Approximate Query Processing: Taming the TeraBytes
Proceedings of the 27th International Conference on Very Large Data Bases
Shark: fast data analysis using coarse-grained distributed memory
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Recurring job optimization in scope
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Re-optimizing data-parallel computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Parallel online aggregation in action
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Making every bit count in wide-area analytics
HotOS'13 Proceedings of the 14th USENIX conference on Hot Topics in Operating Systems
Generation of test databases using sampling methods
Proceedings of the 2013 International Symposium on Software Testing and Analysis
Sampling estimators for parallel online aggregation
BNCOD'13 Proceedings of the 29th British National conference on Big Data
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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 150x faster than Hive on MapReduce and 10--150x faster than Shark (Hive on Spark) over tens of terabytes of data stored across 100 machines, all with an error of 2--10%.