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Modern business applications and scientific databases call for inherentlydynamic data storage environments. Such environments are characterized by twochallenging features: (a) they have little idle system time to devote onphysical design; and (b) there is little, if any, a priori workload knowledge,while the query and data workload keeps changing dynamically. In suchenvironments, traditional approaches to index building and maintenance cannotapply. Database cracking has been proposed as a solution that allows on-the-flyphysical data reorganization, as a collateral effect of query processing.Cracking aims to continuously and automatically adapt indexes to the workloadat hand, without human intervention. Indexes are built incrementally,adaptively, and on demand. Nevertheless, as we show, existing adaptive indexingmethods fail to deliver workload-robustness; they perform much better withrandom workloads than with others. This frailty derives from the inelasticitywith which these approaches interpret each query as a hint on how data shouldbe stored. Current cracking schemes blindly reorganize the data within eachquery's range, even if that results into successive expensive operations withminimal indexing benefit. In this paper, we introduce stochastic cracking, asignificantly more resilient approach to adaptive indexing. Stochastic crackingalso uses each query as a hint on how to reorganize data, but not blindly so;it gains resilience and avoids performance bottlenecks by deliberately applyingcertain arbitrary choices in its decision-making. Thereby, we bring adaptiveindexing forward to a mature formulation that confers the workload-robustnessprevious approaches lacked. Our extensive experimental study verifies thatstochastic cracking maintains the desired properties of original databasecracking while at the same time it performs well with diverse realisticworkloads.

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