{"id":547,"date":"2018-06-13T15:05:19","date_gmt":"2018-06-13T15:05:19","guid":{"rendered":"https:\/\/datascientists.info\/?p=547"},"modified":"2018-06-13T18:42:07","modified_gmt":"2018-06-13T18:42:07","slug":"plumber-getting-r-ready-production","status":"publish","type":"post","link":"https:\/\/datascientists.info\/index.php\/2018\/06\/13\/plumber-getting-r-ready-production\/","title":{"rendered":"Plumber: Getting R ready for production environments?"},"content":{"rendered":"<h2>R Project and Production<\/h2>\n<p>Running R Project in production is a controversially discussed topic, as is everything concerning <a href=\"https:\/\/datascientists.info\/index.php\/2015\/02\/15\/python-vs-r-for-data-science\/\" rel=\"noopener\" target=\"_blank\">R vs Python<\/a>. Lately there have been some additions to the R Project, that made me look into this again. Researching <a href=\"https:\/\/www.r-project.org\/\" target=\"_blank\" rel=\"noopener\">R<\/a> and its usage in production environments I came across several packages \/ project, that can be used as a solution for this:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.opencpu.org\/\" target=\"_blank\" rel=\"noopener\">OpenCPU<\/a> as a hosted version<\/li>\n<li><a href=\"http:\/\/rapache.net\/\" target=\"_blank\" rel=\"noopener\">RApache<\/a> that integrates into the Apache webserver<\/li>\n<li><a href=\"https:\/\/www.rplumber.io\/\" target=\"_blank\" rel=\"noopener\">Plumber<\/a> package<\/li>\n<\/ul>\n<p>There are several more, but those I found the most interesting.<\/p>\n<h2>Plumber<\/h2>\n<p><a href=\"https:\/\/www.rplumber.io\/\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-497 alignright\" src=\"https:\/\/datascientists.info\/wp-content\/uploads\/2018\/01\/plumber.png\" alt=\"\" width=\"177\" height=\"205\" alt=\"Plumber\" \/><\/a><br \/>\nFor reasons of ease of use and because it was not a hosted version, I took a deeper look into <b>Plumber<\/b>. This felt quite natural as it uses function decorators for defining endpoints and parameters. This is similar to <a href=\"https:\/\/spring.io\/projects\/spring-boot\" target=\"_blank\" rel=\"noopener\">Spring Boot<\/a>, which I normally use for programming REST APIs.<br \/>\nSo using Plumber is really straight forward, as the example below shows:<\/p>\n<pre class=\"brush: plain; title: ; notranslate\" title=\"\">\r\n#' return text &quot;Hello&quot;\r\n#' @get \/hello\r\nfunction() {\r\n  list(msg = &quot;hello&quot;)\r\n}\r\n<\/pre>\n<p>The <i>#&#8217; @get<\/i> defines the endpoint for this request. In this case \/hello, so the full url on localhost is http:\/\/127.0.0.1:8001\/hello. To pass in one or more parameters you can use the decorator <i>#&#8217; @param parameter_name parameter_description<\/i>. A more complicated example using Plumber is hosted on our <a href=\"https:\/\/gitlab.com\/datascientists.info\/r-plumber-rest\" target=\"_blank\">Gitlab<\/a>. This example was built with the help of <a href=\"https:\/\/www.tidytextmining.com\/\" target=\"_blank\" rel=\"noopener\">Tidy Textmining<\/a>.<\/p>\n<h2>Production ready?<\/h2>\n<p>Plumber comes with <a href=\"https:\/\/swagger.io\/tools\/swagger-ui\/\" target=\"_blank\" rel=\"noopener\">Swagger<\/a>, so the webserver is automatically available. As the R instance is already running, processing the R code does not take long. If your model is complicated, then, of course, this is reflected in the processing time. But as R is a single thread programming language, Plumber can only process one request at a time.<br \/>\nThere are ways to tweak this of course. You can run several instances of the service, using a Docker image. This is decribed <a href=\"https:\/\/www.knowru.com\/blog\/how-to-scale-up-credit-model-apis-using-aws\/\" target=\"_blank\" rel=\"noopener\">here<\/a>. There is also the option of using a webserver to fork the request to serveral instances of R. Depending on the need of the API, single thread processing can be fast enough. If the service has to be highly available the Docker solutions seems like a good choice, as it comes with a load balancer.<\/p>\n<h2>Conclussion<\/h2>\n<p>After testing Plumber I am surprised by the easy of use. This package makes deploying an REST API in R really easy. Depending on your business needs, it might even be enough for a productive scenario, especially when used in combination with Docker.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>R Project and Production Running R Project in production is a controversially discussed topic, as is everything concerning R vs Python. Lately there have been some additions to the R Project, that made me look into this again. Researching R and its usage in production environments I came across several packages \/ project, that can [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2,5,7,9],"tags":[29,89,66,67,88,90],"ppma_author":[144],"class_list":["post-547","post","type-post","status-publish","format-standard","hentry","category-analytics-platform","category-data-science","category-machine-learning","category-tools","tag-data-science","tag-plumber","tag-python","tag-r","tag-r-project","tag-rest-api","author-marc"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Plumber: Getting R ready for production environments? - DATA DO - \u30c7\u30fc\u30bf \u9053<\/title>\n<meta name=\"description\" content=\"Is R ready for production? 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