๐ช๐ต๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฝ๐ฝ ๐๐ถ๐ฒ๐ ๐๐ ๐ญ ๐ ๐ถ๐น๐น๐ถ๐ผ๐ป ๐ฅ๐ผ๐๐
You build an app. It works. You ship it.
One user uploads a million rows. The loading spinner stays. Memory spikes. The tab crashes.
Your server loads every row. It sends all data to the browser. The browser tries to render a million elements. The system fails.
Do not make code faster. Make it do less. Use the coin flip approach.
Do you need every person in a city to find the average age? No. You ask a small group. You get a close answer. This is probabilistic processing.
Try these techniques:
- Pagination: Load 50 rows. Wait for the user to ask for more.
- Virtual Rendering: Create elements for visible rows. Recycle them as you scroll.
- Streaming: Process data in chunks. Discard data after use.
- Reservoir Sampling: Pick a random sample without loading the whole set.
Look at HyperLogLog. It estimates unique items in a billion rows. It uses a few kilobytes of memory. It stays accurate within 2%. Big companies use this. They trade tiny accuracy for massive speed.
Approximate answers in milliseconds beat exact answers in minutes.
Stop asking how to process all data. Ask how much data you need for a useful answer.
Tell me how you solved a performance problem by doing less.
Source: https://dev.to/iampraveen/the-untold-story-of-why-your-app-dies-at-1000000-rows-2h6k