Popular System Design Posts

This case study describes a production blog platform backed entirely byAmazon DynamoDB. The system serves typical blog workloads such as “latest posts”, “most popular posts”, “posts by author”, and “posts by tag”, while also supporting administrative queries like user moderation and contact messages

What availability means, how it's measured, architecturalavailability patterns, practical trade-offs, and how to validate an availability target. Availabilityis the probability that a system performs its required function at a given point in time. Practically, it answers the question:“Is the service

Scalability is the ability of a system to handle increased load — such as more users, data, or requests — without unacceptable performance degradation. It can be achieved in two main ways: It also includes operational, cost, and development scalability — how the system, budget, and team scale with g

PostgreSQLis an open-source relational database management system (RDBMS) known for standards compliance, extensibility, and strong transactional guarantees. It implementsACIDsemantics by default and supports advanced features such as JSONB, window functions, partial indexes, and custom data types.A

A high-scale lead management system designed to store and retrieve contact information for 200M users. The system supports lookups by phone number, email address, and SSN to resolve associated user profiles.A typical request maps a single contact identifier to exactly one user. ReplacingMergeTreeis

Caching is the process of temporarily storing frequently accessed data in a faster storage layer to reduce latency, save computation, and improve system scalability. Instead of recomputing or refetching data from a slower source like a database or API, a cache serves it quickly from memory or nearby

Relational Database Management Systems (RDBMS)remain foundational infrastructure in modern distributed systems because they provide strong consistency, transactional guarantees, and mature tooling for data integrity. Large-scale systems such as payment platforms, logistics networks, and SaaS product

Modern distributed systems rely on communication protocols that define how services exchange data. As a software developer designing APIs, microservices, real-time systems, or event-driven platforms, choosing the correct protocol directly impacts latency, scalability, observability, cost, and develo

Consistency patternsdescribe how systems behave when multiple actors read and write shared data, especially under concurrency, failure, or distribution. Database transactions are one of the strongest tools for enforcing consistency, but they coexist with weaker models that trade correctness guarante

Domain Name System (DNS)is the distributed naming system that translates human-readable domain names into IP addresses that machines can route. In practical system design, DNS often becomes a hidden dependency whose behavior directly impacts availability, latency, and security.An e-commerce checkout

TheCAP theoremis one of the most fundamental principles in distributed system design. It defines the trade-offs between three key properties —Consistency,Availability, andPartition tolerance— and explains why it’s impossible to achieve all three simultaneously in a distributed data system. Formally

Consistency is one of the foundational concerns when designing distributed systems. It answers the question:when a client reads data, how fresh or correct should that data be compared to the most recent write?Different applications have different tolerances for stale or conflicting data. This articl

Modern software systems must handle rapid growth, unpredictable load, and unexpected failures — all without compromising user experience. Three key pillars define such systems:scalability,availability, andstability. Each comes with its own design patterns and trade-offs that engineers use to build r

Caches can exist on the client (OS or browser), on the server, or in a dedicated cache layer. Content delivery networks (CDNs) act as distributed caches to improve performance and availability. Reverse proxies and caching servers like Varnish can serve static and dynamic content directly. Web server

Online Analytical Processing (OLAP) engines power analytical workloads involving aggregations, scans, and complex queries over large datasets. Selection impacts query latency, cost efficiency, scalability limits, and operational complexity. Different engines optimize for columnar storage, distribute

OLAP databases(Online Analytical Processing) are optimized for complex queries, aggregations, and large-scale analytics workloads. Unlike transactional systems, they focus on read-heavy operations, enabling fast insights across massive datasets, making them essential for BI tools, reporting pipeline

NoSQL engines provide different trade-offs inlatency, consistency, scalability, and query flexibility. Choosing the right engine requires understandingdata access patterns, workload characteristics, and operational complexity, not just database type. Choosing the right NoSQL engine directly impactsl

NoSQL databases are designed to handlelarge-scale, high-velocity, and unstructured datathat traditional relational databases struggle with. They often sacrifice strict consistency forscalability, flexibility, and performance, making them ideal for modern distributed systems. Choosing the right NoSQL

Many SaaS applications begin with a very simple idea: one user signs up, creates data, and owns that data. This model works extremely well in the early stages of a product. It is easy to reason about, easy to implement, and easy to secure. However, as the product grows, a common requirement appears:

Amazon DynamoDB is a fully managedNoSQLkey–value and document database service provided by AWS. It is designed to deliver single-digit millisecond performance at virtually any scale, without requiring developers to manage servers, storage, or replication. This article covers what DynamoDB is, how it