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research report

Adaptive HTAP through Elastic Resource Scheduling

Raza, Syed Mohammad Aunn  
•
Chrysogelos, Periklis  
•
Anadiotis, Angelos Christos  
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2020

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 an HTAP 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 workload data freshness requirements. We propose an in-memory 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.

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Type
research report
Author(s)
Raza, Syed Mohammad Aunn  
Chrysogelos, Periklis  
Anadiotis, Angelos Christos  
Ailamaki, Anastasia  
Date Issued

2020

Subjects

DBMS

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HTAP

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OLTP

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OLAP

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DIAS  
Available on Infoscience
April 10, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/168151
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