Tinder has improved its recommendation engine for over 75 million users by implementing geosharding, where user data is divided into geographically bound shards. This approach enhances performance, reduces latency, and improves scalability. The system leverages tools like Google's S2 Library and Apache Kafka, and addresses
Table of contents
Building AI Apps on Postgres? Start with pgai (Sponsored)The Initial Single-Index ApproachThe MOST Hands-On Training on AI Tools you’ll ever attend, for free (Sponsored)The Geosharding SolutionAlgorithm and Tools Used For GeoshardingThe Abstraction LayerMulti-Index vs Multi-ClusterHandling Time Zones: Balancing Traffic Across GeoshardsThe Overall Cluster DesignConsistency ChallengesConclusionSPONSOR USSort: