cluster: Create a cluster future whose value will be resolved...

In future: Unified Parallel and Distributed Processing in R for Everyone

Create a cluster future whose value will be resolved asynchronously in a parallel process

Description

WARNING: This function must never be called. It may only be used with plan()

Usage

cluster(
  ...,
  workers = availableWorkers(constraints = "connections"),
  persistent = FALSE
)

Arguments

workers	A cluster object, a character vector of host names, a positive numeric scalar, or a function. If a character vector or a numeric scalar, a cluster object is created using makeClusterPSOCK(workers). If a function, it is called without arguments when the future is created and its value is used to configure the workers. The function should return any of the above types. If workers == 1, then all processing using done in the current/main R session and we therefore fall back to using a sequential future. To override this fallback, use workers = I(1).
persistent	If FALSE, the evaluation environment is cleared from objects prior to the evaluation of the future.
...	Not used.

Details

A cluster future is a future that uses cluster evaluation, which means that its value is computed and resolved in parallel in another process.

This function is must not be called directly. Instead, the typical usages are:

# Evaluate futures via a single background R process on the local machine
plan(cluster, workers = I(1))

# Evaluate futures via two background R processes on the local machine
plan(cluster, workers = 2)

# Evaluate futures via a single R process on another machine on on the
# local area network (LAN)
plan(cluster, workers = "raspberry-pi")

# Evaluate futures via a single R process running on a remote machine
plan(cluster, workers = "pi.example.org")

# Evaluate futures via four R processes, one running on the local machine,
# two running on LAN machine 'n1' and one on a remote machine
plan(cluster, workers = c("localhost", "n1", "n1", "pi.example.org"))

See Also

For alternative future backends, see the 'A Future for R: Available Future Backends' vignette and https://www.futureverse.org/backends.html.

Examples

## Use cluster futures
cl <- parallel::makeCluster(2, timeout = 60)
plan(cluster, workers = cl)

## A global variable
a <- 0

## Create future (explicitly)
f <- future({
  b <- 3
  c <- 2
  a * b * c
})

## A cluster future is evaluated in a separate process.
## Regardless, changing the value of a global variable will
## not affect the result of the future.
a <- 7
print(a)

v <- value(f)
print(v)
stopifnot(v == 0)

## CLEANUP
parallel::stopCluster(cl)

future documentation built on March 14, 2026, 9:07 a.m.