Latest System-Design Articles

Scalability is the ability of a software system to handle increasing load without becoming unacceptably slow, unstable, or expensive.

System design decisions depend heavily on scale. An architecture serving 100 requests per second can look very different from one handling 100,000 requests per second, billions of stored objects, or millions of persistent connections.

A URL shortener converts a long URL into a compact identifier that redirects users to the original destination. The product appears simple, but designing it at large scale introduces important system design questions around identifier generation, storage, caching, database partitioning, redirects, a

Storage architecture affects far more than where bytes are persisted. The storage model influences latency, throughput, consistency, failure recovery, scalability, operational complexity, and cost . A poor storage choice can become an architectural bottleneck long before CPU or network capacity beco

System design interviews are rarely failed because a candidate has never heard of caching, load balancing, replication, queues, or database sharding. More often, the problem is how those concepts are applied : designing before understanding requirements, estimating numbers that never influence the a

Production search systems fail in more ways than returning the wrong document. They can become slow under load, fall behind on indexing, overload databases, produce stale results, create hot shards, return inconsistent rankings, or become impossible to tune because nobody knows why a result was rank

A notification system delivers messages to users through channels such as mobile push, email, and SMS. Sending one notification is straightforward. Sending millions reliably becomes a distributed-systems problem involving asynchronous processing, fan-out, user preferences, provider rate limits, retr

Choosing between object, file, and block storage is an architectural decision about access patterns, latency, consistency, scalability, and operational semantics . All three ultimately persist bytes, but they expose those bytes differently and therefore behave very differently under production workl

Autocomplete looks simple because the interface is small: a user types a few characters and the system returns a short list of likely completions. In production, however, autocomplete is a latency-sensitive search problem with very different requirements from ordinary full-text search.

A search system can return correct results and still fail in production if latency grows under load, indexing falls behind, hot shards overload individual nodes, or expensive queries consume too much CPU and memory.

A system design interview evaluates how an engineer turns an ambiguous product problem into a practical architecture. The goal is rarely to discover one perfect design. Interviewers are more interested in how requirements are clarified, assumptions are made, scale is estimated, components are select

A file storage service allows users to upload, download, organize, and synchronize files across devices. At small scale, files can be written directly to a server's local disk. At large scale, that approach breaks down because application servers are ephemeral, files can be gigabytes in size, storag

At first glance, LeetCode problems and system design seem unrelated. One focuses on algorithms and data structures, while the other deals with distributed systems, scalability, and real-world constraints.

A chat application looks simple from the client perspective: a user sends a message and another user receives it. At scale, that interaction requires persistent connections, message routing, durable storage, ordering, offline delivery, synchronization across multiple devices, and infrastructure capa