Thinking Out Loud

“Our bodies will stay always as they are, fresh and alive, but we must never hesitate to bring death, because it is how we live.” — Lestat, Interview with the Vampire The unwritten rule among vampires was broken the night Claudia was made.

I run dark mode everywhere macOS, browser, editor, terminal.

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.

So, you’ve come across Bloom filters and understand that, despite their probabilistic nature, they are a great fit for your use case.

Durability requirements influence the choice of data protection mechanisms, such as replication, erasure coding, and RAID parity configurations.

This blog post is another take at isolation levels, a topic I first explored in one of my earlier posts.

In my last post, we saw how databases handle conflicts through different philosophies i.e., prevention, validation, versioning, and observation, allowing transactions to run concurrently without violating serializability.

In my last post, we saw how databases achieve concurrency by creating wiggle room for interleaving non-conflicting operations through conflict-free schedules.

In my previous blog, I explored how serializability ensures isolation across transactions while linearizability guarantees real-time consistency of single objects.

How do modern databases and distributed systems ensure correctness while allowing operations to run concurrently?

In the previous post, we looked under the hood at how databases enforce isolation through locks, validations, and snapshots.

In the previous post, we understood isolation levels through the lens of their anomalies i.e., what goes wrong when transactions interleave too freely.

We often think of isolation levels as checkboxes in a database configuration such as Read Committed, Repeatable Read, Serializable.

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.

In the last post, we saw how physical clocks betray us!