SALSA: Self-Adjusting Lean Streaming Analytics

TL;DR

SALSA is a novel method for dynamically resizing counters in data sketching schemes, merging overflowing counters with neighbors to improve accuracy without increasing space, demonstrated on Count-Min Sketch and Count Sketch.

Contribution

SALSA introduces a simple, general approach for dynamic counter resizing in streaming analytics, enhancing accuracy of existing sketching methods.

Findings

Significantly improves accuracy of Count-Min Sketch and Count Sketch.

Allows more counters within the same space by resizing and merging.

Maintains low overhead with effective merging logic.

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 existing 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].