Debugging R Session Crashes Due to Memory Issues in Large Data Operations

Issue

R sessions may crash with a segmentation fault when performing large data operations, particularly when working with extensive datasets and complex data transformations. The crash may appear as:

/usr/lib/rstudio-server/bin/rsession-run: line 266: [PID] Segmentation fault (core dumped)

Description

While these crashes may coincide with SSL certificate errors in logs, the root cause is typically related to memory management issues when handling large datasets or performing memory-intensive operations. Common scenarios that can trigger this include:

• Multiple large data transformations in sequence
• Operations that create many copies of large data frames
• Complex filtering or joining operations on large datasets

Solution

To resolve these memory-related crashes, consider implementing the following code optimization strategies:

1. Break down large operations into smaller steps by using temporary variables:

   # Instead of chaining multiple operations
   temp_data <- base_data %>%
    filter(condition)
   
   data_tmp <- temp_data %>%
    mutate(many_columns)

2. Clean up after operations:

   rm(temp_data)
   gc() # Run garbage collection to free memory

3. Optimize join operations:

   # Use left_join instead of match for better performance
   data <- data %>%
    left_join(lookup_table, by = "key")

4. Convert to factors early if needed:

   # Convert to factor once, not repeatedly
   data$category <- factor(data$category)

Additional recommendations:

• Monitor memory usage during operations using tools like the Performance tab in RStudio
• Consider using data.table instead of dplyr for very large datasets
• Increase the R session memory limit if resources allow:
  memory.limit(size = 8000)

Code improvement suggestions are outside of the Support SLA, but we wanted to share an example to give some ideas of how memory consumption can be reduced. 

Additional information is available here:  http://adv-r.had.co.nz/memory.html