# PostgreSQL vs MySQL 2026: Updated Data Changes the Answer

> PostgreSQL 18 and MySQL 9.7.1 shipped in 2026 with AI-native features, but DB-Engines data shows only one database is still gaining momentum — here's what matters for your next project.

- Canonical: https://www.kunalganglani.com/blog/postgresql-vs-mysql-2026
- Author: Kunal Ganglani
- Published: 2026-07-07 · Updated: 2026-07-07
- Category: Developer Tools · Tags: postgresql, mysql, database, sql, system-design, comparison, 2026, database-comparison

## TL;DR

PostgreSQL and MySQL both shipped major updates in 2025-2026, but the gap between them is growing — not shrinking. PostgreSQL is the only top-4 database gaining popularity, its AI/vector search extension (pgvector) is far more mature than MySQL's native VECTOR type, and cloud platforms are overwhelmingly building on Postgres. For any new project that isn't WordPress or a simple read-heavy app, PostgreSQL is the default choice in 2026.

## PostgreSQL vs MySQL 2026: Updated Data Changes the Answer

PostgreSQL vs MySQL in 2026 is no longer the same comparison it was even 18 months ago. PostgreSQL 18 shipped in September 2025 with a full asynchronous I/O subsystem, PostgreSQL 19 Beta 1 dropped June 4, 2026, and MySQL leapfrogged from 9.0 to 9.7.1 in under two years — adding a native VECTOR data type, JSON Duality Views, and JavaScript stored programs along the way. Most comparison articles online still cite PostgreSQL 17 and MySQL 8.x. This one doesn't.

![laptop screen displaying colorful code](https://cdn.sanity.io/images/vzekdneq/production/859229861f6e7e7104610f1b1b6155f4a12443f6-1200x675.webp)

**Key takeaways:**

- PostgreSQL is the only top-4 database gaining DB-Engines score year-over-year in July 2026 (+6.92), while MySQL dropped 94 points — the momentum gap is widening, not closing.
- MySQL 9.0 introduced a native VECTOR data type for AI workloads, but pgvector's 22,100+ GitHub stars and mature HNSW/IVFFlat indexing still dominate production [RAG](/glossary/rag) and [semantic search](/glossary/semantic-search) deployments.
- For greenfield projects in 2026, PostgreSQL wins on extensibility, AI readiness, and developer sentiment. MySQL wins on legacy LAMP compatibility and raw read-heavy simplicity.
- PostgreSQL's liberal license (PostgreSQL License) carries zero vendor lock-in risk; MySQL's GPL under Oracle ownership is a growing concern for startups.
- Cloud platforms have voted with their defaults: Supabase, Neon, and Vercel Postgres are PostgreSQL-only; PlanetScale is MySQL-only but shut down its free tier.
> For new projects in 2026, PostgreSQL is the default database unless you have a specific, defensible reason to choose MySQL.

| Dimension | PostgreSQL 18 | MySQL 9.7.1 |
| --- | --- | --- |
| **Latest Stable** | 18 (Sept 2025); 19 Beta 1 out | 9.7.1 (June 2026) |
| **License** | PostgreSQL License (liberal) | GPL (Oracle-owned) |
| **ACID Compliance** | Always, all configurations | InnoDB only (default since 5.5) |
| **Vector/AI Support** | pgvector extension (HNSW, IVFFlat) | Native VECTOR type (since 9.0) |
| **JSON** | jsonb + JSON_TABLE() (PG17+) | JSON + JSON Duality Views (9.6+) |
| **Concurrency Model** | Mature MVCC | MVCC (InnoDB) |
| **Async I/O** | Native AIO subsystem (PG18) | Limited |
| **Extension Ecosystem** | 404+ extensions (Pigsty v3.3) | Plugin-based, smaller scope |
| **DB-Engines Score (July 2026)** | 687.80 (+6.92 YoY) | 846.46 (−94.27 YoY) |
| **Stack Overflow 2025** | #1 most admired (65%) | Not in top 3 admired |
| **Best For** | Complex queries, AI/RAG, new projects | Legacy LAMP, read-heavy, WordPress |

## What Changed Since You Last Compared These Two

Most developers picked their database years ago and haven't revisited the decision. That's a mistake. Between mid-2024 and July 2026, both databases shipped more meaningful features than in the preceding five years combined.

![Computer screen displaying code and text](https://cdn.sanity.io/images/vzekdneq/production/04876a288664f2d8e60efdddffc267d1850fabbb-1200x675.webp)

**PostgreSQL's timeline:**

- **PostgreSQL 17** (September 2024): JSON_TABLE() for SQL/JSON standard compliance, incremental backup via `pg_basebackup`, improved VACUUM memory management, and streaming I/O for sequential reads.
- **PostgreSQL 18** (September 2025): Full asynchronous I/O (AIO) subsystem accelerating sequential scans, bitmap heap scans, and VACUUM. Skip scan for multicolumn B-tree indexes. Virtual generated columns. `uuidv7()` for timestamp-ordered UUIDs. OAuth authentication. Temporal constraints for PRIMARY KEY, UNIQUE, and FOREIGN KEY.
- **PostgreSQL 19 Beta 1** (June 4, 2026): Confirms the aggressive annual release cadence continues.
**MySQL's timeline:**

- **MySQL 9.0** (July 2024): Native VECTOR data type. JavaScript stored programs. Generated Invisible Primary Keys (GIPK) improvements.
- **MySQL 9.5** (October 2025): Increased `caching_sha2_password_digest_rounds` default to 10,000. Changed default binlog transaction dependency tracking.
- **MySQL 9.6** (January 2026): JSON Duality Views. Modular Audit Log component. Atomic DDL crash-safety improvements.
- **MySQL 9.7.1** (June 16, 2026): Latest release, continuing the accelerated cadence.
MySQL shipped 9 releases in 24 months — a pace that would have been unthinkable during the MySQL 8.x era. That acceleration is impressive, but it also raises stability questions. If you're running MySQL in production, you need to think carefully about which version to pin to. PostgreSQL's one-major-release-per-year cadence is slower but more predictable for [production AI](/pillars/developer-tools-workflow) workloads.

## License and Ownership: The Risk Nobody Talks About

PostgreSQL uses the PostgreSQL License, which is essentially a liberal BSD-style license. You can do whatever you want with it. Fork it. Embed it. Sell it. No restrictions.

![Code written on a screen, likely programming related.](https://cdn.sanity.io/images/vzekdneq/production/b6c70c135faed179467e40c75291c1c48f7ffb47-1200x675.webp)

MySQL Community Edition uses the GPL, and Oracle owns it. Oracle also sells MySQL Enterprise Edition with proprietary features. If you've watched Oracle's handling of Java licensing or its approach to acquired products over the past decade, you understand why this matters.

As [Tianzhou](https://www.bytebase.com/blog/postgres-vs-mysql/), co-founder of Bytebase and ex-Google Cloud SQL engineer, notes in the Bytebase comparison: the PostgreSQL License gives you complete freedom, while MySQL's GPL under Oracle creates a dependency that many startups are increasingly uncomfortable with.

For a new project in 2026, the licensing delta is real. Not because Oracle is going to sue you — but because the ecosystem builds differently around a liberally licensed project. Neon, Supabase, Crunchy Data, and Timescale all built commercial products on PostgreSQL's liberal license. That ecosystem breadth feeds back into the quality of tooling, extensions, and managed offerings available to you.

## Connection Model and Concurrency

PostgreSQL uses a process-per-connection model. Every client connection spawns a new OS process. This is heavyweight compared to MySQL's thread-per-connection model, which is lighter on resources for large numbers of concurrent connections.

In practice, this means PostgreSQL almost always needs a connection pooler in production. PgBouncer and pgpool-II are the standard choices. MySQL has ProxySQL and MySQL Router, but they're optional rather than essential for most deployments.

Here's the thing nobody says about this: **the connection model difference stopped mattering for most applications around 2020.** PgBouncer adds roughly 1ms of overhead, and every serious PostgreSQL deployment already includes it. If you're evaluating databases in 2026 and the connection model is your deciding factor, you're optimizing the wrong thing.

Both databases implement MVCC (Multi-Version Concurrency Control) for handling concurrent reads and writes. PostgreSQL's MVCC implementation is more mature — it's been the core concurrency model since the database's inception. As [AWS documentation](https://aws.amazon.com/compare/the-difference-between-mysql-vs-postgresql/) states, PostgreSQL is fully ACID compliant in all configurations, while MySQL guarantees full ACID compliance only with the InnoDB storage engine.

For high-concurrency write workloads, PostgreSQL 18's new asynchronous I/O subsystem is a genuine step change. Sequential scans, bitmap heap scans, and VACUUM all benefit from AIO, which means less I/O wait time under heavy concurrent load. This is the kind of infrastructure improvement that compounds — every query benefits, across every workload.

## Performance: Which Is Actually Faster in 2026?

This is the question everyone asks, and the honest answer hasn't changed much: **it depends on the workload.**

MySQL is faster for simple read-heavy workloads with straightforward queries. If your application is 90% `SELECT * FROM users WHERE id = ?`, MySQL will edge out PostgreSQL on raw throughput. This is why MySQL still dominates WordPress hosting, e-commerce product catalogs, and session stores.

PostgreSQL is faster for complex queries involving multiple joins, subqueries, CTEs, window functions, and analytical workloads. PostgreSQL's query optimizer is significantly more sophisticated — it supports hash joins, merge joins, and nested loop joins, and it can parallelize queries across multiple cores.

What changed in 2026:

- **PostgreSQL 18's AIO subsystem** improves sequential scan performance by reducing I/O wait. If your workload involves full table scans or VACUUM-heavy operations, this is a measurable improvement.
- **PostgreSQL 18's skip scan** for multicolumn B-tree indexes means queries that filter on non-leading index columns perform dramatically better. This was a known PostgreSQL weakness for years — it's now fixed.
- **MySQL 9.x's atomic DDL** improvements mean schema changes are safer under load. Historically, DDL operations in MySQL could leave tables in inconsistent states during crashes. That risk is shrinking with each 9.x release.
The performance conversation has also shifted because of AI workloads. If your application stores and queries [vector embeddings](/glossary/vector-embeddings), the performance comparison is entirely different — and that's covered in the next section.

When I built the [LLM pricing tracker](https://www.kunalganglani.com/llm-prices) on this site, the decision to use PostgreSQL came down to exactly this: the workload involves complex aggregations across pricing data from multiple providers, not simple key-value lookups. PostgreSQL's query planner handles that kind of work without breaking a sweat.

## AI and Vector Workloads: pgvector vs MySQL VECTOR Type

This is the section that didn't exist in database comparisons two years ago. In 2026, it's the single most important decision factor for AI-augmented applications.

**pgvector** is a PostgreSQL extension for vector similarity search. It has over 22,100 GitHub stars, supports HNSW and IVFFlat indexes, and version 0.8.0 (November 2024) added iterative index scans to prevent over-filtering plus improved query planning integration. pgvector is the de facto standard for storing and querying embeddings alongside relational data.

**MySQL's native VECTOR type** was introduced in MySQL 9.0 (July 2024). It allows vector similarity search without installing any extension. It's built into the database itself.

On paper, MySQL's approach sounds better — native support should beat an extension. In practice, pgvector is years ahead in capability:

- **Index types:** pgvector offers both HNSW (fast approximate nearest neighbor with tunable recall) and IVFFlat (partition-based ANN). MySQL's VECTOR type currently offers more limited indexing.
- **Ecosystem integration:** pgvector integrates with LangChain, LlamaIndex, and every major [RAG](/glossary/rag) framework. If you're building [retrieval-augmented generation](/glossary/retrieval-augmented-generation) pipelines, pgvector is the path of least resistance.
- **Query planner integration:** pgvector 0.8.0 improved how PostgreSQL's planner selects ANN indexes, meaning the database makes smarter decisions about when to use vector indexes versus sequential scans.
- **Filtering performance:** The iterative scan feature in pgvector 0.8.0 specifically addresses the over-filtering problem — where applying WHERE clauses alongside vector search would return too few results. This is critical for production [RAG](/glossary/rag) workloads that need both semantic similarity and metadata filtering.
Having built a multi-stage RAG pipeline for the Walmart conversational commerce chatbot at Firework — handling millions of queries daily at sub-second response times — I can say confidently that retrieval quality, not model choice, dominated answer quality at scale. The database layer is where retrieval quality lives. pgvector's maturity in index types and filtering directly translates to better retrieval, which translates to better answers from your [LLM](/glossary/large-language-model).

For a deeper comparison of vector database options, I covered this in my [pgvector vs Pinecone](/blog/pgvector-vs-pinecone) and [Milvus vs Qdrant](/blog/milvus-vs-qdrant) comparisons. If your workload is purely vector search with no relational data, a dedicated [vector database](/glossary/vector-database) might make sense. But if you need vectors alongside relational data — which is most real applications — PostgreSQL with pgvector eliminates an entire infrastructure dependency.

[Watch: PostgreSQL vs MySQL](https://www.youtube.com/watch?v=btjBNKP49Rk)

## JSON Support: jsonb vs JSON Duality Views

JSON support has been a PostgreSQL advantage for years. PostgreSQL's `jsonb` type stores JSON in a binary format that supports indexing, and with PostgreSQL 17's JSON_TABLE() function, you can convert JSON documents into relational table representations using standard SQL/JSON syntax.

MySQL 9.6 (January 2026) introduced JSON Duality Views — a feature that lets you view and modify data simultaneously as both relational tables and JSON documents. This is Oracle's approach to bridging the document-relational gap, borrowed from Oracle Database 23c.

JSON Duality Views are genuinely interesting. They let you define a view that presents relational data as a JSON document, and writes to that JSON view propagate back to the underlying relational tables. For applications that need both a document API and relational integrity, this eliminates a lot of application-layer transformation code.

However, PostgreSQL's approach — storing JSON natively in `jsonb` with GIN indexes and querying it with dedicated operators — is more battle-tested. [Salman Ravoof](https://kinsta.com/blog/postgresql-vs-mysql/) at Kinsta notes in their extensive comparison that PostgreSQL's JSON handling has been production-grade since PostgreSQL 9.4 (2014), giving it over a decade of optimization.

The practical difference: if you're building a new API that serves JSON and stores relational data, PostgreSQL's `jsonb` is the simpler, more proven path. If you're retrofitting a relational MySQL schema to serve document-style APIs without rewriting your data layer, JSON Duality Views are a compelling MySQL-specific feature.

## Extensibility and the 404-Extension Ecosystem

This is where PostgreSQL pulls away from MySQL in a way that's hard to overstate.

The Pigsty v3.3 distribution (March 2025) packages 404 PostgreSQL extensions, including pg_documentdb (Microsoft), pg_collection (AWS), and pg_tracing (Datadog). That number — 404 extensions from a single distribution — illustrates that PostgreSQL's extension ecosystem has become an enterprise platform in its own right.

PostgreSQL extensions aren't just convenience add-ons. They fundamentally expand what the database can do:

- **PostGIS** turns PostgreSQL into a geographic information system
- **pgvector** adds vector similarity search for AI workloads
- **TimescaleDB** adds time-series capabilities
- **Citus** adds horizontal sharding and distributed queries
- **pg_stat_statements** provides query performance monitoring
MySQL has a plugin system, but its scope is narrower. MySQL plugins can add storage engines, authentication methods, and audit logging, but they can't extend the SQL language or add new data types the way PostgreSQL extensions can.

This extensibility gap is why cloud platforms have overwhelmingly bet on PostgreSQL for new products. Supabase, Neon, and Vercel Postgres are PostgreSQL-only. They chose PostgreSQL because extensions let them build differentiated features — Supabase's real-time subscriptions and auth, Neon's serverless branching — on top of a proven relational core. PlanetScale bet on MySQL but shut down its free tier in 2024, and the MySQL-native cloud platform space is noticeably thinner.

If you're interested in how managed Postgres platforms compare, I covered the two biggest in [Neon vs Supabase](/blog/neon-vs-supabase-2026).

## Replication, High Availability, and Security

Both databases support synchronous and asynchronous replication. Both can achieve high availability with proper configuration. But the details matter.

**PostgreSQL** supports streaming replication (physical) and logical replication. PostgreSQL 17 added logical replication failover control with `pg_createsubscriber`, making it easier to promote a logical replica to primary. PostgreSQL 18 added OAuth authentication support, which is significant for enterprises using identity providers.

**MySQL** supports binary log (binlog) replication and Group Replication for multi-primary setups. MySQL 9.5 changed the default binlog transaction dependency tracking and increased the default `caching_sha2_password_digest_rounds` to 10,000 — both meaningful security improvements for existing deployments.

On security specifically, PostgreSQL supports row-level security (RLS) out of the box, which is why Supabase built its entire authorization model on it. MySQL has role-based access control but lacks native RLS. For applications that need per-user or per-tenant data isolation at the database level, PostgreSQL's RLS is a significant advantage.

## Indexing: Where PostgreSQL 18 Closed a Gap

PostgreSQL supports a wider variety of index types: B-tree, hash, GiST, SP-GiST, GIN, and BRIN. MySQL primarily uses B-tree and hash indexes (with InnoDB).

The practical impact: PostgreSQL's GIN indexes make `jsonb` queries fast. GiST indexes power PostGIS spatial queries. BRIN indexes efficiently handle time-series data on naturally ordered columns.

PostgreSQL 18's skip scan for multicolumn B-tree indexes deserves special mention. Previously, if you had a composite index on `(status, created_at)` and queried only on `created_at`, PostgreSQL couldn't use that index efficiently. Skip scan fixes this by jumping over distinct values of the leading column. MySQL has supported loose index scan (its equivalent) for longer, so this was a known PostgreSQL weakness that's now resolved.

For AI workloads specifically, pgvector adds HNSW and IVFFlat index types that don't exist in MySQL's native VECTOR implementation at the same maturity level. If your application queries high-dimensional embedding vectors — which is increasingly common in 2026 — PostgreSQL's index diversity is a concrete advantage.

## When to Use PostgreSQL in 2026

Choose PostgreSQL when:

- You're building a **new project** and don't have legacy constraints pulling you toward MySQL
- Your application involves **complex queries**: joins, CTEs, window functions, subqueries
- You need **AI/vector search** alongside relational data (pgvector)
- You're building a **multi-tenant SaaS** and need row-level security
- You want **extensibility** — PostGIS, TimescaleDB, Citus, or any of the 404+ available extensions
- Your team values a **liberal open-source license** without Oracle dependency
- You're deploying on **Supabase, Neon, or Vercel Postgres**
Don't choose PostgreSQL if your application is a simple read-heavy workload with minimal query complexity and your team already has deep MySQL expertise. Switching databases for the sake of switching is engineering waste.

## When to Use MySQL in 2026

Choose MySQL when:

- You're running **WordPress** or a LAMP-stack application — MySQL is the native database and switching adds unnecessary risk
- Your workload is **heavily read-dominated** with simple queries — MySQL's thread-per-connection model and query execution are optimized for this
- You need **JSON Duality Views** for bridging document and relational access patterns without application-layer transformation
- Your team has **deep MySQL expertise** and the application doesn't need PostgreSQL-specific features
- You're deploying on **PlanetScale** or have existing MySQL infrastructure you can't migrate from
MySQL is still the #2 database globally on DB-Engines with a score of 846.46 in July 2026. Calling it irrelevant is wrong. But the trajectory matters: MySQL's score dropped 94.27 points year-over-year while PostgreSQL gained 6.92. PostgreSQL is the only top-4 database with a rising score. Oracle dropped 84.69 points and SQL Server dropped 72.18.

[Mark Smallcombe](https://www.integrate.io/blog/postgresql-vs-mysql-which-one-is-better-for-your-use-case/) at Integrate.io puts it well: MySQL is preferred for read-only commands, while PostgreSQL is preferred for read-write operations, large datasets, and complex queries. That framing has been accurate for years, but the rise of AI workloads has tilted the balance further toward PostgreSQL for new projects.

## Is PostgreSQL Replacing MySQL?

The DB-Engines data tells a clear story. In July 2026, PostgreSQL scored 687.80 (up 6.92 year-over-year) while MySQL scored 846.46 (down 94.27). MySQL still has a larger install base, but PostgreSQL is the only top-4 database gaining ground.

The [Stack Overflow 2025 Developer Survey](https://survey.stackoverflow.co/2025/technology) — based on 49,000+ responses from 177 countries — shows PostgreSQL as the most admired database (65% of developers) and most desired database (46%) for three consecutive years. MySQL doesn't appear in the top 3 for either category.

Is PostgreSQL "replacing" MySQL? Not exactly. Legacy MySQL installations aren't disappearing overnight — WordPress alone ensures MySQL's relevance for years. But for new projects, the gravitational pull toward PostgreSQL is unmistakable. The developer sentiment data, the cloud platform defaults, the extension ecosystem, and the AI workload story all point the same direction.

Based on the pricing and benchmark data I maintain at [kunalganglani.com/llm-prices](https://www.kunalganglani.com/llm-prices), I've watched the AI tooling ecosystem standardize on PostgreSQL as its default relational backend. Every major [LLM](/glossary/large-language-model) framework — LangChain, LlamaIndex, Haystack — documents PostgreSQL/pgvector integration first. MySQL VECTOR support documentation is thinner, with fewer community examples and production case studies.

## The 2026 Verdict: What New Projects Should Choose

If you're starting a new project in 2026 and asking which database to pick, the answer is PostgreSQL unless you have a specific, defensible reason to choose MySQL.

The defensible reasons for MySQL are real but narrow: you're building on WordPress, you're extending an existing MySQL codebase, or your workload is genuinely simple read-heavy traffic with no anticipated complexity growth.

For everything else — SaaS applications, AI-augmented products, analytical workloads, multi-tenant platforms, APIs serving JSON — PostgreSQL is the stronger foundation. The extension ecosystem gives you room to grow. pgvector means you won't need a separate [vector database](/glossary/vector-database) for embedding search. The license means you won't wake up to an Oracle surprise.

MySQL isn't dying. It shipped 9 releases in 24 months and added genuinely useful features like JSON Duality Views and the native VECTOR type. Oracle is investing. But the developer ecosystem is voting with its feet, and it's walking toward PostgreSQL.

Here's what I think happens next: by the time the 2027 Stack Overflow survey publishes, the PostgreSQL admiration gap will widen further. The AI workload story alone — pgvector's maturity versus MySQL's nascent VECTOR type — is enough to drive the next wave of adoption. If you're building something today that will still be running in 2030, PostgreSQL is the bet with the most favorable odds.

The boring answer is the right one. Pick PostgreSQL. Build your [agent architecture](/blog/ai-agent-control-flow-architecture) on top of it. Use pgvector for your embeddings. Stop second-guessing, and start shipping.

## FAQ

### Is PostgreSQL better than MySQL in 2026?

For most new projects, yes. PostgreSQL leads in extensibility, AI/vector workload support (pgvector), developer sentiment (65% admiration in Stack Overflow 2025), and is the only top-4 database gaining DB-Engines score year-over-year. MySQL is still better for simple read-heavy workloads, WordPress sites, and teams with deep MySQL expertise.

### Which is faster: PostgreSQL or MySQL?

MySQL is generally faster for simple, read-heavy queries with straightforward WHERE clauses. PostgreSQL outperforms MySQL on complex queries involving joins, CTEs, window functions, and analytical workloads thanks to its more sophisticated query optimizer. PostgreSQL 18's async I/O subsystem also narrowed the gap on sequential scan performance.

### Does MySQL support vector search like PostgreSQL's pgvector?

Yes. MySQL 9.0 (July 2024) introduced a native VECTOR data type. However, pgvector is significantly more mature — it offers HNSW and IVFFlat index types, iterative scans to prevent over-filtering, deep integration with RAG frameworks like LangChain and LlamaIndex, and has 22,100+ GitHub stars of community momentum. For production AI workloads, pgvector remains the stronger choice.

### What is JSON Duality Views in MySQL 9.6?

JSON Duality Views, introduced in MySQL 9.6 (January 2026), let you view and modify relational data simultaneously as JSON documents. Writes to the JSON view propagate back to underlying relational tables. It bridges the document-relational gap without application-layer transformation code. PostgreSQL takes a different approach with native jsonb storage and JSON_TABLE() for converting JSON to relational format.

### Should I use PostgreSQL or MySQL for a new AI project in 2026?

PostgreSQL with pgvector. The extension supports HNSW and IVFFlat indexes for approximate nearest neighbor search, integrates with every major LLM framework, and lets you store embeddings alongside your relational data in a single database. MySQL's native VECTOR type is functional but less mature for production AI workloads.

### Is MySQL still relevant in 2026?

Absolutely. MySQL is ranked #2 globally on DB-Engines (score 846.46) and powers most WordPress installations, many legacy LAMP applications, and read-heavy web services. Oracle's accelerated release cadence (9 releases in 24 months) shows active investment. However, its year-over-year score decline of 94.27 points suggests mindshare is shifting toward PostgreSQL for new projects.

Photo by Đào Hiếu on Unsplash.

## FAQ

### Is PostgreSQL better than MySQL in 2026?

For most new projects, yes. PostgreSQL leads in extensibility, AI/vector workload support (pgvector), developer sentiment (65% admiration in Stack Overflow 2025), and is the only top-4 database gaining DB-Engines score year-over-year. MySQL is still better for simple read-heavy workloads, WordPress sites, and teams with deep MySQL expertise.

### Which is faster: PostgreSQL or MySQL?

MySQL is generally faster for simple, read-heavy queries with straightforward WHERE clauses. PostgreSQL outperforms MySQL on complex queries involving joins, CTEs, window functions, and analytical workloads thanks to its more sophisticated query optimizer. PostgreSQL 18's async I/O subsystem also narrowed the gap on sequential scan performance.

### Does MySQL support vector search like PostgreSQL's pgvector?

Yes. MySQL 9.0 (July 2024) introduced a native VECTOR data type. However, pgvector is significantly more mature — it offers HNSW and IVFFlat index types, iterative scans to prevent over-filtering, deep integration with RAG frameworks like LangChain and LlamaIndex, and has 22,100+ GitHub stars. For production AI workloads, pgvector remains the stronger choice.

### What is JSON Duality Views in MySQL 9.6?

JSON Duality Views, introduced in MySQL 9.6 (January 2026), let you view and modify relational data simultaneously as JSON documents. Writes to the JSON view propagate back to underlying relational tables. It bridges the document-relational gap without application-layer transformation code. PostgreSQL takes a different approach with native jsonb storage and JSON_TABLE().

### Should I use PostgreSQL or MySQL for a new AI project in 2026?

PostgreSQL with pgvector. The extension supports HNSW and IVFFlat indexes for approximate nearest neighbor search, integrates with every major LLM framework, and lets you store embeddings alongside your relational data in a single database. MySQL's native VECTOR type is functional but less mature for production AI workloads.

### Is MySQL still relevant in 2026?

Absolutely. MySQL is ranked #2 globally on DB-Engines (score 846.46) and powers most WordPress installations, many legacy LAMP applications, and read-heavy web services. Oracle's accelerated release cadence shows active investment. However, its year-over-year score decline of 94.27 points suggests mindshare is shifting toward PostgreSQL for new projects.