In the previous post, we saw how distributed systems escape the chaos of unreliable clocks by using logical time to measure causality instead of seconds. But there’s one domain where time keeps sneaking back in i.e., Coordination. And as already explained in Part I of this series, coordination…
In the last post, we saw how physical clocks betray us! They drift, skew, jump, and bend, creating logs where effects appear before causes and refunds happen before payments. So what do we do when time itself can’t be trusted? We stop measuring time, and start measuring causality. Second…
Distributed systems often behave as if time were universal, objective truth as if two machines agreeing on a timestamp meant they also agreed on the order of events. But clocks in computers are not laws of nature! They drift with time, freeze under load, stumble over leap seconds, and lags…
Consistency models are often introduced as abstract rules: causal, sequential, linearizable, and so on. But what they really do is draw boundaries around the kinds of mistakes a distributed system can make. If consistency is about agreement, anomalies are the cracks where that agreement breaks. Each model exists precisely to…
Distributed systems differ not only in what they store but in how they order events. Every read, write, and replica update follows an underlying ordering guarantee, a rule defining how operations appear across replicas. As these guarantees strengthen, systems exchange availability and latency for stronger forms of consistency. Every distributed…