PL/R Language

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PL/R Language

About Greenplum Database PL/R

PL/R is a procedural language. With the Greenplum Database PL/R extension you can write database functions in the R programming language and use R packages that contain R functions and data sets.

For information about supported PL/R versions, see the Greenplum Database Release Notes.

Installing R

For RHEL and CentOS, installing the PL/R package installs R in $GPHOME/ext/R-<version> and updates $GPHOME/ for Greenplum Database to use R.

To use PL/R on Ubuntu host systems, you must install and configure R on all Greenplum Database host systems before installing PL/R.

Note: You can use the gpssh utility to run bash shell commands on multiple remote hosts.
  1. To install R, run these apt commands on all host systems.
    $ sudo apt update && sudo apt install r-base

    Installing r-base also installs dependent packages including r-base-core.

  2. To configure Greenplum Database to use R, add the R_HOME environment variable to $GPHOME/ on all hosts. This example command returns the R home directory.
    $ R RHOME

    Using the previous R home directory as an example, add this line to the file on all hosts.

    export R_HOME=/usr/lib/R
  3. Source $GPHOME/ and restart Greenplum Database. For example, run these commands on the Greenplum Database master host.
    $ source $GPHOME/
    $ gpstop -r

Installing PL/R

The PL/R extension is available as a package. Download the package from Pivotal Network and install it with the Greenplum Package Manager (gppkg).

The gppkg utility installs Greenplum Database extensions, along with any dependencies, on all hosts across a cluster. It also automatically installs extensions on new hosts in the case of system expansion and segment recovery.

Installing the Extension Package

Before you install the PL/R extension, make sure that your Greenplum Database is running, you have sourced, and that the $MASTER_DATA_DIRECTORY and $GPHOME variables are set.

  1. Download the PL/R extension package from Pivotal Network.
  2. Copy the PL/R package to the Greenplum Database master host.
  3. Install the software extension package by running the gppkg command. This example installs the PL/R extension on a Linux system:
    $ gppkg -i plr-3.0.3-gp6-rhel7_x86_64.gppkg
  4. Source the file $GPHOME/
  5. Restart Greenplum Database.
    $ gpstop -r

Enabling PL/R Language Support

For each database that requires its use, register the PL/R language with the SQL command CREATE EXTENSION. Because PL/R is an untrusted language, only superusers can register PL/R with a database. For example, run this command as the gpadmin user to register the language with the database named testdb:

$ psql -d testdb -c 'CREATE EXTENSION plr;'

PL/R is registered as an untrusted language.

Note: Using the deprecated createlang command to enable PL/R generates an error.

Uninstalling PL/R

When you remove PL/R language support from a database, the PL/R routines that you created in the database will no longer work.

Remove PL/R Support for a Database

For a database that no longer requires the PL/R language, remove support for PL/R with the SQL command DROP EXTENSION. Because PL/R is an untrusted language, only superusers can remove support for the PL/R language from a database. For example, run this command as the gpadmin user to remove support for PL/R from the database named testdb:

$ psql -d testdb -c 'DROP EXTENSION plr;'

The default command fails if any existing objects (such as functions) depend on the language. Specify the CASCADE option to also drop all dependent objects, including functions that you created with PL/R.

Note: Using the deprecated droplang command to remove support for PL/R generates an error.

Uninstall the Extension Package

If no databases have PL/R as a registered language, uninstall the Greenplum PL/R extension with the gppkg utility. This example uninstalls PL/R package version 3.0.3.

$ gppkg -r plr-3.0.3

On RHEL and CentOS systems, uninstalling the extension uninstalls the R software that was installed with the extension.

You can run the gppkg utility with the options -q --all to list the installed extensions and their versions.

For Ubuntu systems, remove the R_HOME environment variable from on all Greenplum Database host systems.

Source the file $GPHOME/ and restart the database.

$ gpstop -r

Uninstall R (Ubuntu)

For Ubuntu systems, remove R from all Greenplum Database host systems. These commands remove R from an Ubuntu system.
$ sudo apt remove r-base
$ sudo apt remove r-base-core

Removing r-base does not uninstall the R executable. Removing r-base-core uninstalls the R executable.


The following are simple PL/R examples.

Example 1: Using PL/R for single row operators

This function generates an array of numbers with a normal distribution using the R function rnorm().

CREATE OR REPLACE FUNCTION r_norm(n integer, mean float8,
  std_dev float8) RETURNS float8[ ] AS

The following CREATE TABLE command uses the r_norm() function to populate the table. The r_norm() function creates an array of 10 numbers.

CREATE TABLE test_norm_var
  AS SELECT id, r_norm(10,0,1) as x
  FROM (SELECT generate_series(1,30:: bigint) AS ID) foo

Example 2: Returning PL/R data.frames in Tabular Form

Assuming your PL/R function returns an R data.frame as its output, unless you want to use arrays of arrays, some work is required to see your data.frame from PL/R as a simple SQL table:

  • Create a TYPE in a Greenplum database with the same dimensions as your R data.frame:
    CREATE TYPE t1 AS ...
  • Use this TYPE when defining your PL/R function
    ... RETURNS SET OF t1 AS ...

Sample SQL for this is given in the next example.

Example 3: Hierarchical Regression using PL/R

The SQL below defines a TYPE and runs hierarchical regression using PL/R:

--Create TYPE to store model results
CREATE TYPE wj_model_results AS (
  cs text, coefext float, ci_95_lower float, ci_95_upper float,
  ci_90_lower float, ci_90_upper float, ci_80_lower float,
  ci_80_upper float);

--Create PL/R function to run model in R
DROP FUNCTION IF EXISTS wj_plr_RE(float [ ], text [ ]);
CREATE FUNCTION wj_plr_RE(response float [ ], cs text [ ])
RETURNS SETOF wj_model_results AS
  y<- log(response)
  cs<- cs
  d_temp<- data.frame(y,cs)
  m0 <- lmer (y ~ 1 + (1 | cs), data=d_temp)
  cs_unique<- sort(unique(cs))
  n_cs_unique<- length(cs_unique)
  temp_m0<- data.frame(matrix0,n_cs_unique, 7))
  for (i in 1:n_cs_unique){temp_m0[i,]<-
    c(exp(coef(m0)$cs[i,1] + c(0,-1.96,1.96,-1.65,1.65,
  names(temp_m0)<- c("Coefest", "CI_95_Lower",
    "CI_95_Upper", "CI_90_Lower", "CI_90_Upper",
   "CI_80_Lower", "CI_80_Upper")
  temp_m0_v2<- data.frames(cs_unique, temp_m0)

--Run modeling plr function and store model results in a
DROP TABLE IF EXISTS wj_model_results_roi;
CREATE TABLE wj_model_results_roi AS SELECT *
  FROM wj_plr_RE((SELECT wj_droi2_array),
  (SELECT cs FROM wj_droi2_array));

Downloading and Installing R Packages

R packages are modules that contain R functions and data sets. You can install R packages to extend R and PL/R functionality in Greenplum Database.

Greenplum Database provides a collection of data science-related R libraries that can be used with the Greenplum Database PL/R language. You can download these libraries in .gppkg format from Pivotal Network. For information about the libraries, see R Data Science Library Package.

Note: If you expand Greenplum Database and add segment hosts, you must install the R packages in the R installation of the new hosts.
  1. For an R package, identify all dependent R packages and each package web URL. The information can be found by selecting the given package from the following navigation page:

    As an example, the page for the R package arm indicates that the package requires the following R libraries: Matrix, lattice, lme4, R2WinBUGS, coda, abind, foreign, and MASS.

    You can also try installing the package with R CMD INSTALL command to determine the dependent packages.

    For the R installation included with the Greenplum Database PL/R extension, the required R packages are installed with the PL/R extension. However, the Matrix package requires a newer version.

  2. From the command line, use the wget utility to download the tar.gz files for the arm package to the Greenplum Database master host:
  3. Use the gpscp utility and the hosts_all file to copy the tar.gz files to the same directory on all nodes of the Greenplum Database cluster. The hosts_all file contains a list of all the Greenplum Database segment hosts. You might require root access to do this.
    gpscp -f hosts_all Matrix_0.9996875-1.tar.gz =:/home/gpadmin 
    gpscp -f /hosts_all arm_1.5-03.tar.gz =:/home/gpadmin
  4. Use the gpssh utility in interactive mode to log into each Greenplum Database segment host (gpssh -f all_hosts). Install the packages from the command prompt using the R CMD INSTALL command. Note that this may require root access. For example, this R install command installs the packages for the arm package.
    $R_HOME/bin/R CMD INSTALL Matrix_0.9996875-1.tar.gz   arm_1.5-03.tar.gz
  5. Ensure that the package is installed in the $R_HOME/library directory on all the segments (the gpssh can be use to install the package). For example, this gpssh command list the contents of the R library directory.
    gpssh -s -f all_hosts "ls $R_HOME/library"

    The gpssh option -s sources the file before running commands on the remote hosts.

  6. Test if the R package can be loaded.

    This function performs a simple test to if an R package can be loaded:

    CREATE OR REPLACE FUNCTION R_test_require(fname text)
    RETURNS boolean AS
    LANGUAGE 'plr';

    This SQL command checks if the R package arm can be loaded:

    SELECT R_test_require('arm');

Displaying R Library Information

You can use the R command line to display information about the installed libraries and functions on the Greenplum Database host. You can also add and remove libraries from the R installation. To start the R command line on the host, log into the host as the gadmin user and run the script R from the directory $GPHOME/ext/R-3.3.3/bin.

This R function lists the available R packages from the R command line:

> library()

Display the documentation for a particular R package

> library(help="package_name")
> help(package="package_name")

Display the help file for an R function:

> help("function_name")
> ?function_name

To see what packages are installed, use the R command installed.packages(). This will return a matrix with a row for each package that has been installed. Below, we look at the first 5 rows of this matrix.

> installed.packages()

Any package that does not appear in the installed packages matrix must be installed and loaded before its functions can be used.

An R package can be installed with install.packages():

> install.packages("package_name")
> install.packages("mypkg", dependencies = TRUE, type="source")
Load a package from the R command line.
> library(" package_name ") 

An R package can be removed with remove.packages

> remove.packages("package_name")

You can use the R command -e option to run functions from the command line. For example, this command displays help on the R package MASS.

$ R -e 'help("MASS")'

Loading R Modules at Startup

PL/R can automatically load saved R code during interpreter initialization. To use this feature, you create the plr_modules database table and then insert the R modules you want to auto-load into the table. If the table exists, PL/R will load the code it contains into the interpreter.

In a Greenplum Database system, table rows are usually distributed so that each row exists at only one segment instance. The R interpreter at each segment instance, however, needs to load all of the modules, so a normally distributed table will not work. You must create the plr_modules table as a replicated table in the default schema so that all rows in the table are present at every segment instance. For example:

CREATE TABLE public.plr_modules {
  modseq int4,
  modsrc text

See for more information about using the PL/R auto-load feature.

References - The R Project home page - The home page for PivotalR, a package that provides an R interface to operate on Greenplum Database tables and views that is similar to the R data.frame. PivotalR also supports using the machine learning package MADlib directly from R.

The following links highlight key topics from the R documentation.