{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:13:53Z","timestamp":1784099633549,"version":"3.55.0"},"reference-count":23,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,7]]},"abstract":"<jats:p>Main memories are becoming sufficiently large that most OLTP databases can be stored entirely in main memory, but this may not be the best solution. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). It is still more economical to store the coldest records on secondary storage such as flash. This paper introduces Siberia, a framework for managing cold data in the Microsoft Hekaton main-memory database engine. We discuss how to migrate cold data to secondary storage while providing an interface to the user to manipulate both hot and cold data that hides the actual data location. We describe how queries of different isolation levels can read and modify data stored in both hot and cold stores without restriction while minimizing number of accesses to cold storage. We also show how records can be migrated between hot and cold stores while the DBMS is online and active. Experiments reveal that for cold data access rates appropriate for main-memory optimized databases, we incur an acceptable 7-14% throughput loss.<\/jats:p>","DOI":"10.14778\/2732967.2732968","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"931-942","source":"Crossref","is-referenced-by-count":59,"title":["Trekking through Siberia"],"prefix":"10.14778","volume":"7","author":[{"given":"Ahmed","family":"Eldawy","sequence":"first","affiliation":[{"name":"University of Minnesota"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Levandoski","sequence":"additional","affiliation":[{"name":"Microsoft Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Per-\u00c5ke","family":"Larson","sequence":"additional","affiliation":[{"name":"Microsoft Research"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2014,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/2556549.2556556"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1498759.1498761"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2556549.2556575"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2463710"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/2350229.2350258"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/1921071.1921077"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376713"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454211"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767867"},{"issue":"2","key":"e_1_2_1_10_1","first-page":"6","volume":"36","author":"Lahiri T.","year":"2013","unstructured":"T. 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IEEE Data Engineering Bulletin 36(2): 28--33 (2013).","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_2_1_13_1","volume-title":"ICDE","author":"Levandoski J.","year":"2013","unstructured":"J. Levandoski , P. \u00c5. Larson , and R. Stoica . Classifying Hot and Cold Data in a Main Memory OLTP Engine . In ICDE , 2013 . J. Levandoski, P. \u00c5. Larson, and R. Stoica. Classifying Hot and Cold Data in a Main Memory OLTP Engine. In ICDE, 2013."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544834"},{"issue":"2","key":"e_1_2_1_15_1","first-page":"14","volume":"36","author":"Lindstr\u00f6m J.","year":"2013","unstructured":"J. Lindstr\u00f6m , V. Raatikka , J. Ruuth , P. Soini , K. Vakkila . IBM solidDB: In-Memory Database Optimized for Extreme Speed and Availability. IEEE Data Engineering Bulletin 36 ( 2 ): 14 -- 20 ( 2013 ). J. Lindstr\u00f6m, V. Raatikka, J. Ruuth, P. Soini, K. Vakkila. IBM solidDB: In-Memory Database Optimized for Extreme Speed and Availability. IEEE Data Engineering Bulletin 36(2): 14--20 (2013).","journal-title":"Availability. IEEE Data Engineering Bulletin"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/564870.564881"},{"key":"e_1_2_1_17_1","unstructured":"L. Sidirourgos and P. \u00c5. Larson Adjustable and Updatable Bloom Filters. Available from the authors.  L. Sidirourgos and P. \u00c5. Larson Adjustable and Updatable Bloom Filters. Available from the authors ."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2485278.2485285"},{"key":"e_1_2_1_19_1","volume-title":"VLDB","author":"M. Stonebraker","year":"2007","unstructured":"M. Stonebraker et al. The End of an Architectural Era (Its Time for a Complete Rewrite) . In VLDB , 2007 . M. Stonebraker et al. The End of an Architectural Era (Its Time for a Complete Rewrite). In VLDB, 2007."},{"issue":"2","key":"e_1_2_1_20_1","first-page":"21","volume":"36","author":"Stonebraker M.","year":"2013","unstructured":"M. Stonebraker and A. Weisberg . The VoltDB Main Memory DBMS. IEEE Data Engineering Bulletin 36 ( 2 ): 21 -- 27 ( 2013 ). M. Stonebraker and A. Weisberg. The VoltDB Main Memory DBMS. 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