Tomograph: Highlighting Query Parallelism in a Multi-Core System

Provided by: Association for Computing Machinery
Topic: Big Data
Format: PDF
Query parallelism improves serial query execution performance by orders of magnitude. Getting optimal performance from an already parallelized query plan is however difficult due to its dependency on run time factors such as correct operator scheduling, memory pressure, disk IO performance, and operating system noise. Identifying the exact problems in a parallel query execution is difficult due to inter-dependence of these factors. In this paper, the authors present Tomograph, a tool to visualize the parallel query execution performance bottlenecks. Tomograph provides a time ordered view of operator execution aligned with CPU, memory, and disk IO usage, in an operator at a time execution model.

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