How Instagram Scaled to 14 Million Users with a Simple, Reliable Stack

Guiding Principles

Keep it simple

Avoid reinventing the wheel

Prefer proven, stable technologies

1. Technology Stack Overview

Infrastructure

Instagram’s early services ran on Amazon EC2 instances with Ubuntu Linux. An Amazon Elastic Load Balancer (ELB) distributed incoming traffic to three NGINX reverse‑proxy instances, which then forwarded requests to the application tier.

Backend

Application servers were built with Django (Python) and served by Gunicorn. Deployment automation used Fabric to execute commands in parallel across more than 25 high‑CPU EC2 instances. The servers were stateless, allowing horizontal scaling by adding identical instances.

Data Storage

PostgreSQL stored core metadata (users, photo IDs, etc.) and was accessed through PgBouncer for connection pooling.

Data were sharded: logical shards were mapped to a small number of physical shards, supporting >25 photo uploads and 90 likes per second.

IDs were 64‑bit time‑ordered values: 41 bits for milliseconds (≈41 years), 13 bits for logical shard ID, and 10 bits for a per‑millisecond sequence (up to 1 024 IDs per shard per ms).

Photo Storage and Delivery

Photos were stored in Amazon S3 and delivered to users via the CloudFront CDN, providing low‑latency access to terabytes of image data.

Caching Layer

Redis held a mapping of roughly 300 million photo‑to‑user IDs. A custom hashing scheme kept this mapping under 5 GB of RAM and the data were sharded across multiple Redis nodes.

Memcached ran six instances for general purpose caching of recent query results.

2. Push Notifications and Asynchronous Tasks

Push notifications were sent using PyAPNs, an open‑source APNS provider; by the time of writing Instagram had delivered over one billion notifications.

Asynchronous work (e.g., sharing a photo to Twitter or fan‑out distribution to followers) was queued with Gearman. Approximately 200 Python worker processes consumed tasks from the Gearman queue.

3. Monitoring and Alerting

Sentry (Django integration) captured runtime exceptions in real time.

Munin visualized system metrics; custom plugins tracked application‑level counters such as uploads per second.

Pingdom monitored external dependencies, while PagerDuty handled incident escalation.

4. High‑Availability Architecture

Both PostgreSQL and Redis were deployed in a primary‑replica configuration with Amazon EBS snapshots for frequent backups.

Overall, Instagram’s rapid growth to 14 million users in just over a year was achieved by selecting simple, battle‑tested components and applying disciplined engineering practices that emphasized scalability, reliability, and fast deployment.