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Learned Indexes for a Google-scale Disk-based Database
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There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees. In this paper, we work to remove some of that uncertainty by demonstrating how a learned index can be integrated in a distributed, disk-based database system: Google's Bigtable. We detail several design decisions we made to integrate learned indexes in Bigtable. Our results show that integrating learned index significantly improves the end-to-end read latency and throughput for Bigtable.
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Cited by 1 Pith paper
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Evaluating Learned Indexes in LSM-tree Systems: Benchmarks,Insights and Design Choices
A unified benchmark shows learned indexes beat fence pointers on memory-latency tradeoff in LSM-trees, with position boundary and SSTable granularity as the key tuning knobs.
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