Popular System Design Posts

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

PostgreSQL is one of the strongest default database choices for modern backend systems. It combines relational modeling, ACID transactions, rich SQL support, JSONB, extensibility, and a mature ecosystem.

Availability is one of the core goals in system design. It answers a simple but critical question: can users access the system when they need it? A highly available system continues to work despite server failures, database issues, traffic spikes, deployments, network problems, or even regional outa

Caching is one of the most effective techniques for improving performance, scalability, and availability in software systems. Instead of repeatedly fetching data from a slower source such as a database, API, object storage, or another service, a cache stores frequently used data in a faster layer.

Scalability is the ability of a software system to handle growth without unacceptable performance degradation. Growth can mean more users, more requests, more data, more tenants, more background jobs, or more complex queries.

AWS Lambda concurrency is one of the most important concepts to understand before running serverless applications in production. Lambda can scale quickly, but that does not mean every downstream system can handle unlimited parallel requests.

AWS Lambda is often introduced as a simple way to run code without managing servers. But in real production systems, Lambda is rarely used alone. It usually becomes part of a larger architecture with API Gateway , SQS , SNS , EventBridge , DynamoDB , S3 , Step Functions , and other AWS services.

PostgreSQL is easy to start with, but production PostgreSQL has many behaviors that developers usually learn only after slow queries, lock contention, failed migrations, or painful bulk imports.

Modern applications rarely do only one thing at a time. They call APIs, query databases, read files, send notifications, process messages, and wait for external services. If every operation runs one after another, the application often spends most of its time waiting instead of doing useful work.

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.

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.

Cloud computing replaces the traditional on-premises model , where companies had to buy, install, and maintain physical servers in their own data centers. AWS shifts this responsibility to a cloud provider, allowing developers to focus on building applications instead of managing hardware.

CP systems guarantee that all nodes maintain a consistent state even during partitions, but they reject or delay requests when coordination cannot be achieved. This is typically implemented using quorum-based replication or consensus protocols that require agreement before committing writes.

ClickHouse is an open-source, column-oriented database management system designed for online analytical processing ( OLAP ).

This benchmark compares PostgreSQL and ClickHouse using identical data and queries to evaluate performance in a read-heavy analytical workload.

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 pipelin

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 prod