research-article

Scalable lineage capture for debugging DISC analytics

Authors:

Dionysios Logothetis,

Kenneth YocumAuthors Info & Claims

SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing

Article No.: 17, Pages 1 - 15

https://doi.org/10.1145/2523616.2523619

Published: 01 October 2013 Publication History

Abstract

A fundamental challenge for big-data analytics is how to efficiently tune and debug multi-step dataflows. This paper presents Newt, a scalable architecture for capturing and using record-level data lineage to discover and resolve errors in analytics. Newt's flexible instrumentation allows system developers to collect this fine-grain lineage from a range of data intensive scalable computing (DISC) architectures, actively recording the flow of data through multi-step, user-defined transformations. Newt pairs this API with a scale-out, fault-tolerant lineage store and query engine.

We find that while active collection can be expensive, it incurs modest runtime overheads for real-world analytics (<36%) and enables novel lineage-based debugging techniques. For instance, Newt can efficiently recreate errors (crashes or bad outputs) or remove input data from the dataflow to enable data cleaning strategies. Additionally, Newt's active lineage collection allows retro-spective analyses of a dataflow's behavior, such as identifying anomalous processing steps. As case studies, we instrument two DISC systems, Hadoop and Hyracks, with less than 105 lines of additional code for each. Finally, we use Newt to systematically clean input data to a Hadoop-based de novo genome assembler, improving the quality of the output assembly.

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Cited By

Morán JBertolino Ade la Riva CTuya J(2024)Automatic Debugging of Design Faults in MapReduce ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2024.336976650:4(956-978)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3369766
Guedes TMattoso MBedo Mde Oliveira D(2024) Version - [SAMbA-RaP is music to scientists’ ears: Adding provenance support to spark-based scientific workflows] SoftwareX10.1016/j.softx.2024.10192728(101927)Online publication date: Dec-2024
https://doi.org/10.1016/j.softx.2024.101927
Humayun AKim MGulzar MChandra SBlincoe KTonella P(2023)Co-dependence Aware Fuzzing for Dataflow-Based Big Data AnalyticsProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616298(1050-1061)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616298
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Scalable lineage capture for debugging DISC analytics

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cover image ACM Conferences

SOCC '13: Proceedings of the 4th annual Symposium on Cloud Computing

October 2013

427 pages

ISBN:9781450324281

DOI:10.1145/2523616

General Chair:
Guy Lohman

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 October 2013

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October 1 - 3, 2013

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SOCC '13 Paper Acceptance Rate 23 of 114 submissions, 20%;

Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

Morán JBertolino Ade la Riva CTuya J(2024)Automatic Debugging of Design Faults in MapReduce ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2024.336976650:4(956-978)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1109/TSE.2024.3369766
Guedes TMattoso MBedo Mde Oliveira D(2024) Version - [SAMbA-RaP is music to scientists’ ears: Adding provenance support to spark-based scientific workflows] SoftwareX10.1016/j.softx.2024.10192728(101927)Online publication date: Dec-2024
https://doi.org/10.1016/j.softx.2024.101927
Humayun AKim MGulzar MChandra SBlincoe KTonella P(2023)Co-dependence Aware Fuzzing for Dataflow-Based Big Data AnalyticsProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616298(1050-1061)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616298
Yang CBrower-Sinning RLewis GKÄStner C(2022)Data Leakage in Notebooks: Static Detection and Better ProcessesProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3556918(1-12)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3556918
Yamada MKitagawa HAmagasa TMatono A(2022)Augmented lineage: traceability of data analysis including complex UDF processingThe VLDB Journal10.1007/s00778-022-00769-7Online publication date: 23-Nov-2022
https://doi.org/10.1007/s00778-022-00769-7
Grafberger SGroth PStoyanovich JSchelter S(2022)Data distribution debugging in machine learning pipelinesThe VLDB Journal10.1007/s00778-021-00726-w31:5(1103-1126)Online publication date: 31-Jan-2022
https://doi.org/10.1007/s00778-021-00726-w
Ye QLu M(2021)s2p: Provenance Research for Stream Processing SystemApplied Sciences10.3390/app1112552311:12(5523)Online publication date: 15-Jun-2021
https://doi.org/10.3390/app11125523
Gulzar MKim M(2021)OptDebugProceedings of the ACM Symposium on Cloud Computing10.1145/3472883.3487016(359-372)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1145/3472883.3487016
Li YInterlandi MPsallidas FWang WZaniolo C(2021)SEIZE: Runtime Inspection for Parallel Dataflow SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2020.303517032:4(842-854)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TPDS.2020.3035170
Rupprecht LDavis JArnold CGur YBhagwat D(2020)Improving reproducibility of data science pipelines through transparent provenance captureProceedings of the VLDB Endowment10.14778/3415478.341555613:12(3354-3368)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3415478.3415556
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