{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T02:09:03Z","timestamp":1789610943944,"version":"build-2803163510"},"reference-count":7,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,6]]},"abstract":"<jats:p>\n            This paper analyzes the performance of three systems for in-memory data management:\n            <jats:italic>Memcached, Redis<\/jats:italic>\n            and the\n            <jats:italic>Resilient Distributed Datasets (RDD)<\/jats:italic>\n            implemented by Spark. By performing a thorough performance analysis of both analytics operations and fine-grained object operations such as\n            <jats:italic>set\/get<\/jats:italic>\n            , we show that neither system handles efficiently both types of workloads. For Memcached and Redis the CPU and I\/O performance of the TCP stack are the bottlenecks -- even when serving in-memory objects within a single server node. RDD does not support efficient\n            <jats:italic>get operation<\/jats:italic>\n            for random objects, due to a large startup cost of the\n            <jats:italic>get<\/jats:italic>\n            job. Our analysis reveals a set of features that a system must support in order to achieve efficient in-memory data management.\n          <\/jats:p>","DOI":"10.14778\/2732951.2732956","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"833-836","source":"Crossref","is-referenced-by-count":23,"title":["Efficient in-memory data management"],"prefix":"10.14778","volume":"7","author":[{"given":"Hao","family":"Zhang","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogdan Marius","family":"Tudor","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,6]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Aredis java redis client. http:\/\/aredis.sourceforge.net\/.  Aredis java redis client. http:\/\/aredis.sourceforge.net\/."},{"key":"e_1_2_1_2_1","unstructured":"Memcached. http:\/\/memcached.org.  Memcached. http:\/\/memcached.org."},{"key":"e_1_2_1_3_1","unstructured":"Redis. http:\/\/redis.io.  Redis. http:\/\/redis.io."},{"key":"e_1_2_1_4_1","unstructured":"Spymemcached memcached client. https:\/\/code.google.com\/p\/spymemcached\/.  Spymemcached memcached client. https:\/\/code.google.com\/p\/spymemcached\/."},{"key":"e_1_2_1_5_1","unstructured":"Stanford large network dataset collection. https:\/\/snap.stanford.edu\/data\/.  Stanford large network dataset collection. https:\/\/snap.stanford.edu\/data\/."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2485922.2485926"},{"key":"e_1_2_1_7_1","volume-title":"NSDI","author":"Zaharia M.","year":"2012","unstructured":"M. Zaharia , M. Chowdhury , T. Das , A. Dave , J. Ma , M. McCauley , M. J. Franklin , S. Shenker , and I. Stoica . Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing . In NSDI , 2012 . M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica. Resilient Distributed Datasets: A Fault-tolerant Abstraction for In-memory Cluster Computing. In NSDI, 2012."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/2732951.2732956","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:57:48Z","timestamp":1672225068000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/2732951.2732956"}},"subtitle":["an analysis"],"short-title":[],"issued":{"date-parts":[[2014,6]]},"references-count":7,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2014,6]]}},"alternative-id":["10.14778\/2732951.2732956"],"URL":"https:\/\/doi.org\/10.14778\/2732951.2732956","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2014,6]]}}}