Database Guru

The database world is full of hype and marketing fog. This column cuts through it with blunt commentary, case studies, and technical deep dives.

A conversation between Mike Stonebraker (MIT CSAIL, Turing Award Winner, Creator of PostgreSQL), Andy Pavlo (Carnegie Mellon University), and the DBOS team.

MySQL is to the internet what baijiu is to China: harsh, hard to swallow, yet worshipped because culture demands obedience. Both are loyalty tests—will you endure discomfort to fit in?

VictoriaMetrics is brutally efficient—using a fraction of Prometheus + Loki’s resources for multiples of the performance. Pigsty v4 swaps to the Victoria stack; here’s the beta for anyone eager to try it.

MinIO just entered maintenance mode. What replaces it? Can RustFS step in? I tested the contenders so you don’t have to.

MinIO announces it is entering maintenance mode, the dragon-slayer has become the dragon – how MinIO transformed from an open-source S3 alternative to just another commercial software company

Your ability to ask questions—and your taste in what to ask—determines your position in the AI era. When answers become commodities, good questions become the new wealth. We are living in the moment this prophecy comes true.

In serious production you can’t rely on an upstream that explicitly says “no guarantees.” When someone says “don’t count on me,” the right answer is “then I’ll run it myself.”

Tons of users running the official docker postgres image got burned during recent minor version upgrades. A friendly reminder: think twice before containerizing production databases.

The second edition of Designing Data-Intensive Applications has released ten chapters. I translated them into Chinese and rebuilt a clean Hugo/Hextra web version for the community.

Imagine a “closed-course” shootout for domestic databases and clouds, the way Dongchedi just humiliated 30+ autonomous cars. This industry needs its own stress test.

Google recently launched a database MCP toolbox, perhaps the first production-ready solution.

Who will be revolutionized first - OLTP or OLAP? Integration vs specialization, how to choose? Where will DBAs go in the AI era? Feng’s views from the HOW 2025 conference roundtable, organized and published.

The database for the AI era has been settled. Capital markets are making intensive moves on PostgreSQL targets, with PG having become the default database for the AI era.

At PGConf.Dev 2025, Bohan Zhang from OpenAI shared a session titled Scaling Postgres to the next level at OpenAI, giving us a peek into the database usage of a top-tier unicorn.

Plenty. If you’re rolling your own Kubernetes, odds are you’ll crash because etcd ships with a 2 GB time bomb.

Future software = Agent + Database. No middle tiers, just agents issuing CRUD. Database skills age well, and PostgreSQL is poised to be the agent-era default.

A 2025 reality check on where PostgreSQL stands relative to MySQL across features, performance, quality, and ecosystem.

If you ask me, we’re on the brink of a cosmic collision in database-land, and Postgres + DuckDB is the meteor we should all be watching.

The PG community has started punching up: Cybertec’s Laurenz Albe breaks down how Oracle’s transaction system stacks against PostgreSQL.

Databases are the core of business architecture, but what happens if we go further and let databases become the business architecture itself?

Is PostgreSQL the king of boring databases? Here are seven databases worth studying in 2025: PostgreSQL, SQLite, DuckDB, ClickHouse, FoundationDB, TigerBeetle, and CockroachDB—each deserving a week of deep exploration.

Supabase is great, own your own Supabase is even better. A tutorial for self-hosting production-grade supabase on local/cloud/ VM/BMs.

The Linux community is essentially imperial — and Linus himself is the earliest and most successful technical dictator. People are used to Linus’s generosity but forget this point.

Programmers are expensive, scarce biological computing cores, the anchor point of software costs — please prioritize optimizing biological cores before optimizing CPU cores.

MongoDB has a terrible track record on integrity, lackluster products and technology, gets beaten by PG in correctness, performance, and functionality, with collapsing developer reputation, declining popularity, stock price halving, and expanding losses. Provocative marketing against PG can’t save it with “good marketing.”

MongoDB 3.2’s analytics subsystem turned out to be an embedded PostgreSQL database? A whistleblowing story from MongoDB’s partner about betrayal and disillusionment.

This vulnerability affects EL9, Ubuntu 22.04, Debian 12. Users should promptly update OpenSSH to fix this vulnerability.

Friends often ask me, can Chinese domestic databases really compete? To be honest, it’s a question that offends people. So let’s try speaking with data - I hope the charts provided in this article can help readers understand the database ecosystem landscape and establish more accurate proportional awareness.

Today we discuss the fair pricing of commercial databases, open-source databases, cloud databases, and domestic Chinese databases.

Redis “going non-open source” is not a disgrace to Redis, but a disgrace to “open source/OSI” and even more so to public cloud. What truly matters has always been software freedom, while open source is just one means to achieve software freedom.

MySQL’s transaction ACID has flaws and doesn’t match documentation promises. This may lead to serious correctness issues - use with caution.

Whether databases should be housed in Kubernetes/Docker remains highly controversial. It has fundamental drawbacks with stateful services.

Vector storage and retrieval is a real need, but specialized vector databases are already dead. Small needs are solved by OpenAI directly, standard needs are captured by existing mature databases with vector extensions. The ecological niche left for specialized vector databases might support one company, but trying to build an industry around AI stories is impossible.

Many “domestic databases” are just shoddy, inferior products that can’t be helped. Xinchuang domestic OS/databases are essentially IT pre-made meals in schools. Users hold their noses while migrating, developers pretend to work hard, and everyone plays along with leaders who neither understand nor care about technology. The infrastructure software industry isn’t being strangled by anyone - the real chokehold comes from the so-called “insiders.”

RHEL-series OS distribution compatibility level: RHEL = Rocky ≈ Anolis > Alma > Oracle » Euler. Recommend using RockyLinux 8.8, or Anolis 8.8 for domestic requirements.

When we talk about self-reliance and control, what are we really talking about? Operational self-reliance vs. R&D self-reliance - what nations/users truly need is the former, not flashy “self-research”.

The cost-cutting imperative has triggered a reevaluation of all technologies, including databases. This series critiques hot DB technologies and poses fundamental questions about their trade-offs: Are cloud databases, distributed databases, microservices, and containerization real needs or false hype?

Similar to Maslow’s hierarchy of needs, user demands for databases also have a progressive hierarchy: physiological needs, safety needs, belonging needs, esteem needs, cognitive needs, aesthetic needs, self-actualization needs, and transcendence needs.

As hardware technology advances, the capacity and performance of standalone databases have reached unprecedented heights. which makes distributed (TP) databases appear utterly powerless, much like the “data middle platform,” donning the emperor’s new clothes in a state of self-deception.

Thou shalt not run a prod database inside a container

A proper understanding of time is very helpful for correctly handling time-related issues in work and life. For example, time representation and processing in computers, as well as time handling in databases and programming languages.

Without understanding the basic principles of character encoding, even simple string operations like comparison, sorting, and random access can easily lead you into pitfalls. This article attempts to clarify these issues through a comprehensive explanation.

Concurrent programs are hard to write correctly and even harder to write well. Many programmers simply throw these problems at the database… But even the most sophisticated databases won’t help if you don’t understand concurrency anomalies and isolation levels.

The technical essence, functionality, and evolution of blockchain is distributed databases. Specifically, it’s a Byzantine Fault Tolerant (resistant to malicious node attacks) distributed (leaderless replication) database.

The term “consistency” is heavily overloaded, representing different concepts in different contexts. For example, the C in ACID and the C in CAP actually refer to different concepts.

Those who only know how to code are just programmers; learn databases well, and you can at least make a living; but for excellent engineers, merely using databases is far from enough.