TAILIEUCHUNG - Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores

We choose the kinesins to illustrate similarities and differences between protein family databases. Kinesin and its relatives are motor proteins that utilize ATP hydrolysis to move along microtubules in eukaryotic cells. The motor portion of a kinesin is structurally very similar to that of the myosin motor, which moves along actin filaments, although no sequence similarity is evident between them. This is an example of likely divergence from an ancestral fold that is beyond current sequence- based comparison methods to detect. Kinesin subfamilies based on sequence similarities betweenmotor domains are strongly predictive of cellular function, indicating diver- gence from an ancestral kinesin-like motor. Kinesins are multidomain proteins, with a coiled-coil stalk attached to the. | Stochastic Database Cracking Towards Robust Adaptive Indexing in Main-Memory Column-Stores Felix Halim Stratos Idreos Panagiotis Karras0 Roland H. C. Yap National University of Singapore tCWI Amsterdam 0Rutgers University halim ryap @ idreos@ karras@ ABSTRACT Modern business applications and scientific databases call for inherently dynamic data storage environments. Such environments are characterized by two challenging features a they have little idle system time to devote on physical design and b there is little if any a priori workload knowledge while the query and data workload keeps changing dynamically. In such environments traditional approaches to index building and maintenance cannot apply. Database cracking has been proposed as a solution that allows on-the-fly physical data reorganization as a collateral effect of query processing. Cracking aims to continuously and automatically adapt indexes to the workload at hand without human intervention. Indexes are built incrementally adaptively and on demand. Nevertheless as we show existing adaptive indexing methods fail to deliver workload-robustness they perform much better with random workloads than with others. This frailty derives from the inelasticity with which these approaches interpret each query as a hint on how data should be stored. Current cracking schemes blindly reorganize the data within each query s range even if that results into successive expensive operations with minimal indexing benefit. In this paper we introduce stochastic cracking a significantly more resilient approach to adaptive indexing. Stochastic cracking also uses each query as a hint on how to reorganize data but not blindly so it gains resilience and avoids performance bottlenecks by deliberately applying certain arbitrary choices in its decisionmaking. Thereby we bring adaptive indexing forward to a mature formulation that confers the workload-robustness previous approaches lacked. Our .

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