Wormhole: A Fast Ordered Index for In-memory Data Management
Xingbo Wu, Fan Ni, Song Jiang

TL;DR
Wormhole is a novel ordered index structure that significantly improves lookup speed for large in-memory datasets by combining hash tables, prefix trees, and B+ trees, achieving O(log L) lookup time.
Contribution
The paper introduces Wormhole, a new ordered index that offers faster lookup times by integrating multiple data structures, outperforming existing indexes in key throughput.
Findings
Wormhole outperforms skip list, B+ tree, ART, and Masstree in key lookup throughput.
It achieves up to 8.4x speedup over skip list.
Wormhole maintains space efficiency comparable to traditional indexes.
Abstract
In-memory data management systems, such as key-value stores, have become an essential infrastructure in today's big-data processing and cloud computing. They rely on efficient index structures to access data. While unordered indexes, such as hash tables, can perform point search with O(1) time, they cannot be used in many scenarios where range queries must be supported. Many ordered indexes, such as B+ tree and skip list, have a O(log N) lookup cost, where N is number of keys in an index. For an ordered index hosting billions of keys, it may take more than 30 key-comparisons in a lookup, which is an order of magnitude more expensive than that on a hash table. With availability of large memory and fast network in today's data centers, this O(log N) time is taking a heavy toll on applications that rely on ordered indexes. In this paper we introduce a new ordered index structure, named…
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Taxonomy
TopicsDistributed systems and fault tolerance · Caching and Content Delivery · Advanced Data Storage Technologies
