H-Tree: An efficient index structure for event matching in publish/subscribe systems

Content-based publish/subscribe systems have been employed to deal with complex distributed information flows in many applications. It is well recognized that event matching is a fundamental component of such large-scale systems. Event matching is to search in a space which is composed of all subscriptions. As the scale and complexity of a system grow, the efficiencies of event matching become more critical to the system performance. Most existing methods suffer performance degradation problem when a system has both large number of subscriptions and large number of constraints. In this paper, we present H-Tree (Hash Tree), a highly efficient index structure for event matching. H-Tree is a hash table in nature which is a combination of hash lists and hash chaining. A hash list is realized on an indexed attribute by dividing the attribute's value domain into cells. Multiple hash lists are chained into a hash tree. The basic idea behind H-Tree is that matching efficiencies are improved when the search space is substantially reduced by pruning most of the impossible subscriptions. We have implemented H-Tree and conducted extensive experiments in different settings. Experimental results show that H-Tree outperforms its counterparts to a large degree. In particular, the matching time is faster by three order of magnitude than its counterparts when both the number of subscriptions and the number of constraints are large.