28 articles on Caching for iOS performance
Showing 20 of 28 articles (Page 1 of 2)
Essam Fahmy from Deloitte reveals why 70% of users abandon slow apps—and shares the exact techniques that cut load times by up to 70%. If your mobile team treats performance as an afterthought, this breakdown w...
Todoist's iOS app became nearly unusable for some users after migrating from Realm to GRDB. The culprit? A SQL query generating 128,136 intermediate rows from just 399 actual records.
CRED Engineering faced a nightmare: caching every query combination would require 45,768+ TB of storage. Here's how they got it down to 135 MB.
Swiggy's mobile apps serve millions of users across wildly different devices and network conditions. How do you deliver high-quality images and videos without killing performance or burning through bandwidth co...
Slack's mobile team faces a unique challenge: users expect desktop-level performance while bouncing between subway tunnels and spotty cellular networks.
Swiggy was bleeding money on video bandwidth costs. The culprit? Videos being requested at 270px, 272px, 275px... you get the idea.
Swiggy's mobile team faced a common problem: juggling disk caches, LRU caches, and databases without creating a maintenance nightmare. Here's their elegant solution.
Swiggy's iOS team built Instagram-style video stories and saved 49GB of user data in just two weeks. Here's how they did it.
Delivery Hero was burning through 9TB of image data daily. Their apps were sluggish, their network costs were astronomical, and users were waiting too long for images to load.
Pinterest's API team just freed up 4.5GB of memory per host with a clever database swap. The result? Fewer servers, happier users, and a masterclass in optimization.
Klarna's team removed their caching layer and saw a 25% performance boost. Wait, what?
LazyPay was serving unoptimized images directly from S3, killing app performance and burning bandwidth. Their migration to Cloudinary cut image sizes by 70%.
DoorDash makes millions of ML predictions per second, but their Redis-based feature store was becoming a massive cost and scalability bottleneck. Could client-side caching be the answer?
Spotify just open-sourced the tool that slashed their iOS build times by 70%. For teams drowning in long compile times, this is a game-changer.
Eric Robertson from AWS AppSync reveals how pipeline resolver caching slashed database requests by 99% for some customers. If your GraphQL API is hitting backend services too hard, this changes everything.
AWS Amplify just shipped CDN caching improvements that delivered a 98% reduction in response times for static assets. Cookie handling was the culprit.
Slack cut load times by 65% for large teams by doing less work, not more. Their secret? Strategic laziness.
Slack's desktop client was grinding to a halt as teams scaled. The culprit? An over-reliance on browser LocalStorage that seemed smart at first.
Emre Havan from Freeletics ditched third-party analytics SDKs and built a custom tracking system using CoreData. The result? Full control over batching, performance, and data flow without external dependencies.
AWS just shipped two features that solve opposite problems: making GraphQL APIs blazing fast and keeping DynamoDB writes bulletproof. Here's how they change the game for mobile backends.