Blink and it's done: Interactive queries on very large data

Sameer Agarwal, Aurojit Panda, Barzan Mozafari, Anand P. Iyer, Samuel Madden, Ion Stoica

Research output: Contribution to journalArticlepeer-review


In this demonstration, we present BlinkDB, a massively parallel, sampling-based approximate query processing framework for running interactive queries on large volumes of data. The key observation in BlinkDB is that one can make reasonable decisions in the absence of perfect answers. BlinkDB extends the Hive/HDFS stack and can handle the same set of SPJA (selection, projection, join and aggregate) queries as supported by these systems. BlinkDB provides real-time answers along with statistical error guarantees, and can scale to petabytes of data and thousands of machines in a fault-tolerant manner. Our experiments using the TPC-H benchmark and on an anonymized real-world video content distribution workload from Conviva Inc. show that BlinkDB can execute a wide range of queries up to 150× faster than Hive on MapReduce and 10-150× faster than Shark (Hive on Spark) over tens of terabytes of data stored across 100 machines, all with an error of 2 - 10%.

Original languageEnglish (US)
Pages (from-to)1902-1905
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - Aug 2012

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science


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