Consistency latest published posts

Consistency patternsdescribe how systems behave when multiple actors read and write shared data, especially under concurrency, failure, or distribution. Database transactions are one of the strongest tools for enforcing consistency, but they coexist with weaker models that trade correctness guarante

TheCAP theoremis one of the most fundamental principles in distributed system design. It defines the trade-offs between three key properties —Consistency,Availability, andPartition tolerance— and explains why it’s impossible to achieve all three simultaneously in a distributed data system. Formally

Many production data bugs originate not from missing transactions, but from misunderstanding what transactions actually guarantee. Systems often wrap logic inBEGINandCOMMITand assume correctness follows automatically. Under concurrent load, these systems still oversell inventory, double-charge users

Consistency is one of the foundational concerns when designing distributed systems. It answers the question:when a client reads data, how fresh or correct should that data be compared to the most recent write?Different applications have different tolerances for stale or conflicting data. This articl