{"title":"CausalSpartan:使用混合逻辑时钟的分布式数据存储的因果一致性","authors":"Mohammad Roohitavaf, M. Demirbas, S. Kulkarni","doi":"10.1109/SRDS.2017.27","DOIUrl":null,"url":null,"abstract":"Causal consistency is an intermediate consistency model that can be achieved together with high availability and high-performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using clocks is more efficient than explicit dependency tracking by sending dependency check messages. Existing clock-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new clock-based algorithm, CausalSpartan, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartan decreases the visibility latency for a given data item comparing to existing clock-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. Like previous protocols, CausalSpartan assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":"30 1","pages":"184-193"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"CausalSpartan: Causal Consistency for Distributed Data Stores Using Hybrid Logical Clocks\",\"authors\":\"Mohammad Roohitavaf, M. Demirbas, S. Kulkarni\",\"doi\":\"10.1109/SRDS.2017.27\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Causal consistency is an intermediate consistency model that can be achieved together with high availability and high-performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using clocks is more efficient than explicit dependency tracking by sending dependency check messages. Existing clock-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new clock-based algorithm, CausalSpartan, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartan decreases the visibility latency for a given data item comparing to existing clock-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. Like previous protocols, CausalSpartan assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.\",\"PeriodicalId\":6475,\"journal\":{\"name\":\"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)\",\"volume\":\"30 1\",\"pages\":\"184-193\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SRDS.2017.27\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SRDS.2017.27","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CausalSpartan: Causal Consistency for Distributed Data Stores Using Hybrid Logical Clocks
Causal consistency is an intermediate consistency model that can be achieved together with high availability and high-performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using clocks is more efficient than explicit dependency tracking by sending dependency check messages. Existing clock-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new clock-based algorithm, CausalSpartan, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartan decreases the visibility latency for a given data item comparing to existing clock-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. Like previous protocols, CausalSpartan assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.