Distributed Evaluation of Continuous Equi-join Queries over Large Structured Overlay Networks

We study the problem of continuous relational query processing in Internet-scale overlay networks realized by distributed hash tables. We concentrate on the case of continuous two-way equi-join queries. Joins are hard to evaluate in a distributed continuous query environment because data from more than one relations is needed, and this data is inserted in the network asynchronously. Each time a new tuple is inserted, the network nodes have to cooperate to check if this tuple can contribute to the satisfaction of a query when combined with previously inserted tuples. We propose a series of algorithms that initially index queries at network nodes using hashing. Then, they exploit the values of join attributes in incoming tuples to rewrite the given queries into simpler ones, and reindex them in the network where they might be satisfied by existing or future tuples. We present a detailed experimental evaluation in a simulated environment and we show that our algorithms are scalable, balance the storage and query processing load and keep the network traffic low.