R: Create a future

future {future} | R Documentation

## Create a future

### Description

Creates a future that evaluates an R expression or a future that calls an R function with a set of arguments. How, when, and where these futures are evaluated can be configured using `plan()` such that it is evaluated in parallel on, for instance, the current machine, on a remote machine, or via a job queue on a compute cluster. Importantly, any R code using futures remains the same regardless on these settings and there is no need to modify the code when switching from, say, sequential to parallel processing.

### Usage

    future(expr, envir = parent.frame(), substitute = TRUE, globals = TRUE,
      packages = NULL, lazy = FALSE, seed = NULL, evaluator = plan("next"),
      ...)

    futureAssign(x, value, envir = parent.frame(), substitute = TRUE,
      lazy = FALSE, seed = NULL, globals = TRUE, ..., assign.env = envir)

    x %<-% value

    futureCall(FUN, args = NULL, envir = parent.frame(), lazy = FALSE,
      seed = NULL, globals = TRUE, packages = NULL,
      evaluator = plan("next"), ...)

### Arguments

`expr, value` | An R expression to be evaluated.
`envir` | The environment from where global objects should be identified. Depending on the future strategy (the `evaluator`), it may also be the environment in which the expression is evaluated.
`substitute` | If TRUE, argument `expr` is `substitute()`:ed, otherwise not.
`globals` | (optional) A logical, a character vector, or a named list for controlling how globals are handled. For details, see below section.
`packages` | (optional) a character vector specifying packages to be attached in the R environment evaluating the future.
`lazy` | Specifies whether a future should be resolved lazily or eagerly (default).
`seed` | (optional) A L'Ecuyer-CMRG RNG seed.
`evaluator` | The actual function that evaluates the future expression and returns a Future. The evaluator function should accept all of the same arguments as the ones listed here (except `evaluator`, `FUN` and `args`). The default evaluator function is the one that the user has specified via `plan()`.
`...` | Additional arguments passed to the "evaluator".
`x` | the name of a future variable, which will hold the value of the future expression (as a promise).
`assign.env` | The environment to which the variable should be assigned.
`FUN` | A function to be evaluated.
`args` | A list of arguments passed to function `FUN`.

### Details

The state of a future is either unresolved or resolved. The value of a future can be retrieved using `v <- value(f)`. Querying the value of a non-resolved future will block the call until the future is resolved. It is possible to check whether a future is resolved or not without blocking by using `resolved(f)`.

For a future created via a future assignment (`x %<-% value` or `futureAssign("x", value)`), the value is bound to a promise, which when queried will internally call `value()` on the future and which will then be resolved into a regular variable bound to that value. For example, with future assignment `x %<-% value`, the first time variable `x` is queried the call blocks if (and only if) the future is not yet resolved. As soon as it is resolved, and any succeeding queries, querying `x` will immediately give the value.

The future assignment construct `x %<-% value` is not a formal assignment per se, but a binary infix operator on objects `x` and expression `value`. However, by using non-standard evaluation, this constructs can emulate an assignment operator similar to `x <- value`. Due to R's precedence rules of operators, future expressions often needs to be explicitly bracketed, e.g. `x %<-% { a + b }`.

### Value

`f <- future(expr)` creates a Future `f` that evaluates expression `expr`, the value of the future is retrieved using `v <- value(f)`.

`x %<-% value` (a future assignment) and `futureAssign("x", value)` create a Future that evaluates expression `expr` and binds its value (as a promise) to a variable `x`. The value of the future is automatically retrieved when the assigned variable (promise) is queried. The future itself is returned invisibly, e.g. `f <- futureAssign("x", expr)` and `f <- (x %<-% expr)`. Alternatively, the future of a future variable `x` can be retrieved without blocking using `f <- futureOf(x)`. Both the future and the variable (promise) are assigned to environment `assign.env` where the name of the future is `.future_<name>`.

`f <- futureCall(FUN, args)` creates a Future `f` that calls function `FUN` with arguments `args`, where the value of the future is retrieved using `x <- value(f)`.

### Eager or lazy evaluation

By default, a future is resolved using eager evaluation (`lazy = FALSE`). This means that the expression starts to be evaluated as soon as the future is created.

As an alternative, the future can be resolved using lazy evaluation (`lazy = TRUE`). This means that the expression will only be evaluated when the value of the future is requested. Note that this means that the expression may not be evaluated at all - it is guaranteed to be evaluated if the value is requested.

For future assignments, lazy evaluation can be controlled via the `%lazy%` operator, e.g. `x %<-% { expr } %lazy% TRUE`.

### Globals used by future expressions

Global objects (short globals) are objects (e.g. variables and functions) that are needed in order for the future expression to be evaluated while not being local objects that are defined by the future expression. For example, in

      a <- 42
      f <- future({ b <- 2; a * b })

variable `a` is a global of future assignment `f` whereas `b` is a local variable. In order for the future to be resolved successfully (and correctly), all globals need to be gathered when the future is created such that they are available whenever and wherever the future is resolved.

The default behavior (`globals = TRUE`) of all evaluator functions, is that globals are automatically identified and gathered. More precisely, globals are identified via code inspection of the future expression `expr` and their values are retrieved with environment `envir` as the starting point (basically via `get(global, envir = envir, inherits = TRUE)`). In most cases, such automatic collection of globals is sufficient and less tedious and error prone than if they are manually specified.

However, for full control, it is also possible to explicitly specify exactly which the globals are by providing their names as a character vector. In the above example, we could use

      a <- 42
      f <- future({ b <- 2; a * b }, globals = "a")

Yet another alternative is to explicitly specify also their values using a named list as in

      a <- 42
      f <- future({ b <- 2; a * b }, globals = list(a = a))

or

      f <- future({ b <- 2; a * b }, globals = list(a = 42))

Specifying globals explicitly avoids the overhead added from automatically identifying the globals and gathering their values. Furthermore, if we know that the future expression does not make use of any global variables, we can disable the automatic search for globals by using

      f <- future({ a <- 42; b <- 2; a * b }, globals = FALSE)

Future expressions often make use of functions from one or more packages. As long as these functions are part of the set of globals, the future package will make sure that those packages are attached when the future is resolved. Because there is no need for such globals to be frozen or exported, the future package will not export them, which reduces the amount of transferred objects. For example, in

      x <- rnorm(1000)
      f <- future({ median(x) })

variable `x` and `median()` are globals, but only `x` is exported whereas `median()`, which is part of the stats package, is not exported. Instead it is made sure that the stats package is on the search path when the future expression is evaluated. Effectively, the above becomes

      x <- rnorm(1000)
      f <- future({
        library("stats")
        median(x)
      })

To manually specify this, one can either do

      x <- rnorm(1000)
      f <- future({
        median(x)
      }, globals = list(x = x, median = stats::median)

or

      x <- rnorm(1000)
      f <- future({
        library("stats")
        median(x)
      }, globals = list(x = x))

Both are effectively the same.

When using future assignments, globals can be specified analogously using the `%globals%` operator, e.g.

      x <- rnorm(1000)
      y %<-% { median(x) } %globals% list(x = x, median = stats::median)

### See Also

How, when and where futures are resolved is given by the future strategy, which can be set by the end user using the `plan()` function. The future strategy must not be set by the developer, e.g. it must not be called within a package.

### Examples

    ## Evaluate futures in parallel
    plan(multiprocess)

    ## Data
    x <- rnorm(100)
    y <- 2 * x + 0.2 + rnorm(100)
    w <- 1 + x ^ 2

    ## (1) Regular assignments (evaluated sequentially)
    fitA <- lm(y ~ x, weights = w)      ## with offset
    fitB <- lm(y ~ x - 1, weights = w)  ## without offset
    fitC <- {
      w <- 1 + abs(x)  ## Different weights
      lm(y ~ x, weights = w)
    }
    print(fitA)
    print(fitB)
    print(fitC)

    ## (2) Future assignments (evaluated in parallel)
    fitA %<-% lm(y ~ x, weights = w)      ## with offset
    fitB %<-% lm(y ~ x - 1, weights = w)  ## without offset
    fitC %<-% {
      w <- 1 + abs(x)
      lm(y ~ x, weights = w)
    }
    print(fitA)
    print(fitB)
    print(fitC)

    ## (3) Explicitly create futures (evaluated in parallel)
    ## and retrieve their values
    fA <- future( lm(y ~ x, weights = w) )
    fB <- future( lm(y ~ x - 1, weights = w) )
    fC <- future({
      w <- 1 + abs(x)
      lm(y ~ x, weights = w)
    })
    fitA <- value(fA)
    fitB <- value(fB)
    fitC <- value(fC)
    print(fitA)
    print(fitB)
    print(fitC)

    ## (4) Explit future assignments (evaluated in parallel)
    futureAssign("fitA", lm(y ~ x, weights = w))
    futureAssign("fitB", lm(y ~ x - 1, weights = w))
    futureAssign("fitC", {
      w <- 1 + abs(x)
      lm(y ~ x, weights = w)
    })
    print(fitA)
    print(fitB)
    print(fitC)

[Package future version 1.8.0 Index]