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…
So, you’ve come across Bloom filters and understand that, despite their probabilistic nature, they are a great fit for your use case. You’ve decided to integrate them into your system design, but you’re unsure about the optimal size and the number of hash functions needed for your…
Durability requirements influence the choice of data protection mechanisms, such as replication, erasure coding, and RAID parity configurations. Achieving higher durability involves trade-offs between redundancy, storage usage ratio, and computational complexity. Replication achieves durability by creating multiple copies of data, which increases redundancy but reduces the storage usage ratio. In…
In my last post, we saw how databases achieve concurrency by creating wiggle room for interleaving non-conflicting operations through conflict-free schedules. This ability to reoder independent operations without changing the final outcome is what gives serializable systems their performance edge. But we also saw that conflicting operations fix the relative…
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, essentially exploring how valid serial transaction orders are nothing but a means to achieve concurrency through conflict-free scheduling. We…