PostgreSQL, MongoDB, and what “cannot scale” really means

Last week, I read The Register’s coverage of MongoDB CEO Chirantan “CJ” Desai telling analysts that a “super-high growth AI company … switched from PostgreSQL to MongoDB because PostgreSQL could not just scale.” I believe you can show the value of your own technology without tearing down another. That is really what this post is about.
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PostgreSQL License: Free to Use, Enterprise-Ready, and Cost-Efficient in Production

Do you need a PostgreSQL license for critical production use? Short answer: No. The open-source PostgreSQL database is free to download, use, modify, and distribute. There are no per-CPU, per-core, per-socket, or per-instance license fees. What you do need is a realistic plan for operational costs and expertise, the parts that make PostgreSQL truly production-grade. Many teams search for “PostgreSQL license” while budgeting for a new system or replacing proprietary databases. They want to know whether PostgreSQL is free like a hobby project or free like a platform you can trust with revenue. It is the latter - enterprise-reliable and secure, provided you run it with the right architecture and operational discipline.
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Scaling Up Wasn’t the Plan — Until It Was the Only Plan

If you have ever generated a complex report in Odoo only to watch the loading spinner for minutes, you are not alone. One of our customers ran into exactly this scenario: their system ground to a near stall whenever they tried to compile business reports. After a systematic investigation, we achieved a 93 % performance improvement, but only by choosing the last resort: upgrading the instance’s resources.
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NUMA, Linux, and PostgreSQL before libnuma Support

This entry in this series covers the general interaction of PostgreSQL and Linux on NUMA systems. This topic is complex and so there are cases of simplification. However this distills the general information about running PostgreSQL 17 and below (or Postgres 18 without libnuma support) on NUMA systems, with Linux as a reference point. By the end of this blog entry, you should both be able to run Postgres on a NUMA system and also understand why the libnuma support in PostgreSQL 18 is so important.
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What Are “Dirty Pages” in PostgreSQL?

PostgreSQL stores data in fixed‑size blocks (pages), normally 8 KB. When a client updates or inserts data, PostgreSQL does not immediately write those changes to disk. Instead, it loads the affected page into shared memory (shared buffers), makes the modification there, and marks the page as dirty. A “dirty page” means the version of that page in memory is newer than the on‑disk copy.
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Introduction to NUMA

PostgreSQL and NUMA, part 1 of 4 This series covers the specifics of running PostgreSQL on large systems with many processors. My experience is that people spend months often learning the basics when confronted with the problem. This series tries to dispel these difficulties by providing a clear background into the topics in question. The hope is that future generations of database engineers and administrators don’t have to spend months figuring things out through trial and error. This entry in the series focuses on the low-level hows and whys of Non-Uniform Memory Access so that it is possible to understand the solutions and recommendations later with a focus on conceptual details. Unfortunately in many details this requires focusing on technical details as often the concepts without the details are confusing at best. Further entries will build upon the information in this post. We recommend reading it first, and then referring back as needed.
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Configuring Linux Huge Pages for PostgreSQL

Huge pages are a Linux kernel feature that allocates larger memory pages (typically 2 MB or 1 GB instead of the normal 4 KB). PostgreSQL’s shared buffer pool and dynamic shared memory segments are often tens of gigabytes, and using huge pages reduces the number of pages the processor must manage. Fewer page‑table entries mean fewer translation‑lookaside‑buffer (TLB) misses and fewer page table walks, which reduces CPU overhead and improves query throughput and parallel query performance.
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Understanding Disaster Recovery in PostgreSQL

System outages, hardware failures, or accidental data loss can strike without warning. What determines whether operations resume smoothly or grind to a halt is the strength of the disaster recovery setup. PostgreSQL is built with powerful features that make reliable recovery possible. This post takes a closer look at how these components work together behind the scenes to protect data integrity, enable consistent restores, and ensure your database can recover from any failure scenario.
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Don’t Skip ANALYZE: A Real-World PostgreSQL Story

Recently, we worked on a production PostgreSQL database where a customer reported that a specific SELECT query was performing extremely slowly. The issue was critical since this query was part of a daily business process that directly impacted their operations.
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