Algorithms

Imagine you’re running a service with 10 servers, each capable of handling 1,000 requests per second. You set up a round-robin load balancer—simple, elegant, fair. Every server gets its turn in sequence. Traffic flows smoothly until suddenly, at 2 AM, your monitoring alerts start screaming. Half your servers are overwhelmed, queues are growing, latencies are spiking, and the other half of your servers are nearly idle. What went wrong? The servers weren’t identical. Three of them were newer machines with faster CPUs and more memory. Three were legacy boxes running older hardware. The round-robin algorithm, in its mechanical fairness, sent exactly the same number of requests to a struggling legacy server as it did to a powerful new one. The legacy servers couldn’t keep up, requests piled up in their queues, and eventually they started timing out—cascading into a partial outage that woke up half your engineering team. ...

In 1970, Burton Howard Bloom faced a problem that would feel familiar to any modern software engineer working with large datasets. He needed to check whether words required special hyphenation rules, but storing 500,000 dictionary entries in memory was prohibitively expensive. His solution—a data structure that uses dramatically less space than any traditional approach—became one of the most widely deployed probabilistic data structures in computing history. The insight was radical: what if you could trade certainty for space? A Bloom filter will never tell you an item is absent when it’s actually present (no false negatives), but it might occasionally claim an item exists when it doesn’t (false positives). For many applications, this trade-off is not just acceptable—it’s transformative. ...