E. Liarou, R. Goncalves, and S. Idreos, “
Exploiting the power of relational databases for efficient stream processing,” in
Proceedings of the 19th International Conference on Extending Database Technology (EDBT), Saint-Petersburg, Russia, 2009, pp. 323-334.
AbstractStream applications gained significant popularity over the last years that lead to the development of specialized stream engines. These systems are designed from scratch with a different philosophy than nowadays database engines in order to cope with the stream applications requirements. However, this means that they lack the power and sophisticated techniques of a full fledged database system that exploits techniques and algorithms accumulated over many years of database research.
In this paper, we take the opposite route and design a stream engine directly on top of a database kernel. Incoming tuples are directly stored upon arrival in a new kind of system tables, called baskets. A continuous query can then be evaluated over its relevant baskets as a typical one-time query exploiting the power of the relational engine. Once a tuple has been seen by all relevant queries/operators, it is dropped from its basket. A basket can be the input to a single or multiple similar query plans. Furthermore, a query plan can be split into multiple parts each one with its own input/output baskets allowing for flexible load sharing query scheduling. Contrary to traditional stream engines, that process one tuple at a time, this model allows batch processing of tuples, e.g., query a basket only after x tuples arrive or after a time threshold has passed. Furthermore, we are not restricted to process tuples in the order they arrive. Instead, we can selectively pick tuples from a basket based on the query requirements exploiting a novel query component, the basket expressions.
We investigate the opportunities and challenges that arise with such a direction and we show that it carries significant advantages. We propose a complete architecture, the DataCell, which we implemented on top of an open-source column-oriented DBMS. A detailed analysis and experimental evaluation of the core algorithms using both micro benchmarks and the standard Linear Road benchmark demonstrate the potential of this new approach.
LGI_EDBT2009.pdf S. Idreos, M. L. Kersten, and S. Manegold, “
Self-organizing tuple reconstruction in column-stores,” in
Proceedings of the 29th ACM SIGMOD International Conference on Management of Data, Providence, Rhode Island, 2009, pp. 297-308.
AbstractColumn-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.
IKM_SIGMOD09.pdf