How Discord Stores Trillions of Messages - A deep dive

This episode explores Discord's journey in managing its massive message storage, transitioning from billions to trillions of messages. Against the backdrop of initial reliance on MongoDB and a subsequent migration to Cassandra, the podcast details the performance bottlenecks encountered with Cassandra as the message volume exploded. More significantly, the host analyzes the inherent limitations of Cassandra's Log-Structured Merge-tree (LSM) architecture, particularly concerning read performance degradation due to hot partitions and the challenges of compaction. The solution involved a multi-pronged approach: migrating to ScyllaDB (a Cassandra-compatible database written in C++), implementing a Rust-based data service layer for request coalescing, and employing consistent hashing for load balancing. For instance, the data service dramatically reduced read requests by grouping concurrent queries for the same data. Ultimately, this resulted in a significant reduction in nodes, improved latency, and faster write speeds, highlighting the interplay between database choice, architectural design, and the importance of addressing performance bottlenecks proactively. This case study underscores the evolving demands of large-scale data management and the need for adaptable solutions to maintain optimal performance.