What is causing the `external pointer is not valid` error in `parallel::parSapply`?

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05-10-2024

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Unraveling the "External Pointer is Not Valid" Error in R's parallel::parSapply

Parallel processing in R, facilitated by the parallel package, can dramatically speed up your computations. However, you might encounter a frustrating error message: "external pointer is not valid" when using parallel::parSapply. This error can be perplexing, as it suggests a problem with memory management or data structures.

Let's break down what causes this error and how to troubleshoot it.

The Scenario

Imagine you have a function, my_function, that takes a vector as input and performs some calculations. You want to apply this function to multiple vectors in parallel using parSapply. Your code might look something like this:

library(parallel)

# Function to apply in parallel
my_function <- function(x) {
  # Some calculations with x
  return(result)
}

# Data to be processed
data_list <- list(c(1,2,3), c(4,5,6), c(7,8,9))

# Apply function in parallel
results <- parSapply(cl = makeCluster(detectCores() - 1), 
                     X = data_list,
                     FUN = my_function)

However, when you run this code, you get the infamous error: "external pointer is not valid".

The Root of the Issue

The "external pointer is not valid" error usually arises when the function you are applying in parallel uses objects or functions that are not accessible within the forked processes created by parSapply.

Here's the breakdown:

• Forking Processes: parSapply works by creating multiple copies (or "forks") of the main R process, each running in its own environment.
• Data Access: These forked processes cannot directly access the data and functions from the original (parent) process's environment.
• Closure Problems: If your function relies on objects defined outside its scope (e.g., global variables or functions), those objects might not be available within the child processes. This is where the error arises.

Debugging and Solutions

1. Identify the Culprit: The key is to pinpoint the object or function that is causing the issue. Examine my_function to see if it uses any variables, functions, or data structures defined outside its own scope.

2. Explicitly Pass Objects: If your function relies on variables from the parent environment, ensure you explicitly pass them as arguments:

   my_function <- function(x, some_variable) {
     # ...calculations using 'x' and 'some_variable'
     return(result)
   }
   
   results <- parSapply(cl = makeCluster(detectCores() - 1), 
                        X = data_list,
                        FUN = my_function,
                        some_variable = some_variable)
   

3. Package and Export: If you have a large number of dependent functions or objects, package them into a separate file and use clusterExport to send them to the forked processes:

   # ... define functions and variables in 'my_functions.R'
   
   clusterExport(cl = makeCluster(detectCores() - 1), 
                 varlist = c("my_function", "some_variable"), 
                 envir = environment())
   

4. Consider clusterApply: If your function needs access to a large amount of data, consider using clusterApply instead of parSapply. This allows you to pass data and objects directly to each worker process:

   results <- clusterApply(cl = makeCluster(detectCores() - 1),
                       X = data_list, 
                       fun = function(x) my_function(x, some_variable))
   

5. RStudio Workaround: In RStudio, the "external pointer is not valid" error might occur if the code is running in a different workspace than the forked processes. Restarting RStudio can often resolve this issue.

Remember:

• Minimize Dependencies: If possible, design functions to be self-contained and avoid external dependencies for better parallel performance.
• Clear Communication: Use the clusterEvalQ function to send code to worker processes and ensure they have the necessary functions or variables defined.

By understanding the error's origins and applying these solutions, you can overcome the "external pointer is not valid" hurdle and harness the power of parallel processing in your R scripts.