Notes from Making Instagram Fast articles

Written on December 11, 2019


Instagram Engineering published a series of 4-part blog series on how they improved the performance on their website. Here’s my notes from these articles for a quick overview.

child racing on a go-kart

Part 1

Making faster: Part 1

  • intelligent pre-loading of fonts and GraphQL queries
    • Approach 1: (suggested by Chrome team)Have preload tag after the critical <script> tags
    • Approach 2: (implemented by Instagram) Use preload for all critical tags and order them in the order it is necessary.
  • preloading images:
    • Preload images when the browser is free. This can be done by using the requestIdleCallback API. (There is more to this but, this is a good start)
    • For the lazy-loaded images, load it sequentially so that the image closes to viewport gets rendered first.

Part 2

Making faster: Part 2

  • Pushing data early
    • HTTP Chunked Transfer: This can split the HTML response and has universal support (as compared to complex HTTP/2 push). Every platform has some kind of streaming response library - use that.
    • Push response/cache first approach: This implementation is a bit involved and custom. For general websites, my gut feel is that a ServiceWorker based approach would work.

Part 3

Making faster: Part 3 — cache first

  • Cache first approach:
    • This is basically providing a mechanism to load the entire view and queue up the actions before the first load.
    • It is similar to the approach of git branch with the website as the master.
    • For most regular website, this is similar to providing an offline cached experience.
    • My take-away is simply to cache as much as possible.
  • API:
    • This is specific to the caching implementation.
    • My take-away is that there needs to be a robust mechanism for cache management and XHR call management so that they don’t block and cause cache collisions.

Part 4

Making faster: Code size and execution optimizations (Part 4)

  • Send less code, especially to lower end devices:

There’s a common assumption in the industry that the size of the JavaScript that gets downloaded over the network is what’s important (i.e. the size post-compression), however we found that what’s really important is the size pre-compression as this is what has to be parsed and executed on the user’s device, even if it’s cached locally.

  • Reduce JS code size:
    • JS size, uncompressed more strongly determines performance if it is in the critical render path.
    • Size of JS for lazy loaded component has lower impact on performance.
    • One simple approach is to use the require in a lazy fashion. This will ensure that the modules are loaded only if the code execution path reaches a point the really requires this module.
  • Move to ES2017
    • Browser support is quite good.
    • Run your own checks and keep 2 builds if that is absolutely required. One build will be ES2017 and another one could be ES5 or polyfill depending on user base.


Preloading is a big win, if you can use it correctly. Don’t under-estimate the power of boring technology like HTTP chunked encoding. Use browser-native features like ES2017’s async/await to reduce code. For lazy-loading of images, consider browser-native methods and where it is not available, try to implement standards based solution like IntersectionObserver and requestIdleCallback API.