Hasso-Plattner-Institut
Prof. Dr. Tilmann Rabl
 

Manos Athanassoulis

Affiliation: Boston University
Title: Elevating Bitmap to General Secondary Indexes: A tale of UpBit, CUBIT, and beyond

 

Abstract

In this talk, I will describe our journey to elevate bitmap indexes from read-only indexes, to update-friendly, highly-concurrent secondary indexes that can be used as general-purpose secondary indexes. Our design relies on three principles. First, we employ a horizontal bitwise representation of updated bits, which enables efficient atomic updates without locking entire bitvectors. Second, we propose a lightweight snapshotting mechanism that allows queries to run on separate snapshots and provides a wait-free progress guarantee. Third, we consolidate updates in a latch-free manner, providing a strong progress guarantee. Our evaluation shows that CUBIT offers 3–16x higher throughput and 3–220x lower latency than state-of-the-art updatable bitmap indexes. CUBIT’s update-friendly nature widens the applicability of bitmap indexing. Experimenting with OLAP workloads with standard, batched updates shows that CUBIT overcomes the maintenance downtime and outperforms DuckDB by 1.2–2.7x on TPC-H. For HTAP workloads with real-time updates, CUBIT achieves 2–11x performance improvement over the state-of-the-art approaches. We will conclude by discussing future directions about bitmap indexes and query processing with bitvectors.

Short CV

Manos Athanassoulis is an Associate Professor of Computer Science at Boston University, Director and Founder of the BU Data-intensive Systems and Computing Laboratory, and co-director of the BU Massive Data Algorithms and Systems Group. He also spent a summer as a Visiting Faculty at Meta. His research is in the area of data management, focusing on building data systems that efficiently exploit modern hardware (computing units, storage, and memories), are deployed in the cloud, and can adapt to the workload both at setup time and dynamically, at runtime. Before joining Boston University, Manos was a postdoc at Harvard University. Earlier, he obtained his PhD from EPFL, Switzerland, and spent one summer at IBM Research, Watson. Manos’ work has been recognized by awards like “Best of SIGMOD” in 2016, “Best of VLDB” in 2010 and 2017, “Most Reproducible Paper” at SIGMOD in 2017, "Best Demo" for VLDB 2023, and "Distinguished PC Member" for SIGMOD 2018, 2023, 2024, 2025 and EDBT 2025, and has been supported by multiple NSF grants including an NSF CRII and an NSF CAREER award, and industry funds including a Facebook Faculty Research Award, multiple Red Hat Research Incubation Awards and gifts from Cisco, Red Hat, and Meta.
He has served or serving as Associate Edtior for ACM SIGMOD Record, ACM SIGMOD Availability and Reproducibility Co-Chair (2021, 2022, 2023, 2024, 2025), VLDB Ambassador for Industry Relations (2022, 2023, 2024), Industrial Track Co-chair for ICWE 2024, Proceedings Chair for VLDB 2023, Area Chair for ACM SIGMOD 2026, IEEE ICDE 2026, VLDB 2025, and IEEE ICDE 2022, Publicity Chair for VLDB 2022 and IEEE ICDE 2021, and as a PC member on multiple top data management venues.