research-article

Workload analysis of a large-scale key-value store

Authors:

Eitan Frachtenberg,

Mike PalecznyAuthors Info & Claims

SIGMETRICS '12: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems

Pages 53 - 64

https://doi.org/10.1145/2254756.2254766

Published: 11 June 2012 Publication History

Abstract

Key-value stores are a vital component in many scale-out enterprises, including social networks, online retail, and risk analysis. Accordingly, they are receiving increased attention from the research community in an effort to improve their performance, scalability, reliability, cost, and power consumption. To be effective, such efforts require a detailed understanding of realistic key-value workloads. And yet little is known about these workloads outside of the companies that operate them. This paper aims to address this gap.

To this end, we have collected detailed traces from Facebook's Memcached deployment, arguably the world's largest. The traces capture over 284 billion requests from five different Memcached use cases over several days. We analyze the workloads from multiple angles, including: request composition, size, and rate; cache efficacy; temporal patterns; and application use cases. We also propose a simple model of the most representative trace to enable the generation of more realistic synthetic workloads by the community.

Our analysis details many characteristics of the caching workload. It also reveals a number of surprises: a GET/SET ratio of 30:1 that is higher than assumed in the literature; some applications of Memcached behave more like persistent storage than a cache; strong locality metrics, such as keys accessed many millions of times a day, do not always suffice for a high hit rate; and there is still room for efficiency and hit rate improvements in Memcached's implementation. Toward the last point, we make several suggestions that address the exposed deficiencies.

References

[1]

http://voldemort-project.com.

[2]

Ahmad, I. Easy and efficient disk I/O workload characterization in VMware ESX server. In Proceedings of IEEE International Symposium on Workload Characterization (Sept. 2007).

Digital Library

[3]

Anand, A., Muthukrishnan, C., Kappes, S., Akella, A., and Nath, S. Cheap and large CAMs for high performance data-intensive networked systems. In Proceedings of the 7th USENIX conference on Networked Systems Design and Implementation (Apr. 2010).

Digital Library

[4]

Andersen, D. G., Franklin, J., Kaminsky, M., Phanishayee, A., Tan, L., and Vasudevan, V. FAWN: a fast array of wimpy nodes. In Proceedings of the 22nd ACM SIGOPS Symposium on Operating Systems Principles (SOSP) (Big Sky, Montana, 2009), ACM, pp. 1--14.

Digital Library

[5]

Arlitt, M., Friedrich, R., and Jin, T. Workload characterization of a web proxy in a cable modem environment. ACM SIGMETRICS - Performance Evaluation Review 27 (1999), 25--36.

Digital Library

[6]

Arlitt, M. F., and Williamson, C. L. Internet web servers: Workload characterization and performance implications. IEEE/ACM Transactions on Networking 5 (October 1997), 631--645.

Digital Library

[7]

Barford, P., Bestavros, A., Bradley, A., and Crovella, M. Changes in web client access patterns. In World Wide Web Journal, Special Issue on Characterization and Performance Evaluation (1999).

Digital Library

[8]

Beaver, D., Kumar, S., Li, H. C., Sobel, J., and Vajgel, P. Finding a needle in haystack: Facebook's photo storage. In Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation (Oct. 2010).

Digital Library

[9]

Berezecki, M., Frachtenberg, E., Paleczny, M., and Steele, K. Many-core key-value store. In Proceedings of the Second International Green Computing Conference (Orlando, FL, Aug. 2011).

Digital Library

[10]

Breslau, L., Cao, P., Fan, L., Phillips, G., and Shenker, S. Web caching and zipf-like distributions: Evidence and implications. In Proceedings of the 18th Annual IEEE International Conference on Computer Communications (1999).

[11]

Carns, P., Latham, R., Ross, R., Kamil Iskra, S. L., and Riley, K. 24/7 characterization of petascale I/O workloads. In Proceedings of the 4th Workshop on Interfaces and Architectures for Scientific Data Storage (Nov. 2009).

[12]

Cunha, C. R., Bestavros, A., and Crovella, M. E. Characteristics of WWW client-based traces. In Technical Report TR-95-010, Boston University Department of Computer Science, (July 1995).

Digital Library

[13]

Debnath, B. K., Sengupta, S., and Li, J. Flashstore: High throughput persistent key-value store. Proceedings of 36th International Conference on Very Large Data Bases (VLDB) 3, 2 (2010).

Digital Library

[14]

Debnath, B. K., Sengupta, S., and Li, J. SkimpyStash: RAM space skimpy key-value store on flash-based storage. In Proceedings of the Annual ACM SIGMOD Conference (June 2010), pp. 25--36.

Digital Library

[15]

DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Pilchin, A., Sivasubramanian, S., Vosshall, P., and Vogels, W. Dynamo: Amazon's highly available key-value store. In Proceedings of the 21st ACM SIGOPS Symposium on Operating Systems Principles (SOSP) (Stevenson, WA, 2007), pp. 205--220.

Digital Library

[16]

Duska, B. M., Marwood, D., and Feeley, M. J. The measured access characteristics of world-wide web client proxy caches. In Proceedings of USENIX Symposium of Internet Technologies and Systems (Dec. 1997).

Digital Library

[17]

Feitelson, D. G. Workload modeling for performance evaluation. In Performance Evaluation of Complex Systems: Techniques and Tools, M. C. Calzarossa and S. Tucci, Eds., vol. 2459 of Lecture Notes in Computer Science. Springer-Verlag, Sept. 2002, pp. 114--141. www.cs.huji.ac.il/ feit/papers/WorkloadModel02chap.ps.gz.

Digital Library

[18]

Fitzpatrick, B. Distributed caching with memcached. Linux Journal, 124 (Aug. 2004), 72--78. www.linuxjournal.com/article/7451?page=0,0.

Digital Library

[19]

Jiang, S., and Zhang, X. LIRS: an efficient low inter-reference recency set replacement policy to improve buffer cache performance. In Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems (2002), SIGMETRICS'02, ACM, pp. 31--42.

Digital Library

[20]

Kavalanekar, S., Worthington, B., Zhang, Q., and Sharda, V. Characterization of storage workload traces from production windows servers. In Proceedings of IEEE International Symposium on Workload Characterization (Sept. 2008).

[21]

Keeton, K., Alistair Veitch, D. O., and Wilkes, J. I/O characterization of commercial workloads. In Proceedings of the 3rd Workshop on Computer Architecture Evaluation using Commercial Workloads (Jan. 2000).

[22]

Kim, Y., Gunasekaran, R., Shipman, G. M., Dillow, D. A., Zhang, Z., and Settlemyer, B. W. Workload characterization of a leadership class storage cluster. In Proceedings of Petascale Data Storage Workshop (Nov. 2010).

[23]

Lim, H., Fan, B., Andersen, D. G., and Kaminsky, M. Silt: A memory-eficient, high-performance key-value store. In Proceedings of the 23rd ACM Symposium on Operating Systems Principles (Oct. 2011).

Digital Library

[24]

Lublin, U., and Feitelson, D. G. The workload on parallel supercomputers: Modeling the characteristics of rigid jobs. Journal of Parallel and Distributed Computing 63, 11 (Nov. 2003), 1105--1122. www.cs.huji.ac.il/ feit/papers/Rigid01TR.ps.gz.

Digital Library

[25]

Manley, S., and Seltzer, M. Web facts and fantasy. In Proceedings of USENIX Symposium on Internet Technologies and Systems (Dec. 1997).

Digital Library

[26]

Petrovic, J. Using Memcached for data distribution in industrial environment. In Proceedings of the Third International Conference on Systems (Washington, DC, 2008), IEEE Computer Society, pp. 368--372.

Digital Library

[27]

Reddi, V. J., Lee, B. C., Chilimbi, T., and Vaid, K. Web search using mobile cores: Quantifying and mitigating the price of efficiency. In Proceedings of the 37th International Symposium on Computer Architecture (ISCA) (June 2010), ACM. portal.acm.org/citation.cfm?id=1815961.1816002.

Digital Library

[28]

Roselli, D., Lorch, J. R., and Anderson, T. E. A comparison of file system workloads. In Proceedings of the 2000 USENIX Annual Technical Conference (June 2000).

Digital Library

[29]

Vasudevan, V. R. Energy-Efficient Data-intensive Computing with a Fast Array of Wimpy Nodes. PhD thesis, Carnegie Mellon University, Oct. 2011.

[30]

Wang, F., Xin, Q., Hong, B., Miller, E. L., Long, D. D. E., Brandt, S. A., and McLarty, T. T. File system workload analysis for large scientific computing applications. In Proceedings of 21st IEEE / 12th NASA Goddard Conference on Mass Storage Systems and Technologies (Apr. 2004).

[31]

Zhao, H. HipHop for PHP: Move fast. https://developers.facebook.com/blog/post/358/, Feb. 2010.

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Workload analysis of a large-scale key-value store

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Published In

cover image ACM Conferences

SIGMETRICS '12: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems

June 2012

450 pages

ISBN:9781450310970

DOI:10.1145/2254756

General Chair:
Peter Harrison
Imperial College London, United Kingdom
,
Program Chairs:
Martin Arlitt
HP Labs, USA and University of Calgary, Canada
,
Giuliano Casale
Imperial College London, United Kingdom

ACM SIGMETRICS Performance Evaluation Review Volume 40, Issue 1
Performance evaluation review
June 2012
433 pages
ISSN:0163-5999
DOI:10.1145/2318857
Issue’s Table of Contents

Copyright © 2012 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 ACM 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: 11 June 2012

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SIGMETRICS '12: ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

June 11 - 15, 2012

London, England, UK

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https://dl.acm.org/doi/10.5555/3650697.3650715
Gong ZWu SXu Y(2024)Hybrid fault tolerance in distributed in-memory storage systemsJUSTC10.52396/JUSTC-2022-012554:4(0406)Online publication date: 2024
https://doi.org/10.52396/JUSTC-2022-0125
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https://doi.org/10.2298/CSIS230923018P
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