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Clojure(Script) library for declarative data description and validation

A Clojure(Script) library for declarative data description and validation.

Leiningen dependency (Clojars): [prismatic/schema "0.4.3"]. Latest codox API docs.

This is an alpha release. The API and organizational structure are subject to change. Comments and contributions are much appreciated.

One of the difficulties with bringing Clojure into a team is the overhead of understanding the kind of data (e.g., list of strings, nested map from long to string to double) that a function expects and returns. While a full-blown type system is one solution to this problem, we present a lighter weight solution: schemas. (For more details on why we built Schema, check out this post on the Prismatic blog.)

As of version 0.2.0, Schema also supports schema-driven data transformations, with coercion being the main application fleshed out thus far. See this post for a detailed overview, or check out the Coercion section below for an example.

Meet Schema

A Schema is a Clojure(Script) data structure describing a data shape, which can be used to document and validate functions and data.

(ns schema-examples
  (:require [schema.core :as s 
             :include-macros true ;; cljs only

(def Data
  "A schema for a nested data type"
  {:a {:b s/Str
       :c s/Int}
   :d [{:e s/Keyword
        :f [s/Num]}]})

  {:a {:b "abc"
       :c 123}
   :d [{:e :bc
        :f [12.2 13 100]}
       {:e :bc
        :f [-1]}]})
;; Success!

  {:a {:b 123
       :c "ABC"}})
;; Exception -- Value does not match schema:
;;  {:a {:b (not (instance? java.lang.String 123)),
;;       :c (not (integer? "ABC"))},
;;   :d missing-required-key}

The simplest schemas describe leaf values like Keywords, Numbers, and instances of Classes (on the JVM) and prototypes (in ClojureScript):

;; s/Any, s/Bool, s/Num, s/Keyword, s/Symbol, s/Int, and s/Str are cross-platform schemas.

(s/validate s/Num 42)
;; 42
(s/validate s/Num "42")
;; RuntimeException: Value does not match schema: (not (instance java.lang.Number "42"))

(s/validate s/Keyword :whoa)
;; :whoa
(s/validate s/Keyword 123)
;; RuntimeException: Value does not match schema: (not (keyword? 123))

;; On the JVM, you can use classes for instance? checks
(s/validate java.lang.String "schema")

;; On JS, you can use prototype functions
(s/validate Element (js/document.getElementById "some-div-id"))

From these simple building blocks, we can build up more complex schemas that look like the data they describe. Taking the examples above:

;; list of strings
(s/validate [s/Str] ["a" "b" "c"])

;; nested map from long to String to double
(s/validate {long {String double}} {1 {"2" 3.0 "4" 5.0}})

Since schemas are just data, you can also def them and reuse and compose them as you would expect:

(def StringList [s/Str])
(def StringScores {String double})
(def StringScoreMap {long StringScores})

What about when things go bad? Schema's s/check and s/validate provide meaningful errors that look like the bad parts of your data, and are (hopefully) easy to understand.

(s/validate StringList ["a" :b "c"])
;; RuntimeException: Value does not match schema:
;;  [nil (not (instance? java.lang.String :b)) nil]

(s/validate StringScoreMap {1 {"2" 3.0 "3" [5.0]} 4.0 {}})
;; RuntimeException: Value does not match schema:
;;  {1 {"3" (not (instance? java.lang.Double [5.0]))},
;;   (not (instance? java.lang.Long 4.0)) invalid-key}

See the "More Examples" section below for more examples and explanation.

Beyond type hints

If you've done much Clojure, you've probably seen code like this:

(defrecord StampedNames
  [^Long date
   names ;; a list of Strings

(defn ^StampedNames stamped-names
  "names is a list of Strings"
  (StampedNames. (str (System/currentTimeMillis)) names))

Clojure's type hints make great documentation, but they fall short for complex types, often leading to ad-hoc descriptions of data in comments and doc-strings. This is better than nothing, but these ad hoc descriptions are often imprecise, hard to read, and prone to bit-rot.

Schema provides macros defrecord, defn, and fn that help bridge this gap, by allowing arbitrary schemas as type hints on fields, arguments, and return values. This is a graceful extension of Clojure's type hinting system, because every type hint is a valid Schema, and Schemas that represent valid type hints are automatically passed through to Clojure.

(s/defrecord StampedNames
  [date :- Long
   names :- [s/Str]])

(s/defn stamped-names :- StampedNames
  [names :- [s/Str]]
  (StampedNames. (str (System/currentTimeMillis)) names))

Here, x :- y means that x must satisfy schema y, replacing and extending the more familiar metadata hints such as ^y x.

As you can see, these type hints are precise, easy to read, and shorter than the comments they replace. Moreover, they produce Schemas that are data, and can be inspected, manipulated, and used for validation on-demand (did you spot the bug in stamped-names?)

;; You can inspect the schemas of the record and function

(s/explain StampedNames)
==> (record user.StampedNames {:date java.lang.Long, :names [java.lang.String]})

(s/explain (s/fn-schema stamped-names))
==> (=> (record user.StampedNames {:date java.lang.Long, :names [java.lang.String]})

;; And you can turn on validation to catch bugs in your functions and schemas
  (stamped-names ["bob"]))
==> RuntimeException: Output of stamped-names does not match schema:
     {:date (not (instance? java.lang.Long "1378267311501"))}

;; Oops, I guess we should remove that `str` from `stamped-names`.

Schemas in practice

We've already seen how we can build up Schemas via composition, attach them to functions, and use them to validate data. What does this look like in practice?

First, we ensure that all data types that will be shared across namespaces (or heavily used within namespaces) have Schemas, either by defing them or using s/defrecord. This allows us to compactly and precisely refer to this data type in more complex data types, or when documenting function arguments and return values.

This documentation is probably the most important benefit of Schema, which is why we've optimized Schemas for easy readability and reuse -- and sometimes, this is all you need. Schemas are purely descriptive, not prescriptive, so unlike a type system they should never get in your way, or constrain the types of functions you can write.

After documentation, the next-most important benefit is validation. Thus far, we've found three key use cases for validation. First, you can globally turn on function validation within a given test namespace by adding this line:

(use-fixtures :once schema.test/validate-schemas)

As long as your tests cover all call boundaries, this means you should catch any 'type-like' bugs in your code at test time.

Second, we manually call s/validate to check any data we read and write over the wire or to persistent storage, ensuring that we catch and debug bad data before it strays too far from its source. If you need maximal performance, you can avoid the schema processing overhead on each call by create a validator once with s/validator and calling the resulting function on each datum you want to validate (s/defn does this under the hood).

Alternatively, you can force validation for key functions (without the need for with-fn-validation):

(s/defn ^:always-validate stamped-names ...)

Thus, each time you invoke stamped-names, Schema will perform validation.

To reduce generated code size, you can use the *assert* flag and set-compile-fn-validation! functions to control when validation code is generated (details).

Finally, we use validation with coercion for API inputs and outputs. See the coercion section below for details.

More examples

The source code in schema/core.cljx provides a wealth of extra tools for defining schemas, which are described in docstrings. The file schema/core_test.cljx demonstrates a variety of sample schemas and many examples of passing & failing clojure data. We'll just touch on a few more examples here, and refer the reader to the code for more details and examples (for now).

Map schema details

In addition to uniform maps (like String to double), map schemas can also capture maps with specific key requirements:

(def FooBar {(s/required-key :foo) s/Str (s/required-key :bar) s/Keyword})

(s/validate FooBar {:foo "f" :bar :b})
;; {:foo "f" :bar :b}

(s/validate FooBar {:foo :f})
;; RuntimeException: Value does not match schema:
;;  {:foo (not (instance? java.lang.String :f)),
;;   :bar missing-required-key}

For the special case of keywords, you can omit the required-key, like {:foo s/Str :bar s/Keyword}. You can also provide specific optional keys, and combine specific keys with generic schemas for the remaining key-value mappings:

(def FancyMap
  "If foo is present, it must map to a Keyword.  Any number of additional
   String-String mappings are allowed as well."
  {(s/optional-key :foo) s/Keyword
    s/Str s/Str})

(s/validate FancyMap {"a" "b"})

(s/validate FancyMap {:foo :f "c" "d" "e" "f"})

Sequence schema details

Similarly, you can also write sequence schemas that expect particular values in specific positions:

(def FancySeq
  "A sequence that starts with a String, followed by an optional Keyword,
   followed by any number of Numbers."
  [(s/one s/Str "s")
   (s/optional s/Keyword "k")

(s/validate FancySeq ["test"])
(s/validate FancySeq ["test" :k])
(s/validate FancySeq ["test" :k 1 2 3])
;; all ok

(s/validate FancySeq [1 :k 2 3 "4"])
;; RuntimeException: Value does not match schema:
;;  [(named (not (instance? java.lang.String 1)) "s")
;;   nil nil nil
;;   (not (instance? java.lang.Number "4"))]

Other schema types

schema.core provides many more utilities for building schemas, including maybe, eq, enum, either, both, pred, and more. Here are a few of our favorites:

;; maybe
(s/validate (s/maybe s/Keyword) :a)
(s/validate (s/maybe s/Keyword) nil)

;; enum
(s/validate (s/enum :a :b :c) :a)

;; both and pred
(def OddLong (s/both long (s/pred odd? 'odd?)))
(s/validate OddLong 1)
;; 1
(s/validate OddLong 2)
;; RuntimeException: Value does not match schema: (not (odd? 2))
(s/validate OddLong (int 3))
;; RuntimeException: Value does not match schema: (not (instance? java.lang.Long 3))

;; both & pred can be used for schemas of seqs with at least one element:
(def SetOfAtLeastOneOddLong (s/both #{OddLong} (s/pred seq 'seq)))
(s/validate SetOfAtLeastOneOddLong #{3})
;; => #{3}
(s/validate SetOfAtLeastOneOddLong #{3 5 7})
;; => #{7 3 5}
(s/validate SetOfAtLeastOneOddLong #{})
;; RuntimeException: Value does not match schema: (not (seq #{}))
(s/validate SetOfAtLeastOneOddLong #{2})
;; RuntimeException: Value does not match schema: #{(not (odd? 2))}

You can also define schemas for recursive data types, or create your own custom schemas types.

Transformations and Coercion

As of version 0.2.0, Schema also supports schema-driven data transformations, with coercion being the main application fleshed out thus far. Coercion is like validation, except a schema-dependent transformation can be applied to the input data before validation.

An example application of coercion is converting parsed JSON (e.g., from an HTTP post request) to a domain object with a richer set of types (e.g., Keywords).

(def CommentRequest
  {(s/optional-key :parent-comment-id) long
   :text String
   :share-services [(s/enum :twitter :facebook :google)]})

(def parse-comment-request
  (coerce/coercer CommentRequest coerce/json-coercion-matcher))

(= (parse-comment-request
    {:parent-comment-id (int 2128123123)
     :text "This is awesome!"
     :share-services ["twitter" "facebook"]})
   {:parent-comment-id 2128123123
    :text "This is awesome!"
    :share-services [:twitter :facebook]})
;; ==> true

Here, json-coercion-matcher provides some useful defaults for coercing from JSON, such as:

  • Numbers should be coerced to the expected type, if this can be done without losing precision.
  • When a Keyword is expected, a String can be coerced to the correct type by calling keyword

There's nothing special about json-coercion-matcher though; it's just as easy to make your own schema-specific transformations to do even more.

For more details, see this blog post.

For the Future

Longer-term, we have lots more in store for Schema. Just a few of the crazy ideas we have brewing are:

  • Automatically generate API client libraries based on API schemas
  • Automatically generate test data from schemas
  • Compile to core.typed annotations for more typey goodness, if that's your thing


Please feel free to join the Plumbing mailing list to ask questions or discuss how you're using Schema.

For announcements of new releases, you can also follow on @PrismaticEng on Twitter.

We welcome contributions in the form of bug reports and pull requests; please see in the repo root for guidelines.

Supported Clojure versions

Schema is currently supported on 1.5.1 and 1.6.x and the latest version of ClojureScript.


Copyright (C) 2013 Prismatic and Contributors. Distributed under the Eclipse Public License, the same as Clojure.

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