Engineering Fast Indexes for Big-Data Applications - Databricks

Engineering Fast Indexes for Big-Data Applications

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Contemporary computing hardware offers massive new performance opportunities. Yet high-performance programming remains a daunting challenge.
We present some of the lessons learned while designing faster indexes, with a particular emphasis on compressed bitmap indexes. Compressed bitmap indexes accelerate queries in popular systems such as Apache Spark, Git, Elastic, Druid and Apache Kylin.

About Daniel Lemire

Daniel is a computer science professor at the Université du Québec (TELUQ). He has also been a research officer at the National Research Council of Canada and an entrepreneur. He has written over 50 peer-reviewed publications, including more than 30 journal articles. He has held competitive research grants for the last 15 years. He serves on the program committees of leading computer science conferences (e.g., ACM CIKM, WWW, ACM WSDM, ACM SIGIR, ACM RecSys). His open source software has been used by major corporations such as Google and Facebook. His research interests include databases, information retrieval, and high-performance programming. He blogs regularly on computer science at