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Clojure - clojure.spec - Rationale and Overview

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Tags: clojure clojure.spec generative-testing spec clojure.org
Clipped on: 2016-11-12

clojure.spec - Rationale and Overview

Table of Contents


Docs are not enough

Clojure is a dynamic language. Among other things this means that type annotations are not required for code to run. While Clojure has some support for type hints, they are not an enforcement mechanism, nor comprehensive, and are limited to communicating information to the compiler to aid in efficient code generation. Clojure gets runtime checking of a richer set of types by the JVM itself.

However it has always been a guiding principle of Clojure, widely valued and practiced by the community, to simply represent information as data. Thus important properties of Clojure systems are represented and conveyed by the shape and other predicative properties of the data, not captured or checked anywhere since the runtime types are indistinguishable heterogeneous maps and vectors.

Documentation strings can be used to communicate with human consumers, but they can’t be leveraged by programs or tests, i.e. they have minimal power. Users have turned to various libraries such as Schema and Herbert to get more powerful specifications.

Map specs should be of keysets only

Most systems for specifying structures conflate the specification of the key set (e.g. of keys in a map, fields in an object) with the specification of the values designated by those keys. I.e. in such approaches the schema for a map might say :a-key’s type is x-type and :b-key’s type is y-type. This is a major source of rigidity and redundancy.

In Clojure we gain power by dynamically composing, merging and building up maps. We routinely deal with optional and partial data, data produced by unreliable external sources, dynamic queries etc. These maps represent various sets, subsets, intersections and unions of the same keys, and in general ought to have the same semantic for the same key wherever it is used. Defining specifications of every subset/union/intersection, and then redundantly stating the semantic of each key is both an antipattern and unworkable in the most dynamic cases.

Manual parsing and error reporting is not good enough

Many users, especially beginners, are frustrated and challenged by the error messages produced by hand-written parsing and destructuring code, especially in macros where there are two contexts of execution (the macro runs at compile time and its expansion at runtime, either of which could fail due to user error). This has led to a call for 'macro grammars', but in fact macros are just functions of data→data and any solution for data validation and destructuring should work as well for them as for any other functions. I.e. macros are an instance of the problems above.

Generative testing and robustness

Finally, in all languages, dynamic or not, tests are essential to quality. Too many critical properties are not captured by common type systems. But manual testing has a very low effectiveness/effort ratio. Property-based, generative testing, as implemented for Clojure in test.check, has proved to be far more powerful than manually written tests.

Yet property based testing requires the definition of properties, which require extra effort and expertise to produce, and which, at the function-level, have substantial overlap with function specifications. Many interesting properties at the function level would already be captured by structural+predicative specs. Ideally, specs should integrate with generative testing and provide certain categories of generative tests 'for free'.

A standard approach is needed

In short, Clojure has no standard, expressive, powerful and integrated system for specification and testing.

clojure.spec aims to provide it.



Species - appearance, form, sort, kind, equivalent to spec (ere) to look, regard
               + -iēs abstract noun suffix

Specify - species + -ficus -fic (make)

A specification is about how something 'looks', but is, most importantly, something that is looked at. Specs should be readable, composed of 'words' (predicate functions) programmers are already using, and integrated in documentation.

Unify specification in its various contexts

Specs for data structures, attribute values and functions should all be the same and live in a globally-namespaced directory.

Maximize leverage from specification effort

Writing a spec should enable automatic:

  • Validation

  • Error reporting

  • Destructuring

  • Instrumentation

  • Test-data generation

  • Generative test generation

Minimize intrusion

Don’t require that people e.g. define their functions differently. Minor modifications to doc and macroexpand will allow independently written specs to adorn fn/macro behavior without redefinition.

Decomplect maps/keys/values

Keep map (keyset) specs separate from attribute (key→value) specs. Encourage and support attribute-granularity specs of namespaced keyword to value-spec. Combining keys into sets (to specify maps) becomes orthogonal, and checking becomes possible in the fully-dynamic case, i.e. even when no map spec is present, attributes (key-values) can be checked.

Enable and start a dialog about semantic change and compatibility

Programmers suffer greatly when they redefine things while keeping the names the same. Yet some changes are compatible and some are breaking, and most tools can’t distinguish. Use constructs like set membership and regular expressions for which compatibility can be determined, and provide tools for compatibility checking (while leaving general predicate equality out of scope).


Mistakes will be made

We don’t (and couldn’t) live in a world where we can’t make mistakes. Instead, we periodically check that we haven’t. Amazon doesn’t send you your TV via a UPS<Trucks<Boxes<TV>>>. So occasionally you might get a microwave, but the supply chain isn’t burdened with correctness proof. Instead we check at the edges and run tests.

expressivity > proof

There is no reason to limit our specifications to what we can prove, yet that is primarily what type systems do. There is so much more we want to communicate and verify about our systems. This goes beyond structural/representational types and tagging to predicates that e.g. narrow domains or detail relationships between inputs or between inputs and output. Additionally, the properties we care most about are often those of the runtime values, not some static notion. Thus spec is not a type system.

Names are important

All programs use names, even when the type systems don’t, and they capture important semantics. Int x Int x Int just isn’t good enough (is it length/width/height or height/width/depth?). So spec will not have unlabeled sequence components or untagged union bindings. The utility of this becomes evident when spec needs to talk to users about specs, e.g. in error reporting, and vice versa, e.g. when users want to override generators in specs. When all branches are named, you can talk about parts of specs using paths.

Global (namespaced) names are more important

Clojure supports namespaced keywords and symbols. Note here we are just talking about namespace-qualified names, not Clojure namespace objects. These are tragically underutilized and convey important benefits because they can always co-reside in dictionaries/dbs/maps/sets without conflict. spec will allow (only) namespace-qualified keywords and symbols to name specs. People using namespaced keys for their informational maps (a practice we’d like to see grow) can register the specs for those attributes directly under those names. This categorically changes the self-description of maps, particularly in dynamic contexts, and encourages composition and consistency.

Don’t further add to/overload the (reified) namespaces of Clojure

Nothing will be attached to vars, metadata etc. All functions have namespaced names which can serve as keys to their related data (e.g. spec) that is stored elsewhere.

Code is data (not vice versa)

In Lisps (and thus Clojure), code is data. But data is not code until you define a language around it. Many DSLs in this space drive at a data representation for schemas. But predicative specs have an open and large vocabulary, and most of the useful predicates already exist and are well known as functions in the core and other namespaces, or can be written as simple expressions. Having to 'datafy', possibly renaming, all of these predicates adds little value, and has a definite cost in understanding precise semantics. spec instead leverages the fact that the original predicates and expressions are data in the first place and captures that data for use in communicating with the users in documentation and error reporting. Yes, this means that more of the surface area of clojure.spec will be macros, but specs are overwhelmingly written by people and, when composed, manually so.

Sets (maps) are about membership, that’s it

As per above, maps defining the details of the values at their keys is a fundamental complecting of concerns that will not be supported. Map specs detail required/optional keys (i.e. set membership things) and keyword/attr/value semantics are independent. Map checking is two-phase, required key presence then key/value conformance. The latter can be done even when the (namespace-qualified) keys present at runtime are not in the map spec. This is vital for composition and dynamicity.

Informational vs implementational

Invariably, people will try to use a specification system to detail implementation decisions, but they do so to their detriment. The best and most useful specs (and interfaces) are related to purely information aspects. Only information specs work over wires and across systems. We will always prioritize, and where there is a conflict, prefer, the information approach.


There are very few bottom notions in this space and we will endeavor to stick to them. There are few distinct structural notions - a handful of atomic types, sequential things, sets and maps. Unsurprisingly, these are the Clojure data types and fundamental ops will be provided only for these. Similarly there are mathematical tools for talking about these - set logic for maps and regular expressions for sequences - that have valuable properties. We will prefer these over ad hoc solutions.

Build on test.check but don’t require knowledge of it

The generative testing underpinning of spec will leverage test.check and not reinvent it. But spec users should not need to know anything about test.check until and unless they want to write their own generators or supplement spec's generated tests with further property-based tests of their own. There should be no production runtime dependency on test.check.



Predicative specs

The basic idea is that specs are nothing more than a logical composition of predicates. At the bottom we are talking about the simple boolean predicates you are used to like int? or symbol?, or expressions you build yourself like #(< 42 % 66). spec adds logical ops like spec/and and spec/or which combine specs in a logical way and offer deep reporting, generation and conform support and, in the case of spec/or, tagged returns.


Specs for map keysets provide for the specification of required and optional key sets. A spec for a map is produced by calling keys with :req and :opt keyword arguments mapping to vectors of key names.

:req keys support the logical operators and and or.

(spec/keys :req [::x ::y (or ::secret (and ::user ::pwd))] :opt [::z])

One of the most visible differences between spec and other systems is that there is no place in that map spec for specifying the values e.g. ::x can take. It is the (enforced) opinion of spec that the specification of values associated with a namespaced keyword, like :my.ns/k, should be registered under that keyword itself, and applied in any map in which that keyword appears. There are a number of advantages to this:

  • It ensures consistency for all uses of that keyword in an application where all uses should share a semantic

  • It similarly ensures consistency between a library and its consumers

  • It reduces redundancy, since otherwise many map specs would need to make matching declarations about k

  • Namespaced keyword specs can be checked even when no map spec declares those keys

This last point is vital when dynamically building up, composing, or generating maps. Creating a spec for every map subset/union/intersection is unworkable. It also facilitates fail-fast detection of bad data - when it is introduced vs when it is consumed.

Of course, many existing map-based interfaces take non-namespaced keys. To support connecting them to properly namespaced and reusable specs, keys supports -un variants of :req and :opt

(spec/keys :req-un [:my.ns/a :my.ns/b])

This specs a map that requires the unqualified keys :a and :b but validates and generates them using specs (when defined) named :my.ns/a and :my.ns/b respectively. Note that this cannot convey the same power to unqualified keywords as have namespaced keywords - the resulting maps are not self-describing.


Specs for sequences/vectors use a set of standard regular expression operators, with the standard semantics of regular expressions:

  • cat - a concatenation of predicates/patterns

  • alt - a choice of one among a set of predicates/patterns

  • * - zero or more occurrences of a predicate/pattern

  • + - one or more

  • ? - one or none

  • & - takes a regex op and further constrains it with one or more predicates

These nest arbitrarily to form complex expressions.

Note that cat and alt require all of their components be labeled, and the return value of each is a map with the keys corresponding to the matched components. In this way spec regexes act as destructuring and parsing tools.

user=> (require '[clojure.spec :as s])
(s/def ::even? (s/and integer? even?))
(s/def ::odd? (s/and integer? odd?))
(s/def ::a integer?)
(s/def ::b integer?)
(s/def ::c integer?)
(def s (s/cat :forty-two #{42}
              :odds (s/+ ::odd?)
              :m (s/keys :req-un [::a ::b ::c])
              :oes (s/* (s/cat :o ::odd? :e ::even?))
              :ex (s/alt :odd ::odd? :even ::even?)))
user=> (s/conform s [42 11 13 15 {:a 1 :b 2 :c 3} 1 2 3 42 43 44 11])
{:forty-two 42,
 :odds [11 13 15],
 :m {:a 1, :b 2, :c 3},
 :oes [{:o 1, :e 2} {:o 3, :e 42} {:o 43, :e 44}],
 :ex {:odd 11}}


As you can see above, the basic operation for using specs is conform, which takes a spec and a value and returns the conformed value or :clojure.spec/invalid if the value did not conform. When the value does not conform you can call explain or explain-data to find out why it didn’t.

Defining specs

The primary operations for defining specs are s/def, s/and, s/or, s/keys and the regex ops. There is a spec function that can take a predicate function or expression, a set, or a regex op, and can also take an optional generator which would override the generator implied by the predicate(s).

Note however, that def, and, or, keys spec fns and the regex ops can all take and use predicate functions and sets directly - and do not need them to be wrapped by spec. spec should only be needed when you want to override a generator or to specify that a nested regex starts anew, vs being included in the same pattern.

Data spec registration

In order for a spec to be reusable by name, it has to be registered via def. def takes a namespace-qualified keyword/symbol and a spec/predicate expression. By convention, specs for data should be registered under keywords and attribute values should be registered under their attribute name keyword. Once registered, the name can be used anywhere a spec/predicate is called for in any of the spec operations.

Function spec registration

A function can be fully specified via three specs - one for the args, one for the return, and one for the operation of the function relating the args to the return.

The args spec for a fn is always going to be a regex that specs the arguments as if they were a list, i.e. the list one would pass to apply the function. In this way, a single spec can handle functions with multiple arities.

The return spec is an arbitrary spec of a single value.

The (optional) fn spec is a further specification of the relationship between the arguments and the return, i.e. the function of the function. It will be passed (e.g. during testing) a map containing {:args conformed-args :ret conformed-ret} and will generally contain predicates that relate those values - e.g. it could ensure that all keys of an input map are present in the returned map.

You can fully specify all three specs of a function in a single call to fdef, and recall the specs via fn-specs.

Using specs


Functions specs defined via fdef will appear when you call doc on the fn name. You can call describe on specs to get descriptions as forms.


You can use conform directly in your implementations to get its destructuring/parsing/error-checking. conform can be used e.g. in macro implementations and at I/O boundaries.

During development

You can selectively instrument functions and namespaces with instrument, which swaps out the fn var with a wrapped version of the fn that tests the :args spec. unstrument returns a fn to its original version. You can generate data for interactive testing with gen/sample.

For testing

You can run a suite of spec-generative tests on an entire ns with check. You can get a test.check compatible generator for a spec by calling gen. There are built-in associations between many of the clojure.core data predicates and corresponding generators, and the composite ops of spec know how to build generators atop those. If you call gen on a spec and it is unable to construct a generator for some subtree, it will throw an exception that describes where. You can pass generator-returning fns to spec in order to supply generators for things spec does not know about, and you can pass an override map to gen in order to supply alternative generators for one or more subpaths of a spec.

At runtime

In addition to the destructuring use cases above, you can make calls to conform or valid? anywhere you want runtime checking, and can make lighter-weight internal-only specs for tests you intend to run in production.

Please see the spec Guide and API docs for more examples and usage information.



Many parts of the spec API call for 'predicates' or 'preds'. These arguments can be satisfied by:

  • predicate (boolean) fns

  • sets

  • registered names of specs

  • specs (the return values of spec, and, or, keys)

  • regex ops (the return values of cat, alt, *, +, ?, &)

Note that if you want to nest an independent regex predicate within a regex you will have to wrap it in a call to spec, else it will be considered a nested pattern.


The return values of spec, and, or and keys.

regex ops

The return values of cat, alt, *, +, ?, &. When nested these form a single expression.


conform is the basic operation for consuming specs, and does both validation and conforming/destructuring. Note that conforming is 'deep' and flows through all of the spec and regex operations, map specs etc. Since nil and false are legitimate conformed values, conform returns the distinguished :clojure.spec/invalid when a value cannot be made to conform. valid? can be used instead as a fully-boolean predicate.


When a value fails to conform to a spec you can call explain or explain-data with the same spec+value to find out why. These explanations are not produced during conform because they might perform additional work and there is no reason to incur that cost for non-failing inputs or when no report is desired. An important component of explanations is the path. explain extends the path as it navigates through e.g. nested maps or regex patterns, so you get better information than just the entire or leaf value. explain-data will return a map of paths to problems.


Due to the fact that all branching points in specs are labeled, i.e. map keys, choices in or and alt, and (possibly elided) elements of cat, every subexpression in a spec can be referred to via a path (vector of keys) naming the parts. These paths are used in explain, gen overrides and various error reporting.

Prior Art

Almost nothing about spec is novel. See all the libraries mentioned above, RDF, as well as all the work done on various contract systems, such as Racket’s contracts.

I hope you find spec useful and powerful.

Rich Hickey

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Published 2016-11-04
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