Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 48Software System Performance and ReliabilityAdvanced Database Systems and QueriesCloud Computing and Resource Management

The DOI matches its Crossref record.

Towards Observability Data Management at Scale

Karumuri, Suman, Solleza, Franco, Zdonik, Stan, Tatbul, Nesime·2021·ACM SIGMOD Record
DOI 10.1145/3456859.3456863
Open this paper in CoChat — free account

Abstract

Observability has been gaining importance as a key capability in today's large-scale software systems and services. Motivated by current experience in industry exemplified by Slack and as a call to arms for database research, this paper outlines the challenges and opportunities involved in designing and building Observability Data Management Systems (ODMSs) to handle this emerging workload at scale.

Overview

Integrity signals

Registry

Crossref · 10.1145/3456859.3456863

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 21

  1. R. H. Arpaci-Dusseau et al. 2018 . Cloud-Native File Systems. In USENIX Conference on Hot Topics in Cloud Computing (HotCloud). R. H. Arpaci-Dusseau et al. 2018. Cloud-Native File Systems. In USENIX Conference on Hot Topics in Cloud Computing (HotCloud).
  2. C. Chan etal 2020. Debugging Incidents in Google's Distributed Systems. ACM Queue 18 2 (2020). C. Chan et al. 2020. Debugging Incidents in Google's Distributed Systems. ACM Queue 18 2 (2020). · DOI
  3. 10.1145/2814710.2814713 · DOI
  4. C. Gormley 2015 . Elasticsearch: The Definitive Guide . O'Reilly Media . C. Gormley et al. 2015. Elasticsearch: The Definitive Guide. O'Reilly Media.
  5. N. Narkhede 2017 . Kafka: The Definitive Guide Real-Time Data and Stream Processing at Scale . O'Reilly Media . N. Narkhede et al. 2017. Kafka: The Definitive Guide Real-Time Data and Stream Processing at Scale. O'Reilly Media.
  6. 10.1145/13677.22723 · DOI
  7. 10.1145/3132747.3132749 · DOI
  8. 10.1145/359545.359563 · DOI · Paper page
  9. 10.1145/2815400.2815415 · DOI
  10. S. Niedermaier etal 2019. On Observability and Monitoring of Distributed Systems -- An Industry Interview Study. In ICSOC. S. Niedermaier et al. 2019. On Observability and Monitoring of Distributed Systems -- An Industry Interview Study. In ICSOC. · DOI
  11. F. Özcan et al. 2017 . Hybrid Transactional/Analytical Processing: A Survey. In ACM SIGMOD Conference. 1771--1775 . F. Özcan et al. 2017. Hybrid Transactional/Analytical Processing: A Survey. In ACM SIGMOD Conference. 1771--1775.
  12. 10.1145/2814710.2814719 · DOI
  13. 10.1145/3377391.3377400 · DOI
  14. J. Rodrigues 2017 . Sieve: Actionable Insights from Monitored Metrics in Distributed Systems . In ACM Middleware Conference. 14-- 27 . J. Rodrigues et al. 2017. Sieve: Actionable Insights from Monitored Metrics in Distributed Systems. In ACM Middleware Conference. 14-- 27.
  15. R. Sethi 2019 . Presto: SQL on Everything . In IEEE ICDE. R. Sethi et al. 2019. Presto: SQL on Everything. In IEEE ICDE.
  16. Y. Shkuro . 2019. Mastering Distributed Tracing: Analyzing Performance in Microservices and Complex Systems . Packt Publishing . Y. Shkuro. 2019. Mastering Distributed Tracing: Analyzing Performance in Microservices and Complex Systems. Packt Publishing.
  17. B. H. Sigelman 2010 . Dapper: A Large-Scale Distributed Systems Tracing Infrastructure. Technical Report. Google , Inc . B. H. Sigelman et al. 2010. Dapper: A Large-Scale Distributed Systems Tracing Infrastructure. Technical Report. Google, Inc.
  18. C. Sridharan . 2018. Distributed Systems Observability: A Guide to Building Robust Systems . O'Reilly Media . C. Sridharan. 2018. Distributed Systems Observability: A Guide to Building Robust Systems. O'Reilly Media.
  19. D. Vohra. 2016. Apache Parquet. In Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools. 325--335. D. Vohra. 2016. Apache Parquet. In Practical Hadoop Ecosystem: A Definitive Guide to Hadoop-Related Frameworks and Tools. 325--335. · DOI
  20. A. Wiedemann etal 2019. The DevOps Phenomenon. ACM Queue 17 2 (2019). A. Wiedemann et al. 2019. The DevOps Phenomenon. ACM Queue 17 2 (2019). · DOI
  21. M. Zaharia 2010 . Spark: Cluster Computing withWorking Sets. In USENIX Conference on Hot Topics in Cloud Computing (HotCloud). M. Zaharia et al. 2010. Spark: Cluster Computing withWorking Sets. In USENIX Conference on Hot Topics in Cloud Computing (HotCloud).

Cited by

Metadata

Authors
Karumuri, S., Solleza, F., Zdonik, S., & Tatbul, N.
Affiliations
  • Slack Incorporated (United States) (US)
  • Brown University (US)
  • Intel (United States) (US)
ORCID
Venue
ACM SIGMOD Record
Publisher
Association for Computing Machinery (ACM)
Published
2021-03-08
Volume
49
Issue
4
Pages
18-23
ISSN
0163-5808 (ISSN-L)
DOI
10.1145/3456859.3456863
OpenAlex
W3137837084
Cited by
48
References
0
Citations by year
2026: 7 · 2025: 6 · 2024: 15 · 2023: 8 · 2022: 11 · 2021: 1
FWCI
3.93
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM SIGMOD Record (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
observability, large software systems, monitoring data, database management systems, scalability, distributed systems

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review