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Time-Aware Image Caching

2 posts · Jan 2021 – Mar 2026

What this trail covers

Grab has evolved its image caching strategy by implementing a Time-Aware Least Recently Used (TLRU) cache. This enhancement builds upon the existing DiskLruCache foundation, introducing Time-To-Live (TTL) for automatic eviction of stale content, a minimum cache size threshold to protect essential images, and retaining the maximum cache size for storage management. The TLRU cache aims to reclaim terabytes of storage space by proactively removing outdated images without impacting use. The Drivers

All Grab trails
  1. Reclaiming Terabytes: Optimizing Android image caching with TLRUThis post details the implementation of a Time-Aware Least Recently Used (TLRU) cache by extending Glide's DiskLruCache. The TLRU cache introduces Time-To-Live (TTL) for automatic eviction of stale content, a minimum cache size threshold to protect essential images, and retains the maximum cache size for storage management. The technical implementation involves cloning and modifying DiskLruCache to incorporate these time-based eviction rules, alongside size-based eviction and minimum threshold protection.
  2. Serving Driver-partners Data at Scale Using Mirror CacheThis post introduces 'mirror cache', an in-memory local caching solution for the Drivers Data service. It addresses low cache hit ratios by replicating data across all live instances of the service. The design goals include supporting LRU cache, active invalidation, best-effort replication, and auto-scaling. The implementation uses Ristretto for the in-memory cache and a replicator component with a Membership Store, Notifier, and gRPC Server for data mirroring. The new architecture resulted in a local cache hit ratio of 0.75 and a 5% drop in database calls.