Navid Eslami | Diva: Dynamic Range Filter for Var-Length Keys and Queries | #67
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Navid Eslami | Diva: Dynamic Range Filter for Var-Length Keys and Queries | #67
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In this episode of Disseminate: The Computer Science Research Podcast, Jack sits down with Navid Eslami, PhD researcher at the University of Toronto, to discuss his award-winning paper “DIVA: Dynamic Range Filter for Variable Length Keys and Queries”, which earned Best Research Paper at VLDB.
Navid breaks down how range filters extend the power of traditional filters for modern databases and storage systems, enabling faster queries, better scalability, and theoretical guarantees. We dive into:
- How DIVA overcomes the limitations of existing range filters
- What makes it the “holy grail” of filtering for dynamic data
- Real-world integration in WiredTiger (the MongoDB storage engine)
- Future challenges in data distribution smoothing and hybrid filtering
Whether you're a database engineer, systems researcher, or student exploring data structures, this episode reveals how cutting-edge research can transform how we query, filter, and scale modern data systems.
Links:
- Diva: Dynamic Range Filter for Var-Length Keys and Queries [VLDB'25]
- Diva on GitHub
- Navid's LinkedIn
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