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The Deephaven Core R Client

The Deephaven Core R client is an R package that enables R users to interface with a Deephaven server and perform various server-side operations from the comfort of RStudio or any other R interface.

What can the R client do?

The R Client provides the following functionalities:

  1. Connect to a Deephaven server
    • with anonymous authentication (no username or password)
    • with basic authentication (username and password)
    • with pre-shared key authentication (requires only a key)
    • with custom authentication (general key-value credentials)
  2. Run scripts on the server
    • If the server is equipped with a console, run a script in that console
    • Currently, Python and Groovy are supported
  3. Utilize Deephaven’s vast table API from R

Installation

Currently, the R client is only supported on Ubuntu 20.04 or 22.04 and must be built from source.

  1. We need a working installation of R on the machine where the R client will be built, plus the necessary dependencies for building and creating vignettes. The R client requires R 4.1.2 or newer; you can install R from the standard packages made available by Ubuntu 22.04. If you want a newer R version or if you are running in Ubuntu 20.04, you should install R from CRAN:

    # Download the key and install it
$ wget -qO- https://cloud.r-project.org/bin/linux/ubuntu/marutter_pubkey.asc | \
    sudo gpg --dearmor -o /usr/share/keyrings/r-project.gpg

# Add the R source list to apt's sources list
$ echo "deb [signed-by=/usr/share/keyrings/r-project.gpg] https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/" | \
    sudo tee -a /etc/apt/sources.list.d/r-project.list

# update the apt package list
$ apt -y update

# install R
$ sudo apt -y install r-base r-recommended

    Independently of R itself, install the OS packages required for building vignettes: libxml2-dev and pandoc

    # required during the build for vignettes
$ sudo apt -y install libxml2-dev pandoc

  2. Build the Deephaven C++ client (and dependencies) according to the instructions in the Deephaven C++ client installation guide. Follow the instructions at least through “Build and Install Deephaven C++ client”. After that, you will have both the Deephaven C++ client and any C++ libraries it depends on installed in a particular directory of your choosing. In what follows, we assume that directory is /path/to/dhcpp.

  3. Set environment variables from the C++ client installation required for building the R client:

    source /path/to/dhcpp/env.sh

    where /path/to/dhcpp is the directory you created in step 1 above. Ensure the necessary environment variables are set by checking their values as follows:

    echo $DHCPP
echo $LD_LIBRARY_PATH
echo $NCPUS
echo $CMAKE_PREFIX_PATH

    For faster compilation of the R client and its dependencies (particularly the Arrow R client), use the following command:

     export MAKE="make -j$NCPUS"

    Refer to the instructions on the C++ client installation for more details on the dhcpp directory.

  4. The C++ client installation will have left you with a local clone of the full Deephaven Core Git repository. Navigate to the deephaven-core/R/rdeephaven directory within that clone.

    If you prefer to have an isolated directory where the Deephaven R client source code will live, use git’s sparse-checkout:

    mkdir rdeephaven
cd rdeephaven
git init
git remote add -f origin https://github.com/deephaven/deephaven-core.git
git config core.sparseCheckout true
echo "R/rdeephaven" >> .git/info/sparse-checkout
git pull origin main

  5. Start an R console inside the rdeephaven directory by running R. In that console, install the Deephaven R client dependencies:

    install.packages(c('Rcpp', 'arrow', 'R6', 'dplyr', 'xml2', 'rmarkdown', 'knitr'))

    Then, exit the R console with quit(). From the rdeephaven directory, build and install the R client:

    cd .. && R CMD build rdeephaven && R CMD INSTALL --no-multiarch --with-keep.source rdeephaven_*.tar.gz && rm rdeephaven_*.tar.gz

    This is needed over the typical install.packages() to ensure that the vignettes get built and installed.

  6. Now, run

    in the R session, and start using the client!

    For an introduction to the package, run vignette("rdeephaven").

  7. The LD_LIBRARY_PATH environment variable set in step 2 is necessary for loading the R client once it is installed. Therefore, you must set LD_LIBRARY_PATH manually at the start of each session by running the following command:

    source /path/to/dhcpp/env.sh

    This will set LD_LIBRARY_PATH, as well as the other environment variables from step 2.

    If you would rather not have to run this command every time you open a new session, you can add the following to $HOME/.profile:

    # source DHCPP env variables if dhcpp directory exists
current_dhcpp_dir=/path/to/dhcpp"
if [ -d "$current_dhcpp_dir" ] ; then
    source "$current_dhcpp_dir"/env.sh
fi

    This will automatically set LD_LIBRARY_PATH at the beginning of every session for this user, so that any process (R or RStudio) may access its value. If you move the dhcpp directory, or need to point R to another version of it, current_dhcpp_dir should be modified accordingly.

Common Errors

  • Cannot compile the R client

    If an error like this occurs in step 4: bash client.cpp:7:10: fatal error: deephaven/client/client.h: No such file or directory 7 | #include "deephaven/client/client.h" | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ compilation terminated. this means that the C++ compiler does not know where to find the relevant header files for the Deephaven C++ client. This can happen for a handul of reasons:

    1. Step 1 was skipped, and the Deephaven C++ client was not installed. In this case, please ensure that the client is installed before attempting to build the R client.
    2. The Deephaven C++ client is installed, but the DHCPP environment variable is not set. To test this, run bash echo $DHCPP If this returns an empty string, set DHCPP according to the instructions in step 2a with bash source /path/to/dhcpp/env.sh
    3. The Deephaven C++ client is installed and the DHCPP environment variable is set, but the current project is not configured to allow the compiler to access the Deephaven dhcpp and src directories. This is more difficult to give advice on, as it is an IDE-dependent problem. Consult your IDE’s documentation on C/C++ compiler include paths for more information.

    • Cannot load the R client

      Once the R client is successfully installed, you may try to load it at a later date, only to find this error: bash > library(rdeephaven) Error: package or namespace load failed for ‘rdeephaven’ in dyn.load(file, DLLpath = DLLpath, ...): unable to load shared object '/home/user/R/x86_64-pc-linux-gnu-library/4.4/rdeephaven/libs/rdeephaven.so': libdhclient.so: cannot open shared object file: No such file or directory This very likely means that the LD_LIBRARY_PATH environment variable is not set. To rectify this, run bash source /path/to/dhcpp/env.sh from the parent session and try again. Alternatively, see step 6 for a way to solve this problem semi-permanently.

      RStudio presents its own solution to this problem that RStudio users may want to use instead of the semi-permanent solution in step 6. RStudio supports using a .Renviron file for setting environment variables. If the correct environment variables are currently set (see step 2), you can generate the right content for a .Renviron file by running the script at etc/generate-dotRenviron-lines.sh. Then, copy the output of that script into a new file called .Renviron, and save it in the deephaven-core directory. Then, create a new R project from the existing deephaven-core directory using RStudio, and the corresponding R session will inherit all the necessary environment variables for successful compilation.

      If RStudio Server is being used, the .Renviron files must be set for successful compilation. In addition, run the etc/generate-rserverdotconf-lines.sh script, and the script’s outputs to the rserver.conf file for the RStudio Server installation (the location of that file may depend on your particular RStudio server installation, but a common location is /etc/rstudio/rserver.conf).

Running the unit tests

The Deephaven R client utilizes R’s testthat package to perform unit tests. In order to run these unit tests, install testthat and the other dependent packages:

install.packages(c('testthat', 'lubridate', 'zoo'))

Then, from an R session with rdeephaven installed, run the unit tests:

Debugging

Because the Deephaven R client is written in C++ and wrapped with Rcpp, standard R-level debugging is not sufficient for many kinds of problems associated with C++ code. For this reason, debugging the R client must be done with a C++ debugger. We recommend using Valgrind to check for memory bugs, and using gdb for thorough backtraces and general debugging.

Running R with Valgrind

The following was taken from this blog post, which has proven very useful for getting started with Valgrind. 1. Install Valgrind with sudo apt-get install valgrind or sudo yum install valgrind 2. Run R under Valgrind with R -d valgrind

OS-dependent problems may come up in either step, and the simplest solution is to use a Linux machine or VM if one is available. Attempting these steps in a Linux Docker image may also prove difficult, and will certainly fail if the host architecture is not AMD/X86.

Running R with gdb

This article is a good resource for running R with gdb, and also touches on Valgrind use. There are several ways to run R with gdb, and here we only outline the text-based approach given near the bottom of the page. 1. Install gdb with sudo apt-get install gdb or sudo yum install gdb 2. Start gdb with R attached with R -d gdb. This will start a gdb session denoted by (gdb) in the console. 3. In the gdb session, start an R console with (gdb) run

Both Valgrind and gdb debugging is done through a console, and is not interactive from an IDE. There may be a way to make RStudio play well with Valgrind or gdb, but that is beyond the scope of these instructions.

Enabling DEBUG level logging for gRPC and the C++ layer of the Deephaven R client

The C++ component of the Deephaven R client uses the C++ implementation of gRPC to exchange messages with a Deephaven server. gRPC has an internal logging component that can be configured to log to stderr detail information about connection state and messages exchanged between client and server; the Deephaven R client also uses the same logging component to show client state information. This can be useful for debugging purposes. To enable detailed logging, set the environment variable GRPC_VERVOSITY=DEBUG

Code Styling

The Deephaven R client uses the Tidyverse styleguide for code formatting, and implements this style with the styler package. For contributions, ensure that code is properly styled according to Tidyverse standards by running the following code in your R console, where /path/to/rdeephaven is the path to the root directory of this package.

setwd("/path/to/rdeephaven")
install.packages("styler")
library(styler)
style_pkg()

High-level design overview

The R client uses the Deephaven C++ client as the backend for connecting to and communicating with the server. Any Deephaven-specific feature in the R client is, at some level, an API for an equivalent feature in the C++ client. To make Deephaven’s C++ client API available in R, an R6 class provides an R interface to Rcpp wrapped parts of the C++ API. Deephaven’s C++ API can create Arrow tables, and R has an Arrow library. Because Arrow is an in-memory data format, Arrow data can be transferred between R and C++ simply by passing a pointer between the languages and using the Arrow C Stream Interface. No data copies are required.