{"title":"Spark和Scala(主题演讲)","authors":"Reynold Xin","doi":"10.1145/3136000.3148042","DOIUrl":null,"url":null,"abstract":"Written mostly in Scala and with over 1000 contributors, Apache Spark has become the de facto standard for big data processing. In this talk, I will review the evolution of Spark for the last seven years and our experience using Scala as the main programming language in a high profile open source project with a distributed team. I will outline language features that we can't live without, and features we wish were designed differently. Last but not least, I will discuss how we at Databricks are leveraging native code to further improve performance.","PeriodicalId":158126,"journal":{"name":"Proceedings of the 8th ACM SIGPLAN International Symposium on Scala","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Spark and Scala (keynote)\",\"authors\":\"Reynold Xin\",\"doi\":\"10.1145/3136000.3148042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Written mostly in Scala and with over 1000 contributors, Apache Spark has become the de facto standard for big data processing. In this talk, I will review the evolution of Spark for the last seven years and our experience using Scala as the main programming language in a high profile open source project with a distributed team. I will outline language features that we can't live without, and features we wish were designed differently. Last but not least, I will discuss how we at Databricks are leveraging native code to further improve performance.\",\"PeriodicalId\":158126,\"journal\":{\"name\":\"Proceedings of the 8th ACM SIGPLAN International Symposium on Scala\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM SIGPLAN International Symposium on Scala\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3136000.3148042\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th ACM SIGPLAN International Symposium on Scala","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3136000.3148042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Written mostly in Scala and with over 1000 contributors, Apache Spark has become the de facto standard for big data processing. In this talk, I will review the evolution of Spark for the last seven years and our experience using Scala as the main programming language in a high profile open source project with a distributed team. I will outline language features that we can't live without, and features we wish were designed differently. Last but not least, I will discuss how we at Databricks are leveraging native code to further improve performance.
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