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BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data
| Content Provider | CiteSeerX |
|---|---|
| Abstract | In this paper, we present BlinkDB, a massively parallel, ap-proximate query engine for running interactive SQL queries on large volumes of data. BlinkDB allows users to trade-o query accuracy for response time, enabling interactive queries overmassive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional stratied samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query’s accuracy or response time requirements.We evaluateBlinkDB against the well-known TPC-H benchmarks and a real-world analytic workload derived from Conviva Inc., a company that man-ages video distribution over the Internet. Our experiments on a 100 node cluster show that BlinkDB can answer queries on up to 17 TBs of data in less than 2 seconds (over 200 × faster than Hive), within an error of 2-10%. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Bounded Error Bounded Response Time Large Data Adaptive Optimization Framework Trade-o Query Accuracy Meaningful Error Bar Dynamic Sample Selection Strategy Real-world Analytic Workload Multi-dimensional Stratied Sample Interactive Sql Query Conviva Inc Original Data Response Time Node Cluster Show Man-ages Video Distribution Key Idea Interactive Query Ap-proximate Query Engine Response Time Requirement Large Volume Well-known Tpc-h Benchmark Query Accuracy Data Sample |
| Content Type | Text |
