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PostgreSQL Materialized Views: When Caching Your Query Results Makes Sense (And When It Doesn’t)

Your dashboard queries are timing out at 30 seconds. Your BI tool is showing spinners. Your users are refreshing the page, wondering if something's broken. You've indexed everything. You've tuned shared_buffers. You've rewritten the query three times. The problem isn't bad SQL - it's that you're forcing PostgreSQL to aggregate, join, and scan millions of rows every single time someone opens that report.
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Unlocking High-Performance PostgreSQL: Key Memory Optimizations

PostgreSQL can scale extremely well in production, but many deployments run on conservative defaults that are safe yet far from optimal. The crux of performance optimization is to understand what each setting really controls, how settings interact under concurrency, and how to verify impact with real metrics.This guide walks through the two most important memory parameters : - shared_buffers - work_mem
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Unused Indexes In PostgreSQL: Risks, Detection, And Safe Removal

Indexes exist to speed up data access. They allow PostgreSQL to avoid full table scans, significantly reducing query execution time for read-heavy workloads.From real production experience, we have observed that well-designed, targeted indexes can improve query performance by 5× or more, especially on large transactional tables. However, indexes are not free. And in this blog, we are going to discuss what issues unused indexes can cause and how to remove them from production systems with a rollback plan, safely
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PostgreSQL Column Limits

If you’ve ever had a deployment fail with “tables can have at most 1600 columns”, you already know this isn’t an academic limit. It shows up at the worst time: during a release, during a migration, or right when a customer escalation is already in flight. But here’s the more common reality: most teams never hit 1,600 columns; they hit the consequences of wide tables first
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The Road to Deploy a Production-Grade, Highly Available System with Open-Source Tools

Everyone wants high availability, and that’s completely understandable. When an app goes down, users get frustrated, business stops, and pressure builds.But here’s the challenge: high availability often feels like a big monster. Many people think, If I need to set up high availability, I must master every tool involved. And there’s another common belief too: Open-source tools are not enough for real HA, so I must buy paid tools.
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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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