How ShareChat Performs Aggregations at Scale with Kafka + ScyllaDB

India's largest homegrown social media platform, ShareChat, with approximately 180 million monthly users, has successfully leveraged Kafka streams and ScyllaDB to handle vast quantities of user engagement data, achieving sub-millisecond latencies even at over a million operations per second. The platform, which supports content creation in 15 languages, aggregates user interactions such as likes and shares to enhance user experience and content curation. To manage the high volume of engagement events efficiently, ShareChat selected stream processing over request-response and batch processing due to its ability to offer continuous, non-blocking data processing. The architecture employs Kafka for event capture and processing, with ScyllaDB providing the necessary low-latency database support. The transition to ScyllaDB has not only improved latency but also reduced database costs by over 50%, offering detailed monitoring capabilities that enhance operational transparency. This strategic shift has significantly optimized ShareChat's application performance, enabling them to meet the demands of rapid business growth and providing a more responsive user experience.