The TileDB array data storage manager
Stavros Papadopoulos, Kushal Datta, Samuel R. Madden, Timothy G. Mattson · Proceedings of the VLDB Endowment · 2016
We present a novel storage manager for multi-dimensional arrays that arise in scientific applications, which is part of a larger scientific data management system called TileDB. In contrast to existing solutions, TileDB is optimized for both dense and sparse arrays. Its key idea is to organize array elements into ordered collections called fragments. Each fragment is dense or sparse, and groups contiguous array elements into data tiles of fixed capacity. The organization into fragments turns random writes into sequential writes, and, coupled with a novel read algorithm, leads to very efficient reads. TileDB enables parallelization via multi-threading and multi-processing, offering thread-/process-safety and atomicity via lightweight locking. We show that TileDB delivers comparable performance to the HDF5 dense array storage manager, while providing much faster random writes. We also show that TileDB offers substantially faster reads and writes than the SciDB array database system with both dense and sparse arrays. Finally, we demonstrate that TileDB is considerably faster than adaptations of the Vertica relational column-store for dense array storage management, and at least as fast for the case of sparse arrays.