Philosophy of Everything

 moved to  https://software.mihvoi.ro/2026/04/2-flow-consistency-read-your-writes.html 2. Consistency, Availability and low Latency in Distributed system (workarounding the CAP/PACELC theorems)   Introduction: " 1. Cache and Data Consistency in Distributed systems (CAP/PACELC/CRDT) "   TL;DR Full strong Consistency in geographically Distributed systems can only be achieved by sacrificing Availability (per CAP theorem) and with prohibitive Latency costs (per PACELC theorem). However, we can still design consistent enough systems that continue to function when one geographical region is down and without paying the inter-region latency most of the time. While eventual Consistency is OK many times, there are still cases when we want a strong read-after-write consistency for certain read-after-write flows. There is an optimum design that assures strong Consistency inside read-after-writes flows . Arguably, this is the highest Consistency level that can be assure...

moved to:  https://software.mihvoi.ro/2026/04/1-cache-and-data-consistency-in.html Abstract There is always a tension between data  C onsistency and system  A vailability when  P artitioning a system across datacenters (think  CAP ). Especially data cache-ing poses interesting challenges. This tension becomes way more acute as soon you have 2 data centers separated by more than 10ms latency. I present below some of the problems along with possible solutions. In the end I will present an elegant solution that maximizes  A vailability while providing the needed C onsistency level for read-after-writes flows. The solution requires the client to carry a monotonic  id  along the flow. I would postulate that any solution where the client don't carry some consistency info will provide a higher latency that the presented solution (see chapter "Flow consistency" ) . The examples below are simplified to be intuitive and easy to understand, however the...