Dynamic Query Re-Planning Using QOOP

Modern data processing clusters are highly dynamic – both in terms of the number of concurrently running jobs and their resource usage. To improve job performance, recent works have focused on optimizing the cluster scheduler and the jobs’ query planner with a focus on picking the right query execution plan (QEP) – represented as a directed acyclic graph – for a job in a resource-aware manner, and scheduling jobs in a QEP-aware manner. However, because existing solutions use a fixed QEP throughout the entire execution, the inability to adapt a QEP in reaction to resource changes often leads to large performance inefficiencies. This paper argues for dynamic query re-planning, wherein we re-evaluate and re-plan a job’s QEP during its execution. We show that designing for re-planning requires fundamental changes to the interfaces between key layers of data analytics stacks today, i.e., the query planner, the execution engine, and the cluster scheduler. Instead of pushing more complexity into the scheduler or the query planner, we argue for a redistribution of responsibilities between the three components to simplify their designs. Under this redesign, we analytically show that a greedy algorithm for re-planning and execution alongside a simple max-min fair scheduler can offer provably competitive behavior even under adversarial resource changes. We prototype our algorithms atop Apache Hive and Tez. Via extensive experiments, we show that our design can offer a median performance improvement of 1.47× compared to state-of-the-art alternatives

Adaptive HTAP through Elastic Resource Scheduling

International audienceModern 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