Error Handling in Java and Python

Operational systems, by definition, need to work without human input. Systems are considered “operational” after they have been thoroughly tested and shown to work properly with a variety of input.

However, no software is perfect and no real-world system operates with 100% availability or 100% consistent input. Things occasionally go wrong – perhaps intermittently. In a situation with occasional failures it is vitally important to have robust error handling to deal with both expected and unexpected results.

Other languages used in operational settings have language statements to help with error handling. The code to handle errors looks very similar in java and python:

java

try {
  myFunc(a)
} catch (abcException e) {
  // handle abcException
} catch (defException e) {
  // handle defException
} finally {
  // always executed after handlers
}

python

try:
  myFunc(a)
except abcError:
  # handle abcError
except defError:
  # handle defError
finally:
  # always executed after handlers

Error Handling in R

Not surprisingly, a functional language like R does things differently:

  • No language statements for error handling
  • tryCatch() is a function
  • Need to define warning handler function
  • Need to define error handling function
  • Scope issues

Nevertheless, R’s error handling functions can be made to look similar to java and python:

result <- tryCatch({
  myFunc(a)
}, warning = function(w) {
  # handle all warnings
}, error = function(e) {
  # handle all errors
}, finally = {
  # always executed after handlers
}

For more details see:

Simpler Error Handling in R

In our experience, R’s error handling is too complicated for simple use and requires too much from folks who don’t consider themselves R-gurus.

Instead, we recommend wrapping any block of code that needs error handling in a try() function and then testing the result to see if an error occurred.

For more details see:

This strategy makes it easy to create error handling logic and easy to understand what it does. In the following pseudo-code please note that R considers everything between {} to be a single expression:

result <- try({
  # ...
  # lines of R code
  # ...
}, silent = TRUE)

if ( "try-error" %in% class(result) ) {
err_msg <- geterrmessage()
  # logging of error message
  # detection and handling of particular error strings
  # stop() if necessary with user friendly error strings
}

stopOnError()

The stopOnError() utility function regularizes our handling of errors in operational code. This function tests the first argument for a class of try-error and, if true, performs the following actions:

  1. creates err_msg from a user provided error message or, if NULL, geterrmessage()
  2. allows modification of error messages with arguments prefix and maxLength
  3. logs this message with logger.error(err_msg) if logging has been enabled
  4. throws an updated error message with stop(err_msg)

Encouraging junior R programmers to add error handling to their code is now much easier. They can place any block of R code within a “try block” with the following minimal syntax:

result <- try({
  # ...
  # lines of R code
  # ...
}, silent = FALSE)
stopOnError(result)

Using the %>% pipe operator, we can write this even more concisely without creating the interim result object:

try({
  # ...
  # lines of R code
  # ...
}, silent = FALSE) %>%
  stopOnError()

Working example

Here is a working example demonstrating how a web service might test for user input that may not have been converted from character to numeric. All errors are appropriately logged. (The outer try() blocks in the examples below allow the code to be evaluated for this vignette.).

In the third example, we see how low level error messages that may be hard to understand in the context of a complex, multi-level piece of code can be converted into a message that makes sense in the context of a web service application.

library(MazamaCoreUtils)
logger.setup()
logger.setLevel(TRACE) # force logs to be printed to the console

# Arbitrarily deep in the stack we might have:
myFunc <- function(x) {
  return(log(x))
}

# ----- Example 1:  good user input --------------------------------------------
try({
  
  userInput <- 10
  logger.trace("class(userInput) = %s", class(userInput))
  
  try({
    myFunc(x = userInput)
  }, silent = TRUE) %>%
    stopOnError()
  
  logger.trace("Continue processing ...")
  
}, silent = TRUE)
#> TRACE [2022-08-10 11:52:06] class(userInput) = numeric
#> TRACE [2022-08-10 11:52:06] Continue processing ...

# ----- Example 2:  bad user input ---------------------------------------------
try({
  
  userInput <- "10"
  logger.trace("class(userInput) = %s", class(userInput))
  
  try({
    myFunc(x = userInput)
  }, silent = TRUE) %>%
    stopOnError()
  
  logger.trace("Continue processing ...") # we don't get here
  
}, silent = TRUE)
#> TRACE [2022-08-10 11:52:06] class(userInput) = character
#> ERROR [2022-08-10 11:52:06] Error in log(x) : non-numeric argument to mathematical function

# ----- Example 3:  bad user input, custom error message -----------------------
try({
  
  try({
    logger.trace("class(userInput) = %s", class(userInput))
    myFunc(x = userInput)
  }, silent = TRUE) %>%
    stopOnError("Unable to process user input")
  
  logger.trace("Continue processing ...") # we don't get here
  
}, silent = TRUE)
#> TRACE [2022-08-10 11:52:06] class(userInput) = character
#> ERROR [2022-08-10 11:52:06] Unable to process user input