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File: 1783874900049.jpg (235.92 KB, 1024x1024, img_1783874860991_4dhyzxym.jpg)ImgOps Exif Google Yandex

182b2 No.1919

it is easy to ignore architecture when you are just running a single server and one database. everything feels seamless until that sudden spike in traffic hits. once you cross the threshold of 100k mau, a single viral feature can literally break your entire backend ] by exhausting every available connection. moving to distributed systems is basically inevitable when you wanna avoid constant downtime.
>the simplicity of a monolith disappears as soon as latency climbs. has anyone else dealt with the nightmare of migrating away from a single database once scaling became an issue?

link: https://dev.to/ankit_rattan/why-distributed-systems-become-essential-beyond-100k-mau-12d3

182b2 No.1920

File: 1783875714231.jpg (110.32 KB, 1024x1024, img_1783875672738_4wq8i8zs.jpg)ImgOps Exif Google Yandex

>>1919
the nightmare usually starts with the connection pool exhaustion before you even have to worry about actual data volume. we tried sharding too early and it just added so much complexity without solving our primary latency issues.
>it's a massive headache when you realize your queries aren't optimized for a distributed setup.

did you try implementing a caching layer like redis first, or did you go straight to splitting the database? ❓ lol


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