Trill: A High-Performance Incremental Query Processor for Diverse Analytics
Summary: Trill's tempo-relational model unifies streaming and relational analytics with early results. Streaming batched-columnar data and dynamic compilation yield 2–4x higher streaming throughput, offline queries competitive with modern columnar DBMS, and integration as a high-level library across fabrics. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Badrish Chandramouli (Microsoft)
- 2. Jonathan Goldstein (Microsoft)
- 3. Mike Barnett (Microsoft)
- 4. Robert DeLine (Microsoft)
- 5. Danyel Fisher (Microsoft)
- 6. John C. Platt (Microsoft)
- 7. James F. Terwilliger (Microsoft)
- 8. John Wernsing (Microsoft)
BibTeX Citation
@article{chandramouli_vldb15,
title = {{Trill: A High-Performance Incremental Query Processor for Diverse Analytics}},
author = {Chandramouli, Badrish and Goldstein, Jonathan and Barnett, Mike and DeLine, Robert and Fisher, Danyel and Platt, John C. and Terwilliger, James F. and Wernsing, John},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {4},
pages = {401--412},
doi = {10.14778/2735496.2735502},
url = {https://doi.org/10.14778/2735496.2735502},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 50 of 58 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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