SALSA: Self-Adjusting Lean Streaming Analytics

Citation:

Ran Ben Basat, Gil Einziger, Michael Mitzenmacher, and Shay Vargaftik. 2021. “SALSA: Self-Adjusting Lean Streaming Analytics .” 37th IEEE International Conference on Data Engineering (ICDE 2021). Publisher's Version

Abstract:

Counters are the fundamental building block of many data sketching schemes, which hash items to a small number of counters and account for collisions to provide good approximations for frequencies and other measures. Most exist- ing methods rely on fixed-size counters, which may be wasteful in terms of space, as counters must be large enough to eliminate any risk of overflow. Instead, some solutions use small, fixed-size counters that may overflow into secondary structures. This paper takes a different approach. We propose a simple and general method called SALSA for dynamic re-sizing of counters, and show its effectiveness. SALSA starts with small counters, and overflowing counters simply merge with their neighbors. SALSA can thereby allow more counters for a given space, expanding them as necessary to represent large numbers. Our evaluation demonstrates that, at the cost of a small overhead for its merging logic, SALSA significantly improves the accuracy of popular schemes (such as Count-Min Sketch and Count Sketch) over a variety of tasks. Our code is released as open source [1].