Timeouts in R Programming Language

Timeouts are important for programs that connect to external resources or that otherwise need to bound execution time. Implementing timeouts in R can be achieved using the setTimeLimit function and error handling.

library(parallel)

main <- function() {
  # For our example, suppose we're executing an external
  # call that returns its result after 2 seconds.
  # We'll use the parallel package to simulate this.
  c1 <- mcparallel({
    Sys.sleep(2)
    "result 1"
  })

  # Here we implement a timeout using setTimeLimit.
  # It will throw an error if the operation takes more than 1 second.
  tryCatch({
    setTimeLimit(cpu = Inf, elapsed = 1)
    res <- mccollect(c1, wait = FALSE)[[1]]
    setTimeLimit(cpu = Inf, elapsed = Inf)
    if (!is.null(res)) {
      print(res)
    } else {
      print("timeout 1")
    }
  }, error = function(e) {
    print("timeout 1")
  })

  # If we allow a longer timeout of 3 seconds, then the operation
  # will succeed and we'll print the result.
  c2 <- mcparallel({
    Sys.sleep(2)
    "result 2"
  })

  tryCatch({
    setTimeLimit(cpu = Inf, elapsed = 3)
    res <- mccollect(c2, wait = FALSE)[[1]]
    setTimeLimit(cpu = Inf, elapsed = Inf)
    if (!is.null(res)) {
      print(res)
    } else {
      print("timeout 2")
    }
  }, error = function(e) {
    print("timeout 2")
  })
}

main()

Running this program shows the first operation timing out and the second succeeding.

$ Rscript timeouts.R
[1] "timeout 1"
[1] "result 2"

In this R implementation:

1. We use the parallel package to simulate asynchronous operations.

2. The mcparallel function is used to start a background process that sleeps for 2 seconds and then returns a result.

3. We use setTimeLimit to set a timeout for each operation. If the operation exceeds the time limit, an error is thrown.

4. tryCatch is used to handle the timeout error. If an error occurs (i.e., the operation times out), we print “timeout”.

5. We use mccollect with wait = FALSE to check if a result is available. If it’s not available within the time limit, we consider it a timeout.

6. After each operation, we reset the time limit to infinity to prevent it from affecting subsequent operations.

This implementation provides similar functionality to the original example, demonstrating how to handle timeouts in R.