Latest System Design Posts

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.

SQL databases (also known as relational databases) are systems designed to store, organize, and query structured data using a predefined schema. They are built around the relational model, where data is stored in tables and relationships between entities are explicitly defined.

Choosing between SQL and NoSQL for an MVP is one of the most common early architectural decisions — and one of the most overthought. The honest answer is still: it depends , but in practice, the decision usually comes down to two things: time to build and total cost .

The Domain Name System (DNS) is a distributed naming system that translates human-readable domain names (like example.com) into IP addresses used by machines to communicate over the internet.

A Content Delivery Network (CDN) is a distributed network of servers that cache and deliver content closer to users. Instead of serving every request from a single origin server, a CDN uses geographically distributed edge locations to reduce latency and improve performance.

Amazon Web Services (AWS) is a cloud computing platform that provides compute, storage, networking, and database infrastructure over the internet. Instead of managing physical servers, companies consume infrastructure on-demand using a pay-as-you-go model .

CAP theorem defines three guarantees in distributed systems: consistency , availability , and partition tolerance, with the constraint that only two can be fully satisfied during a network partition. In real-world architectures, partition tolerance is unavoidable, which forces systems to choose betw

This benchmark comparesPostgreSQLandClickHouseusing identical data and queries to evaluate performance in a read-heavy analytical workload. Each query was executed once. Both PostgreSQL and ClickHouse were run with default configurations (no custom tuning). The goal of this article is to demonstrate

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

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

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 managed NoSQL key–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 case study describes a production blog platform backed entirely by Amazon 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 messag