Griffin: Fast Transactional Database Index with Hash and B+-Tree

TL;DR

Griffin introduces a hybrid database index combining hash tables and B+-trees to significantly improve transaction throughput for point and range operations while maintaining serializability and transparency.

Contribution

This paper presents Griffin, a novel hybrid index architecture that integrates hash tables with B+-trees and employs a new locking method to enhance performance in transactional databases.

Findings

Up to 3.1x higher throughput for point operations

Up to 5.4x higher throughput for range operations

Maintains linearizable operations with no additional B+-tree traversal

Abstract

Index access is one of the dominant performance factors in transactional database systems. Many systems use a B+-tree or one of its variants to handle point and range operations. This access pattern has room for performance improvement. Firstly, point operations can potentially be processed in $O(1)$ with a hash table. Secondly, to ensure serializability of transactions, range operations incur overhead from phantom avoidance techniques that involve additional processing or synchronization, such as an extra traversal of the B+-tree. To address these issues, we propose a hybrid index architecture, Griffin. For point operations, Griffin has a hash table that provides access paths in $O(1)$ time, along with a B+-tree. For phantom avoidance, Griffin employs a precision locking method, which does not involve additional traversal of the B+-tree. Despite its hybrid architecture, Griffin transparently provides linearizable operations and an interface of a single database index. We built a Griffin index combining a hash table and BwTree. Compared to a baseline index that is composed of a BwTree only, it achieves up to 3.1x higher throughput in a point operation dominant workload, and up to 5.4x higher throughput in a range operation dominant workload.