In the previous post, we understood isolation levels through the lens of their anomalies i.e., what goes wrong when transactions interleave too freely. But knowing what to prevent is only half the story. Databases must also decide how to prevent it. That’s where concurrency control mechanisms come in.…
We often think of isolation levels as checkboxes in a database configuration such as Read Committed, Repeatable Read, Serializable. But these labels are not arbitrary. They’re the database world’s answer to a simple question: What can possibly go wrong if two transactions run together? Each isolation level exists…
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…
Modern distributed systems are built from many moving parts - databases, services, queues, caches, and schedulers - all operating concurrently and often out of sync. The only reason this doesn’t collapse into chaos is coordination. Coordination is how independent components stay aligned - who leads, who follows, who owns…