3d ago · 3 min read · How to build backend systems that continue to work even when things go wrong In earlier parts, we saw how systems fail under load. Traffic increases, dependencies slow down, and small issues turn into full outages. The goal of system design is not to...
Join discussion4d ago · 3 min read · Why most backend performance issues eventually lead back to the database In Part 1, we saw how systems collapse under pressure.In Part 2, we saw how caching can help or hurt. Now we look at the most common bottleneck in backend systems: The database....
Join discussion4d ago · 3 min read · Why controlling traffic matters more than handling it In Part 1, we saw how systems collapse under pressure.In Part 2 and 3, we looked at caching and database bottlenecks. But there is one concept that directly controls pressure: Rate limiting. Most ...
Join discussionApr 17 · 3 min read · Why most backend performance issues eventually lead back to the database In Part 1, we saw how systems collapse under pressure.In Part 2, we saw how caching can help or hurt. Now we look at the most common bottleneck in backend systems: The database....
Join discussionApr 13 · 3 min read · Most backend systems do not fail during development. They fail later, in production, when everything seems to be working fine. One day the system is fast and stable.The next day it starts throwing errors, slowing down, and eventually becomes unusable...
Join discussionFeb 28 · 4 min read · Cloud storage systems are not accessed by trust — they are accessed by authorization. Modern applications cannot safely embed permanent credentials in frontend apps, mobile devices, or distributed services. Instead, they use temporary, scoped access ...
Join discussionFeb 25 · 3 min read · In the previous article, we saw how to scale reads by reducing the amount of work the database has to do for every query. Writes have a different problem. A write is not just “store this data”.A write often means: Update indexes Maintain order Enfor...
Join discussionFeb 25 · 7 min read · In the previous two articles, we saw something important: To scale reads, we reduce the work the database does per query. [LINK] To scale writes, we reduce the work the database does per insert. [LINK] But what happens when a system has both heavy ...
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