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.
This talk will first introduce BlinkDB; then dive into its integration with Shark and the mathematical foundations behind the project.
Sameer Agarwal is a Spark Committer and Tech Lead in the Data Platform team at Facebook where he works on building distributed systems and databases that scale across geo-distributed clusters of tens of thousands of machines. Before Facebook, Sameer led the open-source Apache Spark team at Databricks. He received his PhD in Databases from UC Berkeley AMPLab where he worked on BlinkDB, an approximate query engine for Spark.