CUBIT: Concurrent Updatable Bitmap Indexing (Extended Version)

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 queries (including range 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 3x - 16x higher throughput and 3x - 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 1.2x - 2.7x on TPC-H. For HTAP workloads with real-time updates, CUBIT achieves 2x - 11x performance improvement over the state-of-the-art approaches.