Distributed Streaming Set Similarity Join

With the prevalence of Internet access and user generated content, a large number of documents/records, such as news and web pages, have been continuously generated in an unprecedented manner. In this paper, we study the problem of efficient stream set similarity join over distributed systems, which has broad applications in data cleaning and data integration tasks, such as on-line near-duplicate detection. In contrast to prefix-based distribution strategy which is widely adopted in offline distributed processing, we propose a simple yet efficient length-based distribution framework which dispatches incoming records by their length. A load-aware length partition method is developed to find a balanced partition by effectively estimating local join cost to achieve good load balance. Our length-based scheme is surprisingly superior to its competitors since it has no replication, small communication cost, and high throughput. We further observe that the join results from the current incoming record can be utilized to guide the index construction, which in turn can facilitate the join processing of future records. Inspired by this observation, we propose a novel bundle-based join algorithm by grouping similar records on-the-fly to reduce filtering cost. A by-product of this algorithm is an efficient verification technique, which verifies a batch of records by utilizing their token differences to share verification costs, rather than verifying them individually. Extensive experiments conducted on Storm, a popular distributed stream processing system, suggest that our methods can achieve up to one order of magnitude throughput improvement over baselines.