Instagram’s Migration from Python 2 to Python 3: Challenges, Solutions, and Performance Gains

Instagram, the photo‑sharing platform with over 3 billion registered users, runs its backend on Python and Django. Despite the language’s reputation for being slow, the company has successfully scaled to hundreds of millions of daily active users, proving that Python + Django can support massive traffic.

The founders chose Django because it was a stable, mature framework known to product managers, and the engineering team has continued to extend it with sharding support, manual garbage‑collection control, and multi‑data‑center deployments.

When Instagram’s user base outgrew the capacity of its Python 2.7/Django 1.3 stack, the team tackled performance bottlenecks by developing internal profiling tools, rewriting critical components in C/C++, and adopting Cython. They also began exploring asynchronous I/O via the standard asyncio module.

The decisive move was upgrading the entire runtime to Python 3. This required ensuring that all heavily used third‑party packages were Python‑3 compatible, removing unused packages, and replacing incompatible ones. Instagram used the modernize tool, fixing compatibility issues file‑by‑file to keep code reviews manageable.

Key technical challenges during the migration included:

Unicode handling: converting string arguments to bytes before calling APIs such as hmac.new . Example: value = 'abc' if isinstance(value, six.text_type): value = value.encode('utf-8') mymac = hmac.new(value)

Pickle protocol differences: Python 3 uses protocol 4, which Python 2 cannot read. Instagram isolated memcache namespaces for each Python version to avoid cross‑version deserialization errors.

Iterator semantics: functions like map return iterators in Python 3, causing one‑off bugs when the iterator was consumed prematurely. Converting to a list (e.g., builds = list(map(BuildProcess, CYTHON_SOURCES)) ) resolved the issue.

Dictionary order variability across Python versions, fixed by always calling json.dumps(..., sort_keys=True) .

After completing the migration, Instagram observed a 12 % reduction in CPU instructions per request and a 30 % drop in memory usage for Celery workers, while maintaining rapid feature development thanks to a robust suite of thousands of unit tests and a master‑branch‑centric workflow.

The experience demonstrates that Python 3 is the future for large‑scale services, that thorough testing and incremental development are essential for successful migrations, and that performance can be improved without sacrificing developer velocity.