Missing Database Index Crashed a Live Launch With 400,000 Records Loaded

·1 views

In 2021, a first-time startup developer in India helped build a full-stack app that was set to launch with over 400,000 pre-loaded user records in a MongoDB database. During the live launch event, the server began crashing after just 50 mid-level users signed up, largely because the database was running on an AWS free-tier EC2 instance with no room to scale. The root cause turned out to be a missing index on a key collection, forcing the CPU to scan all 400,000 records for every single query and pushing it to 100% utilization. The team quickly scaled up the server and added the missing index, rescuing the event in real time. Five years later, the same platform serves over 5 million users and runs at roughly 0.05 rupees per user per month after ongoing optimizations including Redis integration.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

GitHub Launches Beginner's Guide to Getting Started with Copilot App

GitHub has published an introductory guide aimed at new users of its Copilot app. The guide covers how to start projects and work with AI agents within the platform. It also introduces users to the canvas feature and explains how to streamline development workflows. The resource is designed to help beginners quickly get up to speed with the AI-powered coding assistant.

0 comments Read more at GitHub Blog

ProgrammingDEV Community ·

Dev Tutorial Series Uses Uncle-Nephew Dialogue to Demystify High-Level Design

A DEV Community tutorial series uses a fictional dialogue between an experienced engineer and his nephew to explain software development concepts stage by stage. Episode 3 focuses on High-Level Design (HLD), describing it as the process of deciding which system components communicate with each other before any code is written. The uncle illustrates HLD using a simple four-layer Wishlist feature architecture — Frontend, API, Service, and Database — set in a large-scale company context. He demonstrates how a poorly bounded design forces developers to modify existing, working code when new features are added, increasing the risk of unintended breakage. By contrast, a well-structured HLD allows new requirements to be met by adding new components rather than rewriting old ones.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Single PostgreSQL Query Can Handle Pagination but Risks Inefficient Execution Plans

A SQL technique allows developers to write one prepared statement in PostgreSQL that handles both first-page and subsequent-page pagination, using a NULL-check condition to toggle filtering. The approach uses a WHERE clause structured as '$1::timestamptz IS NULL OR timestamp > $1', eliminating the need for two separate queries as typically required in MongoDB implementations. When PostgreSQL uses a custom plan, it detects the NULL check at parse time and applies an efficient index scan, reading only the required rows. However, when the database falls back to a generic plan — as can happen after repeated executions — the index condition is dropped, causing a far slower full index scan across thousands of rows. Developers are advised to be aware of this trade-off when relying on generic prepared statements for performance-critical pagination queries.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Simple formula can reveal whether an ad experiment is doomed before it starts

Most inconclusive ad experiments fail not because the idea was bad, but because the test never had enough statistical power to detect a meaningful result. A minimum detectable effect (MDE) formula shows that at a 3% conversion rate, even 20,000 clicks per arm can only reliably detect lifts of 16% or more — far above typical campaign improvements. Real account data compounds the problem, as analysis of 31 advertiser accounts totalling roughly $133 million in spend found that 62% of conversions come from just the top 1% of search terms, shrinking the effective sample size well below raw click counts. Resampling search terms rather than individual clicks provides a more accurate standard error, and the resulting 'design effect' can double the required sample size. Marketers can use these two checks before launching any experiment to determine whether their traffic volume can actually support the conclusion they are hoping to reach.

0 comments Read more at DEV Community