#Distributed-Systems

How Two-Phase Locking Keeps Concurrent Bank Transactions From Corrupting Your Data

Two transfers hit the same account in the same millisecond and one payment vanishes. This is the lost update, and it is why every payment ledger runs on a concurrency control layer. This article builds the theory from a fintech example up to two-phase locking (2PL): the transaction model, the math of conflict-serializability and the precedence graph, why the 2PL theorem guarantees correctness, pessimistic versus optimistic control in PostgreSQL, strict 2PL, and how deadlocks are detected and prevented.

Load Balancing Algorithms: Each One Fixes the Last One's Flaw

Spreading requests across N servers sounds like a one-liner: pick a server, send the request. Then one backend is slower, or bigger, or holds a session, and the naive choice falls apart. This walks the classic algorithms as a chain where each one exists to fix the previous one's blind spot: round robin, weighted, least connections, power of two choices, and consistent hashing, in Go, ending with the failure every tutorial forgets.

Zanzibar Demystified: How Google Answers 'Can This User Do This?'

Authorization looks like a one-line if statement until you run it ten million times a second across every product Google ships. Zanzibar is the system that made that question fast, consistent and global. This walks from the naive permission check to relationship-based access control, the tuple model, consistency with zookies, and the open-source heirs like OpenFGA you can actually run today.

Retry Storms: How Good Clients Take Down Healthy Servers

A retry looks harmless: the request failed, so try again. Multiply that by every client, add one slow dependency, and retries turn into a self-inflicted DDoS. This walks from the naive retry loop to exponential backoff, jitter, retry budgets and circuit breakers, the caller-side half of resilience that pairs with rate limiting on the server.

Why Redis Rate Limiting Breaks at Scale (and What Uber Does Instead)

A token bucket in memory is trivial. Put it behind Redis and it works, until it doesn't. This walks through rate limiting from one node, to a shared Redis, to why that model collapses at millions of requests per second, and the shift Uber made: enforce locally, coordinate globally, and drop by probability.