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Stochastic Database Cracking: Towards Robust Adaptive Indexing in Main-Memory Column-Stores
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 decision-making. Thereby, we bring adaptive
indexing forward to a mature formulation that confers the workload-robustness
previous approaches lacked. Our extensive experimental study verifies that
stochastic cracking maintains the desired properties of original database
cracking while at the same time it performs well with diverse realistic
workloads.Comment: VLDB201
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