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[Demo] Low-latency Spark Queries on Updatable Data

Summary: Demo: Indexed DataFrame—cached Spark DataFrame with an integrated index for fast lookups and joins on updatable data. Supports multi-version concurrency for updates; evaluated on growing social-network graphs with microbenchmarks and real-world queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5797
Venue
SIGMOD
Year
2019
Pagerank
5.2422003e-05
Overall Rank
9,655 | 33.76%
DOI
10.1145/3299869.3320227

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{uta_sigmod19,
        title = {{[Demo] Low-latency Spark Queries on Updatable Data}},
        author = {Uta, Alexandru and Ghit, Bogdan and Dave, Ankur and Boncz, Peter},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3320227},
        url = {https://dl.acm.org/doi/10.1145/3299869.3320227},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,927 Hyperspace: The Indexing Subsystem of Azure Synapse 2021 VLDB 5.3483178e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
24 Spark SQL: Relational Data Processing in Spark 2015 SIGMOD 0.00054865648
426 The LDBC Social Network Benchmark: Interactive Workload 2015 SIGMOD 0.00018692185
1,190 Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark 2018 SIGMOD 0.00011743246
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