Sup, fellow engineer! 👋 Let's break down how PostgreSQL keeps your data blazing fast, especially when your app is serving thousands of requests per second. We'll start with the "warm cache" and then dive deep into how your data actually gets pulled from disk.

🔥 What's the "Warm Cache" in PostgreSQL?

When we talk about "warm cache" in PostgreSQL, we're really talking about data that's living its best life in memory, specifically in the shared buffer pool.

• Think of it like this: Your computer's RAM is super-fast, while your disk (SSD/HDD) is comparatively slow.
• The Goal: PostgreSQL wants to serve as much data as possible from RAM to avoid hitting the slower disk.
• "Warm" Data: Data that's already in the shared buffer pool because it's been accessed recently or frequently. It's ready for instant retrieval! ⚡
• "Cold" Data: Data that's not in memory and needs to be fetched from disk. This takes a bit longer. 🐢

PostgreSQL's smart algorithms (like LRU - Least Recently Used) constantly manage this buffer pool, making sure the most relevant data stays "warm."

📏 How Much Warm Data Can We Hold? (shared_buffers)

The size of your warm cache is controlled by the shared_buffers configuration parameter.

• Typical Size: A common starting point is 25% of your total system RAM. So, on a 4GB server, 1GB is a very reasonable setting.
• The Unit: shared_buffers holds 8KB data blocks (pages).
• Quick Math! If you allocate 1GB to shared_buffers and each block is 8KB: That's over 131,000 individual 8KB chunks of your tables and indexes, ready to go! 🎉
• Balancing Act: Too large, and you starve the OS and other processes of RAM. Too small, and you'll hit the disk too often. Monitoring your "buffer hit ratio" is key to finding the sweet spot!

💡 How Does PostgreSQL Cache? (It's NOT Query Results!)

PostgreSQL's shared_buffers does NOT cache the results of your SQL queries (like a SELECT * FROM users WHERE id = 1).