Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning

The physical data layout significantly impacts performance when database systems access cold data. In addition to the traditional row store and column store designs, recent research proposes to partition tables hierarchically, starting from either horizontal or vertical partitions and then determining the best partitioning strategy on the other dimension independently for each partition. All these partitioning strategies naturally produce rectangular partitions. Coarse-grained rectangular partitioning reads unnecessary data when a table cannot be partitioned along one dimension for all queries. Fine-grained rectangular partitioning produces many small partitions which negatively impacts I/O performance and possibly introduces a high tuple reconstruction overhead.