@conference {DBLP:conf/sigmod/IdreosKM09,
	title = {Self-organizing tuple reconstruction in column-stores},
	booktitle = {Proceedings of the 29th ACM SIGMOD International Conference on Management of Data},
	year = {2009},
	pages = {297-308},
	address = {Providence, Rhode Island},
	abstract = {Column-stores gained popularity as a promising physical design alternative. Each attribute of a relation is physically stored as a separate column allowing queries to load only the required attributes. The overhead incurred is on-the-fly tuple reconstruction for multi-attribute queries. Each tuple reconstruction is a join of two columns based on tuple IDs, making it a significant cost component. The ultimate physical design is to have multiple presorted copies of each base table such that tuples are already appropriately organized in multiple different orders across the various columns. This requires the ability to predict the workload, idle time to prepare, and infrequent updates.

In this paper, we propose a novel design, partial sideways cracking, that minimizes the tuple reconstruction cost in a self-organizing way. It achieves performance similar to using presorted data, but without requiring the heavy initial presorting step itself. Instead, it handles dynamic, unpredictable workloads with no idle time and frequent updates. Auxiliary dynamic data structures, called cracker maps, provide a direct mapping between pairs of attributes used together in queries for tuple reconstruction. A map is continuously physically reorganized as an integral part of query evaluation, providing faster and reduced data access for future queries. To enable flexible and self-organizing behavior in storage-limited environments, maps are materialized only partially as demanded by the workload. Each map is a collection of separate chunks that are individually reorganized, dropped or recreated as needed. We implemented partial sideways cracking in an open-source column-store. A detailed experimental analysis demonstrates that it brings significant performance benefits for multi-attribute queries.},
	author = {Stratos Idreos and Martin L. Kersten and Stefan Manegold}
}
