How Instagram Scaled to Billions with Python 3: Migration Lessons and Performance Gains

Introduction

Instagram, a mobile photo‑sharing app founded in 2010, grew from 13 employees to over 30 billion registered users, handling more than 7 billion monthly active users, all powered by Python and Django.

Why Python and Django

The founders, both product managers, selected Django for its stability and maturity. Even after surpassing 2 billion user IDs, Django remained a non‑bottleneck, and Instagram extended it with sharding support, manual garbage‑collection control, and multi‑data‑center deployments.

Advantages of Python

Python’s simplicity fosters a developer‑centric culture focused on problem solving, using proven technologies, and delivering user‑visible value. Instagram prioritises development velocity over raw execution speed.

At Instagram, our bottleneck is development velocity, not pure code execution.

Improving Runtime Efficiency

To address scaling challenges, Instagram built internal performance‑tuning tools, rewrote critical components in C/C++, and employed Cython. They also explored asynchronous I/O and newer Python runtimes.

Why Upgrade to Python 3

Running on Python 2.7/Django 1.3 for years, Instagram decided to migrate to Python 3 to leverage new features such as type annotations, asyncio, and community support, while maintaining zero downtime and uninterrupted feature development.

Key Migration Factors

Type annotations

def compose_from_max_id(max_id):
    '''@param str max_id'''

Ensuring correct types prevents runtime errors.

Technical Challenges

Unicode handling differences required explicit binary conversion, e.g. using ensure_binary(). Pickle protocol mismatches (Python 3 uses protocol 4) forced separate memcache namespaces. Iterator semantics changed, turning map() into a lazy iterator that skipped the first Cython source file unless converted to a list.

builds = list(map(BuildProcess, CYTHON_SOURCES))

Dictionary ordering variations across Python versions were solved by using json.dumps(..., sort_keys=True).

Performance Gains After Migration

Post‑migration metrics showed a 12% reduction in CPU instructions per request and a 30% memory usage drop for Celery workers, though request‑per‑second remained unchanged due to differing memory optimisation settings.

if uwsgi.opt.get('optimize_mem', None) == b'True':
    optimize_mem()

Final Outcomes

12% overall CPU usage reduction (Django/uwsgi)

30% memory savings (Celery)

During the migration, monthly active users grew from 400 million to 600 million, and new features like comment filtering and live streaming were released.

Takeaways for Engineers

Python + Django can reliably serve billions of users.

Extensive unit testing is essential for large‑scale migrations.

Leverage developers as product users for early feedback.

Adopt a master‑branch‑centric workflow with small, frequent commits.

Embrace Python 3 for new projects to benefit from modern language features.