In my previous blog, I explored how serializability ensures isolation across transactions while linearizability guarantees real-time consistency of single objects. This post builds on that discussion by diving deeper into serializability itself. Concurrency control in databases is all about ensuring that transactions running in parallel behave as if they had…
In my last post Bloom Filters: MathMania, we worked through the derivations for optimal number of bits to allocate and the right number of hash functions to use – striking a balance between space, computation, and false positives. But all of that math rested on one simple assumption: the set of…
How do modern databases and distributed systems ensure correctness while allowing operations to run concurrently? Two foundational guarantees — serializability and linearizability — offer different answers depending on whether you care about logical correctness or real-time visibility. In this post, we’ll explore what they are, how they differ, and why it’…
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…