Critical debug info
Sensible logging in web applications
10 min. read

Logging is not only a powerful technique during the development phase, it rather is an inherent responsibility of every application that runs in production: logs allow us to see what’s going on, trace back bugs and optimize our applications in accordance with their actual usage patterns.

In order to know what to log and in which places, it is crucial to understand which people will work with the logs later on and what exact insights they are trying to retrieve from them. This blog post outlines how logging can be setup in modern web applications, especially if they are hosted on fully-managed cloud platforms (PAAS). Whatever the case will be, though: the most important thing is to define a consistent strategy and stick to it.

Log levels

The usage of a certain set of log levels has been established to be a common best practice over the years. These categories are widely supported by libraries and logging tools across different languages and platforms. Some libraries define additional log levels and therefore allow for a more fine-granular shading or slightly different semantics. But at its heart it boils down to five essential ones: critical, error, warning, info, debug.


A “critical” log means that the application is in an overall undefined or inconsistent state. Critical errors are usually not scoped to one request, but they affect the application on a global level. They usually deserve immediate reaction, because either the application fails as a whole or more harm is about to be done. For example:

Bottom line: something is fundamentally going wrong and requires immediate fixing. Use this level if you want an alert to be triggered.


A log event with level “error” means that the current task failed to execute, but the application is (assumably) in an overall healthy state:

Bottom line: some business functionality has failed and the application can neither recover from the failure nor gracefully deal with it. Use this level if you want an error to be reviewed and taken care of individually.

The difference between “error” and “critical” is mostly about scope and impact of a problem. The second scenario is a good example here: depending on the case, a data inconsistency might also be critical if you would expect the malformed data to spread and cause more damage.


A warning is an indication that an error is likely about to happen near future. Imagine something along the lines of:

Bottom line: nothing has gone wrong yet, but chances are that something will go wrong very soon – precautionary action might be necessary. Use this level if you want to bring something to the attention of software engineers proactively. (However, be disciplined and don’t overuse it.)

One thought about warnings from personal experience: I use warnings very scarcely and almost always find that there are more effective ways to receive preventive notices about potentially precarious system state; of which more later.


“info” logs should yield the broad picture of the things that are going on in the application. They can be crucial for fixing bugs or tracing issues that get reported by users. Moreover, they allow for comprehensive statistical analysis of the app usage.

Bottom line: use this level to describe the current state of a request from a higher perspective.


While “info” logs tell you what has happend, the “debug” logs provide context to understand why and how exactly something occurred. Context is fluent or at least transient, meaning that it will vanish or change over time. With “debug” statements you can capture the most important bits in the logs.

Bottom line: use this level to provide contextual information that allow you to backtrace and understand an issue later on. (Tip: maybe it helps you to put yourself into the shoes of a developer who needs to fix a bug. What information would they wish for?)

The only limits for debug logs are probably disk space and data privacy. If debug logs become too cluttered, you can consider to introduce the “trace” level for even more detail.

Logging in practice

Knowing what log levels there are and understanding the idea behind them is only half the battle. In order to use logging in a sensible way there are practical aspects you should be aware of. Let me illustrate this with some piece of code: in the following example you see a method from a service layer of a web server. Its job is to read a post (e.g. a blog post) from a database and serve it to the user – of course only in case the post was found and the user has appropriate permissions to access it.


Post read(String postId, User principal)
throws PostException, DatabaseException
{"User requesting post.")
    log.debug("Post id: " + postId);
    log.debug("User id: " + principal.getId());

    try {
        Post post = database.findPostById(postId);

        if (post == null) {
  "Not found: Post is not existing.");
            throw new NotFoundException();

        Auth auth = post.canBeReadBy(principal);
        if (!auth.isGranted()) {
  "Access denied: user not allowed to see post.");
            log.debug("Reason: " + auth.getReason());
            log.debug("User roles: " + user.getRoles());
            throw new AccessDeniedException();

        return post;
    } catch (ConnectionException e) {
        log.error("Database connection failed.", e);
        throw e;

Log severity and client errors are separate things

Only because something failed on the server doesn’t mean that the client must bring this to the attention of the user. The same holds true the other way around: only because the client displays an error doesn’t mean that something went wrong on the server.

Consider the database connection failure: a read operation on a post resource is idempotent and a network failure is most likely a temporary thing. If the server did translate ConnectionException into HTTP status 503, the client could just wait a couple of seconds and then retry the operation. The retry is likely to succeed and the user would hardly ever notice about it.

On the other hand, imagine the cases where the post is not found or the user doesn’t have proper rights to access it. The client will show an error to the user explaining them why the post cannot be retrieved. However, from the point of view of the server this is actually well-defined behaviour. (Performing these checks is literally in the job description of that method.) The server only informs in the logs about this occurrence, but there is nothing going wrong in the first place – hence the “info” level.

Logging alone is only one side of the coin

Log statements should be written from the current point of view of the application. They shouldn’t make assumptions about any external circumstances that might have led to the failure to begin with:

Especially if you consider the second case it’s arguably not wrong to bump the log level to “warning”. However, the effect that you are aiming for cannot be accomplished by logging alone; instead you might even end up with producing a lot of noise in the logs due to false positives.

A more suitable way is to complement your logging setup with tools2 that are able to perform heuristic analysis of logs, preferrably in realtime. This allows you – on an overarching level – to define precise thresholds, visualise borderline behaviour and respond to unfolding deficiencies effectively.

Log statements should be machine processable

In the example above I use “info” logs for plain descriptions and provide concrete values in “debug” logs. This is only a personal preference: you can also find good reasons to provide the values within one single “info” statement. But no matter how you do it eventually, you should make sure that logs can be found and aggregated by means of their message.

Imagine we replaced the three log statements in the authentication failure condition with one single line and inserted the data into the continuous text, like so: “User with id %s is not allowed to access post with id %s because their role is %s. This would be a format that is unnecessarily hard to search for:

One related thing: it is very convenient if you can identify all logs on a per-request basis. That way, if you see an error in the logs, you can inspect all log messages of that particular request that got issued prior to the error. This brings the additional benefit that you don’t have to provide the same contextual data all over again (such as ids). Consult the documentation of your HTTP framework resp. log library on how to do that. Supplementing this technique with a log processing pipeline can make for a very powerful setup.

Logging is an impure operation

Logging is a meta level procedure, because it is not tightly related to the business logic itself. Therefore logging might feel like an extraordinary thing to do. If you use battle-proof libraries (which you should) it is furthermore a cheap and resilient operation. Keep in mind though, that every log command gets written or sent somewhere; it’s an ordinary side effect that relies on global state and in that it’s no different from writing to a file directly. Hence, bear logging in mind while developing your code architecture.

In the example above canBeReadBy could also just return a boolean value instead of an Auth object. However, then we either wouldn’t get to know why the authentication check has failed in the first place, or we would need to log within that method. The first might not be acceptable from a debugging perspective, whereas the latter might not be acceptable from a code design perspective (e.g. as the permission check is designed to be strictly pure). Returning the authentication result in form of an object allows us to pass on the responsibility of logging to the caller while still providing enough of the original context.

Another implication of logging can be its verbosity: if you copy & pasted the example into an editor and omit all log statements you would see how much more concise the code will become all of a sudden. I personally prefer to keep logging in the upper layers of my application, because this is the place which is engaged with side effects anyway. There, however, I leave it as explicit and granular as necessary.

  1. This is the method written in Java. Just assume this: 1) The principal represents the inquiring user. 2) The log and database objects were injected into the class upon construction. 3) Exceptions are handled by an upper layer and get translated into HTTP status codes there. ↩︎

  2. Examples for full-featured logging tools are Kibana, Google Stackdriver or AWS CloudWatch↩︎

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