HIGHLIGHTS
- What: The authors propose a novel hierarchical predictor: the predictor. The authors design a systematic approach to use these models that best leverages optimality (cache) instance-optimization (local model) and transferable knowledge about (global model). Experimentally the authors show that the predictor makes more accurate and robust predictions while maintaining a practical inference latency and memory overhead. Inspired by the recent advance in zero-shot cost model , the authors design the global model as a graph neural_network that takes a physical execution plan of a query as input to predict its exec-time.
- Who: Query . . .
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