What Database Architecture Works Best for Real-Time Chat Applications?

Real-time chat systems require a complex architecture because no single database can handle all aspects efficiently due to diverse workload demands, such as maintaining message order, fast pagination, and search functionalities. This complexity is exemplified by platforms like Discord, Slack, and Meta, which utilize a combination of wide-column stores like Cassandra or ScyllaDB for messages, relational databases like Postgres or MySQL for user and channel metadata, Redis for ephemeral states like presence and typing indicators, inverted indexes like Elasticsearch for search, and object storage like S3 for media attachments. The intricacies involve ensuring messages are stored in an append-heavy manner while allowing for edits and deletions, managing user-specific states, and providing rapid search and access capabilities, all while maintaining data integrity and performance. The architecture typically involves a change data capture pipeline to ensure search indexes remain synchronized with message stores without blocking writes, and object storage is used to offload media files for efficient retrieval through CDNs. This setup underscores the necessity of specialized databases and storage solutions to manage the varied requirements of chat applications effectively.