Cerberus is a lightweight and extensible data validation library for Python. Beta has been around since 2012. During this time Cerberus has been serving as the validation system for Eve core. It has been also adopted by a quite a lot open source projects, averaging around 18K downloads per month on PyPI and collecting some remarkable endorsements.

All things considered, I would dare to claim that Cerberus is battle tested to death. This is, in fact, one reason why I believe that the time for a canonical and stable release has come. Another reason is that next release is a major one. It brings a ton of important new features along with very significant code refactoring and a redesigned, powerful API. Third, next release breaks backward compatibility, and we want to signal that in the version number.

So next Cerberus release will be 1.0. If you have been following the development this will come as no surprise, as a Release Candidate has been out for a while. As a Cerberus user you will want to take the plunge and upgrade to 1.0 because well, it is just too cool to be true. If new to Cerberus you will also want to adopt 1.0 right away, for the same reason. If you are new however, make sure you get the basics covered before reading further. By the way, at latest PyCon Italy I gave a talk on Cerberus which also included a preview of several 1.0 features. You can check the slides to get a general idea of the tool, its usage, and upcoming features.

Let’s now look at some of the relevant features and changes introduced with Cerberus 1.0. For a (mostly) accurate list of changes and new features, have a look at the changelog.

Transformation and normalization

Big news in the normalization department. Similar to validated(), the new normalized() method returns a normalized copy of a document without validating it.

Renaming of Fields

You can now define a field to be renamed before any further processing.

To let a callable rename a field or arbitrary fields, you can define a handler for renaming. If the constraint is a string, it points to a custom method.

If the constraint is an iterable, the value is processed through that chain.

Purging Unknown Fields

After renaming, unknown fields will be purged if the purge_unknown property of a Validator instance is True.

You can set the property per keyword-argument upon initialization or as rule for subdocuments like allow_unknown. The default is False.

Default Values

You can set default values for missing fields in the document by using the default rule.

You can also define a default setter callable to set the default value dynamically. The callable gets called with the current (sub)document as the only argument. If the constraint is a string, it points to a custom method.

Value Coercion

Coercion has been introduced with 0.9. It allows you to apply a callable to a value before the document is validated. The return value of the callable replaces the new value in the document. This can be used to convert values or sanitize data before it is validated.

If the constraint is an iterable, the value is processed through that chain.

Please note that coerce kicks in with validate(), not with normalized().

Schema Validation

This is another area the is seeing a lot of changes and new, powerful things.


Schema registries are awesome if your schemas shall include references to themselves (recursion) and if they contain a lot of reused parts and are supposed to be serialized. There are two default registries in the cerberus module namespace. You can use schema_registry to store definitions for schemas which can later be re-used:

And you can extend rules_set_registry with rules-sets which can then be referenced in validation schemas:

Schema Constraints in docstrings

Validation schemas themselves are validated when passed to the validator or a new set of rules is set for a document’s field. A SchemaError is raised when an invalid validation schema is encountered.

Now you can provide constraints as literal Python expression in the docstring of the rule’s implementing method to validate the arguments given in a schema for that rule. Either the docstring contains solely the literal or the literal is placed at the bottom of the docstring preceded by the following sentence: The rule's arguments are validated against this schema.

The example below is comes directly from Cerberus’ own test suite:

Validation Rules


Opposite to allowed, this new rule validates if a value is any but one of the defined values:

min, max

Up to 0.9 you could only use min and max to compare numeric types. Now they define minimum and maximum values allowed for any types that implement comparison operators.


For better consistency, the propertyschema rule has been renamed to keyschema. This is the counterpart to valueschema and validates the keys of a dict.


Data type validation now also supports the binary type.

Breaking Changes

This is a major release which breaks backward compatibility in several ways. Don’t worry, these changes are for the better. However, if you are upgrading, then you should really take the time to read the list of changes in the changelog and consider their impact on your codebase, especially so if you have custom validators sitting around. For your convenience, there are also some upgrade notes available.


Cerberus 1.0 would not exist in its current form without the incredible work done by Frank “funkyfuture” Sachsenheim. After his initial, timid, set of v0.9 contributions, Frank went on a rage. He really adopted the project and went on touching on every aspect of it: documentation, new features, proposal, fixes, refactoring, ticket triaging, you-name-it. His efforts have been instrumental in taking the project to the next level. As a token of appreciation for his work, I’m tagging this release with the funkyfuture codename.

Of course he was not alone. Other contributors were Matthew Ellison, Dominik Kellner, Damián Nohales, calve, Roman Redkovich. And then are all the other people who helped by opening tickets and spreading the word around. Thank you all, folks.

Closing notes

Cerberus 1.0 is an important milestone. I consider the API to be reasonably stable and, once it is released, I plan to let it settle down for a while. Release Candidate will stay out for a couple more weeks, which is ideal for you to experiment a little bit before the final release.

If you are a Eve user you probably see the potential that new Cerberus can offer to your RESTful API, especially in the normalization department. Don’t get too excited though as I do not plan on immediately adding Cerberus 1.0 to Eve. Next Eve release (v0.7), which is also upcoming, will be focused on MongoDB Aggregation Framework and other things. That is more than enough for a major release. I am targeting the following release (v0.8) for Cerberus 1.0 support.

So, have fun with New Cerberus. As usual, please report any issue on the ticket system.

If you want to get in touch, I am @nicolaiarocci on Twitter.