{"title":"推断分布式事务的序列化顺序","authors":"Khuzaima S. Daudjee, K. Salem","doi":"10.1109/ICDE.2006.82","DOIUrl":null,"url":null,"abstract":"Data partitioning is often used to scale-up a database system. In a centralized database system, the serialization order of commited update transactions can be inferred from the database log. To achieve this in a shared-nothing distributed database, the serialization order of update transactions must be inferred from multiple database logs. We describe a technique to generate a single stream of updates from logs of multiple database systems. This single stream represents a valid serialization order of update transactions at the sites over which the database is partitioned.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"5 1","pages":"154-154"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Inferring a Serialization Order for Distributed Transactions\",\"authors\":\"Khuzaima S. Daudjee, K. Salem\",\"doi\":\"10.1109/ICDE.2006.82\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data partitioning is often used to scale-up a database system. In a centralized database system, the serialization order of commited update transactions can be inferred from the database log. To achieve this in a shared-nothing distributed database, the serialization order of update transactions must be inferred from multiple database logs. We describe a technique to generate a single stream of updates from logs of multiple database systems. This single stream represents a valid serialization order of update transactions at the sites over which the database is partitioned.\",\"PeriodicalId\":6819,\"journal\":{\"name\":\"22nd International Conference on Data Engineering (ICDE'06)\",\"volume\":\"5 1\",\"pages\":\"154-154\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference on Data Engineering (ICDE'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2006.82\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference on Data Engineering (ICDE'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2006.82","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Inferring a Serialization Order for Distributed Transactions
Data partitioning is often used to scale-up a database system. In a centralized database system, the serialization order of commited update transactions can be inferred from the database log. To achieve this in a shared-nothing distributed database, the serialization order of update transactions must be inferred from multiple database logs. We describe a technique to generate a single stream of updates from logs of multiple database systems. This single stream represents a valid serialization order of update transactions at the sites over which the database is partitioned.