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arXiv2020

Learned Indexes for a Google-scale Disk-based Database

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.

cs.DB↗

arXiv2013

On the k-Atomicity-Verification Problem

Modern Internet-scale storage systems often provide weak consistency in exchange for better performance and resilience. An important weak consistency property is k-atomicity, which bounds the staleness of values returned by read operations. The k-atomicity-verification problem (or k-AV for short) is the problem of deciding whether a given history of operations is k-atomic. The 1-AV problem is equivalent to verifying atomicity/linearizability, a well-known and solved problem. However, for k > 2, no polynomial-time k-AV algorithm is known. This paper makes the following contributions towards solving the k-AV problem. First, we present a simple 2- AV algorithm called LBT, which is likely to be efficient (quasilinear) for histories that arise in practice, although it is less efficient (quadratic) in the worst case. Second, we present a more involved 2-AV algorithm called FZF, which runs efficiently (quasilinear) even in the worst case. To our knowledge, these are the first algorithms that solve the 2-AV problem fully. Third, we show that the weighted k-AV problem, a natural extension of the k-AV problem, is NP-complete.

cs.DC↗