Latest System Design Posts

ClickHouse is an open-source, column-oriented database management system designed for online analytical processing (OLAP). It is built to process massive datasets quickly and efficiently, making it ideal for analytics rather than transactional workloads. Traditional databases are optimized for trans

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

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

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

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

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 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

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

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

Availability patternsdescribe architectural techniques that keep systems operational despite failures, overload, or partial outages. High availability matters most in systems that support continuous business processes, such as payment authorization or customer-facing APIs, where downtime directly tr

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

Many production data bugs originate not from missing transactions, but from misunderstanding what transactions actually guarantee. Systems often wrap logic inBEGINandCOMMITand assume correctness follows automatically. Under concurrent load, these systems still oversell inventory, double-charge users

Database transactions are the foundation of correctness in almost every non-trivial system. Any system handling money transfers, inventory updates, or user state changes relies on transactions to prevent partial writes and inconsistent reads. A payment service that debits an account but fails to cre

This 4th part of the series builds on the previous discussion inScalability for Dummies - Part 3: Cache. Imagine trying to book a popular concert ticket online. A user selects a seat, but instead of an immediate confirmation, the website asks to return in two hours when the order is processed. This

After followingScalability for Dummies - Part 2: Database, the database can now handle massive volumes of data and high concurrency. Despite this, users may still experience slow page loads when queries fetch large amounts of data. The bottleneck is no longer the database itself but the repeated ret