Annotated Logger

-The annotated-logger package provides a decorator that can inject an annotatable logger object into a method or class. This logger object is a drop in replacement for logging.logger with additional functionality. You can read about the Annotated Logger in the announcement blog post.

Install

pip install annotated-logger

Background

Annotated Logger is actively used by GitHub's Vulnerability Management team to help to easily add context to our logs in splunk. It is more or less feature complete for our current use cases, but we will add additional features/fixes as we discover a need for them. But, we'd love feature requests, bug report and or PRs for either (see our contribution guidelines for more information if you wish to contribute).

Requirements

Annotated Logger is a Python package. It should work on any version of Python 3, but it is currently tested on 3.9 and higher.

Usage

The annotated-logger package allows you to decorate a function so that the start and end of that function is logged as well as allowing that function to request an annotated_logger object which can be used as if it was a standard python logger. Additionally, the annotated_logger object will have added annotations based on the method it requested from, any other annotations that were configured ahead of time and any annotations that were added prior to a log being made. Finally, any uncaught exceptions in a decorated method will be logged and re-raised, which allows you to when and how a method ended regardless of if it was successful or not.

from annotated_logger import AnnotatedLogger

annotated_logger = AnnotatedLogger(
    annotations={"this": "will show up in every log"},
)
annotate_logs = annotated_logger.annotate_logs

@annotate_logs()
def foo(annotated_logger, bar):
    annotated_logger.annotate(bar=bar)
    annotated_logger.info("Hi there!", extra={"mood": "happy"})

foo("this is the bar parameter")

{"created": 1708476277.102495, "levelname": "INFO", "name": "annotated_logger.fe18537a-d293-45d7-83c9-51dab3a4c436", "message": "Hi there!", "mood": "happy", "action": "__main__:foo", "this": "will show up in every log", "bar": "this is the bar parameter", "annotated": true}
{"created": 1708476277.1026022, "levelname": "INFO", "name": "annotated_logger.fe18537a-d293-45d7-83c9-51dab3a4c436", "message": "success", "action": "__main__:foo", "this": "will show up in every log", "bar": "this is the bar parameter", "run_time": "0.0", "success": true, "annotated": true}

The example directory has a few files that exercise all of the features of the annotated-logger package. The Calculator class is the most fully featured example (but not a fully featured calculator 😉). The logging_config example shows how to configure a logger via a dictConfig, like django uses. It also shows some of the interactions that can exist between a logging logger and an annotated_logger if logging is configured to use the annotated logger filter.

Here is a more complete example that makes use of a number of the features.

import os
from annotated_logger import AnnotatedLogger
al = AnnotatedLogger(
    name="annotated_logger.example",
    annotations={"branch": os.environ.get("BRANCH", "unknown-branch")}
)
annotate_logs = al.annotate_logs

@annotate_logs()
def split_username(annotated_logger, username):
    annotated_logger.annotate(username=username)
    annotated_logger.info("This is a very important message!", extra={"important": True})
    return list(username)

>>> split_username("crimsonknave")
{"created": 1733349907.7293086, "levelname": "DEBUG", "name": "annotated_logger.example.c499f318-e54b-4f54-9030-a83607fa8519", "message": "start", "action": "__main__:split_username", "branch": "unknown-branch", "annotated": true}
{"created": 1733349907.7296104, "levelname": "INFO", "name": "annotated_logger.example.c499f318-e54b-4f54-9030-a83607fa8519", "message": "This is a very important message!", "important": true, "action": "__main__:split_username", "branch": "unknown-branch", "username": "crimsonknave", "annotated": true}
{"created": 1733349907.729843, "levelname": "INFO", "name": "annotated_logger.example.c499f318-e54b-4f54-9030-a83607fa8519", "message": "success", "action": "__main__:split_username", "branch": "unknown-branch", "username": "crimsonknave", "success": true, "run_time": "0.0", "count": 12, "annotated": true}
['c', 'r', 'i', 'm', 's', 'o', 'n', 'k', 'n', 'a', 'v', 'e']
>>>
>>> split_username(1)
{"created": 1733349913.719831, "levelname": "DEBUG", "name": "annotated_logger.example.1c354f32-dc76-4a6a-8082-751106213cbd", "message": "start", "action": "__main__:split_username", "branch": "unknown-branch", "annotated": true}
{"created": 1733349913.719936, "levelname": "INFO", "name": "annotated_logger.example.1c354f32-dc76-4a6a-8082-751106213cbd", "message": "This is a very important message!", "important": true, "action": "__main__:split_username", "branch": "unknown-branch", "username": 1, "annotated": true}
{"created": 1733349913.7200255, "levelname": "ERROR", "name": "annotated_logger.example.1c354f32-dc76-4a6a-8082-751106213cbd", "message": "Uncaught Exception in logged function", "exc_info": "Traceback (most recent call last):\n  File \"/home/crimsonknave/code/annotated-logger/annotated_logger/__init__.py\", line 758, in wrap_function\n    result = wrapped(*new_args, **new_kwargs)  # pyright: ignore[reportCallIssue]\n  File \"<stdin>\", line 5, in split_username\nTypeError: 'int' object is not iterable", "action": "__main__:split_username", "branch": "unknown-branch", "username": 1, "success": false, "exception_title": "'int' object is not iterable", "annotated": true}
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<makefun-gen-0>", line 2, in split_username
  File "/home/crimsonknave/code/annotated-logger/annotated_logger/__init__.py", line 758, in wrap_function
    result = wrapped(*new_args, **new_kwargs)  # pyright: ignore[reportCallIssue]
  File "<stdin>", line 5, in split_username
TypeError: 'int' object is not iterable

There are a few things going on in this example. Let's break it down piece by piece.

• The Annotated Logger requires a small amount of setup to use; specifically, you need to instantiate an instance of the AnnotatedLogger class. This class contains all of the configuration for the loggers.
  • Here we set the name of the logger. (You will need to update the logging config if your name does not start with annotated_logger or there will be nothing configured to log your messages.)
  • We also set a branch annotation that will be sent with all log messages.
• After that, we create an alias for the decorator. You don't have to do this, but I find it's easier to read than @al.annotate_logs().
• Now, we decorate and define our method, but this time we're going to ask the decorator to provide us with a logger object, annotated_logger. This annotated_logger variable can be used just like a standard logger object, but has some extra features.
  • This annotated_logger argument is added by the decorator before calling the decorated method.
  • The signature of the decorated method is adjusted so that it does not have an annotated_logger parameter (see how it's called with just name).
  • There are optional parameters to the decorator that allow type hints to correctly parse the modified signature.
• We make use of one of those features right away by calling the annotate method, which will add whatever kwargs we pass to the extra field of all log messages that use the logger.
  • Any field added as an annotation will be included in each subsequent log message that uses that logger.
  • You can override an annotation by annotating again with the same name
• At last, we send a log message! In this message we also pass in a field that's only for that log message, in the same way you would when using logger.
• In the second call, we passed an int to the name field and list threw an exception.
  • This exception is logged automatically and then re-raised.
  • This makes it much easier to know if/when a method ended (unless the process was killed).

Let's break down each of the fields in the log message:

Field	Source	Description
created	logging	Standard Logging field.
levelname	logging	Standard Logging field.
name	annotated_logger	Logger name (set via class instantiation).
message	logging	Standard Logging field for log content.
action	annotated_logger	Method name the logger was created for.
branch	AnnotatedLogger()	Set from the configuration's branch annotation.
annotated	annotated_logger	Boolean indicating if the message was sent via Annotated Logger.
important	annotated_logger.info	Annotation set for a specific log message.
username	annotated_logger.annotate	Annotation set by user.
success	annotated_logger	Indicates if the method completed successfully (True/False).
run_time	annotated_logger	Duration of the method execution.
count	annotated_logger	Length of the return value (if applicable).

The success, run_time and count fields are added automatically to the message ("success") that is logged after a decorated method is completed without an exception being raised.

Features

Primary Interactions

The Annotated Logger interacts with Logging via two main classes: AnnotatedAdapter and AnnotatedFilter. AnnotatedAdapter is a subclass of logging.LoggerAdapter and is what all annotated_logger arguments are instances of. AnnotatedFilter is a subclass of logging.Filter and is where the annotations are actually injected into the log messages. As a user outside of config and plugins, the only part of the code you will only interact with are AnnotatedAdapter in methods and the decorator itself. Each instance of the AnnotatedAdapter class has an AnnotatedFilter instance, the AnnotatedAdapter.annotate method passes those annotations on to the filter where they are stored. When a message is logged, that filter will calculate all the annotations it should have and then update the existing LogRecord object with those annotations.

Because each invocation of a method gets its own AnnotatedAdapter object it also has its own AnnotatedFilter object. This ensures that there is no leaking of annotations from one method call to another.

Type Hinting

The Annotated Logger is fully type hinted internally and fully supports type hinting of decorated methods. But a little bit of additional detail is required in the decorator invocation. The annotate_logs method takes a number of optional arguments. For type hinting, _typing_self, _typing_requested, _typing_class and provided are relevant. The three arguments that start with _typing have no impact on the behavior of the decorator and are only used in method signature overrides for type hinting. Setting provided to True tells the decorator that the annotated_logger should not be created and will be provided by the caller (thus the signature shouldn't be altered).

_typing_self defaults to True as that is how most of my code is written. provided, _typing_class and _typing_requested default to False.

class Example:
    @annotate_logs(_typing_requested=True)
    def foo(self, annotated_logger):
        ...

e = Example()
e.foo()

Plugins

There are a number of plugins that come packaged with the Annotated Logger. Plugins allow for the user to hook into two places: when an exception is caught by the decorator and when logging a message. You can create your own plugin by creating a class that defines the filter and uncaught_exception methods (or inherits from annotated_logger.plugins.BasePlugin which provides noop methods for both).

The filter method of a plugin is called when a message is being logged. Plugins are called in the order they are set in the config. They are called by the AnnotatedFilter object of the AnnotatedAdapter and work like any logging.Filter. They take a record argument which is a logging.LogRecord object. They can manipulate that record in any way they want and those modifications will persist. Additionally, just like any logging filter, they can stop a message from being logged by returning False.

The uncaught_exception method of a plugin is called when the decorator catches an exception in the decorated method. It takes two arguments, exception and logger. The logger argument is the annotated_logger for the decorated method. This allows the plugin to annotate the log message stating that there was an uncaught exception that is about to be logged once the plugins have all processed their uncaught_exception methods.

Here is an example of a simple plugin. The plugin inherits from the BasePlugin, which isn't strictly needed here since it implements both filter and uncaught_exception, but if it didn't, inheriting from the BasePlugin means that it would fall back to the default noop methods. The plugin has an init so that it can take and store arguments. The filter and uncaught_exception methods will end up with the same result: flagged=True being set if a word matches. But they do it slightly differently, filter is called while a given log message is being processed and so the annotation it adds is directly to that record. While uncaught_exception is called if an exception is raised and not caught during the execution of the decorated method, so it doesn't have a specific log record to interact with and set an annotation on the logger. The only difference in outcome would be if another plugin emitted a log message during its uncaught_exception method after FlagWordPlugin, in that case, the additional log message would also have flagged=True on it.

from annotated_logger.plugins import BasePlugin

class FlagWordPlugin(BasePlugin):
    """Plugin that flags any log message/exception that contains a word in a list."""
    def __init__(self, *wordlist):
        """Save the wordlist."""
        self.wordlist = wordlist

    def filter(self, record):
    """Add annotation if the message contains words in the wordlist."""
    for word in self.wordlist:
        if word in record.msg:
            record.flagged = True

    def uncaught_exception(self, exception, logger):
    """Add annotation if exception title contains words in the wordlist."""
    for word in self.wordlist:
        if word in str(exception)
            logger.annotate(flagged=True)


AnnotatedLogger(plugins=[FlagWordPlugin("danger", "Will Robinson")])

Plugins are stored in a list and the order they are added can matter. The BasePlugin is always the first plugin in the list; any that are set in configuration are added after it.

When a log message is being sent the filter methods of each plugin will be called in the order they appear in the list. Because the filter methods often modify the record directly, one filter can break another if, for example, one filter removed or renamed a field that another filter used. Conversely, one filter could expect another to have added or altered a field before its run and would fail if it was ahead of the other filter. Finally, just like in the logging module, the filter method can stop a log from being emitted by returning False. As soon as a filter does so the processing ends and any Plugins later in the list will not have their filter methods called.

If the decorated method raises an exception that is not caught, then the plugins will again execute in order. The most common interaction is plugins attempting to set/modify the same annotation. The BasePlugin and RequestsPlugin both set the exception_title annotation. Since the BasePlugin is always first, the title it sets will be overridden. Other interactions would be one plugin setting an annotation before or after another plugin that emits a log message or sends data to a third-party. In both of those cases the order will impact if the annotation is present or not.

Plugins that come with the Annotated Logger:

• GitHubActionsPlugin - Set a level of log messages to also be emitted in actions notation (notice::).
• NameAdjusterPlugin - Add a pre/postfix to a name to avoid collisions in any log processing software (source is a field in Splunk, but we often include it as a field and it's just hidden).
• RemoverPlugin - Remove a field. Exclude password/key fields and set an object's attributes to the log if you want or ignore fields like taskName that are set when running async, but not sync.
• NestedRemoverPlugin - Remove a field no matter how deep in a dictionary it is.
• RenamerPlugin - Rename one field to another (don't like levelname and want level, this is how you do that).
• RequestsPlugin - Adds a title and status code to the annotations if the exception inherits from requests.exceptions.HTTPError.
• RuntimeAnnotationsPlugin - Sets dynamic annotations.

dictConfig

When adding the Annotated Logger to an existing project, or one that uses other packages that log messages (flask, django and so on), you can configure all of the Annotated Logger via dictConfig by supplying a dictConfig compliant dictionary as the config argument when initializing the Annotated Logger class. If, instead, you wish to do this yourself you can pass config=False and reference annotated_logger.DEFAULT_LOGGING_CONFIG to obtain the config that is used when none is provided and alter/extract as needed.

There is one special case where the Annotated Logger will modify the config passed to it: if there is a filter named annotated_filter that entry will be replaced with a reference to a filter that is created by the instance of the Annotated Logger that's being created. This allows any annotations or other options set to be applied to messages that use that filter. You can instead create a filter that uses the AnnotatedFilter class, but it won't have any of the config the rest of your logs have.

Notes

dictConfig partly works when merging dictionaries. I have found that some parts of the config are not overwritten, but other parts seem to lose their references. So, I would encourage you to build up a logging config for everything and call it once only. If you pass config, the Annotated Logger will call logging.config.dictConfig on your config after it has the option to add/adjust the config.

The logging_config.py example has a much more detailed breakdown and set of examples.

Pytest mock

Included with the package is a pytest mock to assist in testing for logged messages. I know that there are some strong opinions about testing log messages, and I don't suggest doing it extensively, or frequently, but sometimes it's the easiest way to check a loop, or the log message is tied to an alert, and it is important how it's formatted. In these cases, you can ask for the annotated_logger_mock fixture which will intercept, record and forward all log messages.

def test_logs(annotated_logger_mock):
    with pytest.raises(KeyError):
        complicated_method()
    annotated_logger_mock.assert_logged(
        "ERROR",  # Log level
        "That's not the right key",  # Log message
        present={"success": False, "key": "bad-key"},  # annotations and their values that are required
        absent=["fake-annotations"],  # annotations that are forbidden
        count=1  # Number of times log messages should match
    )

The assert_logged method makes use of pychoir for flexible matching. None of the parameters are required, so feel free to use whichever makes sense. Below is a breakdown of the default and valid values for each parameter.

Parameter	Default Value	Valid Values	Description
level	Matches anything	String or string-based matcher	Log level to check (e.g., "ERROR").
message	Matches anything	String or string-based matcher	Log message to check.
present	{}	Dictionary with string keys and any value	Annotations required in the log.
absent	set()	"ALL", set, or list of strings	Annotations that must not be present in the log.
count	All positive integers	Integer or integer-based matcher	Number of times the log message should match.

The present key is often what makes the mock truly useful. It allows you to require the things you care about and ignore the things you don't care about. For example, nobody wants their tests to fail because the run_time of a method went from 0.0 to 0.1 or fail because the hostname is different on different test machines. But both of those are useful things to have in the logs. This mock should replace everything you use the caplog fixture for and more.

Other features

Class decorators and persist

Classes can be decorated with @annotate_logs as well. These classes will have an annotated_logger attribute added after the init (I was unable to get it to work inside the __init__). Any decorated methods of that class will have an annotated_logger that's based on the class logger. Calls to annotate that pass persist=True will set the annotations on the class Annotated Logger and so subsequent calls of any decorated method of that instance will have those annotations. The class instance's annotated_logger will also have an annotation of class specifying which class the logs are coming from.

Iterators

The Annotated Logger also supports logging iterations of an enumerable object. annotated_logger.iterator will log the start, each step of the iteration, and when the iteration is complete. This can be useful for pagination in an API if your results object is enumerable, logging each time a page is fetched instead of sitting for a long time with no indication if the pages are hanging or there are simply many pages.

By default the iterator method will log the value of each iteration, but this can be disabled by setting value=False. You can also specify the level to log the iterations at if you don't want the default of info.

Provided

Because each decorated method gets its own annotated_logger calls to other methods will not have any annotations from the caller. Instead of simply passing the annotated_logger object to the method being called, you can specify provided=True in the decorator invocation. This does two things: first, it means that this method won't have an annotated_logger created and passed automatically, instead it requires that the first argument be an existing annotated_logger, which it will use as a basis for the annotated_logger object it creates for the function. Second, it adds the annotation of subaction and sets the decorated function's name as its value, the action annotation is preserved as from the method that called and provided the annotated_logger. Annotations are not persisted from a method decorated with provided=True to the method that called it, unless the class of the calling method was decorated and the called action annotated with persist=True, in which case the annotation is set on the annotated_logger of the instance and shared with all methods as is normal for decorated classes.

The most common use of this is with private methods, especially ones created during a refactor to extract some self contained logic. But other uses are for common methods that are called from a number of different places.

Split Messages

Long messages wreak havoc on log parsing tools. I've encountered cases where the HTML of a 500 error page was too long for Splunk to parse, causing the entire log entry to be discarded and its annotations to go unprocessed. Setting max_length when configuring the Annotated Logger will break long messages into multiple log messages each annotated with split=True, split_complete=False, message_parts=# and message_part=#. The last part of the long message will have split_complete=True when it is logged.

Only messages can be split like this; annotations will not trigger the splitting. However, a plugin could truncate any values with a length over a certain size.

Pre/Post Hooks

You can register hooks that are executed before and after the decorated method is called. The pre_call and post_call parameters of the decorator take a reference to a function and will call that function right before passing in the same arguments that the function will be/was called with. This allows the hooks to add annotations and/or log anything that is desired (assuming the decorated function requested an annotated_logger).

Examples of this would be having a set of annotations that annotate fields on a model and a pre_call that sets them in a standard way. Or a post_call that logs if the function left a model in an unsaved state.

Runtime Annotations

Most annotations are static, but sometimes you need something that's dynamic. These are achieved via the RuntimeAnnotationsPlugin in the Annotated Logger config. The RuntimeAnnotationsPlugin takes a dict of names and references to functions. These functions will be called and passed the log record when the plugin's filter method is invoked just before the log message is emitted. Whatever is returned by the function will be set as the value of the annotation of the log message currently being logged.

A common use case is to annotate a request/correlation id, which identifies all of the log messages that were part of a given API request. For Django, one way to do this is via django-guid.

Tips, Tricks And Gotchas

• When using the decorator in more than one file, it's useful to do all of the configuration in a file like log.py. That allows you to from project.log import annotate_logs everywhere you want to use it and you know it's all configured and everything will be using the same setup.
• Namespacing your loggers helps when there are two projects that both use the Annotated Logger (a package and a service that uses the package). If you are setting anything via dictConfig you will want to have a single config that has everything for all Annotated Loggers.
• In addition to setting a correlation id for the API request being processed, passing the correlation id of the caller and then annotating that will allow you to trace from the logs of service A to the specific logs in Service B that relate to a call made by service A.
• Plugins are very flexible. For example:
  • Send every exception log message to a service like Sentry.
  • Suppress logs from another package like Django, that you don't want to see (assuming you've configured Django's logs to use a filter for your Annotated Logger).
  • Add annotations for extra information about specific types of exceptions (see the RequestsPlugin).
  • Set run time annotations on a subset of messages (instead of all messages with RuntimeAnnotationsPlugin)

License

This project is licensed under the terms of the MIT open source license. Please refer to MIT for the full terms.

Maintainers

This project is primarily maintained by crimsonknave on behalf of GitHub's Vulnerability Management team as it was initially developed for our internal use.

Support

Reported bugs will be addressed, pull requests are welcome, but there is limited bandwidth for work on new features.