The AHA-Tree: An Adaptive Index for HTAP Workloads

TL;DR

The paper introduces the AHA-Tree, an adaptive index that dynamically morphs between write-optimized and read-optimized states in HTAP systems, enabling seamless workload transitions without downtime.

Contribution

The paper presents the design and implementation of the AHA-Tree, a novel adaptive index that transitions between LSM-tree and B+-tree states without system downtime.

Findings

AHA-Tree effectively morphs between index states during workload changes.

The index supports concurrent operations without blocking.

Benchmark results show improved adaptability and performance.

Abstract

In this demo, we realize data indexes that can morph from being write-optimized at times to being read-optimized at other times nonstop with zero-down time during the workload transitioning. These data indexes are useful for HTAP systems (Hybrid Transactional and Analytical Processing Systems), where transactional workloads are write-heavy while analytical workloads are read-heavy. Traditional indexes, e.g., B+-tree and LSM-Tree, although optimized for one kind of workload, cannot perform equally well under all workloads. To migrate from the write-optimized LSM-Tree to a read-optimized B+-tree is costly and mandates some system down time to reorganize data. We design adaptive indexes that can dynamically morph from a pure LSM-tree to a pure buffered B-tree back and forth, and has interesting states in-between. There are two challenges: allowing concurrent operations and avoiding system down time. This demo benchmarks the proposed AHA-Tree index under dynamic workloads and shows how the index evolves from one state to another without blocking.