Error handling: return values and exceptions

My colleague edulix started a discussion on the golang list about the merits of go’s error handling. This got me thinking about the problem of error handling in general, and that no language seems to have has gotten it quite right. What follows is a quick braindump.

Two approaches

There are two prominent approaches to error handling in software engineering today, using return values and exceptions.

With return value error handling, errors are indicated through the values returned from functions, the caller has to write checks on these values to detect error conditions. Originally, error returns were encoded as falling outside the range of data corresponding to normal operation.

For example, when opening a file with fopen in C++, the normal return is a pointer to an object representing the file. But if an error occurs the function returns null (additional information about the error is available from a global variable, errno). Just as the caller of the function uses normal return values, said caller is responsible for dealing with errors present in the return.

Exceptions establish a separate channel to relay error information back from functions. When using exceptions, a function’s return only holds values that correspond to normal behavior. In this sense the values returned by functions are more immediately identified with the function’s purpose, details regarding what can and does go wrong are separated into the exceptional channel.

For example, the purpose of the Java’s parseInt function is to parse an integer. So the return value’s content is exclusively a consequence of this purpose: the integer parsed from the string. If something goes wrong when attempting to achieve this purpose it will be represented in the throwing of an exception which holds error details. Whereas with return values there is no need for additional machinery beyond that provided by normal function calls, exception requires an extra mechanism: try/catch blocks.

Exceptions’ claimed virtues


Exceptions try to declutter code (

Exceptions aim to be an improvement over return values. Here are some possible shortcomings of the return value approach:

  • The code is cluttered with error checking that reduces its readability, intent is obscured.
  • Sometimes the scope at which an error occurs is not the best one to handle it. In these cases one has to manually propagate error’s backwards, using several returns until an adequate error handling scope is reached. This is cumbersome.
  • If one forgets to check for errors the program may continue to run in an inconsistent state, causing fatal errors down the line that may not be easy to understand.

Exceptions try to address these possible shortcomings:

  • With exceptions normal program flow is separated from error handling code, which lives in special catch blocks for that purpose. This declutters normal behaviour code, making intent clear and readable.
  • With exceptions errors programmers have the option of handling errors at the right scope, without having to manually return across several levels. When exceptions are not handled at the scope the error occurs, they are propagated automatically up the stack until a suitable handler is found.
  • If exceptions are not handled, the program crashes immediately instead of continuing in an inconsistent state. A stack trace shows the error, and the call stack leading to the error.

But it’s not all good

The story doesn’t end here of course, exceptions have their problems. Some say that they are worse than the solution they were trying to improve upon. This is the position that many proponents of Go’s error handling defend, stating that returns with multiple values (feature not present orignally in C, hence errno and the like) are not only sufficient but simply better than exceptions:

  • Because exceptions propagate up and can be handled elsewhere, programmers are tempted to ignore errors and “let someone else deal with it”. This lazy behaviour can lead to errors being handled too late or not at all.
  • It is in principle impossible to know whether a given function call can throw an exception, short of analyzing its entire forward call tree. As a consequence, the use of exceptions in a language is equivalent to hidden goto’s that can jump control back up the call stack at any function invocation. In contrast, when using return values error information is formally present in the function signature.

Whereas before we listed exception propagation as an improvement (allowing error handling at the right scope), here it is listed as a weakness: it can encourage bad programming practice. But it is the second criticism that I find more significant: exceptions are opaque and may lead to unpredictable behaviour. With such an unpredictable ingredient, the programmer is unable to ancipate and control the potential paths his/her code can take, as lurking under every function call is the potential for a jump in execution back up the call stack.

Of course, in real world practice, exceptions are not the randomness disaster that this description may suggest[1]. Properly documented functions do communicate to the programmer, to a reasonable approximation, what can and cannot happen exception-wise. And properly implemented functions do not whimsically throw exceptions at every opportunity, “magically” making your code jump to a random location.

Good intentions: checked exceptions

What about checked exceptions, you say? The rationale for checked exceptions in Java is precisely one that formally addreses the very problem we just described, because

With checked exceptions, function calls must, in their type signature, specify what exceptions can be thrown.

Checked exceptions are part of a function’s type and the compiler ensures that exceptions are either caught or declared to be thrown (a use of the type system that fits well with what I’ve advocated here). Isn’t this exactly what’s needed? The subtle problem is that the phrase “caught or declared to be thrown” does not mean the same as “correctly handled”. The use of checked exceptions encourages lazy behaviour from programmers that makes things even worse. When forced by the compiler to deal with checked exceptions, programmers bypass it by

  • Indiscriminately adding throws clauses, which just add noise to the code
  • Writing empty try/catch blocks that never get populated with error handling code, potentially resulting in silent failures

The second problem is especially dangerous, it can result in exceptions being swallowed and silenced until some larger error occurs down the line, an error which will be very hard to diagnose. If you recall, this is precisely the third drawback we listed for error handling via return values. Both problems arise, not due to the language feature itself, but out of imperfect programming.

The bottom line for a language feature is not what some ideal programmer can do with it, but the use it encourages in the real world. In this case the argument goes that checked exceptions (and as we saw above, error return values) make it harder to write correct error handling code; it requires additional discipline not to shoot oneself in the foot. Which is a shame, since the rationale behind checked exceptions is very appealing: documentation at the type level and compiler enforcement of proper error handling.

Quick recap. Exceptions aim to be an improvement over return values offering benefits mentioned above: code decluttering, error handling at the right scope via propagation and failing fast to avoid hidden errors. Of these three features, the second is questioned and undermined by the two points that proponents of return values make. Opacity in particular is a strong drawback. Checked exceptions aim to resolve this by formally including exception information in the function type and enforcing error handling through the compiler, but the industry consensus seems to be that they do more harm than good.

The way forward

It’s difficult to reach a general conclusion in favor of exceptions or return values, the matter is unclear. But let’s assume that exceptions are no good. Granted this assumption, it would not mean that the problems with return values magically went away. Is there some way, besides exceptions, to address these problems?

One approach I find promising achieves decluttering but within the confines of errors as return values, using a functional style. This technique exploits several programming language features: sum types, pattern matching and monads. But those are just details, what’s important is the end result, see this example in Rust:

The first two lines of the function are operations that can fail, they return a sum type that can represent either a success or failure. The desired behaviour is such that if any error occurs at these lines, then the function from_file should stop executing and must return that error.

Notice how the logic that achieves this is not cluttering the code, it is embedded in the try! macro, which uses pattern matching on the sum type to either return a value to be used in the next line (the file variable), or short circuit program flow back to the caller. If everything works the code eventually returns Ok. Let’s restate the main points

  • from_file has a sum type return that indicates that the function may fail.
  • the individual invocations within the function itself also return that type.
  • the try! macro extracts successful values from these invocations, or shorts circuit execution passing the error as the return of from_file.
  • the caller of the from_file function can proceed the same way or or deal with errors directly (via pattern matching)

Besides the internal machinery and language features in use, I hope it’s clear that this style of handling errors mimics some of the positive aspects of exceptions without using anything but return values. Here is another example which has a similar[2] intent, this time in Scala

This code shows a series of operations that can each fail: getting a row from a database, getting one of its columns, and then doing a lookup on a map. Finally, if any of the steps fail a default value is assigned. The equivalent code with standard null checking would be an ugly and repetitive series of nested if-blocks checking for null. As in the previous example, this error checking logic is not cluttering the code, but occurs behind the scenes thanks to the Option monad. Again, no exceptions, just return values.

Error handling is hard. After years of using exceptions it is still controversial whether they are a net gain or a step back. We cannot expect that a language feature will turn up and suddenly solve everything. Having said that, it seems to me that the functional style we have seen has something to offer in at least one of the areas where traditional return values fall short. Time will tell whether this approach is a net gain.


[1] If real world application of exceptions was in fact disastrous, software written with exceptions would just never work, and apparently it does; exceptions are widely in use in countless systems today.

So a reasonable position to take is that criticisms of exceptions are on the mark when pointing out that the exception mechanism is opaque and potentially unpredictable. This criticism does not mean that software written with exceptions is inherently flawed, but that exceptions make correct error handling hard. But then again, error handling is a hard problem to begin with. Does the opacity of exceptions null its advantages?

[2] In this case the problem is how to deal with failure in the form of null values.

[3] In Rust for example, the problem of swallowing errors is also addressed with #[must_use] , see

Further reading


Checked exceptions


Scala handling-Handling exceptions!topic/scala-debate/K6wv6KphJK0%5B101-125-false%5D

Querying in Slick with many optional constraints

It’s a common use case to have to write queries with multiple constraints (ie where conditions) where each of these may or may not be present. For example, you could have an interface where the user may wish to filter according to different columns or criteria. In the old days this meant having to do very nasty sql generation by hand, by constructing some base query and then adding where clauses to it.

And even If you were not doing the sql by hand and using some kind of abstraction on top, the code would still require tedious and repetitive logic, polluting the code with if’s for each constraint that you may want to filter on.

One of the nice properties of Slick is how operations on queries are accumulated in the typical functional way. Queries are immutable, adding a constraint to a query returns another query that inherits all the operations done until then plus the new operation. This makes accumulation of constraints very readable, and makes queries composable and reusable. But how do we succinctly support the notion of accumulating optional operations?

Remember, this is Scala, this is the kind of thing we expect to be able to do concisely and elegantly, just like the Option type allows us to handle chaining of operations that may fail without having to write all those ugly if-else blocks. But unlike the case with Option, what we want to do is operate on a value that does exist, but where the operation itself is what is optional.

I’m convinced that there must be a well known functional pattern for this use case, but I don’t know what it is. Feel free to let me know in the coments, much appreciated. Anyhow, here’s what I came up with

// optionally filter on a column with a supplied predicate
case class MaybeFilter[X, Y](val query: scala.slick.lifted.Query[X, Y]) {
def filter[T](data: Option[T])(f: T => X => scala.slick.lifted.Column[Boolean]) = { => MaybeFilter(query.filter(f(v)))).getOrElse(this)

It looks more complicated than it is because of the type annotations to make it generic, but the mechanism is quite simple. If the optional constraint is present, return a new query with the filtering operation accumulated. Otherwise return the existing accumulated query unchanged.

And here’s how you use it, in this example there are five optional constraints

// example use case
def find(id: Option[Int], createdMin: Option[Date], createdMax: Option[Date], modifiedMin: Option[Date], modifiedMax: Option[Date]) = {

val query = MaybeFilter(Query(this))
.filter(id)(v => d => === v)
.filter(createdMin)(v => d => d.created >= v)
.filter(createdMax)(v => d => d.created <= v)
.filter(modifiedMin)(v => d => d.modified >= v)
.filter(modifiedMax)(v => d => d.modified <= v)



Where did the if‘s go!

Parallel collections for vote processing

At AgoraVoting we recently completed a very important feature, cryptographically secure voting. Among many other things, this adds a lot of heavy number crunching to the process of carrying out elections. One of the steps in the process validates votes, using something called proofs of knowledge. I won’t go into the math details here, just note that like other domains such as 3D graphics, processing votes is embarrassingly parallel. So we carried out an experiment to see how Scala’s parallel collections can achieve parallelism for one particular task.

In this experiment, we have to parse voting records which are then transformed into collections for their validation. The technique is to first obtain a collection with all the necessary data, and then compute in parallel on it. But first, lets see what happens with sequential code, for comparison. I’ve left out the preprocessing code that first obtains the collection, here’s the compute-intensive fragment:


ctexts.foreach( vote => {
vote.foreach( question => {

val pk_p = BigInt((question(2) \ "p").as[String])
val pk_g = BigInt((question(2) \ "g").as[String])

val commitment = BigInt((question(0) \ "commitment").as[String])
val response = BigInt((question(0) \ "response").as[String])
val challenge = BigInt((question(0) \ "challenge").as[String])
val alpha = BigInt((question(1) \ "alpha").as[String])

val toHash = alpha + "/" + commitment
val digest = MessageDigest.getInstance("SHA-256")
val hash = digest.digest(toHash.getBytes("UTF-8"))
val expected = BigInt(1, hash)

assert (challenge == expected)

val first_part = pk_g.modPow(response, pk_p)
val second_part = commitment * (alpha.modPow(challenge, pk_p)) % pk_p

assert(first_part == second_part)


Here’s what happens when running this code:


as you can see, the cores are underutilized. This test run took 1175.254 seconds. Now let’s turn ctexts into a parallel collection before processing on it:

[scala highlight=”1″]

ctexts.par.foreach( vote => {
vote.foreach( question => {

// the same code here …



Yes, that’s a difference of just three characters, par converts ctexts into a parallel collection. Here’s what happens:


All the cores are maxed out, total time: 307.184 seconds. Not bad!