Articles
Deep dives on .NET, React, cloud and everything in between.
Redis Failure Under Extreme Traffic: How to Prevent a Database Crash When 1 Million Requests Hit at Once
Learn how to design a resilient caching architecture when Redis suddenly goes down and millions of requests fall back to the database. Explore cache stampede, thundering herd, circuit breakers, rate limiting, request coalescing, cache warming, graceful degradation, Redis high availability, TTL strategies, and protecting the database from sudden traffic spikes.
07 Oct 2026 · 36 views
Production Database Disaster: How to Recover 10 Million Deleted Rows Without a WHERE Clause
A real-world database disaster scenario: 10 million rows were accidentally deleted in production because a DELETE query was executed without a WHERE clause. Learn how to respond, stop further damage, identify the recovery point, restore data using backups and transaction logs, perform point-in-time recovery, validate the recovered data, and design safeguards to prevent such incidents in the future.
07 Oct 2026 · 34 views
Designing a High-Scale Railway Ticket Booking System: Handling 2 Million Users for 500 Seats
Learn how to design a highly scalable railway ticket booking system that can handle 2 million users competing for just 500 seats. Explore traffic spikes, virtual queues, rate limiting, seat inventory management, distributed locking, Redis, database consistency, reservation timeouts, payment processing, idempotency, fault tolerance, and strategies to prevent double booking.
07 Oct 2026 · 24 views
Cache-Aside — System Design
Cache-Aside is the most commonly used caching pattern in system design. Reading from the database when cache misses Putting the database result into cache.
27 Sep 2026 · 24 views
What is Caching?
Caching means storing frequently accessed data in a faster storage layer so that future requests can be served faster without repeatedly accessing the primary database or another slower system.
27 Sep 2026 · 39 views
Hot, Warm & Cold DR — Disaster Recovery
When production goes down because of a region failure, database failure, infrastructure failure, or major outage, we need a Disaster Recovery (DR) strategy.
27 Sep 2026 · 36 views
RTO — Database Scaling
RTO (Recovery Time Objective) defines how quickly a system must be restored after a failure. In simple words, RTO = How much downtime can the business tolerate?
27 Sep 2026 · 23 views
RPO — Database Scaling
RPO (Recovery Point Objective) defines how much data an application can afford to lose after a failure.
27 Sep 2026 · 23 views
Disaster Recovery — Database Scaling
Disaster Recovery (DR) is the strategy used to restore an application's database, application services, and supporting infrastructure after a major failure.
27 Sep 2026 · 38 views