Capturing and Querying Structural Provenance in Spark with Pebble
Summary: Pebble captures and queries structural provenance for nested data in Spark, tracking access and modification of both top-level and nested items. Tree-pattern provenance queries and a Jupyter GUI enable debugging pipelines in a standalone Spark library. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ralf Diestelkämper (University of Stuttgart)
- 2. Melanie Herschel (University of Stuttgart)
BibTeX Citation
@inproceedings{diestelkamper_sigmod19,
title = {{Capturing and Querying Structural Provenance in Spark with Pebble}},
author = {Diestelkämper, Ralf and Herschel, Melanie},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3320225},
url = {https://dl.acm.org/doi/10.1145/3299869.3320225},
year = {2019}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,366 | Provenance for Generalized Map and Reduce Workflows | 2011 | CIDR | 0.00011016972 |
| 1,786 | Putting Lipstick on Pig: Enabling Database-style Workflow Provenance | 2012 | VLDB | 9.7690421e-05 |
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