In this paper, we present BlinkDB, a massively parallel, sampling-based
approximate query engine for running ad-hoc, interactive SQL queries on large
volumes of data. The key insight that BlinkDB builds on is that one can often
make reasonable decisions in the absence of perfect answers. For example,
reliably detecting a malfunctioning server using a distributed collection of
system logs does not require analyzing every request processed by the system.
Based on this insight, BlinkDB allows one to trade-off query accuracy for
response time, enabling interactive queries over massive data by running
queries on data samples and presenting results annotated with meaningful error
bars. To achieve this, BlinkDB uses two key ideas that differentiate it from
previous work in this area: (1) an adaptive optimization framework that builds
and maintains a set of multi-dimensional, multi-resolution 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 and/or
response time requirements. We have built an open-source version of BlinkDB and
validated its effectiveness using the well-known TPC-H benchmark as well as a
real-world analytic workload derived from Conviva Inc. Our experiments on a 100
node cluster show that BlinkDB can answer a wide range of queries from a
real-world query trace on up to 17 TBs of data in less than 2 seconds (over
100\times faster than Hive), within an error of 2 - 10%.

本文介绍了 BlinkDB，一种基于采样的大规模并行近似查询引擎，用于在大量数据上运行临时交互式 SQL 查询。BlinkDB 允许用户在查询精度和响应时间之间权衡，通过在数据样本上运行查询并呈现带有有意义误差条的结果，从而实现对海量数据的交互式查询。