What do you do when you’ve caught an exception?

Abort, Retry, Ignore

This article is a follow up to “Don’t Catch Exceptions“, which advocates that exceptions should (in general) be passed up to a “unit of work”, that is, a fairly coarse-grained activity which can reasonably be failed, retried or ignored. A unit of work could be:

  • an entire program, for a command-line script,
  • a single web request in a web application,
  • the delivery of an e-mail message
  • the handling of a single input record in a batch loading application,
  • rendering a single frame in a media player or a video game, or
  • an event handler in a GUI program

The code around the unit of work may look something like

[01] try {
[02]   DoUnitOfWork()
[03] } catch(Exception e) {
[04]    ... examine exception and decide what to do ...
[05] }

For the most part, the code inside DoUnitOfWork() and the functions it calls tries to throw exceptions upward rather than catch them.

To handle errors correctly, you need to answer a few questions, such as

  • Was this error caused by a corrupted application state?
  • Did this error cause the application state to be corrupted?
  • Was this error caused by invalid input?
  • What do we tell the user, the developers and the system administrator?
  • Could this operation succeed if it was retried?
  • Is there something else we could do?

Although it’s good to depend on existing exception hierarchies (at least you won’t introduce new problems), the way that exceptions are defined and thrown inside the work unit should help the code on line [04] make a decision about what to do — such practices are the subject of a future article, which subscribers to our RSS feed will be the first to read.

The cause and effect of errors

There are a certain range of error conditions that are predictable,  where it’s possible to detect the error and implement the correct response.  As an application becomes more complex,  the number of possible errors explodes,  and it becomes impossible or unacceptably expensive to implement explicit handling of every condition.

What do do about unanticipated errors is a controversial topic.  Two extreme positions are: (i) an unexpected error could be a sign that the application is corrupted, so that the application should be shut down, and (ii) systems should bend but not break: we should be optimistic and hope for the best.  Ultimately, there’s a contradiction between integrity and availability, and different systems make different choices.  The ecosystem around Microsoft Windows,  where people predominantly develop desktop applications,   is inclined to give up the ghost when things go wrong — better to show a “blue screen of death” than to let the unpredictable happen.  In the Unix ecosystem,  more centered around server applications and custom scripts,  the tendency is to soldier on in the face of adversity.

What’s at stake?

Desktop applications tend to fail when unexpected errors happen:  users learn to save frequently.  Some of the best applications,  such as GNU emacs and Microsoft Word,  keep a running log of changes to minimize work lost to application and system crashes.  Users accept the situation.

On the other hand,   it’s unreasonable for a server application that serves hundreds or millions of users to shut down on account of a cosmic ray.  Embedded systems,  in particular,  function in a world where failure is frequent and the effects must be minimized.   As we’ll see later,  it would be a real bummer if the Engine Control Unit in your car left you stranded home because your oxygen sensor quit working.

The following diagram illustrates the environment of a work unit in a typical application:  (although this application accesses network resources,  we’re not thinking of it as a distributed application.  We’re responsible for the correct behavior of the application running in a single address space,  not about the correct behavior of a process swarm.)

The Input to the work unit is a potential source of trouble.  The input could be invalid,  or it could trigger a bug in the work unit or elsewhere in the system (the “system” encompasses everything in the diagram)   Even if the input is valid,  it could contain a reference to a corrupted resource,  elsewhere in the system.  A corrupted resource could be a damaged data structure (such as a colored box in a database),  or an otherwise malfunctioning part of the system (a crashed server or router on the network.)

Data structures in the work unit itself are the least problematic,  for purposes of error handling,  because they don’t outlive the work unit and don’t have any impact on future work units.

Static application data,  on the other hand,  persists after the work unit ends,  and this has two possible consequences:

  1. The current work unit can fail because a previous work unit caused a resource to be corrupted, and
  2. The current work unit can corrupt a resource,  causing a future work unit to fail

Osterman’s argument that applications should crash on errors is based on this reality:  an unanticipated failure is a sign that the application is in an unknown (and possibly bad) state,  and can’t be trusted to be reliable in the future.  Stopping the application and restarting it clears out the static state,  eliminating resource corruption.

Rebooting the application,  however,  might not free up corrupted resources inside the operating system.  Both desktop and server applications suffer from operating system errors from time to time,  and often can get immediate relief by rebooting the whole computer.

The “reboot” strategy runs out of steam when we cross the line from in-RAM state to persistent state,  state that’s stored on disks,  or stored elsewhere on the network.  Once resources in the persistent world are corrupted,  they need to be (i) lived with,  or repaired by (ii) manual or (iii) automatic action.

In either world,  a corrupted resource can have either a narrow (blue) or wide (orange) effect on the application.  For instance,  the user account record of an individual user could be damaged,  which prevents that user from logging in.  That’s bad,  but it would hardly be catastrophic for a system that has 100,000 users.   It’s best to ‘ignore’ this error,  because a system-wide ‘abort’ would deny service to 99,999 other users;  the problem can be corrected when the user complains,  or when the problem is otherwise detected by the system administrator.

If,  on the other hand,  the cryptographic signing key that controls the authentication process were lost,  nobody would be able to log in:  that’s quite a problem.  It’s kind of the problem that will be noticed,  however,  so aborting at the work unit level (authenticated request) is enough to protect the integrity of the system while the administrators repair the problem.

Problems can happen at an intermediate scope as well.  For instance,  if the system has damage to a message file for Italian users,  people who use the system in the Italian language could be locked out.  If Italian speakers are 10% of the users,  it’s best to keep the system running for others while you correct the problem.


There are several tools for dealing with corruption in persistent data stores. In a one-of-a-kind business system, a DBA may need to intervene occasionally to repair corruption. More common events can be handled by running scripts which detect and repair corruption, much like the fsck command in Unix or the chkdsk command in Windows. Corruption in the metadata of a filesystem can, potentially, cause a sequence of events which leads to massive data loss, so UNIX systems have historically run the fsck command on filesystems whenever the filesystem is in a questionable state (such as after a system crash or power failure.) The time do do an fsck has become an increasing burden as disks have gotten larger, so modern UNIX systems use journaling filesystems that protect  filesystem metadata with transactional semantics.

Release and Rollback

One role of an exception handler for a unit of work is to take steps to prevent corruption. This involves the release of resources, putting data in a safe state, and, when possible, the rollback of transactions.

Although many kinds of persistent store support transactions, and many in-memory data structures can support transactions, the most common transactional store that people use is the relational database. Although transactions don’t protect the database from all programming errors, they can ensure that neither expected or unexpected exceptions will cause partially-completed work to remain in the database.

A classic example in pseudo code is the following:

[06] function TransferMoney(fromAccount,toAccount,amount) {
[07]   try {
[08]      BeginTransaction();
[09]      ChangeBalance(toAccount,amount);
[10]      ... something throws exception here ...
[11]      ChangeBalance(fromAccount,-amount);
[12]      CommitTransaction();
[13]   } catch(Exception e) {
[14]      RollbackTransaction();
[15]   }
[16] }

In this (simplified) example, we’re transferring money from one bank account to another. Potentially an exception thrown at line [05] could be serious, since it would cause money to appear in toAccount without it being removed from fromAccount. It’s bad enough if this happens by accident, but a clever cracker who finds a way to cause an exception at line [05] has discovered a way to steal money from the bank.

Fortunately we’re doing this financial transaction inside a database transaction. Everything done after BeginTransaction() is provisional: it doesn’t actually appear in the database until CommitTransaction() is called. When an exception happens, we call RollbackTransaction(), which makes it as if the first ChangeBalance() had never been called.

As mentioned in the “Don’t Catch Exceptions” article, it often makes sense to do release, rollback and repairing operations in a finally clause rather than the unit-of-work catch clause because it lets an individual subsystem take care of itself — this promotes encapsulation. However, in applications that use databases transactionally, it often makes sense to push transaction management out the the work unit.

Why? Complex database operations are often composed out of simpler database operations that, themselves, should be done transactionally. To take an example, imagine that somebody is opening a new account and funding it from an existing account:

[17] function OpenAndFundNewAccount(accountInformation,oldAccount,amount) {
[18]    if (amount<MinimumAmount) {
[19]       throw new InvalidInputException(
[20]          "Attempted To Create Account With Balance Below Minimum"
[21]       );
[22]    }
[23]    newAccount=CreateNewAccountRecords(accountInformation);
[24]    TransferMoney(oldAccount,newAccount,amount);|
[25] }

It’s important that the TransferMoney operation be done transactionally, but it’s also important that the whole OpenAndFundNewAccount operation be done transactionally too, because we don’t want an account in the system to start with a zero balance.

A straightforward answer to this problem is to always do banking operations inside a unit of work, and to begin, commit and roll back transactions at the work unit level:

[26] AtmOutput ProcessAtmRequest(AtmInput in) {
[27]    try {
[28]       BeginTransaction();
[29]       BankingOperation op=AtmInput.ParseOperation();
[30]       var out=op.Execute();
[31]       var atmOut=AtmOutput.Encode(out);
[32]       CommitTransaction();
[33]       return atmOut;
[34]    }
[35]    catch(Exception e) {
[36]       RollbackTransaction();
[37]       ... Complete Error Handling ...
[38]    }

In this case, there might be a large number of functions that are used to manipulate the database internally, but these are only accessable to customers and bank tellers through a limited set of BankingOperations that are always executed in a transaction.


There are several parties that could be notified when something goes wrong with an application, most commonly:

  1. the end user,
  2. the system administrator, and
  3. the developers.

Sometimes, as in the case of a public-facing web application, #2 and #3 may overlap. In desktop applications, #2 might not exist.

Let’s consider the end user first. The end user really needs to know (i) that something went wrong, and (ii) what they can do about it. Often errors are caused by user input: hopefully these errors are expected, so the system can tell the user specifically what went wrong: for instance,

[39] try {
[40]   ... process form information ...
[42]    if (!IsWellFormedSSN(ssn))
[43]       throw new InvalidInputException("You must supply a valid social security number");
[45]    ... process form some more ...
[46] } catch(InvalidInputException e) {
[47]    DisplayError(e.Message);
[48] }

other times, errors happen that are unexpected. Consider a common (and bad) practice that we see in database applications: programs that write queries without correctly escaping strings:

[49] dbConn.Execute("
[50]   INSERT INTO people (first_name,last_name)
[51]      VALUES ('"+firstName+"','+lastName+"');
[52] ");

this code is straightforward, but dangerous, because a single quote in the firstName or lastName variable ends the string literal in the VALUES clause, and enables an SQL injection attack. (I’d hope that you know better than than to do this, but large projects worked on by large teams inevitably have problems of this order.) This code might even hold up well in testing, failing only in production when a person registers with

[53] lastName="O'Reilly";

Now, the dbConn is going to throw something like a SqlException with the following message:

[54] SqlException.Message="Invalid SQL Statement:
[55]   INSERT INTO people (first_name,last_name)
[56]      VALUES ('Baba','O'Reilly');"

we could show that message to the end user, but that message is worthless to most people. Worse than that, it’s harmful if the end user is a cracker who could take advantage of the error — it tells them the name of the affected table, the names of the columns, and the exact SQL code that they can inject something into. You might be better off showing users something like:

and telling them that they’ve experienced an “Internal Server Error.”  Even so,  the discovery that a single quote can cause an “Internal Server Error” can be enough  for a good cracker to sniff out the fault and develop an attack in the blind.. What can we do? Warn the system administrators. The error handling system for a server application should log exceptions, stack trace and all. It doesn’t matter if you use the UNIX syslog mechanism, the logging service in Windows NT, or something that’s built into your server, like Apache’s error_log. Although logging systems are built into both Java and .Net, many developers find that Log4J and Log4N are especially effective.

There really are two ways to use logs:

  1. Detailed logging information is useful for debugging problems after the fact. For instance, if a user reports a problem, you can look in the logs to understand the origin of the problem, making it easy to debug problems that occur rarely: this can save hours of time trying to understand the exact problem a user is experiencing.
  2. A second approach to logs is proactive: to regularly look a logs to detect problems before they get reported. In the example above, the SqlException would probably first be thrown by an innocent person who has an apostrophe in his or her name — if the error was detected that day and quickly fixed, a potential security hole could be fixed long before it would be exploited.  Organizaitons that investigate all exceptions thrown by production web applications run the most secure and reliable applications.

In the last decade it’s become quite common for desktop applications to send stack traces back to the developers after a crash: usually they pop up a dialog box that asks for permission first. Although developers of desktop applications can’t be as proactive as maintainers of server applications, this is a useful tool for discovering errors that escape testing, and to discover how commonly they occur in the field.

Retry I: Do it again!

Some errors are transient: that is, if you try to do the same operation later, the operation may succeed. Here are a few common cases:

  • An attempt to write to a DVD-R could fail because the disk is missing from the drive
  • A database transaction could fail when you commit it because of a conflict with another transaction: an attempt to do the transaction again could succeed
  • An attempt to deliver a mail message could fail because of problems with the network or destination mail server
  • A web crawler that crawls thousands (or millions) of sites will find that many of them are down at any given time: it needs to deal with this reasonably, rather than drop your site from it’s index because it happened to be down for a few hours

Transient errors are commonly associated with the internet and with remote servers; errors are frequent because of the complexity of the internet, but they’re transitory because problems are repaired by both automatic and human intervention. For instance, if a hardware failure causes a remote web or email server to go down, it’s likely that somebody is going to notice the problem and fix it in a few hours or days.

One strategy for dealing with transient errors is to punt it back to the user: in a case like this, we display an error message that tells the user that the problem might clear up if they retry the operation. This is implicit in how web browsers work: sometimes you try to visit a web page, you get an error message, then you hit reload and it’s all OK. This strategy is particularly effective when the user could be aware that there’s a problem with their internet connection and could do something about it: for instance, they might discover that they’ve moved their laptop out of Wi-Fi range, or that the DSL connection at their house has gone down for the weekend.

SMTP, the internet protocol for email, is one of the best examples of automated retry. Compliant e-mail servers store outgoing mail in a queue: if an attempt to send mail to a destination server fails, mail will stay in the queue for several days before reporting failure to the user. Section 4.5.4 of RFC 2821 states:

   The sender MUST delay retrying a particular destination after one
   attempt has failed.  In general, the retry interval SHOULD be at
   least 30 minutes; however, more sophisticated and variable strategies
   will be beneficial when the SMTP client can determine the reason for

   Retries continue until the message is transmitted or the sender gives
   up; the give-up time generally needs to be at least 4-5 days.  The
   parameters to the retry algorithm MUST be configurable.

   A client SHOULD keep a list of hosts it cannot reach and
   corresponding connection timeouts, rather than just retrying queued
   mail items.

   Experience suggests that failures are typically transient (the target
   system or its connection has crashed), favoring a policy of two
   connection attempts in the first hour the message is in the queue,
   and then backing off to one every two or three hours.

Practical mail servers use fsync() and other mechanisms to implement transactional semantics on the queue: the needs of reliability make it expensive to run an SMTP-compliant server, so e-mail spammers often use non-compliant servers that don’t correctly retry (if they’re going to send you 20 copies of the message anyway, who cares if only 15 get through?) Greylisting is a highly effective filtering strategy that tests the compliance of SMTP senders by forcing a retry.

Retry II: If first you don’t succeed…

An alternate form of retry is to try something different. For instance, many programs in the UNIX environment will look in many different places for a configuration file: if the file isn’t in the first place tried, it will try the second place and so forth.

The online e-print server at arXiv.org has a system called AutoTex which automatically converts documents written in several dialects of TeX and LaTeX into Postscript and PDF files. AutoTex unpacks the files in a submission into a directory and uses chroot to run the document processing tools in a protected sandbox. It tries about of ten different configurations until it finds one that successfully compiles the document.

In embedded applications,  where availability is important,  it’s common to fall back to a “safe mode” when normal operation is impossible.  The Engine Control Unit in a modern car is a good example:

Since the 1970′s,   regulations in the United States have reduced emissions of hydrocarbons and nitrogen oxides from passenger automobiles by more than a hundred fold.  The technology has many aspects,  but the core of the system in an Engine Control Unit that uses a collection of sensors to monitor the state of the engine and uses this information to adjust engine parameters (such as the quantity of fuel injected) to balance performance and fuel economy with environmental compliance.

As the condition of the engine,  driving conditions and composition of fuel change over the time,  the ECU normally operates in a “closed-loop” mode that continually optimizes performance.   When part of the system fails (for instance,  the oxygen sensor) the ECU switches to an “open-loop” mode.  Rather than leaving you stranded,  it lights the “check engine” indicator and operates the engine with conservative assumptions that will get you home and to a repair shop.


One strength of exceptions,  compared to the older return-value method of error handling is that the default behavior of an exception is to abort,  not to ignore.  In general,  that’s good,  but there are a few cases where “ignore” is the best option.  Ignoring an error makes sense when:

  1. Security is not at stake,  and
  2. there’s no alternative action available,  and
  3. the consequences of an abort are worse than the consequences of avoiding an error

The first rule is important,  because crackers will take advantage of system faults to attack a system.  Imagine,  for instance,  a “smart card” chip embedded in a payment card.  People have successfully extracted information from smart cards by fault injection:  this could be anything from a power dropout to a bright flash of light on an exposed silicon surface.  If you’re concerned that a system will be abused,  it’s probably best to shut down when abnormal conditions are detected.

On the other hand,  some operations are vestigial to an application.  Imagine,  for instance,  a dialog box that pops when an application crashes that offers the user the choice of sending a stack trace to the vendor.  If the attempt to send the stack trace fails,  it’s best to ignore the failure — there’s no point in subjecting the user to an endless series of dialog boxes.

“Ignoring” often makes sense in the applications that matter the most and those that matter the least.

For instance,  media players and video games operate in a hostile environment where disks,  the network, sound and controller hardware are uncooperative.  The “unit of work” could be the rendering of an individual frame:  it’s appropriate for entertainment devices to soldier on despite hardware defects,  unplugged game controllers,  network dropouts and corrupted inputs,  since the consequences of failure are no worse than shutting the system down.

In the opposite case,  high-value systems and high-risk should continue functioning no matter what happen.  The software for a space probe,  for instance,  should never give up.  Much like an automotive ECU,  space probes default to a “safe mode” when contact with the earth is lost:  frequently this strategy involves one or more reboots,  but the goal is to always regain contact with controllers so that the mission has a chance at success.


It’s most practical to catch exceptions at the boundaries of relatively coarse “units of work.” Although the handling of errors usually involves some amount of rollback (restoring system state) and notification of affected people, the ultimate choices are still what they were in the days of DOS: abort, retry, or ignore.

Correct handling of an error requires some thought about the cause of an error: was it caused by bad input, corrupted application state, or a transient network failure? It’s also important to understand the impact the error has on the application state and to try to reduce it using mechanisms such as database transactions.

“Abort” is a logical choice when an error is likely to have caused corruption of the application state, or if an error was probably caused by a corrupted state. Applications that depend on network communications sometimes must “Retry” operations when they are interrupted by network failures. Another form of “Retry” is to try a different approach to an operation when the first approach fails. Finally, “Ignore” is appropriate when “Retry” isn’t available and the cost of “Abort” is worse than soldiering on.

This article is one of a series on error handling.  The next article in this series will describe practices for defining and throwing exceptions that gives exception handlers good information for making decisions.  Subscribers to our RSS Feed will be the first to read it.