascript tutorial' IN BOOLEAN MODE) AS score
FROM articles
WHERE MATCH(title, body) AGAINST('+javascript +tutorial' IN BOOLEAN MODE)
ORDER BY score DESC;

-- MySQL FULLTEXT limitations:
-- Minimum word length (default 4 chars)
-- No phrase proximity search
-- English-only stemming
-- No custom ranking control

Winner: PostgreSQL ??More powerful with configurable dictionaries, proximity search, rich ranking, and the ability to index JSONB alongside text.

Transactions and Locking

-- PostgreSQL: MVCC (Multi-Version Concurrency Control)
-- Readers never block writers, writers never block readers

-- Isolation levels
BEGIN TRANSACTION ISOLATION LEVEL READ COMMITTED;  -- default
BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ;
BEGIN TRANSACTION ISOLATION LEVEL SERIALIZABLE;    -- strictest

-- PostgreSQL serializable actually detects and prevents anomalies
-- (MySQL serializable uses locks, not true serializability)

-- Explicit row locking
SELECT * FROM orders WHERE id = 1 FOR UPDATE;           -- lock for update
SELECT * FROM orders WHERE id = 1 FOR UPDATE SKIP LOCKED; -- skip locked rows (queue pattern)
SELECT * FROM orders WHERE id = 1 FOR SHARE;            -- shared lock

-- MySQL InnoDB also uses MVCC
-- But has some differences:

-- Phantom reads in REPEATABLE READ:
-- MySQL prevents them with gap locks (can cause deadlocks)
-- PostgreSQL prevents them with MVCC snapshot (no gap locks)

-- Table locking:
-- MySQL MyISAM: table-level locks (avoid for writes)
-- MySQL InnoDB: row-level locks (same as PostgreSQL)

ACID and ?�strictness??in the real world

Atomicity, consistency, isolation, durability are not marketing labels?�they are the contract your application leans on when you say ?�one logical operation.??InnoDB and PostgreSQL both offer durable commits with MVCC-style read isolation, but the details of isolation behavior and locking differ enough that you should not assume one engine?�s REPEATABLE READ means the same as the other?�s in edge cases (phantom reads, write skew, and deadlock patterns).

Topic	PostgreSQL (practical note)	MySQL 8 + InnoDB (practical note)
Default isolation	`READ COMMITTED` (configurable)	`REPEATABLE READ` in InnoDB (snapshot semantics with gap locks)
?�Strong??serializability	`SERIALIZABLE` + SSI?�style protection (fails fast on dangerous patterns)	`SERIALIZABLE` exists; verify behavior for your specific edge cases in tests
Partial writes	Statement-level atomicity; multi?�stmt transactions as expected with proper client usage	InnoDB groups row changes; avoid mixing transactional engines in one statement
*Replication vs ?�truth??	Async replicas can be stale; use sync rep or read?�your?�writes strategies	Classic async topologies: measure replica lag; avoid assuming instant reads on replicas
Foreign keys	Enforced with clear errors	`FOREIGN_KEY_CHECKS` and engine choice must be right (InnoDB)

A fair take: if your app truly requires serious isolation proofs, the database choice is necessary but not sufficient?�you still need clear transaction boundaries, idempotency, and test coverage for race conditions. PostgreSQL?�s richer isolation documentation and SKIP LOCKED queue patterns are frequently cited in systems that process ordered work; MySQL is widely proven for very high simple read/write QPS with careful tuning and pooling.

Window Functions and CTEs

Both support these, but PostgreSQL has broader support.

-- Window functions (both support)
SELECT
  name,
  department,
  salary,
  AVG(salary) OVER (PARTITION BY department) AS dept_avg,
  RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank,
  LAG(salary, 1) OVER (ORDER BY hire_date) AS prev_salary
FROM employees;

-- CTEs (both support)
WITH ranked_sales AS (
  SELECT
    salesperson_id,
    SUM(amount) AS total,
    RANK() OVER (ORDER BY SUM(amount) DESC) AS rank
  FROM sales
  GROUP BY salesperson_id
)
SELECT * FROM ranked_sales WHERE rank <= 10;

-- Recursive CTEs (both support)
WITH RECURSIVE org_chart AS (
  SELECT id, name, manager_id, 0 AS depth
  FROM employees WHERE manager_id IS NULL
  UNION ALL
  SELECT e.id, e.name, e.manager_id, depth + 1
  FROM employees e
  JOIN org_chart o ON e.manager_id = o.id
)
SELECT * FROM org_chart ORDER BY depth;

-- PostgreSQL extras:
-- FILTER clause in aggregations
SELECT
  COUNT(*) FILTER (WHERE status = 'active') AS active_count,
  COUNT(*) FILTER (WHERE status = 'inactive') AS inactive_count
FROM users;

-- LATERAL joins
SELECT u.name, recent.order_id
FROM users u,
LATERAL (
  SELECT order_id FROM orders WHERE user_id = u.id
  ORDER BY created_at DESC LIMIT 3
) AS recent;

Indexes

-- Standard B-tree index (both)
CREATE INDEX idx_users_email ON users (email);

-- Partial index ??PostgreSQL (index subset of rows)
CREATE INDEX idx_active_users ON users (email) WHERE active = true;
-- Only indexes active users ??smaller, faster for queries with WHERE active = true

-- Composite index
CREATE INDEX idx_orders_user_date ON orders (user_id, created_at DESC);

-- GIN index ??PostgreSQL (arrays, JSONB, full-text)
CREATE INDEX idx_tags ON posts USING GIN (tags);         -- array column
CREATE INDEX idx_meta ON posts USING GIN (metadata);     -- JSONB column

-- GiST index ??PostgreSQL (geometric, range types, full-text)
CREATE INDEX idx_location ON places USING GIST (coordinates);  -- PostGIS

-- MySQL-specific
-- FULLTEXT index
CREATE FULLTEXT INDEX idx_search ON articles (title, body);

-- Invisible index (MySQL 8.0) ??test impact without dropping
ALTER TABLE users ALTER INDEX idx_email INVISIBLE;

-- Both: covering index (include extra columns to avoid table lookups)
-- PostgreSQL:
CREATE INDEX idx_orders_covering ON orders (user_id) INCLUDE (status, total);
-- MySQL:
CREATE INDEX idx_orders_covering ON orders (user_id, status, total);

Replication

PostgreSQL replication options:
  Streaming replication (built-in)
    - Primary ??one or more standbys
    - Synchronous or asynchronous
    - Standbys can be used for read queries
  Logical replication (PostgreSQL 10+)
    - Table-level, row-level filtering
    - Replicate to different PostgreSQL versions
  Third-party: Patroni (HA), pgpool-II (connection pooling + HA)

MySQL replication options:
  Classic async replication (simple, widely used)
  Semi-synchronous replication (primary waits for at least one replica)
  Group Replication (multi-primary, built-in)
  InnoDB Cluster (MySQL Shell + Group Replication)
  ProxySQL (connection routing to primary/replicas)

-- PostgreSQL: check replication status
SELECT
  client_addr,
  state,
  sent_lsn - write_lsn AS write_lag,
  sent_lsn - flush_lsn AS flush_lag,
  sent_lsn - replay_lsn AS replay_lag
FROM pg_stat_replication;

-- MySQL: check replication status
SHOW REPLICA STATUS\G
-- Shows: Seconds_Behind_Source, Replica_IO_Running, Replica_SQL_Running

High availability, failover, and what ?�zero downtime??really costs

Neither database magically eliminates outages. The difference is in patterns your team is comfortable running. PostgreSQL is often paired with Patroni, repmgr, or managed RDS/Aurora/Cloud SQL/Neon-style services that provide automated failover, backups, and PITR. MySQL has decades of experience with async primaries and replicas, ProxySQL routing, and InnoDB Cluster / Group Replication for multi?�primary (with operational complexity). A candid comparison: MySQL?�s replication ecosystem is ubiquitous; PostgreSQL?�s logical replication is powerful for selective upgrades and data movement but requires explicit monitoring of slots and LSNs.

HA need	Common PostgreSQL approach	Common MySQL approach
Automatic failover	Patroni, cloud HA agents	Orchestrator, cloud HA, InnoDB Cluster tooling
Read scaling	Hot standbys, logical subscribers	One primary + N replicas, routes via proxy
Geographic distribution	Logical replication, careful conflict rules	Async replication, multi?�region with lag awareness
Online schema changes	`pg_repack`, managed blue/green, careful locks	`pt-online-schema-change`, `gh-ost` culture is strong in MySQL shops

Fair warning: the fastest way to a bad day is a split?�brain or a replica that answers stale reads as if fresh. If you are building payments or inventory, invest in explicit session routing and monitor lag p99.

Scalability, sharding, and when to look beyond a single node

Vertical scaling (bigger instance, better disks, more RAM) remains the first lever for most OLTP. Read replicas help read?�heavy, eventually?�consistent use cases. Sharding (partitioning data across many primaries) is a product decision as much as a database decision. Application?�level sharding (Vitess, custom tenant keys) is common in large MySQL estates; in PostgreSQL, Citus and distributed patterns exist but are not universal defaults.

Path	When it fits	Cost / trade?�off
Bigger node + good indexes	Sub?�ms reads, working set in memory	Simpler ops; eventual ceiling on CPU/IO
Read replicas	Dashboards, search indexes, report queries	Staleness, routing complexity, duplicate cache invalidation work
Application sharding	Very large, tenant?�isolated data	Migrations, cross?�shard queries, heavy engineering
Specialized store	Pure OLAP, time?�series at huge scale	Second system to operate (often worth it)

A balanced judgment: if you are a typical web product, a single well?�tuned instance plus replicas takes you very far. If you know you will outgrow it, start with a sharding key in your data model even before you split physical nodes?�it is painful to retro?�fit.

Ecosystem: drivers, hosting, and operations

Layer	PostgreSQL	MySQL / MariaDB
Drivers (Node, Go, etc.)	`pg` / `pgx` / `libpq` family	`mysql2`, official connectors; massive examples online
ORMs	Prisma, Drizzle, SQLAlchemy, Rails?�first?�class	Same ORMs, excellent Laravel/WordPress heritage
Migrations	`sqitch`, `flyway`, `liquibase`, Prisma Migrate	Same; MySQL?�s DDL + locking behavior differs (measure on large tables)
Managed cloud	RDS, Aurora PG, Cloud SQL, Azure PG, Neon, etc.	RDS, Aurora MySQL, Cloud SQL, PlanetScale (MySQL protocol)
Observability	`pg_stat_statements`, `auto_explain`, great logs	`performance_schema`, `EXPLAIN ANALYZE` (8.0+), slow log

This is an ecosystem tie for many teams: pick what your SREs already run at night. The ecosystem advantage often goes to the engine your hosting provider and framework defaults optimize for.

Use?�case?�based selection (without tribalism)

You are building??	Sensible default	Why it is a reasonable bias
B2B SaaS with complex reporting	PostgreSQL	Rich SQL, `JSONB`, analytics?�friendly
High?�QPS, simple queries, existing MySQL SREs	MySQL 8 (InnoDB)	Mature playbooks, huge community corpus
Geospatial	PostgreSQL + PostGIS	Industry standard for GIS in SQL
WordPress / PHP CMS	MySQL / MariaDB	Ecosystem and hosting defaults
Serverless/edge	Whichever the vendor nails (e.g. Neon, PlanetScale)	Vendor product can matter more than engine theology

If two cells both seem true, use the one your team can operate safely. A slightly ?�weaker??database with strong backups and on?�call runbooks beats a ?�stronger??engine with nobody watching replication lag.

Production experience (patterns that survive audits)

What actually breaks in production is rarely a missing SQL function?�it is connection storms, long transactions holding locks, N+1 queries, and unbounded background jobs hammering the primary. I have seen PostgreSQL instances shine in organizations that invested in pgbouncer pool sizing, regular VACUUM health checks, and pg_stat_statements to kill regressions. I have seen MySQL fleets carry enormous traffic with disciplined schema discipline (proper InnoDB, no accidental MyISAM), careful replication lag dashboards, and gh-ost for low?�drama migrations. Both engines can surprise you after major version upgrades?�always run replay tests and check planner changes, not just ?�it boots.?? A fair lesson: standardize on migrations in CI, synthetic canaries for your hottest queries, and automated restore drills. Those practices pay for either database?�and they pay for your sleep.

Performance Tips

-- EXPLAIN ANALYZE (PostgreSQL)
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT u.name, COUNT(o.id) AS order_count
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.created_at > NOW() - INTERVAL '30 days'
GROUP BY u.id;

-- Key things to look for:
-- Seq Scan ??missing index
-- Hash Join vs Nested Loop vs Merge Join
-- Actual rows vs Estimated rows (large diff = stale statistics)
-- Buffers: hit vs read (cache hit ratio)

-- Update statistics
ANALYZE users;  -- Update table statistics
VACUUM ANALYZE; -- Reclaim space + update statistics

-- EXPLAIN in MySQL
EXPLAIN FORMAT=JSON
SELECT u.name, COUNT(o.id)
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.created_at > DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY u.id;

-- Check index usage
SHOW INDEX FROM users;
SELECT * FROM information_schema.STATISTICS WHERE table_name = 'users';

-- Connection pooling (critical for both)
-- PostgreSQL: PgBouncer
-- MySQL: ProxySQL or MySQL Router

-- PostgreSQL: table bloat (MVCC creates dead tuples)
-- Regular VACUUM prevents bloat
ALTER TABLE orders SET (autovacuum_vacuum_scale_factor = 0.05);  -- More frequent vacuum for large tables

-- PostgreSQL: check table size
SELECT
  relname AS table_name,
  pg_size_pretty(pg_total_relation_size(relid)) AS total_size,
  pg_size_pretty(pg_relation_size(relid)) AS table_size,
  pg_size_pretty(pg_indexes_size(relid)) AS index_size
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC;

Extensions (PostgreSQL Only)

PostgreSQL’s extension system is a significant differentiator.

-- List available extensions
SELECT name, default_version, comment FROM pg_available_extensions ORDER BY name;

-- PostGIS ??geospatial data
CREATE EXTENSION postgis;
SELECT ST_Distance(
  ST_GeographyFromText('POINT(-122.4194 37.7749)'),  -- San Francisco
  ST_GeographyFromText('POINT(-118.2437 34.0522)')   -- Los Angeles
) / 1000 AS distance_km;

-- pg_trgm ??trigram similarity (fuzzy search)
CREATE EXTENSION pg_trgm;
CREATE INDEX idx_name_trgm ON users USING GIN (name gin_trgm_ops);
SELECT name FROM users WHERE name % 'Alce';  -- typo-tolerant search
SELECT name, similarity(name, 'Alice') AS sim FROM users ORDER BY sim DESC LIMIT 10;

-- uuid-ossp ??UUID generation
CREATE EXTENSION "uuid-ossp";
INSERT INTO items (id, name) VALUES (uuid_generate_v4(), 'Widget');

-- pg_stat_statements ??query performance tracking
CREATE EXTENSION pg_stat_statements;
SELECT query, calls, mean_exec_time, total_exec_time
FROM pg_stat_statements
ORDER BY total_exec_time DESC LIMIT 10;

-- timescaledb ??time-series data
CREATE EXTENSION timescaledb;
SELECT create_hypertable('metrics', 'time');

Node.js Integration

// PostgreSQL with node-postgres (pg)
import { Pool } from 'pg';

const pool = new Pool({
  connectionString: process.env.DATABASE_URL,
  max: 20,
  idleTimeoutMillis: 30000,
});

const result = await pool.query(
  'SELECT * FROM users WHERE email = $1',  // Parameterized query (prevents SQL injection)
  ['[email protected]']
);

// MySQL with mysql2
import mysql from 'mysql2/promise';

const pool = mysql.createPool({
  uri: process.env.DATABASE_URL,
  waitForConnections: true,
  connectionLimit: 20,
});

const [rows] = await pool.execute(
  'SELECT * FROM users WHERE email = ?',   // ? for parameters
  ['[email protected]']
);

// Both work with ORMs ??Prisma supports both
// prisma/schema.prisma
datasource db {
  provider = "postgresql"  // or "mysql"
  url      = env("DATABASE_URL")
}

[Prisma ORM Complete Guide](/en/blog/prisma-complete-guide/
[Redis Complete Guide](/en/blog/redis-advanced-guide/
[Docker Compose Complete Guide](/en/blog/docker-compose-complete-guide/

On?�page FAQ (quick links to details above)

When should I apply this in production?

A. When you are choosing an engine, designing replication, or planning JSON and search. Use the decision matrix and use?�case table to align the database with your access patterns, not a generic blog ranking.

What should I read next?

A. Follow the Related posts at the end of this article for Prisma, Redis, and Docker Compose, which are the usual companions to a production SQL deployment.

Where can I go deeper on internals?

A. The official PostgreSQL documentation and MySQL Reference Manual are authoritative. For performance, prioritize running EXPLAIN (ANALYZE) and measuring p95/p99 on your own data.

Search keywords: PostgreSQL, MySQL, SQL, database, backend, performance, replication, OLTP, OLAP, JSONB, InnoDB.

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