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Querying Petabytes of Data in Seconds

Reynold Xin (Databricks), Sameer Agarwal (UC Berkeley)
Hadoop and Beyond
GA Ballroom J
Average rating: ***..
(3.50, 6 ratings)

There is an exponential growth in data that is being collected and stored. This has created an unprecedented demand for processing and analyzing massive amounts of data. Furthermore, analysts and data scientists want results fast to enable explorative data analysis, while more and more applications require data processing to happen in near real time.

In this talk, we present BlinkDB, which uses a radically different approach where queries are always processed in near real time, regardless of the size of the underlying dataset. This is enabled by not looking at all the data, but rather operating on statistical samples of the underlying datasets. More precisely, BlinkDB gives the user the ability to trade between the accuracy of the results and the time it takes to compute queries. The challenge is to ensure that query results are still meaningful, even though only a subset of the data has been processed. Here we leverage recent advances in statistical machine learning and query processing. Using statistical bootstrapping, we can resample the data in parallel to compute confidence intervals that tell the quality of the sampled results. To compute the sampled data in parallel, we build on the Shark distributed query engine, which can compute tens of thousands of queries per second.

BlinkDB is being integrated in Shark and in Facebook Presto and is also in the process of being deployed at a number of companies. This talk will feature an overview of the BlinkDB architecture and its design philosophy. We will also cover how the audience can leverage this new technology to gain insights in real-time using a variety of real-world use cases from our early adopters.

Photo of Reynold Xin

Reynold Xin

Cofounder, Databricks

Reynold Xin is an Apache Spark committer and the lead developer for Shark and GraphX, two computation frameworks built on top of Spark. He is also a co-founder of Databricks. Before Databricks, he was pursuing a PhD focusing on large scale data systems in the UC Berkeley AMPLab.

Photo of Sameer Agarwal

Sameer Agarwal

PhD Student, UC Berkeley

Sameer Agarwal is a Ph.D. student in the AMPLab at Berkeley working on large-scale approximate query processing frameworks. His research interests are at the intersection of distributed systems, databases and machine learning. He received his B.Tech in Computer Science and Engineering from the Indian Institute of Technology, Guwahati and was awarded the President of India Gold Medal in 2009. He is supported by the Qualcomm Innovation Fellowship during 2012-13 and the Facebook Graduate Fellowship during 2013-14.