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BUZZARD: A NUMA-Aware In-Memory Indexing System

Summary: BUZZARD: NUMA-aware in-memory indexing using a prefix-tree partitioned across nodes, routing requests to local workers to minimize remote traffic. Adaptive partitioning with batch flushes and owner-local indexing reduces contention, boosting throughput on NUMA (220%) vs a single index. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4810
Venue
SIGMOD
Year
2013
Pagerank
5.2043672e-05
Overall Rank
9,876 | 32.25%
DOI
10.1145/2463676.2465342

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{maas_sigmod13,
        title = {{BUZZARD: A NUMA-Aware In-Memory Indexing System}},
        author = {Maas, Lukas M. and Kissinger, Thomas and Habich, Dirk and Lehner, Wolfgang},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2465342},
        url = {https://dl.acm.org/doi/10.1145/2463676.2465342},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,479 P-MOSS: Scheduling Main-Memory Indexes Over NUMA Servers Using Next Token Prediction 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
8,625 QPPT: Query Processing on Prefix Trees 2013 CIDR 5.3979222e-05
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