Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some aggregation function over distributed data items, for example, to know (a) the average of temperatures sensed by a set of sensors (b) the value of index of mid-cap stocks. In these queries a client specifies a coherency requirement as part of the query. In this paper we present a low-cost, scalable technique to answer continuous aggregation queries using a content distribution network of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Just as various fragments of a dynamic web-page are served by one or more nodes of a content distribution network, our technique involves decomposing a client query into sub-queries and executing sub-queries on judiciously chosen data aggregators with their individual sub-query incoherency bounds. We provide a technique of getting the optimal query plan (i.e., set of subqueries and their chosen data aggregators) which satisfies client query’s coherency requirement with least cost, measured in terms of the number of refresh messages sent from aggregators to the client. For estimating query execution cost, we build a continuous query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. Performance results using real-world traces show that our cost based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.

Many techniques have been proposed to scale web applications. However, the data interdependencies between the database queries and transactions issued by the applications limit their efficiency. We claim that major scalability improvements can be gained by restructuring the web application data into multiple independent data services with exclusive access to their private data store. While this restructuring does not provide performance gains by itself, the implied simplification of each database workload allows a much more efficient use of classical techniques. We illustrate the data denormalization process on three benchmark applications: TPC-W, RUBiS and RUBBoS. We deploy the resulting service-oriented implementation of TPC-W across an 85-node cluster and show that restructuring its data can provide at least an order of magnitude improvement in the maximum sustainable throughput compared to master-slave database replication, while preserving strong consistency and transactional properties.