PostgreSQL Closes Operational Gap with REPACK CONCURRENTLY

The cool part, IMO, is the extension / tool to core-feature path. Adding major changes to core is challenging, so proving out the concept in an extension / tool first and providing this value in the ecosystem is a great path forward. A lot of folks who suggest new Postgres contributions are disappointed when they get “this sounds like an extension” as a response, but it’s actually good advice.

I am m starting a deep dive series on PostgreSQL Internals Most developers use PostgreSQL every day. but very few understand what actually happens inside the database. So I am breaking it down step by step in a simple, practical way. Part 1: PostgreSQL Architecture Explained In this first post, I cover: Why PostgreSQL uses processes instead of threads What happens when you connect to a database How a SQL query is actually executed What shared memory means in simple terms Why PostgreSQL is so reliable in production systems Read full article here...

PostgreSQL Reality Check: “VACUUM Doesn’t Free Space (The Way You Think)” Many people believe that running VACUUM will free up disk space. However, this is not entirely accurate. What Actually Happens? PostgreSQL employs Multi-Version Concurrency Control (MVCC). When you execute a command like: UPDATE users SET name = 'Ali' WHERE id = 1; PostgreSQL does not overwrite the existing row; instead, it creates a new version of the row, leaving the old row as a “dead tuple.” So What Does VACUUM Do? VACUUM performs the following functions: - Marks dead tuples as reusable - Cleans visibility maps - Assists future inserts in reusing space However, it does NOT return space to the operating system. The Common Misunderstanding If you delete 1 million rows and then run: VACUUM users; you may notice that the disk size remains unchanged. When Do You Actually Reclaim Space? To reclaim space, you need to run: VACUUM FULL users; Keep in mind that this operation: - Locks the table - Rewrites the entire table - Is a heavy operation not suitable for peak production hours Better Approach (Production Safe) To manage this effectively: - Enable autovacuum properly - Monitor table bloat using pg_stat_user_tables - Schedule maintenance windows as needed Pro Insight To check for dead tuples, you can run: SELECT relname, n_dead_tup FROM pg_stat_user_tables ORDER BY n_dead_tup DESC; A high n_dead_tup indicates that bloat is building up. Golden Rule: “DELETE doesn’t delete immediately in PostgreSQL — it just hides data.” Final Thought: If your database is experiencing slowdowns over time, it’s likely not due to your queries but rather table bloat quietly impacting performance. #PostgreSQL #PostgreSQLDBA #PostgreSQLPerformance

At what point should you abandon PostgreSQL (pgvector) for a dedicated Vector DB like Qdrant? Many engineers assume "data size" is the trigger. However, after benchmarking 500,000 records, I found that the true boundary lies in Selectivity (Metadata Hit Rate), not just volume. My results exposed a fascinating "U-Curve" in PostgreSQL's performance: ・High Selectivity (60%): PostgreSQL wins (12ms). No network overhead. ・Ultra-Low Selectivity (0.1%): PostgreSQL wins again (5ms). The optimizer intelligently switches to B-Tree + KNN. ・The "Performance Cliff" (5%): PostgreSQL spikes to 83ms, while Qdrant stays flat at 18ms. This 5% "sweet spot" is where HNSW index traversal becomes a liability for Postgres, and where Qdrant’s single-stage filtering becomes a mathematical necessity. I’ve documented the full breakdown of why this U-Curve happens and how to detect the "Performance Cliff" using EXPLAIN ANALYZE. If you're debating your RAG stack, I hope this data helps you make an architectural decision based on measurements rather than assumptions. To ensure this reaches as many people as possible, I've placed the link to the full technical breakdown in the first comment below.

🚨 New Medium Article Alert! Ever wondered what actually happens behind the scenes when you run a simple SELECT query in PostgreSQL? We write queries every day… SELECT * FROM users WHERE id = 42; But under the hood, PostgreSQL is doing a LOT more than just fetching rows. In my latest article, I break it down in a simple, visual, and easy-to-understand way: 🔍 What happens after you hit enter ⚙️ How the query parser, planner, and executor work 📊 Why indexes matter 🚀 How PostgreSQL optimizes your queries for performance If you're working with databases, backend systems, or building data pipelines — this is something you must understand. 💡 Whether you're a beginner or an experienced engineer, this will change how you think about query performance. 👉 Read the full article here: https://lnkd.in/gRkF_gZk #PostgreSQL #Databases #BackendEngineering #SystemDesign #PerformanceOptimization #SQL #TechExplained

🚀 Medium Article Alert: PostgreSQL Write Path Explained What really happens when you run an INSERT in PostgreSQL? You type: INSERT INTO users VALUES (...) → hit Enter → INSERT 0 1 ✅ Simple, right? But under the hood, PostgreSQL is doing some serious heavy lifting to ensure your data is durable, consistent, and crash-safe — even if the power goes out right after execution. From WAL (Write-Ahead Logging) to buffer management and checkpoints, this article breaks down the complete write path in a simple, intuitive way. 📖 If you're working with databases, distributed systems, or building data pipelines — this is a must-read. 👉 Read the full article here: https://lnkd.in/gqMN5fnQ #PostgreSQL #Databases #DataEngineering #SystemDesign #Backend #WAL #TechDeepDive

10 years of PostgreSQL development, on one page. I made this after reading Robert Haas' latest decade-of-Postgres review on pgsql-hackers https://lnkd.in/gKNeMYvT and decided to visualize. Here is it: https://lnkd.in/g29ghrJR A few things jumped out: - 2025 was the most active year on record by almost every measure Haas tracks - 266 distinct authors contributed -- the widest pool ever - But only 31 people hold commit keys (this is how Postgres dev process is organized – "committers" is a very small group). That ratio hasn't moved much in 10 years - It's hard to overstate how central Tome Lane is to Postgres development Bonus – part 2 (beyond original Robert's data): https://lnkd.in/gxwDAmnV

🔍 Case Study: Why PostgreSQL Slowed Down Overnight Everything looked fine—until nightly operations began. Queries slowed, storage kept growing, and nothing seemed obviously broken. The root cause? A hidden mix of large data cleanups and backups that quietly created table “bloat”—extra space PostgreSQL couldn’t reuse efficiently. We tackled it with a combination of online reorganization and smarter data cleanup, restoring performance and freeing up disk space—without downtime. 👉 Check the comments for the full case study. Hossted – Beyond Support. #PostgreSQL #OpenSource #DatabasePerformance #CaseStudy #Hossted #DBA #DevOps #SRE #PerformanceTuning #CloudInfra #SQL #PostgresTips

How VACUUM can impact queries (and create vacuum lag) PostgreSQL doesn’t delete rows immediately. It marks them as dead. VACUUM later cleans these dead rows and frees space. When VACUUM can’t keep up (due to high write load, long-running transactions, or misconfigured auto-vacuum), dead tuples accumulate. This leads to vacuum lag, slower queries, bloated tables, and inefficient index scans. VACUUM isn’t just maintenance. It’s a critical part of query performance and database health. Ignoring it doesn’t fail fast & it fails quietly. #PostgreSQL #DatabaseInternals #VACUUM #SystemDesign #BackendEngineering #PerformanceEngineering #SoftwareArchitecture #Databases

🚀 Important PostgreSQL Concepts Every Developer Should Learn If you're working with PostgreSQL, just knowing basic queries isn’t enough. To become a strong backend developer, you need to understand these core concepts 👇 🧠 1️⃣ Indexing Speeds up your queries drastically. Learn B-Tree, Hash indexes, and when to use them. 🔗 2️⃣ Joins Master INNER, LEFT, RIGHT joins to combine data across tables efficiently. ⚡ 3️⃣ Query Optimization Understand EXPLAIN / ANALYZE to debug slow queries and improve performance. 🔄 4️⃣ Transactions & ACID Learn how PostgreSQL ensures data consistency using transactions (COMMIT, ROLLBACK). 📦 5️⃣ Normalization Design clean and efficient schemas by avoiding data redundancy. 🔐 6️⃣ Locks & Concurrency Understand how PostgreSQL handles multiple users accessing data at the same time. 🧾 7️⃣ Views & Materialized Views Simplify complex queries and improve performance for repeated reads. ⚙️ 8️⃣ Stored Procedures & Functions Move business logic closer to the database for efficiency. 📊 9️⃣ Partitioning Handle large datasets by splitting tables for better performance. 🔁 🔟 Replication Learn read replicas & high availability for production systems. 🔥 Real Insight: Most developers stop at CRUD — but real growth happens when you understand how the database works internally. #PostgreSQL #Database #BackendDeveloper #SystemDesign #SQL #SoftwareEngineering #LearnToCode