CUBIT: Concurrent Updatable Bitmap Indexing (Extended Version)
Junchang Wang, Manos Athanassoulis

TL;DR
CUBIT introduces a novel concurrent bitmap index that supports efficient real-time updates, significantly improving throughput and latency for hybrid transactional/analytical workloads without compromising query performance.
Contribution
It proposes a new design for bitmap indexes enabling concurrent updates using atomic operations, snapshotting, and latch-free consolidation, which was not previously achievable.
Findings
CUBIT achieves 3x-16x higher throughput than existing indexes.
It reduces latency by 3x-220x compared to state-of-the-art.
Outperforms DuckDB by 1.2x-2.7x on TPC-H workloads.
Abstract
Bitmap indexes are widely used for read-intensive analytical workloads because they are clustered and offer efficient reads with a small memory footprint. However, they are notoriously inefficient to update. As analytical applications are increasingly fused with transactional applications, leading to the emergence of hybrid transactional/analytical processing (HTAP), it is desirable that bitmap indexes support efficient concurrent real-time updates. In this paper, we propose Concurrent Updatable Bitmap indexing (CUBIT) that offers efficient real-time updates that scale with the number of CPU cores used and do not interfere with queries. 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Database Systems and Queries · Semantic Web and Ontologies · Data Management and Algorithms
