Don't Forget Range Delete! Enhancing LSM-based Key-Value Stores with More Compatible Lookups and Deletes
Fan Wang, Dingheng Mo, Siqiang Luo

TL;DR
This paper introduces GLORAN, a novel method for range deletes in LSM-trees that improves point lookup efficiency and overall performance by using a global index and entry validity estimation.
Contribution
GLORAN provides a new range delete technique compatible with modern LSM-based systems, significantly reducing lookup latency and I/O costs while maintaining high throughput.
Findings
GLORAN achieves up to 10.6x faster point lookups.
GLORAN increases overall throughput by 2.7x.
It reduces I/O complexity from O(N/λ) to near logarithmic scales.
Abstract
LSM-trees are featured by out-of-place updates, where key deletion is handled by inserting a tombstone to mark its staleness instead of removing it in place. This defers actual removal to compactions with greatly reduced overhead. However, this classic strategy struggles with another fundamental operator--range deletes--which removes all keys within a specified range, requiring the system to insert numerous tombstones and causing severe performance issues. To address this, modern LSM-based systems introduce range tombstones that record the start and end keys to avoid per-key tombstones. Although this achieves impressive range delete efficiency, such a solution is incompatible with lookups. In particular, our experiments show that point lookup latency can increase by 30% even with just 1% range deletions in workloads. Further to our surprise, this issue has not been raised before,…
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Taxonomy
TopicsAdvanced Data Storage Technologies · Network Packet Processing and Optimization · Caching and Content Delivery
