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Adaptive HTAP through Elastic Resource Scheduling

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Abstract

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, and are optimized for a fixed range of freshness requirements, addressed at a performance cost for either OLTP or OLAP. The data freshness and the performance requirements of both engines, however, may vary with the workload. We approach HTAP as a scheduling problem, addressed at runtime through elastic resource management. We model anHTAP system as a set of three individual engines: an OLTP, an OLAP and a Resource and Data Exchange (RDE) engine. We devise a scheduling algorithm which traverses the HTAP design spectrum through elastic resource management, to meet the data freshness requirements of the workload. We propose an inmemory system design which is non-intrusive to the current state-of-art OLTP and OLAP engines, and we use it to evaluate the performance of our approach. Our evaluation shows that the performance benefit of our system for OLAP queries increases over time, reaching up to 50% compared to static schedules for 100 query sequences,while maintaining a small, and controlled, drop in the OLTP throughput.

Dates and versions

hal-03104617 , version 1 (09-01-2021)

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Cite

Aunn Raza, Periklis Chrysogelos, Angelos Christos Anadiotis, Anastasia Ailamaki. Adaptive HTAP through Elastic Resource Scheduling. SIGMOD/PODS '20 - International Conference on Management of Data, Jun 2020, Portland OR USA, United States. pp.2043-2054, ⟨10.1145/3318464.3389783⟩. ⟨hal-03104617⟩
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