Latest System Design Articles

Write-through caching is a caching strategy where application writes update the cache and the underlying database as part of the same write path. A write is considered successful only after the authoritative storage has been updated.

Vertical scaling, also called scaling up , increases a system's capacity by giving an existing machine more resources: additional CPU cores, memory, faster storage, higher network bandwidth, or a larger cloud instance.

Availability is the ability of a software system to remain accessible and usable when users need it.

Reliability is the ability of a software system to keep doing the correct thing over time, even when parts of the system fail.

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

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.

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.

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

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

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

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

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

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

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

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 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

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.