How Instagram Scales to 2.5B Users: Architecture, Consistency & Performance

Background

Two Stanford graduates originally built a location‑check‑in app, but the most used feature became photo sharing, leading to the creation of Instagram. Within two years the service reached 30 million users and later over 2.5 billion, forcing the team to continuously expand the infrastructure.

Key Scaling Challenges

Resource utilization : Adding servers increased capacity, yet each server’s CPU and memory were under‑utilized because Python processes were limited by the Global Interpreter Lock (GIL).

Data consistency : Global user growth required multiple data‑center deployments, making strong consistency across regions difficult.

Performance : Synchronous request handling and heavy PostgreSQL reads caused latency spikes and risk of overload.

Engineering Solutions

1. Improving Resource Usage

Python code was rewritten with Cython and critical functions were moved to native C/C++ libraries, reducing CPU cycles per request and allowing more users per server.

Memory was optimized by moving frequently accessed objects to shared memory, decreasing per‑process private memory and enabling a higher number of Python workers.

2. Data Consistency with Regional Cassandra Clusters

Instagram deployed independent Cassandra clusters per continent, using eventual consistency within each cluster. The Akkio data‑placement service routes user requests to the appropriate cluster based on cached location data.

When a request arrives, the flow is:

User request reaches the application.

Application forwards it to the Akkio proxy.

Akkio checks its cache; on miss it queries a location database.

The proxy returns the correct Cassandra cluster address.

The application contacts that cluster directly and caches the mapping.

Akkio adds roughly 10 ms of latency, which is acceptable. If a user migrates continents, Akkio tracks request counts per region and moves the user’s data to a closer cluster when a threshold is exceeded.

3. PostgreSQL Replication and Caching

Instagram runs PostgreSQL in a leader‑follower topology: writes go to the leader, reads are served by followers in the same data center. To protect PostgreSQL from traffic spikes, a Memcache layer fronts read requests.

Cache invalidation is handled by a dedicated service that watches PostgreSQL write streams; when data changes, the service evicts the relevant Memcache entries, forcing the next read to hit the database.

Expensive queries (e.g., counting likes for a media item) are accelerated by denormalized tables:

SELECT COUNT(*) FROM user_likes_media WHERE media_id = 42;

SELECT COUNT FROM media_likes WHERE media_id = 42;

4. Async I/O and Request Timeouts

Python’s asynchronous I/O is used for external service calls, and the system prefers processes over threads to avoid the GIL. Requests taking longer than 12 seconds are terminated to free resources.

5. Memcache Leasing to Avoid Thundering Herd

When a cache miss occurs, the first request is forwarded to PostgreSQL while subsequent concurrent requests are held. The first response updates Memcache, and later requests receive fresh data, preventing a flood of simultaneous database hits.

Conclusion

Through a combination of language‑level optimizations, region‑aware data stores, intelligent routing via Akkio, asynchronous processing, and careful cache management, Instagram’s engineering team achieved extreme scalability while maintaining data consistency and low latency for billions of users.