It’s that time of year again. It seems like about once a year I get interested designing a schema library. This post is a collection of my latest ideas and design goals, mostly based on what I’ve learnt from the previous three or four implementations.

This topic is probably interesting to a tiny subset of developers, and super boring to everyone else. I’ve tried to write this post in a way that is accessible to a wider developer audience, but you have been warned!

WTF Is A Schema Library

The word “schema” comes from the ancient Greek for “form” or “shape”. In computery terms, a schema is a data structure that describes the shape of data. Schemas are metadata: data about data. They are typically used to filter/validate/coerce values in some way.

The fastest way for me to demonstrate the concept is probably to just show a few different implementations.

React PropTypes are schemas that do type checking on React component properties.

AuthorComponent.propTypes = {
  name: PropTypes.string,
  isVerified: PropTypes.bool,

Database table schemas describe the columns of a database table. In PostgreSQL this can be queried like a table, so it’s kind of a table of data that describes the shape of other tables.

SELECT column_name, data_type
FROM information_schema.columns
WHERE table_name = 'authors';
 column_name | data_type
id           | integer
name         | text
is_verified  | boolean
(4 ROWS)

Making our way into Ruby land, Rails Strong Parameters are (in my opinion very poor) schemas that describe the structure of request parameters.

params.permit(:name, { emails: [] },
              friends: [ :name,
                         { family: [ :name ], hobbies: [] }])

The stronger_parameters gem is a more-featureful alternative to Strong Parameters, and looks a lot more like a typical schema library.

  name: Parameters.string.required, # will not accept a nil or a non-String
  is_verified: Parameters.boolean   # optional, may be omitted

I think the best schema library in Ruby is currently dry-schema.

AuthorSchema = Dry::Schema.Params do

To summarize, what all these implementations have in common is that they describe the structure and/or types (i.e. the “shape”) of complex values.

On the off chance that this appears on Hacker News or any other wretched hive of scum and vapidity, I should address the “lol dynlang people reinventing compilers badly” reaction. There is a lot of overlap between type systems and schemas libraries, but schemas are primarily designed for checking user input, not function signatures. Let me know when you’re running your compiler in production to handle web requests.

Schemas In Web Apps

The typical use case for a web app is to sanitise inputs before actually performing a request. My current view is that this can be broken down into three distinct phases or steps:

  1. Coercion
  2. Constraint checking
  3. Validation

These need to happen in order, and ideally should report failures in a standardised way.

Step 1. Coercion

Coercion is the process of converting a value to its “real” type. How do we know what the “real” type is? The schema tells us.

For example, web forms submit everything as strings, so if you want an integer you need to do string-to-integer coercion.

Similarly, we do string-to-time coercion quite often. If you’ve ever parsed an input string into a date/time object, you’ve implemented a kind of coercion.

Like the other steps, this can fail. Not all strings can be converted into an integer in a sane way. There will be more on failures later.

Step 2. Constraint checking

Just converting a value to the right class doesn’t necessarily mean it’s the right type. For example, the string "-1" can be coerced to the integer -1, but that’s still not a valid value if the type is supposed to be an unsigned integer (i.e. a non-negative integer).

This constraint checking step narrows down the set of valid values from everything (e.g. all integers) to a subset (e.g. non-negative integers).

As another example, a schema library might differentiate between any string and strings with a specific format. Let’s say we’re writing a JSON web API that takes UUIDs as input. They will probably come in as strings, but we don’t want to accept any old string, we only want to accept strings that have the correct UUID format. The format is a constraint that reduces the infinite set of all strings down to a finite subset of valid strings.

Step 3. Validation

These are business rules. If an integer value represents money to send, you might validate that there are sufficient funds to cover the amount.

This is outside the scope of a schema library but failures are usually rendered/handled in the same way across all steps. This brings us to…

Working With Failures

Schema failures and validation failures should be compatible, or at least easy to map from one to the other, because all three steps happen on the same pipeline. A piece of user input enters the application, and needs to be cleaned up, converted, sanitised and checked before it makes its way further into the system.

Ideally, failures at any point in the pipeline get returned to the requester in a consistent, unified way. We don’t want to be writing three independent ways to render failures just because we’re using three different libraries. That means that all the different parts of the pipeline need to play nice together, when it comes to returning information about failures.

For example, Rails apps tend to be built around ActiveModel::Errors, so when schema validation fails it probably makes sense that the schema library returns ActiveModel::Errors objects (ew) or returns something that can be mapped to an ActiveModel::Errors object without losing fidelity.

Raising an exception with a single message string is not good enough. At a minimum, I would expect:

  • The ability to get all the failures, not just the first one that was encountered.
  • I18n compatibility, for human interfaces
  • Symbolic, namespaced error names with machine-readable details, for computer interfaces
  • Enough structural information that the failure can be associated with the exact input field that produced the failure, within a complex form

Why Make A Distinction Between Validation And Constraint Checking?

Personally, I think general-purpose input validation is outside the scope of a schema library. You have to draw the line somewhere.

In my mind, schemas are intuitively good for simple checks like:

  • Is this an integer?
  • Is it non-negative?
  • Is this a Hash?
  • Is this hash missing any required keys?
  • Is this a string?
  • Is the string empty?

These are all easy checks that can be applied to a single value. They are the kind of thing one might expect from a type system, and schema libraries are kind of type systems if you squint your eyes and tilt your head slightly.

Don't hurt me, Haskell people. I'm just joking in the category of endofunctors of 𝑋, with product ✕ replaced by composition of endofunctors and unit set by the identity endofunctor.

What one would not normally expect from a type system is that the type of one variable depends on the run-time value of a different variable, or that type checking requires access to a database. Yes, there are fancy type systems with dependent types that can do these kind of things, but they are not widely used. I mean, they are so obscure that even Haskell doesn’t have that capability, and nobody actually uses Haskell, they just say that they do.

The story is different when it comes to general-purpose validation. Checking a value against something in the database is absolutely necessary, and checking two separate values against each other is common (e.g. validating that starts_at is earlier than ends_at). I would rather leave this higher-level business logic to some other library that specialises in that functionality.

There is no reason that a lower-level schema library and a higher-level validation library can’t work together. Schema libraries are a bad fit for business logic, and validation libraries are usually too cumbersome for lots of pedantic type checking, so they actually complement each other quite well. Rails has both Strong Parameters and model validations, and together they work… passably.

Coercion Is Context-Dependent

Constraint checking (Step 2) is universal. A non-negative integer is a non-negative integer, regardless of whether it came from a JSON API, a web form, CLI arguments, etc. The same is true for validation (Step 3) — it doesn’t matter how you requested a money transfer, there are either sufficient funds or there are not.

But the same is not true for coercion (Step 1). Coercion is context-dependent, and that has implications for designing a schema library.

Apple's bug was caused by type inference without a schema. Schema-based coercion would have never converted lastName to a boolean, because the schema would have indicated that lastName should be a string. That's their whole purpose.

As an example, let’s say we have a schema for a boolean value. If the input comes from a JSON request body then no coercion is necessary, because JSON can represent true and false natively. Not only is it unnecessary, it’s probably also undesirable. If a client provides a string where it should have provided a boolean, that’s a mistake that the client should be made aware of. Silently coercing strings to booleans is how Rachel True got locked out of her iCloud account for six months with the error message “cannot set value `true` to property `lastName`”.

But if that boolean input comes from a checkbox in a web form, then coercion is a must, and it’s also kind of complicated. Firstly, the value comes in as a string. The default string is "on", but it can actually be set to anything. That’s right, the strings "off" and "false" could actually indicate that the checkbox was checked. So what string gets sent when the checkbox is unchecked? That’s a trick question — the browser doesn’t send anything, as if the form had no checkbox at all. But wait, is this a Rails app? Because Rails adds hidden form inputs that only get submitted if the checkbox is unchecked, so the string "0" should be interpreted as false.

Things went from no coercion to woah coercion, real fast. And the schema shouldn’t be concerned about any of this. There absolutely should not be a JSONBoolean type and WebFormBoolean type. A Boolean is a Boolean — where the input comes from is irrelevant.

One of the mistakes I made in previous designs was treating schema objects as if they were functions that could be applied to input — something like this:

schema = RSchema.define { boolean }

result = schema.validate("1")
result.value #=> true

This is a mistake because it assumes that there could be a single implementation of coercion that is correct for all situations, when in reality there are many different ways to coerce a boolean, and the correct choice depends on the context.

To avoid this mistake next time, I plan to…

Consider Schemas As Data Structures

Don't hurt me, object-oriented people. I'm just making a joke using the polymorphic joke builder interface encapsulating the concrete joke builder implementation that I got from the abstract joke builder factory which got setter-injected by the DI framework. I specifically used setter instead of constructor injection so nobody can accuse me of using immutability.

OOP teaches us that good code combines encapsulated state (instance variables) with behaviour that is privy to that state (methods). Not to go on a rant, but that’s a bunch of poppycock and flapdoodle. As Freud once said, “sometimes a data structure is just a data structure”.

Probably the primary application of schemas is validating input, but there are several useful applications.

  • Various different flavours of coercion
  • User input validation
  • Code generation
  • Automated API documentation
  • Generating random valid inputs for property-based testing
  • Versioning structured data (e.g. event payloads)
  • Run-time assertions
  • Conversion to other schema formats (e.g. JSON Schema)

That is way too many responsibilities to cram into a single class, if we were to use one class per type.

The solution to this design problem is pretty simple, in hindsight. Just stop thinking of schemas as objects. Think of them as data structures, and think of every application as a function that takes the schema as an argument.

# no
coerced_input = schema.coerce_json(json_input)
coerced_input = schema.coerce_web(form_input)
coerced_input = schema.coerce_cli(cli_args)
result = schema.validate(coerced_input)
docs = schema.to_documentation
value = schema.random_value
json = schema.to_json_schema

# yes (pseudocode)
coerced_input = coerce_json(input, schema)
coerced_input = coerce_web(input, schema)
coerced_input = coerce_cli(input, schema)
result = validate(coerced_input, schema)
docs = generate_documentation_for(schema)
value = generate_random_value_for(schema)
json = json_schema_for(schema)

This solves a lot of design problems I have previously created for myself. I’m kind of kicking myself for not realising this earlier. There are plenty of examples of people doing this already (e.g. JSON Schema). I was blinded by focusing too much on the validation use case.

Next time, I’m not going to implement any kind of validation behaviour on schema classes (or any other kind of feature). The single responsibility of a schema will be to represent a type, as a simple tree structure. There will probably be some behaviour for traversing the tree using a common interface, but that’s about all it needs.

Coercion, validation, and all the other features can then be implemented separately, as independent functions. Some of those functions will be complicated — coercion, for example, requires walking two tree structures in synchrony (the schema and the input), triple dispatch (applying behaviour based on the kind of coercion, the kind of schema, and the kind of input), and building a complex return value (see Working With Failures above) — but I think decomposing the functionality this way will be a huge simplification overall.

I’m toying with a proof-of-concept implementation, and it’s looking good so far. I want to keep an eye on performance — not too many allocations, and doing coercion and validation with a single traversal of the input/schema tree (instead of two traversals). The other tricky part is making everything extensible, so that user-defined types can opt-in to functionality in a piecemeal way. Defining a new type from scratch should be easy, and shouldn’t require implementing functionality that you don’t intend to use — that is, if you don’t intend to use your new type for validation, you shouldn’t be forced to write any code related to validation.


What I’m looking for from a schema library is:

  • a suite of built-in types that represent the most common stuff like strings, numbers, arrays and hashes

  • the ability to add custom types to the suite easily, and have the custom types be 100% as powerful as the built-in ones

  • when implementing custom types, to be able to only implement the functionality I intend to use, ignoring the features I don’t care about

  • a suite of built-in coercion behaviours, and the ability to add custom implementations that are just as powerful

  • the ability to reuse a single schema across multiple different contexts, with different kinds of coercion behaviour

  • context-independent constraint checking, that goes beyond just looking at a value’s class

  • rich failure details, that can be integrated into various different validation failure workflows (e.g. ActiveModel::Errors in Rails) without losing fidelity

  • an extensible way to implement new applications for schemas, such that one gem could provide this new feature, a completely separate gem could provide some custom types, and a third gem could provide the integration between the new feature and the custom types

  • the ability to treat schemas as simple data structures, that can be converted to, and imported from, other formats (e.g. JSON Schema)

  • good performance, in terms of memory allocations and efficient traversal of the input/schema tree

I’m not aware of any schema library that meets all these criteria, in any programming language. Even Clojure’s schema and spec libraries, considered by some to be best in class, do not tick all the boxes. I know, because I’ve tried to steal parts of their design to improve my own, and discovered that they have the exact same limitations I was running into.

I’m in the very early stages of progress towards this ideal schema library. I’ve been tinkering with a proof of concept design in Ruby recently. There are still some unknowns to iron out, but it’s looking promising at the moment. This is no announcement or guarantee that I will finish and release a library, but it’s a topic that I keep coming back to year after year, so maybz it will happen some day.