基于弹性资源调度的自适应HTAP
Adaptive HTAP through Elastic Resource Scheduling
摘要：
现代混合事务处理/分析处理（HTAP）系统使用集成的数据处理引擎，该数据处理引擎对从事务引擎摄取的新鲜数据执行分析。HTAP系统通常在设计时考虑数据新鲜度，并针对固定范围的新鲜度要求进行优化，以OLTP或OLAP的性能为代价。然而，两个引擎的数据新鲜度和性能要求可能随工作负载而变化。我们接近HTAP作为一个调度问题，在运行时通过弹性资源管理解决。我们将HTAP系统建模为一组三个独立的引擎：OLTP，OLAP和资源和数据交换（RDE）引擎。我们设计了一个调度算法，通过弹性资源管理，遍历HTAP设计频谱，以满足数据的新鲜度要求的工作负载。我们提出了一个内存系统的设计，这是非侵入性的当前最先进的OLTP和OLAP引擎，我们用它来评估我们的方法的性能。我们的评估表明，我们的OLAP查询系统的性能优势随着时间的推移而增加，达到50%相比，静态时间表为100个查询序列，同时保持一个小的，可控的，在OLTP吞吐量下降。
英文摘要：
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 data freshness requirements of the workload. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
