Discover 41 articles on Caching in mobile performance
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Shopify's home feed was consuming 30% of their total database load and slowing down their entire app. Off-the-shelf caching solutions wouldn't cut it.
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.
Florina Muntenescu and Rohit Sathyanarayana from Google reveal why SharedPreferences has been secretly blocking your UI thread and causing ANRs. Their solution? Jetpack DataStore, a complete reimagining of Andr...
Microsoft Teams Android engineers cut WebView initialization time by 70% using a clever caching technique. Here's how they avoided memory leaks while sharing WebViews across activities.
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.
Vrbo eliminated loading screens in their Android app entirely. Here's how they used Apollo GraphQL's normalized cache to make navigation feel instant.
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.
Skyscanner's iOS developers were waiting hours for builds to complete. Their CI/CD team cut that time in half with a clever caching strategy.
Netflix's Titus container platform hit a wall: their singleton leader couldn't handle the API query load. Here's how they scaled horizontally without breaking consistency guarantees.
Instagram serves 800 million monthly users, 80% outside the US. How do you make the app feel instant for everyone, regardless of network quality?
Instagram's background prefetching could easily drain batteries and waste data. Here's how they built a system that's both fast and responsible.
Zhiyao Wang and the Airbnb team tackled a massive problem: their mobile inbox was loading like a 2001 webmail client. With 100k+ messages per hour, every tap meant a network request, killing UX on slow connecti...
Twitter open sourced their iOS image pipeline after hitting a breaking point: 2GB caches, corrupted images, and no way to clear data when users logged out. Here's how they rebuilt it from scratch.
Dan Lew from Atlassian reveals how Trello Android went from dropping the entire database on every schema change to implementing proper upgrades. The reason? They were secretly building offline mode.
LinkedIn rebuilt their Android data pipeline from scratch for their flagship app. The result? A system that handles everything from feed updates to messaging while keeping data consistent across screens.
Skyscanner needed to store 1.6 billion weights to rank hotel prices. Their in-memory approach couldn't scale, so they turned to AWS.
Dropbox engineers faced a brutal reality: reading 5,000 photos from SQLite took a full second on a Nexus 5. For users with 100,000+ photos, the standard approach would be unusable.