Concurrent data structures for efficient streaming aggregation

We briefly describe our study on the problem of streaming multiway aggregation, where large data volumes are received from multiple input streams. Multiway aggregation is a fundamental computational component in data stream management systems, requiring low-latency and high throughput solutions.We focus on the problem of designing concurrent data structures enabling for low-latency and high-throughput multiway aggregation; an issue that has been overlooked in the literature. We propose two new concurrent data structures and their lock-free linearizable implementations, supporting both order-sensitive and order-insensitive aggregate functions.Results from an extensive evaluation show significant improvement in the aggregation performance,in terms of both processing throughput and latency over the commonly-used techniques based on queues.