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What's New in R Server 9.0.1

Heidi Steen|Last Updated: 1/6/2017
2 Contributors

This release of R Server, built on open source R 3.3.2, includes new and updated packages, plus new operationalization features in the core engine. Key features in this release include the following:

Related Documents

New and updated packages

Microsoft Machine Learning algorithms (MicrosoftML package) is a collection of functions for incorporating machine learning into R code or script that executes on R Server and R Client. It's available in the following Microsoft R products: R Server for Windows, R Client for Windows, and SQL Server R Services. Availability for Linux, Hadoop, and Azure HDInsight is projected for the first quarter of 2017.

To learn more, see Introduction to MicrosoftML.

mrsdeploy package is new in this release and available on all platforms supporting operationalization. Functions provide remote execution on a R Server 9.0.1 instance, and the ability to publish, and subsequently manage, an R code block as a web service.

To learn more, see mrsdeploy Function Reference.

olapR package lets you run MDX queries and connect directly to OLAP cubes on SQL Server 2016 Analysis Services from your R solution. You can manually create and then paste in an MDX query, or use an R-style API to choose a cube, axes, and slicers.

To learn more, see Using Data from OLAP Cubes in R.

RevoScaleR Package is updated to include support for Spark 2.0. For a list of all functions, see RevoScaleR Function Reference.

RxHiveDataGenerate a Hive Data Source object.
RxParquetDataGenerate a Parquet Data Source object.
rxSparkConnectCreate a persistent Spark compute context.
rxSparkDisconnectDisconnect a Spark session and return to a local compute context.
rxSparkListDataList cached RxParquetData or RxHiveData data source objects.
rxSparkRemoveDataRemove cached RxParquetData or RxHiveData data source objects.

Although ScaleR jobs only execute on Spark 2.0 if you have R Server 9.0.1 for Hadoop, you can create solutions containing Hive, Parquet, and Spark-related functions in R Client.

General updates

Operationalization features

Formerly known as DeployR, the operationalization feature is now fully integrated into R Server, with a new ASP .NET core bringing improved support from Microsoft. After installing R Server on select platforms, you'll have everything you need to enable operationalization and configure R Server to host R analytics web services and remote R sessions. For details on which platforms, see Supported platforms.

In many enterprises, the final step is to deploy an interface to the underlying analysis to a broader audience within the organization. The operationalization feature in Microsoft R Server provides the tools to deploy R analytics inside web, desktop, mobile, and dashboard applications as well as backend systems. R Server turns your R scripts into analytics web services, so R code can be easily executed by applications running on a secure server.

An operationalized R server offers the ability to host and bundle R analytics into web services with minimal code changes. R Server accepts interactive commands through mrsdeploy functions for remote execution and web service deployment. Data scientists can use mrsdeploy functions on the command line. Application developers can write code to instrument equivalent operations and integrate web services into their applications using easy-to-consume Swagger-based APIs in any programming language.

The operationalization feature can be configured on a single machine. It can also be scaled for business-critical applications with multiple web and compute nodes on clustered servers for load balancing. This gives you the ability to pipeline data streams that are subsequently transformed, analyzed, and visualized into an R analytics web service.

In a Windows environment, multi-server topologies are supported through Windows clustering methodologies. Compute nodes can be made highly available using Windows server failover clusters in Active-Active mode. Web nodes can be scaled out using Windows network load balancing. Operationalization with R Server also supports production-grade workloads and seamless integration with popular enterprise security solutions.

For feature information and next steps, see Operationalization with R Server and Configure operationalization on R Server.


In the context of operationalization, clustered topologies are composed of standalone servers, not nodes in Hadoop or cloud services in Azure. Feature support is limited to a subset of the supported R Server platforms has the list.

R Server for Linux

This release now supports Ubuntu 14.04 and 16.04 on premises. For installation instructions, see Install R Server for Linux.

R Server for Hadoop (MapReduce and Spark)

  • Support for Spark 1.6 and 2.0.
  • Support for Spark DataFrames through RxHiveData and RxParquetData in ScaleR when using an RxSpark compute context in ScaleR:

      hiveData <- RxHiveData("select * from hivesampletable", ...)
      pqData <- RxParquetData('/share/claimsParquet', ...)

Additional new ScaleR functions for Spark 2.0:

  • Manage Spark persistent sessions: rxSparkConnect, rxSparkDisconnect
  • Manage data in Spark DataFrames : rxSparkListData, rxSparkRemoveData

For installation instructions, see Install R Server for Hadoop. For ScaleR help, see ScaleR Function Reference.

R Server for Windows

As noted, installation of R Server or R Client on Windows delivers the new MicrosoftML package for machine learning.

Additionally, this release adds a simplified setup program for an Server installation on Windows. This setup is in addition to SQL Server Setup, which continues to be a viable option for installation.

Features in the 9.0.1 release are currently only available through simplified setup. In contrast, SQL Server 2016 Setup installs the 8.0.3 version of R Server for Windows, and SQL Server vNext installs the 9.0.0 version. The 9.0.0 version offers the new MicrosoftML and olapR packages, but not operationalization. For a description of the features in 9.0.0, see What's new in SQL Server R Services.

For installation and upgrade instructions, see Install R Server for Windows.


Although the installation experience is changing, licensing is not. R Server for Windows remains a SQL Server enterprise feature, even when installed outside of SQL Server Setup. A SQL Server enterprise license is required for the enterprise edition of R Server for Windows.

New service and support options for R Server for Windows

R Server for Windows can be serviced under the Modern Lifecycle policy or under SQL Server's support policy (search for "SQL Server 2016" on this page). For support information for R Server in general, see Support for Microsoft R Server Versions.

  • Modern lifecycle policy is designed for rapid release cycles. Individual versions age out sooner, but newer features roll out more frequently. The Modern lifecycle policy is in effect when you use simplified setup to install R Server for Windows.

  • SQL Server support policy supports released versions over a longer time frame, but updates are less frequent. This support policy is in effect when you use SQL Server Setup to install R Server for Windows.

  • In the upcoming SQL Server vNext CTP 1.1 release, you will be able to unbind your SQL Server R Services instance and replace it with a 9.0.1 version that offers operationalization features and the Modern Lifecycle Support policy. Check What's new in SQL Server R Services for the latest information on CTP 1.1 when it becomes available.

Previously released features

This section lists the feature announcements of recent previous releases.

Announced in Microsoft R Server 8.0.5

  • On Linux:

    • Support for RedHat RHEL 7.x has been added.

    • Installers are now composed of RPM packages, which can be installed via a top-level install script or as individual RPM packages. This can be convenient for Enterprise IT departments managing extensive deployments.

  • On Hadoop:

    • Installation on Hadoop clusters has been simplified to eliminate manual steps.

    • New support Hadoop on SUSE 11 and Hadoop distributions (Cloudera CDH 5.5-5.7, Hortonworks HDP 2.4, MapR 5.0-5.1)

    • A new distributed compute context RxSpark is available, in which computations are parallelized and distributed across the nodes of a Hadoop cluster via Apache Spark. This provides up to a 7x performance boost compared to RxHadoopMR.

    • New Hadoop diagnostic tool to collect status of MRS and dependencies on all nodes (available through a CSS Support request)

    • Hadoop user directories in HDFS and Linux are now created automatically as needed.

    • New Hadoop administrator script to clean-up orphaned HDFS and Linux user directories.

  • On Teradata:

    • New option has been added to rxPredict to insert into an existing table.

    • New option has been added for use of LDAP authentication with a TPT connection.

  • DeployR Enterprise includes the following changes and improvements:

    • Deployr Enterprise is more secure than ever with improved Web security features for better protection against malicious attacks, improved installation security, and improved Security Policy Management.

    • DeployR Enterprise now relies on an H2 database by default and allows you to easily use a SQL Server or PostgreSQL database instead to fit your production environment.

    • DeployR Enterprise now has a simplified installer for a better customer experience.

    • The XML format for data exchange is deprecated, and will be removed from future versions of DeployR.

    • The API has been updated. See the change history.

    • This release is of DeployR Enterprise only.

Announced in Microsoft R Server 8.0.3

  • R Server 8.0.3 is a Windows-only, SQL-Server-only release. It is installed using SQL Server 2016 setup. For a description of the features in 8.0.3, see What's new in SQL Server R Services.

Announced in Microsoft R Server 8.0.0

  • Revolution R Open is now Microsoft R Open, and Revolution R Enterprise is now generically known as Microsoft R Services, and specifically Microsoft R Server for Linux platforms.

  • Microsoft R Services installs on top of an enhanced version of R 3.2.2, Microsoft R Open for Revolution R Enterprise 8.0.0 (on Windows) or Microsoft R Open for Microsoft R Server (on Linux)

  • Installation of Microsoft R Services has been simplified from three installers to two: the new Microsoft R Open for Revolution R Enterprise/Microsoft R Server installer combines Microsoft R Open with the GPL/LGPL components needed to support Microsoft R Services, so there is no need for the previous “Revolution R Connector” install.

  • RevoScaleR includes:

    • New Fuzzy Matching Algorithms: The new rxGetFuzzyKeys and rxGetFuzzyDist functions provide access to fuzzy matching algorithms for cleaning and analyzing text data.
    • Support for Writing in ODBC Data Sources. The RxOdbcData data source now supports writing
    • Bug Fixes:
      • When using rxDataStep, new variables created in a transformation no longer inherit the rxLowHigh attribute of the variable used to create them.
      • rxGetInfo was failing when an extended class of RxXdfData was used.
      • rxGetVarInfo now respects the newName element of colInfo for non-xdf data sources.
      • If inData for rxDataStep is a non-xdf data source that contains a colInfo specification using newName, the newName should now be used for varsToKeep and varsToDrop.
    • Deprecated and Defunct.
      • NIEDERR is no longer supported as a type of random number generator.
      • scheduleOnce is now defunct for rxPredict.rxDForest and rxPredict.rxBTrees.
      • The compute context RxLsfCluster is now defunct.
      • The compute context RxHpcServer is now deprecated
  • DevelopR - The R Productivity Environment (the IDE provided with Revolution R Enterprise on Windows) is not deprecated, but it will be removed from future versions of Microsoft R Services.

  • RevoMPM, a Multinode Package Manager, is now defunct, as it was deemed redundant. Numerous distributed shells are available, including pdsh, fabric, and PyDSH. More sophisticated provisioning tools such as Puppet, Chef, and Ansible are also available. Any of these can be used in place of RevoMPM.

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