Author: Joe Cheng
Date: 2021-04-20

# Using later from C++

#### Joe Cheng

#### 2021-04-20

# Using later from C++

You can call `later::later` from C++ code in your own packages, to cause your own C-style functions to be called back. This is safe to call from either the main R thread or a different thread; in both cases, your callback will be invoked from the main R thread.

To use the C++ interface, you’ll need to:

- Add `later` to your `DESCRIPTION` file, under both `LinkingTo` and `Imports`
- Make sure that your `NAMESPACE` file has an `import(later)` entry. If your package uses roxygen2, you can do this by adding the following lines to any file under `R/`:

    #' @import later
    NULL

- Add `#include <later_api.h>` to the top of each C++ file that uses the below APIs.

## Executing a C function later

The `later::later` function is accessible from `later_api.h` and its prototype looks like this:

    void later(void (*func)(void*), void* data, double secs)

The first argument is a pointer to a function that takes one `void*` argument and returns void. The second argument is a `void*` that will be passed to the function when it’s called back. And the third argument is the number of seconds to wait (at a minimum) before invoking. In all cases, the function will be invoked on the R thread, when no user R code is executing.

## Background tasks

This package also offers a higher-level C++ helper class called `later::BackgroundTask`, to make it easier to execute tasks on a background thread. It takes care of launching the background thread for you, and returning control back to the R thread at a later point; you’re responsible for providing the actual code that executes on the background thread, as well as code that executes on the R thread before and after the background task completes.

Its public/protected interface looks like this:

    class BackgroundTask {

    public:
      BackgroundTask();
      virtual ~BackgroundTask();

      // Start executing the task
      void begin();

    protected:
      // The task to be executed on the background thread.
      // Neither the R runtime nor any R data structures may be
      // touched from the background thread; any values that need
      // to be passed into or out of the Execute method must be
      // included as fields on the Task subclass object.
      virtual void execute() = 0;

      // A short task that runs on the main R thread after the
      // background task has completed. It's safe to access the
      // R runtime and R data structures from here.
      virtual void complete() = 0;
    }

Create your own subclass, implementing a custom constructor plus the `execute` and `complete` methods.

It’s critical that the code in your `execute` method not mutate any R data structures, call any R code, or cause any R allocations, as it will execute in a background thread where such operations are unsafe. You can, however, perform such operations in the constructor (assuming you perform construction only from the main R thread) and `complete` method. Pass values between the constructor and methods using fields.

    #include <Rcpp.h>
    #include <later_api.h>

    class MyTask : public later::BackgroundTask {
    public:
      MyTask(Rcpp::NumericVector vec) :
        inputVals(Rcpp::as<std::vector<double> >(vec)) {
      }

    protected:
      void execute() {
        double sum = 0;
        for (std::vector<double>::const_iterator it = inputVals.begin();
          it != inputVals.end();
          it++) {

          sum += *it;
        }
        result = sum / inputVals.size();
      }

      void complete() {
        Rprintf("Result is %f\n", result);
      }

    private:
      std::vector<double> inputVals;
      double result;
    };

To run the task, `new` up your subclass and call `begin()`, e.g. `(new MyTask(vec))->begin()`. There’s no need to keep track of the pointer; the task object will delete itself when the task is complete.

    // [[Rcpp::export]]
    void asyncMean(Rcpp::NumericVector data) {
      (new MyTask(data))->begin();
    }

It’s not very useful to execute tasks on background threads if you can’t get access to the results back in R. We’ll soon be introducing a complementary R package that provides a suitable “promise” or “future” abstraction.