How Instagram Scaled to 14 Million Users with Just Three Engineers

Guiding Principles

Keep the architecture simple.

Reuse proven, battle‑tested components.

Prefer reliable, open‑source technologies.

Frontend

Instagram was launched in 2010 as an iOS app written in Objective‑C using the UIKit framework. A user session starts when the app is opened.

Load Balancing

All requests for the main feed are first routed through an AWS Elastic Load Balancer (ELB). The ELB distributes traffic to three NGINX instances that perform health checks and are swapped automatically when unhealthy.

Backend

Application servers run Django (Python) behind Gunicorn, which implements the WSGI interface. Deployment is performed with Fabric, allowing parallel execution of commands across dozens of instances in seconds. The fleet consists of more than 25 AWS CPU‑Optimized Extra‑Large (c4.xlarge) instances; servers are stateless, enabling horizontal scaling.

Data Storage – PostgreSQL

PostgreSQL stores user and photo metadata. Connections are pooled with PgBouncer. Data is sharded: logical shards are mapped to a small number of physical shards via custom code.

Instagram generates 64‑bit, time‑ordered IDs using a Snowflake‑style layout:

41 bits – millisecond‑precision timestamp (covers ~41 years).

13 bits – logical shard identifier.

10 bits – per‑shard auto‑incrementing sequence (max 1024 IDs per shard per millisecond).

This scheme enables fast retrieval of the latest relevant photo IDs.

Photo Storage – Amazon S3 & CloudFront

Photos are stored in Amazon S3 (several terabytes) and served to users through the CloudFront CDN, providing low‑latency delivery.

Caching – Redis & Memcached

Redis holds roughly 300 million photo‑to‑user‑ID mappings in memory, sharded across multiple nodes and compressed with a custom hashing algorithm to fit within ~5 GB. Memcached (six instances) caches auxiliary data, accelerating reads from PostgreSQL.

Replication & Backup

Both PostgreSQL and Redis run in primary‑replica configurations. Frequent backups are taken using Amazon EBS snapshots.

Push Notifications & Asynchronous Tasks

Push notifications are sent via the open‑source pyapns library. Asynchronous work is queued with Gearman; about 200 Python workers process fan‑out tasks such as notifying all followers of a new photo.

Monitoring & Alerting

Runtime errors in Django are captured by the open‑source Sentry integration. System‑wide metrics are visualized with Munin, using custom plugins to track per‑second photo uploads. External health checks are performed with Pingdom, and incident response is coordinated through PagerDuty.

Overall Architecture Overview