{"title":"Microsoft Azure云中的Couchbase服务器:Docker容器方法","authors":"Calin-Marian Iurian, I. Ivanciu, V. Dobrota","doi":"10.1109/ISETC50328.2020.9301052","DOIUrl":null,"url":null,"abstract":"This paper evaluates the Couchbase Server platform performance for storing data items, using NoSQL databases. It involved services for querying and indexing, both deployed in Microsoft Azure Cloud environment and virtual machines. The architecture contained three virtual machines acting as nodes for the Couchbase Server database. Deploying and establishing the database in the public cloud was done with Docker images. Some of the key features and applications from Microsoft Azure were tested, i.e. Monitor, Resource groups, Dashboard and Storage accounts. From the Couchbase perspective, its friendly usage with full text search and real-time analytics proved to be useful for processing of big data.","PeriodicalId":165650,"journal":{"name":"2020 International Symposium on Electronics and Telecommunications (ISETC)","volume":"130 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Couchbase Server in Microsoft Azure Cloud: A Docker Container Approach\",\"authors\":\"Calin-Marian Iurian, I. Ivanciu, V. Dobrota\",\"doi\":\"10.1109/ISETC50328.2020.9301052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper evaluates the Couchbase Server platform performance for storing data items, using NoSQL databases. It involved services for querying and indexing, both deployed in Microsoft Azure Cloud environment and virtual machines. The architecture contained three virtual machines acting as nodes for the Couchbase Server database. Deploying and establishing the database in the public cloud was done with Docker images. Some of the key features and applications from Microsoft Azure were tested, i.e. Monitor, Resource groups, Dashboard and Storage accounts. From the Couchbase perspective, its friendly usage with full text search and real-time analytics proved to be useful for processing of big data.\",\"PeriodicalId\":165650,\"journal\":{\"name\":\"2020 International Symposium on Electronics and Telecommunications (ISETC)\",\"volume\":\"130 3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Electronics and Telecommunications (ISETC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISETC50328.2020.9301052\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Electronics and Telecommunications (ISETC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISETC50328.2020.9301052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Couchbase Server in Microsoft Azure Cloud: A Docker Container Approach
This paper evaluates the Couchbase Server platform performance for storing data items, using NoSQL databases. It involved services for querying and indexing, both deployed in Microsoft Azure Cloud environment and virtual machines. The architecture contained three virtual machines acting as nodes for the Couchbase Server database. Deploying and establishing the database in the public cloud was done with Docker images. Some of the key features and applications from Microsoft Azure were tested, i.e. Monitor, Resource groups, Dashboard and Storage accounts. From the Couchbase perspective, its friendly usage with full text search and real-time analytics proved to be useful for processing of big data.