Query Execution and Optimization

Abstract

Query execution and optimization for streaming data revisits almost all aspects of query execution and optimization over traditional, disk-bound database systems. The reason is that two fundamental assumptions of disk-bound systems are dropped: (i) the data resides on disk, and (ii) the data is finite. As such, new evaluation algorithms and new optimization metrics need to be devised. The approaches can be broadly classified into two categories. First, there are static approaches that follow the traditional optimize-then-execute paradigm by assuming that optimization-time assumptions will continue to hold during execution; the environment is expected to be relatively static in that respect. Alternatively, there are adaptive approaches that assume the environment is completely dynamic and highly unpredictable. In this chapter we explore both approaches and present novel query optimization and evaluation techniques for queries over streaming sources.