# A Future for R: Best Practices for Package Developers

Using future code in package is not much different from other type of package code or when using futures in R scripts. However, there are a few things that are useful to know about in order to minimize the risk for surprises to the end user.

## The future smell test

The by far most common and popular future backend is to parallelize on the local machine, e.g. `plan(multisession)`. This is often good enough in most situations but note that some end-users have access to multiple machines and might want to run your code using all of them to speed it up beyond what a single machine can do. Because of this, avoid as far as possible making assumption about your code will only run on the local machine. A good “smell test” is to ask yourself:

- Will my future code work if it ends up running on the other side of the world?

Regardless of performance, if you answer “Yes”, you have already embraced the core philosophy of the future framework. If you answer “Maybe” or “No”, see if you can rewrite it.

For instance, if your future code made an assumption that it will have access to our local file system, as in:

    f <- future({
      data <- read_tsv(file)
      analyze(data)
    })

you can rewrite the code to load the content of the file before you set up the future, as in:

    data <- read_tsv(file)
    f <- future({
      analyze(data)
    })

Similarly, we should avoid having the future code write to the local file system because the parent R session might not have access to that file system.

By keeping the future smell test in mind when writing future code, we increase the chances that the code can be parallelized in more ways than on just the local computer. Properly written future code will work regardless of what future backend the end-user picks, e.g.

    plan(sequential)
    plan(multisession)
    plan(cluster, workers = rep(c("n1.remote.org", "n2.remote.org", "n3.remote.org"), each = 32))

Remember, as developers we never know what compute resources the end-user has access to right now or they will have access to in six month. Who knows, your code might even end up running on 2,000 cores located on The Moon twenty years from now.

## Avoid changing the future backend

For reasons like the ones mentioned above, refrain from setting `plan()` in a function. It is better to leave it to the end-user to decided how they want to parallelize. One reason for this is that we can never know how and in what context our code will run. For example, they might use futures to parallelize a function call in some other package and that package code calls your package internally. If you set `plan(multisession)` internally without undoing, you might mess up the `plan()` that is already set breaking any further parallelization.

If you still think it is necessary to set `plan()`, make sure to undo when the function exits, also on errors. This can be done by using `with(plan(...), local = TRUE)`, e.g.

    my_fcn <- function(x) {
      with(plan(multisession), local = TRUE)
      y <- analyze(x)
      summarize(y)
    }

The need for setting the future backend within a function often comes from developers wanting to add an argument to their function that allows the end-user to specify whether they want to run the function in parallel or sequentially. This often result in code like:

    my_fcn <- function(x, parallel = FALSE) {
      if (parallel) {
        with(plan(multisession), local = TRUE)
        y <- future_lapply(x, FUN = analyze) ## from future.apply package
      } else {
        y <- lapply(x, FUN = analyze)
      }
      summarize(y)
    }

This way the user can use:

    y <- my_fcn(x, parallel = FALSE)

or

    y <- my_fcn(x, parallel = TRUE)

depending on their needs. However, if another package developer decide to call your function in their function, they now have to expose that `parallel` argument to the users of their function, e.g.

    their_fcn <- function(x, parallel = FALSE) {
      x2 <- preprocess(x)
      y <- my_fcn(x2, parallel = parallel)
      z <- another_fcn(y)
      z
    }

Exposing and passing a “parallel” argument along can become quite cumbersome. Instead, it is neater to use:

    my_fcn <- function(x) {
      y <- future_lapply(x, FUN = analyze) ## from future.apply package
      summarize(y)
    }

and let the user control whether or not they want to parallelize via `plan()`, e.g. `plan(multisession)` and `plan(sequential)`.

## Avoid changing the future options

Just like for other R options, you must not change any of the R `future.*` options. Only the end-user should set these.

If you find yourself having to tweak one of the options, make sure to undo your changes immediately afterward. For example, if you want to bump up the `future.globals.maxSize` limit when creating a future, use something like the following inside your function:

    oopts <- options(future.globals.maxSize = 1.0 * 1e9)  ## 1.0 GB
    on.exit(options(oopts))
    f <- future({ expr })  ## Launch a future with large objects

## Writing examples

If your example sets the future backend at the beginning, make sure to reset the future backend to `plan(sequential)` at the end of the example. The reason for this is that when switching plan, the previous one will be cleaned up. This is particularly important for multisession and cluster futures where `plan(sequential)` will shut down the underlying PSOCK clusters.

For instance, here is an example:

    ## Run the analysis in parallel on the local computer
    future::plan("multisession")

    y <- analyze("path/to/file.csv")

    ## Shut down parallel workers
    future::plan("sequential")

If you forget to shut down the PSOCK cluster, then `R CMD check --as-cran`, or `R CMD check` with environment variable `_R_CHECK_CONNECTIONS_LEFT_OPEN_=true` set, will produce an error on

    $ R CMD check --as-cran mypkg_1.0.tar.gz
    ...
    * checking examples ... ERROR
    Running examples in 'analyze-Ex.R' failed
    ...
    > cleanEx()
    Error: connections left open:
          <-localhost:37400 (sockconn)
          <-localhost:37400 (sockconn)
    Execution halted

If you for some reason do not like to display reset of the future backend in the help documentation, but you still want it run, wrap the statement in an Rd `\dontshow{}` statement.

## Writing vignettes

Importantly, if you use the `multisession` backend in a vignette, you must manually specify the number of workers, i.e.

    plan(multisession, workers = 2)

The reason for having to manually limit the number workers in vignettes, while it is automatically limited for us in package examples and package tests, is that `R CMD check --as-cran` does not set environment variable `_R_CHECK_LIMIT_CORES_` when re-building vignettes, resulting in the default `workers = availableCores()` to use whatever number of cores that the operating system has given our R process.

Don’t forget to stop the parallel workers by calling

    plan(sequential)

at the end of you vignette.

## Testing a package that relies on futures

If you want to make sure your code works when running sequentially as well as when running in parallel, it is often good enough to have package tests that run the code with:

    plan(multisession)

If the code works with this setup, you can be sure that all global variables are properly identified and exported to the workers and that the required packages are loaded on the workers.

Always make sure to shut down your parallel ‘multisession’ workers at the end of each package test by calling:

    plan(sequential)

If not all of your tests are written this way, you can set environment variable `R_FUTURE_PLAN=multisession` before you call `R CMD check`. This will make the default future backend to become ‘multisession’ instead of ‘sequential’. For example,

    $ export R_FUTURE_PLAN=multisession
    $ R CMD check --as-cran mypkg_1.0.tar.gz

## Making sure to stop parallel workers

When creating a `cluster` object, for instance via `plan(multisession)`, in a package help example, in a package vignette, or in a package test, we must remember to stop the cluster at the end of all examples(*), vignettes, and unit tests. This is required in order to not leave behind stray parallel `cluster` workers after our main R session terminates. On Linux and macOS, the operating system often takes care of terminating the worker processes if we forget, but on MS Windows such processes will keep running in the background until they time out themselves, which takes 30 days (sic!).

`R CMD check --as-cran` will indirectly detect these stray worker processes on MS Windows when running R (>= 4.3.0). They are detected, because they result in placeholder `Rscript<hexcode>` files being left behind in the temporary directory. The check NOTE to look out for (only in R (>= 4.3.0)) is:

    * checking for detritus in the temp directory ... NOTE
    Found the following files/directories:
      'Rscript1058267d0c10' 'Rscriptbd4267d0c10'

Those `Rscript<hexcode>` files are from background R worker processes, which almost always are parallel `cluster`:s that we forgot to stop at the end. To stop ‘multisession’ workers, call:

    plan(sequential)

at the end of your examples(*), vignettes, and package tests.

If you create the `cluster` manually using

    cl <- parallelly::makeClusterPSOCK(2)
    plan(cluster, workers = cl)

make sure to stop such clusters at the end using

    plan(sequential)
    parallel::stopCluster(cl)

(*) Currently, examples are excluded from the detritus checks. This was the validated with R-devel revision 82991 (2022-10-02).