No dogs were harmed while making this release

future 1.15.0 is now on CRAN, accompanied by a recent, related update of future.callr 0.5.0. The main update is a change to the Future API:

Although this change does not look much to the world, I’d like to think of this as part of a young person slowly finding themselves. This change in behavior helps us in cases where we create lazy futures upfront;

fs <- lapply(X, future, lazy = TRUE)

Such futures remain dormant until we call value() on them, or, as of this release, when we call resolved() on them. Contrary to value(), resolved() is a non-blocking function that allows us to check in on one or more futures to see if they are resolved or not. So, we can now do:

while (!all(resolved(fs))) {
  do_something_else()
}

to run that loop until all futures are resolved. Any lazy future that is still dormant will be launched when queried the first time. Previously, we would have had to write specialized code for the lazy=TRUE case to trigger lazy futures to launch. If not, the above loop would have run forever. This change means that the above design pattern works the same regardless of whether we use lazy=TRUE or lazy=FALSE (default). There is now one less thing to worry about when working with futures. Less mental friction should be good.

What else?

The Future API now guarantees that value() relays the “visibility” of a future’s value. For example,

> f <- future(invisible(42))
> value(f)
> v <- value(f)
> v
[1] 42

Other than that, I have fixed several non-critical bugs and improved some documentation. See news(package="future") or NEWS for all updates.

What’s next?

• I’ll be talking about futures at rstudio::conf 2020 (San Francisco, CA, USA) at the end of January 2020. Please come and say hi - I am keen to hear your R story.

• I will wrap up the deliverables for the project Future Minimal API: Specification with Backend Conformance Test Suite sponsored by the R Consortium. This project helps to robustify the future ecosystem and validate that all backends fulfill the Future API specification. It also serves to refine the Future API specifications. For example, the above change to resolved() resulted from this project.

• The maintainers of foreach plan to harmonize how foreach() identifies global variables with how the future framework identifies them. The idea is to migrate foreach to use the same approach as future, which relies on the globals package. If you’re curious, you can find out more about this over at the foreach issue tracker. Yeah, the foreach issue tracker is a fairly recent thing - it’s a great addition.

• The progressr package (GitHub only) is a proof-of-concept and a working prototype showing how to signal progress updates when doing parallel processing. It works out of the box with the core Future API and higher-level Future APIs such as future.apply, foreach with doFuture, furrr, and plyr - regardless of what parallel backend is being used. It should also work with all known non-parallel map-reduce frameworks, including base lapply() and purrr. For parallel processing, the “granularity” of progress updates varies with the type of parallel worker used. Right now, you will get live updates for sequential processing, whereas for parallel processing the updates will come in chunks along with the value whenever it is collected for a particular future. I’m working on adding support for “live” progress updates also for some parallel backends including when running on local and remote workers.

Happy futuring!

Links

• future package: CRAN, GitHub
• future.batchtools package: CRAN, GitHub
• future.callr package: CRAN, GitHub
• future.apply package: CRAN, GitHub
• doFuture package: CRAN, GitHub (a foreach adapter)
• progressr package: GitHub
• “So, what happened to the dog?”