Reproduce “Real-time Event Joining in Practice With Kafka and Flink” on OpenResearch
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About this paper
Srijan Saket, Vivek Chandela, Md. Danish Kalim
ShareChat optimized its real-time event joining system for recommendation models, transitioning from a complex, multi-component architecture to one built on Apache Kafka and Apache Flink. This migration achieved a 53% reduction in infrastructure costs while enhancing scalability and data quality for their short-form video content platform.
Key insights
- High-level stream processing frameworks like Apache Flink simplify complex real-time data tasks by offering powerful abstractions and built-in capabilities, such as state management and exactly-once processing.
- Considering the entire data pipeline and optimizing holistically leads to a more coherent and efficient architecture, rather than focusing on isolated component optimizations.
- Careful technology selection and data format choices (e.g., Avro with compression) can yield substantial cost savings at scale in real-time data systems.
Summary by alphaXiv.
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