Abstract:
We introduce a benchmark called TEXTURE (TEXT Under RElations) to measure the relative strengths and weaknesses of combining text processing with a relational workload in an RDBMS. While the well-known TREC benchmarks focus on quality, we focus on efficiency. TEXTURE is a micro-benchmark for query workloads, and considers two central text support issues that previous benchmarks did not: (1) queries with relevance ranking, rather than those that just compute all answers , and (2) a richer mix of text and re-lational processing, reflecting the trend toward seamless integration. In developing this benchmark, we had to address the problem of generating large text collections that reflected the (performance) characteristics of a given " seed " collection; this is essential for a controlled study of specific data characteristics and their effects on performance. In addition to presenting the benchmark, with performance numbers for three commercial DBMSs, we present and validate a synthetic generator for populating text fields.
