S. Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, H. Falaki, Xiangrui Meng, Reynold Xin, A. Ghodsi, M. Franklin, I. Stoica, M. Zaharia
{"title":"SparkR: Scaling R Programs with Spark","authors":"S. Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, H. Falaki, Xiangrui Meng, Reynold Xin, A. Ghodsi, M. Franklin, I. Stoica, M. Zaharia","doi":"10.1145/2882903.2903740","DOIUrl":null,"url":null,"abstract":"R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime is single threaded and can only process data sets that fit in a single machine's memory. We present SparkR, an R package that provides a frontend to Apache Spark and uses Spark's distributed computation engine to enable large scale data analysis from the R shell. We describe the main design goals of SparkR, discuss how the high-level DataFrame API enables scalable computation and present some of the key details of our implementation.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"70 8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"67","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 International Conference on Management of Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2882903.2903740","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 67
Abstract
R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime is single threaded and can only process data sets that fit in a single machine's memory. We present SparkR, an R package that provides a frontend to Apache Spark and uses Spark's distributed computation engine to enable large scale data analysis from the R shell. We describe the main design goals of SparkR, discuss how the high-level DataFrame API enables scalable computation and present some of the key details of our implementation.