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• Learned Query Scheduling

Learned Query Scheduling

Abstract

Query scheduling is a crucial task for analytical database systems that can greatly affect query latency. However, existing scheduling approaches are either based on heuristics or not able to learn a scheduling policy that considers the database-specific characteristics (e.g., operator types, pipelining). As a result, such approaches become not efficient for analytical database systems. In this talk, we introduce LSched: a fully learned workload-aware query scheduler for in-memory analytical database systems. LSched provides an efficient inter-query and intra-query scheduling for dynamic analytical workloads (i.e., different queries can arrive/depart at any time). We integrated LSched with an efficient in-memory analytical database system, and evaluated it with TPCH, SSB, and JOB benchmarks. Our evaluation shows the efficiency of LSched in both streaming and batching query workloads.