{"title":"在分解内存上构建写优化树索引","authors":"Qing Wang, Youyou Lu, J. Shu","doi":"10.1145/3604437.3604448","DOIUrl":null,"url":null,"abstract":"Memory disaggregation architecture physically separates CPU and memory into independent components, which are connected via high-speed RDMA networks, greatly improving resource utilization of database systems. However, such an architecture poses unique challenges to data indexing due to limited RDMA semantics and near-zero computation power at memory side. Existing indexes supporting disaggregated memory either suffer from low write performance, or require hardware modification.","PeriodicalId":346332,"journal":{"name":"ACM SIGMOD Record","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Building Write-Optimized Tree Indexes on Disaggregated Memory\",\"authors\":\"Qing Wang, Youyou Lu, J. Shu\",\"doi\":\"10.1145/3604437.3604448\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Memory disaggregation architecture physically separates CPU and memory into independent components, which are connected via high-speed RDMA networks, greatly improving resource utilization of database systems. However, such an architecture poses unique challenges to data indexing due to limited RDMA semantics and near-zero computation power at memory side. Existing indexes supporting disaggregated memory either suffer from low write performance, or require hardware modification.\",\"PeriodicalId\":346332,\"journal\":{\"name\":\"ACM SIGMOD Record\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGMOD Record\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3604437.3604448\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGMOD Record","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3604437.3604448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Building Write-Optimized Tree Indexes on Disaggregated Memory
Memory disaggregation architecture physically separates CPU and memory into independent components, which are connected via high-speed RDMA networks, greatly improving resource utilization of database systems. However, such an architecture poses unique challenges to data indexing due to limited RDMA semantics and near-zero computation power at memory side. Existing indexes supporting disaggregated memory either suffer from low write performance, or require hardware modification.
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