Design continuums and the path toward self-designing key-value stores that know and learn
Design continuums and the path toward self-designing key-value stores that know and learn Idreos et al., CIDR’19
We’ve seen systems that help to select the best data structure from a pre-defined set of choices (e.g. ‘Darwinian data structure selection’), systems that synthesise data structure implementations given an abstract specification (‘Generalized data structure synthesis’), systems that use learning to tune configurations given a pre-defined set of configuration options (e.g, ‘BOAT: Building auto-tuners with structured Bayesian optimization’), systems that use learned models as key inputs to algorithms (e.g. ‘SageDB: a learned database system’), and systems that use reinforcement learning to discover fit-for-workload policies (‘Towards a hands-free query optimizer through deep learning’). Today’s paper choice jumps right into the middle of this mix, showing how families of data structures can be organised into design continuums with well-understood design parameters and cost models informing selection of a given data-structure design from within the space. As well as helping us to understand and reason about data structures within the space, the constrained feature space with quick-to-evaluate cost models opens the way to practical machine learning guided data structure synthesis and selection. Continue reading


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