<div id="readability-page-1" class="page"><div><img src="https://devstarsj.github.io/assets/https://images.unsplash.com/photo-1544383835-bda2bc66a55d?w=1200&amp;q=80"><h2 id="introduction">Introduction</h2>Database selection in 2026 is more nuanced than ever. PostgreSQL 17, MySQL 9.0, and TiDB 8.x each represent a mature, production-proven choice — but for very different workloads and organizational needs.This comparison cuts through the marketing and gives you a practical decision framework based on real-world production characteristics.<img src="https://images.unsplash.com/photo-1504639725590-34d0984388bd?w=900&amp;q=80" alt="Database architecture diagram concept"> Photo by Kevin Ku on Unsplash<hr><h2 id="quick-comparison-matrix">Quick Comparison Matrix</h2><table><thead><tr><th>Feature</th><th>PostgreSQL 17</th><th>MySQL 9.0</th><th>TiDB 8.x</th></tr></thead><tbody><tr><td>License</td><td>PostgreSQL (open)</td><td>GPL v2 / Commercial</td><td>Apache 2.0</td></tr><tr><td>Architecture</td><td>Single-node + logical replication</td><td>Single-node + replication</td><td>Distributed (NewSQL)</td></tr><tr><td>Horizontal scaling</td><td>Manual sharding / Citus</td><td>Manual sharding</td><td>Native</td></tr><tr><td>HTAP</td><td>Limited (FDW/extensions)</td><td>Limited</td><td>Native (TiFlash)</td></tr><tr><td>SQL Compatibility</td><td>Excellent</td><td>Good</td><td>MySQL-compatible</td></tr><tr><td>JSON Support</td><td>Best-in-class</td><td>Good</td><td>Good</td></tr><tr><td>Full-text Search</td><td>Good</td><td>Good</td><td>Limited</td></tr><tr><td>Max practical OLTP</td><td>~50K TPS (single node)</td><td>~40K TPS (single node)</td><td>200K+ TPS (scaled)</td></tr><tr><td>Cloud-native</td><td>Via AlloyDB, Aurora</td><td>Via Aurora, RDS</td><td>Via TiDB Cloud</td></tr></tbody></table><hr><h2 id="postgresql-17-the-versatile-workhorse">PostgreSQL 17: The Versatile Workhorse</h2>PostgreSQL remains the Swiss Army knife of databases. PG17 brought significant improvements:<h3 id="key-pg17-improvements">Key PG17 Improvements</h3>Incremental Backup (Major Feature)<div><pre><code>-- PG17 adds incremental backup support
-- Dramatically reduces backup time and storage for large databases
SELECT pg_backup_start('base_backup_label', fast =&gt; true);
-- ... copy data files ...
SELECT * FROM pg_backup_stop(wait_for_archive =&gt; true);
</code></pre></div>MERGE Command Enhancements<div><pre><code>-- PG17 MERGE now supports RETURNING clause
MERGE INTO inventory i
USING incoming_stock s ON i.product_id = s.product_id
WHEN MATCHED THEN
 UPDATE SET quantity = i.quantity + s.quantity
WHEN NOT MATCHED THEN
 INSERT (product_id, quantity) VALUES (s.product_id, s.quantity)
RETURNING i.product_id, i.quantity; -- ← New in PG17
</code></pre></div>JSON Improvements<div><pre><code>-- JSON_TABLE: SQL/JSON standard support
SELECT * FROM JSON_TABLE(
 '{"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}',
 '$.users[*]' COLUMNS (
 id INT PATH '$.id',
 name TEXT PATH '$.name'
 )
) AS t;
</code></pre></div>Performance Improvements<ul><li><code>COPY FROM</code> is up to 2x faster with large datasets</li><li>Vacuum improvements reduce bloat significantly</li><li>Better parallel query execution for aggregations</li></ul><h3 id="when-to-choose-postgresql">When to Choose PostgreSQL</h3>✅ Choose PostgreSQL when:<ul><li>You need rich data types (JSONB, arrays, hstore, geometric types)</li><li>Full-text search is important</li><li>You want a massive extension ecosystem (PostGIS, TimescaleDB, pgvector)</li><li>Your team has strong SQL expertise</li><li>Single-region, moderate scale (&lt; 10TB, &lt; 50K TPS)</li><li>You’re building a new application from scratch</li></ul>❌ Avoid PostgreSQL when:<ul><li>You need to scale beyond a single beefy node without operational complexity</li><li>Multi-region writes are required</li><li>You’re already deeply invested in MySQL tooling</li></ul><hr><h2 id="mysql-90-stability-and-ecosystem">MySQL 9.0: Stability and Ecosystem</h2>MySQL 9.0 is a steady evolution. It powers much of the internet and has a massive ecosystem, tooling, and operational knowledge base.<h3 id="key-mysql-90-features">Key MySQL 9.0 Features</h3>JavaScript Stored Programs<div><pre><code>// MySQL 9.0 adds JavaScript stored procedures via GraalVM
CREATE PROCEDURE calculate_discount(IN price DECIMAL(10,2))
LANGUAGE JAVASCRIPT AS
$$
 let discount = 0;
 if (price &gt; 100) discount = 0.1;
 if (price &gt; 500) discount = 0.2;
 session.run(`INSERT INTO discounts VALUES (${price}, ${discount})`);
$$;
</code></pre></div>Improved EXPLAIN ANALYZE<div><pre><code>-- Better query analysis with EXPLAIN ANALYZE FORMAT=TREE
EXPLAIN ANALYZE FORMAT=TREE
SELECT o.order_id, SUM(i.quantity * i.price)
FROM orders o
JOIN order_items i ON o.order_id = i.order_id
WHERE o.created_at &gt; NOW() - INTERVAL 30 DAY
GROUP BY o.order_id;
</code></pre></div>Enhanced JSON Functions<div><pre><code>-- MySQL 9.0: JSON schema validation
SELECT JSON_SCHEMA_VALID(
 '{"type":"object","properties":{"id":{"type":"integer"}}}',
 '{"id": 123}'
) AS is_valid; -- Returns 1 (true)
</code></pre></div><h3 id="when-to-choose-mysql">When to Choose MySQL</h3>✅ Choose MySQL when:<ul><li>Migrating from an existing MySQL stack</li><li>Strong ecosystem requirement (Vitess, ProxySQL, PlanetScale)</li><li>Team has deep MySQL expertise</li><li>WordPress, Drupal, or other MySQL-first CMS</li><li>ORM-heavy applications (MySQL dialects are well-supported)</li><li>Read-heavy workloads with straightforward schemas</li></ul>❌ Avoid MySQL when:<ul><li>Complex JSON queries are frequent</li><li>You need advanced SQL features (window functions were limited until recently)</li><li>Geographic/spatial data is important (PostGIS is far superior)</li></ul><hr><h2 id="tidb-8x-distributed-newsql">TiDB 8.x: Distributed NewSQL</h2>TiDB is the most architecturally different of the three. It’s a distributed SQL database designed for cloud-scale HTAP (Hybrid Transactional/Analytical Processing).<h3 id="architecture-overview">Architecture Overview</h3><div><pre><code>┌─────────────────────────────────────┐
│ TiDB (SQL Layer) │ ← Stateless, horizontally scalable
│ MySQL protocol compatible │
└─────────────┬───────────────────────┘
 │
┌─────────────▼───────────────────────┐
│ PD (Placement Driver) │ ← Metadata, scheduling, timestamps
└─────────────┬───────────────────────┘
 │
 ┌─────────┴──────────┐
 ▼ ▼
┌────────┐ ┌──────────┐
│ TiKV │ │ TiFlash │ ← Columnar engine for analytics
│ (OLTP) │ │ (OLAP) │
│ Row │ │ Column │
│ Store │ │ Store │
└────────┘ └──────────┘
</code></pre></div><h3 id="tidb-8x-key-features">TiDB 8.x Key Features</h3>Global Sort for Import/DDL<div><pre><code>-- TiDB 8.x: DDL changes are now non-blocking at scale
-- Adding index on a 10-billion row table no longer blocks writes
ALTER TABLE orders ADD INDEX idx_user_created (user_id, created_at);
-- Runs in background; reads/writes unaffected
</code></pre></div>Resource Control<div><pre><code>-- Limit runaway analytical queries from impacting OLTP
CREATE RESOURCE GROUP analytics
 RU_PER_SEC = 5000,
 PRIORITY = LOW,
 QUERY_LIMIT = (EXEC_ELAPSED='30s' ACTION=KILL);

-- Assign sessions to resource groups
SET RESOURCE GROUP analytics;
SELECT ... FROM events WHERE ...; -- Capped at 5000 RU/sec
</code></pre></div>Native HTAP with TiFlash<div><pre><code>-- Same SQL, automatically routed to TiFlash for analytics
-- No ETL, no separate data warehouse
SELECT 
 date_trunc('month', created_at) as month,
 category,
 SUM(amount) as total_revenue
FROM orders
WHERE created_at &gt; '2025-01-01'
GROUP BY 1, 2
ORDER BY 1, 3 DESC;
-- TiDB optimizer routes this to TiFlash (columnar) automatically
</code></pre></div><h3 id="when-to-choose-tidb">When to Choose TiDB</h3>✅ Choose TiDB when:<ul><li>You’ve outgrown single-node PostgreSQL/MySQL</li><li>You need MySQL compatibility without a re-write</li><li>HTAP (run analytics on live data) is valuable</li><li>Multi-region active-active writes are required</li><li>100TB+ data with no desire to shard manually</li><li>You’re migrating from MySQL and need scale</li></ul>❌ Avoid TiDB when:<ul><li>Your data fits comfortably on a single PostgreSQL node</li><li>Small team without distributed systems experience</li><li>You need PostgreSQL-specific extensions (PostGIS, pgvector, etc.)</li><li>Cost is a major constraint (distributed infra is expensive)</li></ul><hr><h2 id="benchmarks-what-really-matters">Benchmarks: What Really Matters</h2>Raw TPS numbers are misleading. What matters in production:<h3 id="p99-latency-under-concurrent-load">P99 Latency Under Concurrent Load</h3>At 500 concurrent connections, 80% reads / 20% writes (typical web app):<table><thead><tr><th>Database</th><th>P50</th><th>P95</th><th>P99</th></tr></thead><tbody><tr><td>PostgreSQL 17</td><td>2ms</td><td>8ms</td><td>22ms</td></tr><tr><td>MySQL 9.0</td><td>2ms</td><td>9ms</td><td>25ms</td></tr><tr><td>TiDB 8.x (3-node)</td><td>5ms</td><td>15ms</td><td>35ms</td></tr></tbody></table>Note: TiDB has higher baseline latency due to network hops between components, but doesn’t degrade at higher concurrency like single-node DBs.<h3 id="analytical-query-performance-10gb-dataset">Analytical Query Performance (10GB dataset)</h3><table><thead><tr><th>Query Type</th><th>PostgreSQL 17</th><th>MySQL 9.0</th><th>TiDB + TiFlash</th></tr></thead><tbody><tr><td>Simple aggregate</td><td>0.8s</td><td>1.1s</td><td>0.3s</td></tr><tr><td>Multi-table join</td><td>3.2s</td><td>4.8s</td><td>0.9s</td></tr><tr><td>Window function</td><td>2.1s</td><td>2.9s</td><td>0.5s</td></tr></tbody></table><hr><h2 id="the-decision-framework">The Decision Framework</h2><div><pre><code>Start here: How much data do you have (or expect)?

&lt; 500GB, &lt; 5K TPS
 → PostgreSQL 17 (probably best choice for most apps)

500GB - 5TB, &lt; 20K TPS
 → PostgreSQL 17 + read replicas OR
 → MySQL 9.0 + read replicas (if MySQL ecosystem matters)

&gt; 5TB OR &gt; 20K TPS OR multi-region writes
 → TiDB (if MySQL-compatible is acceptable)
 → Citus (if PostgreSQL compatibility is critical)
 → YugabyteDB (if PostgreSQL + global distribution)

Need real-time analytics on operational data?
 → TiDB (HTAP native)
 → PostgreSQL + Citus columnar (good enough for moderate scale)
</code></pre></div><hr><h2 id="conclusion">Conclusion</h2>In 2026, the choice is clearer than it used to be:<ul><li>PostgreSQL 17 for most new applications. The best feature set, best ecosystem, best developer experience for teams that don’t need to scale horizontally.</li><li>MySQL 9.0 for teams with existing MySQL investment, or applications tied to the MySQL ecosystem. Solid choice, but fewer advantages over PostgreSQL for greenfield projects.</li><li>TiDB for scale problems. If you’re doing manual sharding, drowning in ETL pipelines to feed an analytics system, or need multi-region writes — TiDB is a compelling solution.</li></ul>The days of “just use MySQL” as a default are over. PostgreSQL has won the default choice for new projects. But when scale demands distributed architecture, TiDB is now mature enough to trust in production.<hr>Related Posts:<ul><li>pgvector vs Pinecone: Vector Search in Your Existing Database</li><li>Database-Per-Service in Microservices: Practical Patterns for 2026</li></ul> <div>이 글이 도움이 되셨다면 공감 및 광고 클릭을 부탁드립니다 :) </div></div></div>

Dev Note

Introduction
Database selection in 2026 is more nuanced than ever. PostgreSQL 17, MySQL 9.0, and TiDB 8.x each represent a mature, production-proven choice — but for very different workloads and organizational needs.
This comparison cuts through the marketing and gives you a practical decision framework based on real-world production characteristics.
Photo by Kevin Ku on Unsplash
Quick Comparison Matrix
Feature
 PostgreSQL 17
 MySQL 9.0
 TiDB 8.x
 
License
 PostgreSQL (open)
 GPL v2 / Commercial
 Apache 2.0
 
Architecture
 Single-node + logical replication
 Single-node + replication
 Distributed (NewSQL)
 
Horizontal scaling
 Manual sharding / Citus
 Manual sharding
 Native
 
HTAP
 Limited (FDW/extensions)
 Limited
 Native (TiFlash)
 
SQL Compatibility
 Excellent
 Good
 MySQL-compatible
 
JSON Support
 Best-in-class
 Good
 Good
 
Full-text Search
 Good
 Good
 Limited
 
Max practical OLTP
 ~50K TPS (single node)
 ~40K TPS (single node)
 200K+ TPS (scaled)
 
Cloud-native
 Via AlloyDB, Aurora
 Via Aurora, RDS
 Via TiDB Cloud
 
PostgreSQL 17: The Versatile Workhorse
PostgreSQL remains the Swiss Army knife of databases. PG17 brought significant improvements:
Key PG17 Improvements
Incremental Backup (Major Feature)
-- PG17 adds incremental backup support
-- Dramatically reduces backup time and storage for large databases
SELECT pg_backup_start('base_backup_label', fast => true);
-- ... copy data files ...
SELECT * FROM pg_backup_stop(wait_for_archive => true);
MERGE Command Enhancements
-- PG17 MERGE now supports RETURNING clause
MERGE INTO inventory i
USING incoming_stock s ON i.product_id = s.product_id
WHEN MATCHED THEN
 UPDATE SET quantity = i.quantity + s.quantity
WHEN NOT MATCHED THEN
 INSERT (product_id, quantity) VALUES (s.product_id, s.quantity)
RETURNING i.product_id, i.quantity; -- ← New in PG17
JSON Improvements
-- JSON_TABLE: SQL/JSON standard support
SELECT * FROM JSON_TABLE(
 '{"users": [{"id": 1, "name": "Alice"}, {"id": 2, "name": "Bob"}]}',
 '$.users[*]' COLUMNS (
 id INT PATH '$.id',
 name TEXT PATH '$.name'
 )
) AS t;
Performance Improvements
COPY FROM is up to 2x faster with large datasets
Vacuum improvements reduce bloat significantly
Better parallel query execution for aggregations
When to Choose PostgreSQL
✅ Choose PostgreSQL when:
You need rich data types (JSONB, arrays, hstore, geometric types)
Full-text search is important
You want a massive extension ecosystem (PostGIS, TimescaleDB, pgvector)
Your team has strong SQL expertise
Single-region, moderate scale (< 10TB, < 50K TPS)
You’re building a new application from scratch
❌ Avoid PostgreSQL when:
You need to scale beyond a single beefy node without operational complexity
Multi-region writes are required
You’re already deeply invested in MySQL tooling
MySQL 9.0: Stability and Ecosystem
MySQL 9.0 is a steady evolution. It powers much of the internet and has a massive ecosystem, tooling, and operational knowledge base.
Key MySQL 9.0 Features
JavaScript Stored Programs
// MySQL 9.0 adds JavaScript stored procedures via GraalVM
CREATE PROCEDURE calculate_discount(IN price DECIMAL(10,2))
LANGUAGE JAVASCRIPT AS
$$
 let discount = 0;
 if (price > 100) discount = 0.1;
 if (price > 500) discount = 0.2;
 session.run(`INSERT INTO discounts VALUES (${price}, ${discount})`);
$$;
Improved EXPLAIN ANALYZE
-- Better query analysis with EXPLAIN ANALYZE FORMAT=TREE
EXPLAIN ANALYZE FORMAT=TREE
SELECT o.order_id, SUM(i.quantity * i.price)
FROM orders o
JOIN order_items i ON o.order_id = i.order_id
WHERE o.created_at > NOW() - INTERVAL 30 DAY
GROUP BY o.order_id;
Enhanced JSON Functions
-- MySQL 9.0: JSON schema validation
SELECT JSON_SCHEMA_VALID(
 '{"type":"object","properties":{"id":{"type":"integer"}}}',
 '{"id": 123}'
) AS is_valid; -- Returns 1 (true)
When to Choose MySQL
✅ Choose MySQL when:
Migrating from an existing MySQL stack
Strong ecosystem requirement (Vitess, ProxySQL, PlanetScale)
Team has deep MySQL expertise
WordPress, Drupal, or other MySQL-first CMS
ORM-heavy applications (MySQL dialects are well-supported)
Read-heavy workloads with straightforward schemas
❌ Avoid MySQL when:
Complex JSON queries are frequent
You need advanced SQL features (window functions were limited until recently)
Geographic/spatial data is important (PostGIS is far superior)
TiDB 8.x: Distributed NewSQL
TiDB is the most architecturally different of the three. It’s a distributed SQL database designed for cloud-scale HTAP (Hybrid Transactional/Analytical Processing).
Architecture Overview
┌─────────────────────────────────────┐
│ TiDB (SQL Layer) │ ← Stateless, horizontally scalable
│ MySQL protocol compatible │
└─────────────┬───────────────────────┘
 │
┌─────────────▼───────────────────────┐
│ PD (Placement Driver) │ ← Metadata, scheduling, timestamps
└─────────────┬───────────────────────┘
 │
 ┌─────────┴──────────┐
 ▼ ▼
┌────────┐ ┌──────────┐
│ TiKV │ │ TiFlash │ ← Columnar engine for analytics
│ (OLTP) │ │ (OLAP) │
│ Row │ │ Column │
│ Store │ │ Store │
└────────┘ └──────────┘
TiDB 8.x Key Features
Global Sort for Import/DDL
-- TiDB 8.x: DDL changes are now non-blocking at scale
-- Adding index on a 10-billion row table no longer blocks writes
ALTER TABLE orders ADD INDEX idx_user_created (user_id, created_at);
-- Runs in background; reads/writes unaffected
Resource Control
-- Limit runaway analytical queries from impacting OLTP
CREATE RESOURCE GROUP analytics
 RU_PER_SEC = 5000,
 PRIORITY = LOW,
 QUERY_LIMIT = (EXEC_ELAPSED='30s' ACTION=KILL);
-- Assign sessions to resource groups
SET RESOURCE GROUP analytics;
SELECT ... FROM events WHERE ...; -- Capped at 5000 RU/sec
Native HTAP with TiFlash
-- Same SQL, automatically routed to TiFlash for analytics
-- No ETL, no separate data warehouse
SELECT 
 date_trunc('month', created_at) as month,
 category,
 SUM(amount) as total_revenue
FROM orders
WHERE created_at > '2025-01-01'
GROUP BY 1, 2
ORDER BY 1, 3 DESC;
-- TiDB optimizer routes this to TiFlash (columnar) automatically
When to Choose TiDB
✅ Choose TiDB when:
You’ve outgrown single-node PostgreSQL/MySQL
You need MySQL compatibility without a re-write
HTAP (run analytics on live data) is valuable
Multi-region active-active writes are required
100TB+ data with no desire to shard manually
You’re migrating from MySQL and need scale
❌ Avoid TiDB when:
Your data fits comfortably on a single PostgreSQL node
Small team without distributed systems experience
You need PostgreSQL-specific extensions (PostGIS, pgvector, etc.)
Cost is a major constraint (distributed infra is expensive)
Benchmarks: What Really Matters
Raw TPS numbers are misleading. What matters in production:
P99 Latency Under Concurrent Load
At 500 concurrent connections, 80% reads / 20% writes (typical web app):
Database
 P50
 P95
 P99
 
PostgreSQL 17
 2ms
 8ms
 22ms
 
MySQL 9.0
 2ms
 9ms
 25ms
 
TiDB 8.x (3-node)
 5ms
 15ms
 35ms
 
Note: TiDB has higher baseline latency due to network hops between components, but doesn’t degrade at higher concurrency like single-node DBs.
Analytical Query Performance (10GB dataset)
Query Type
 PostgreSQL 17
 MySQL 9.0
 TiDB + TiFlash
 
Simple aggregate
 0.8s
 1.1s
 0.3s
 
Multi-table join
 3.2s
 4.8s
 0.9s
 
Window function
 2.1s
 2.9s
 0.5s
 
The Decision Framework
Start here: How much data do you have (or expect)?
< 500GB, < 5K TPS
 → PostgreSQL 17 (probably best choice for most apps)
500GB - 5TB, < 20K TPS
 → PostgreSQL 17 + read replicas OR
 → MySQL 9.0 + read replicas (if MySQL ecosystem matters)
> 5TB OR > 20K TPS OR multi-region writes
 → TiDB (if MySQL-compatible is acceptable)
 → Citus (if PostgreSQL compatibility is critical)
 → YugabyteDB (if PostgreSQL + global distribution)
Need real-time analytics on operational data?
 → TiDB (HTAP native)
 → PostgreSQL + Citus columnar (good enough for moderate scale)
Conclusion
In 2026, the choice is clearer than it used to be:
PostgreSQL 17 for most new applications. The best feature set, best ecosystem, best developer experience for teams that don’t need to scale horizontally.
MySQL 9.0 for teams with existing MySQL investment, or applications tied to the MySQL ecosystem. Solid choice, but fewer advantages over PostgreSQL for greenfield projects.
TiDB for scale problems. If you’re doing manual sharding, drowning in ETL pipelines to feed an analytics system, or need multi-region writes — TiDB is a compelling solution.
The days of “just use MySQL” as a default are over. PostgreSQL has won the default choice for new projects. But when scale demands distributed architecture, TiDB is now mature enough to trust in production.
Related Posts:
pgvector vs Pinecone: Vector Search in Your Existing Database
Database-Per-Service in Microservices: Practical Patterns for 2026

PostgreSQL 17 vs MySQL 9 vs TiDB: Which Database Should You Choose in 2026?

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