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Welcome to the third post in our series on Python at scale at Instagram! As we mentioned in the first post in the series, Instagram Server is a several-million-line Python monolith, and it moves quickly: hundreds of commits each day, deployed to production every few minutes. We’ve run into a few pain points working with Python at that scale and speed. This article takes a look at a few that we ima
In recent years instagram.com has seen a lot of changes — we’ve launched stories, filters, creation tools, notifications, and direct messaging as well as a myriad of other features and enhancements. However, as the product grew, a side effect was that our web performance began to slow. Over the last year we made a conscious effort to improve this. This ongoing effort has thus far resulted in almos
And we’re back! As we mentioned in the first part of our blog post series, Instagram Server is a Python monolith with several million lines of code and a few thousand Django endpoints. This post is about how we use types to document and enforce a contract for our Python HTTP APIs. In the next few weeks, we’ll share details on more tools and techniques we’ve developed to manage our codebase’s quali
Instagram Server is entirely Python powered. Well, mostly. There’s also some Cython, and our dependencies include a fair amount of C++ code exposed to Python as C extensions. Our server app is a monolith, one big codebase of several million lines and a few thousand Django endpoints [1], all loaded up and served together. A few services have been split out of the monolith, but we don’t have any pla
In recent years, instagram.com has seen a lot of changes — we’ve launched stories, filters, creation tools, notifications, and direct messaging as well as myriad other features and enhancements. However, as the product grew, one unfortunate side effect was that our web performance began to suffer. Over the last year, we made a conscious effort to improve this. Our ongoing efforts have thus far res
Story of a ServiceSuppose you’re writing a simple service: it handles inbound requests from your users, but doesn’t hold any data itself. This means that in order to handle any inbound requests it needs to refer to a backend to actually fetch whatever is required. This is a great feature as it means your service is stateless — it’s easier to test, simpler to scale, and typically easier to understa
At Instagram, we have the world’s largest deployment of the Django web framework, which is written entirely in Python. We began using Python early on because of its simplicity, but we’ve had to do many hacks over the years to keep it simple as we’ve scaled. Last year we tried dismissing the Python garbage collection (GC) mechanism (which reclaims memory by collecting and freeing unused data), and
With more than 25 photos and 90 likes every second, we store a lot of data here at Instagram. To make sure all of our important data fits into memory and is available quickly for our users, we’ve begun to shard our data — in other words, place the data in many smaller buckets, each holding a part of the data. Our application servers run Django with PostgreSQL as our back-end database. Our first qu
Today we are excited to announce we’re open-sourcing MonkeyType, our tool for automatically adding type annotations to your Python 3 code via runtime tracing of types seen. MotivationAt Instagram we have hundreds of engineers working on well over a million lines of Python 3. Every day we have new engineers joining the team from other projects and other languages who need to ramp up quickly and get
At Instagram and Facebook, we’ve seen how adopting React Native in product development has allowed our engineers to move and iterate faster on products. Viewing the effects of our changes is now just a single tap of ⌘R away! But working with native code doesn’t provide the same experience. Instead, engineers need to recompile, link, install, and launch the application they’re developing every time
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