Distributed Systems

In April 2025, Meta’s Llama 4 Scout achieved something previously thought impossible: processing 10 million tokens in a single context window. To put this in perspective, that’s roughly 20 novels, 40 hours of video, or an entire mid-sized codebase—all in one prompt. The secret behind this breakthrough isn’t a revolutionary new model architecture or exotic hardware. It’s a clever distributed computing technique called Ring Attention that fundamentally rethinks how we compute attention across multiple GPUs. ...

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 1985, three researchers—Michael Fischer, Nancy Lynch, and Michael Paterson—published a result that would fundamentally reshape how we think about distributed systems. Their theorem, now known simply as FLP, demonstrated something unsettling: in an asynchronous distributed system where even a single process can fail, there exists no deterministic algorithm that is guaranteed to solve consensus. This wasn’t a limitation of current technology or a gap in our knowledge. It was a mathematical impossibility—a fundamental boundary that no amount of engineering cleverness can overcome. Yet today, distributed databases coordinate across continents, consensus algorithms power everything from cloud infrastructure to blockchain networks, and systems achieve agreement millions of times per second. How do we reconcile this apparent contradiction? ...

On September 20, 2015, Amazon DynamoDB in US-East-1 went dark for over four hours. The root cause wasn’t a hardware failure or a cyberattack—it was a feedback loop. Storage servers couldn’t retrieve their partition assignments from a metadata service, so they retried. The metadata service became overwhelmed. More timeouts. More retries. More overload. Engineers eventually had to firewall the metadata service from storage servers entirely, effectively taking DynamoDB offline to break the cycle. ...

A financial services company migrated their payment processing to a message queue architecture. The design seemed straightforward: producers publish payment requests, workers consume and process them. Six months later, they discovered their customers had been double-charged for approximately 3% of transactions. The queue was working exactly as configured—the problem was that “working” meant something different than they expected. Message queues occupy a strange position in distributed systems. They appear deceptively simple on the surface: put message in, get message out. But beneath that simplicity lies a maze of trade-offs involving durability, ordering, delivery guarantees, and failure modes. Understanding these trade-offs isn’t academic—it’s the difference between a reliable system and one that silently corrupts data. ...

When Amazon engineers published the Dynamo paper in 2007, they revealed a technique that had been quietly powering some of the world’s largest distributed systems. The core idea—consistent hashing—originated from a 1997 MIT paper by David Karger and colleagues, but it took a decade before the industry fully embraced its elegance. Today, consistent hashing underpins Apache Cassandra, Amazon DynamoDB, Discord’s messaging infrastructure, Netflix’s content delivery network, and virtually every modern distributed database. The algorithm solves a deceptively simple problem: how do you distribute data across servers when those servers keep joining and leaving? ...

A major e-commerce platform celebrated when their cache hit ratio hit 99.2%. Their dashboard showed beautiful green charts. Three days later, their database collapsed during a flash sale. The cache hit ratio never dropped below 98%. What went wrong? The team optimized for the wrong metric. While their cache served 99% of requests from memory, the 1% that missed were the most expensive queries—complex aggregations, joins across multiple tables, and expensive computations. A cache hit ratio tells you how often you avoid work, not how much work you’re avoiding. ...

At 2:17 AM on a Tuesday, a major e-commerce platform’s API went down. The incident report later revealed the root cause: a misconfigured rate limiter had allowed a burst of requests through at exactly the boundary between two time windows, overwhelming downstream services. The platform had implemented a fixed window counter—the simplest rate limiting algorithm—and paid the price for its simplicity. Rate limiting seems straightforward: allow N requests per time period. But the algorithm you choose determines not just whether your system survives traffic spikes, but how fairly it treats users, how much memory it consumes, and whether it creates new failure modes you never anticipated. The difference between algorithms isn’t academic—it’s the difference between a system that degrades gracefully and one that cascades into total failure. ...