Sade: A Smooth Operator for PartitioningData in the Background

ABSTRACTParallel databases must partition or shard data across multiple nodes, ideally in a dynamic fashion

that adjusts to changes in data and workload distributions. This is typically done via a partitioning

operator p mapping items to nodes. From calculus we have that p is a smooth function if it has

continuous derivatives of all orders at all points x 2 R. Smooth functions are well suited to

dynamic, fine-grained background tasks due to their infinite di↵erentiability. We identify such a

smooth operator, called Sade, for fine-grained dynamic data partitioning.

We present an extensive empirical evaluation of Sade on workloads that are geographically dis-

tributed across data centers in the cloud (Coast-to-Coast, L.A.-to-Chicago). We find that Sade

is an excellent background task for a variety of applications including online shopping, sexting

and remote dentistry. In elevator scheduling and telemarketing applications, however, we measure

noticeably increased wait time perception, which we attribute to inappropriate use of Sade as a

foreground process.

BODYInfinitely di↵erentiable (smooth) functions are well-suited to background tasks.

Sade is such a smooth operator for data partitioning.

Volume X of Tiny Transactions on Computer Science

This content is released under the Creative Commons Attribution-NonCommercial ShareAlike License. Permission to

make digital or hard copies of all or part of this work is granted without fee provided that copies are not made or

distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.

CC BY-NC-SA 3.0: http://creativecommons.org/licenses/by-nc-sa/3.0/.