A Comparison of C-Store and Row-Store in a Common Framework

Recently, a “column store” system called CStore has shown significant performance benefits by utilizing storage optimizations for a read-mostly query workload. The authors of the C-Store paper compared their optimized column store to a commercial row store RDBMS that is optimized for a mixture of reads and writes, which obscures the relative benefits of row and column stores. In this paper, we describe two storage optimizations for a row store architecture given a read-mostly query workload – “super tuples” and “column abstraction.” We implement both our optimized row store and C-Store in a common framework in order to perform an “applesto-apples” comparison of the optimizations in isolation and combination. We also develop a detailed cost model for sequential scans to break down time spent into three categories – disk I/O, iteration cost, and local tuple reconstruction cost. We conclude that, while the C-Store system offers tremendous performance benefits for scanning a small fraction of columns from a table, our optimized row store provides disk storage savings, reduced sequential scan times, and low additional CPU overheads while requiring only evolutionary changes to a standard row store. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 32nd VLDB Conference, Seoul, Korea, 2006