spec-provider

This is a library that will produce a best-guess Clojure spec based on multiple examples of in-memory data. The inferred spec is not meant to be used as is and without human intervention, it is rather a starting point that can (and should) be refined.

The idea is analogous to F# type providers -- specifically the JSON type provider, but the input in the case of spec-provider is any in-memory Clojure data structure.

Since Clojure spec is still in alpha, this library should also be considered to be in alpha -- so, highly experimental, very likely to change, possibly flawed.

This library works in both Clojure and ClojureScript.

Maturity level: mature and useful. Has not reached full potential as some ideas are still unexplored.

Usage

To use this library, add this dependency to your Leiningen project.clj file:

[spec-provider "0.4.14"]

Version history

Use cases

The are two main use cases for spec-provider:

1. You have a lot of examples of raw data (maybe in a JSONB column of a PostreSQL table) and you'd like to:

   • See a summary of what shape the data is. You can use spec-provider as a way to explore new datasets.

   • You already know what shape your data is, and you just want some help getting started writing a spec for it because your data is deeply nested, has a lot of corner cases, you're lazy etc.

   • You think you know what shape your data is, but because it's neither typed checked nor contract checked, some exceptions have sneaked into it. Instead of eyeballing 100,000 maps, you run spec-provider on them and to your surprise you find that one of the fields is (s/or :integer integer? :string string?) instead of just string as you expected. You can use spec-provider as a data debugging tool.

2. You have an un-spec'ed function and you also have a good way to exercise it (via unit tests, actual usage etc). You can instrument the function with spec-provider, run it a few times with actual data, and then ask spec-provider for the function spec based on the data that flowed through the function.

Inferring the spec of raw data

To infer a spec of a bunch of data just pass the data to the infer-specs function:

> (require '[spec-provider.provider :as sp])

> (def inferred-specs
    (sp/infer-specs
     [{:a 8  :b "foo" :c :k}
      {:a 10 :b "bar" :c "k"}
      {:a 1  :b "baz" :c "k"}]
     :toy/small-map))

> inferred-specs

((clojure.spec.alpha/def :toy/c (clojure.spec/or :keyword keyword? :string string?))
 (clojure.spec.alpha/def :toy/b string?)
 (clojure.spec.alpha/def :toy/a integer?)
 (clojure.spec.alpha/def :toy/small-map (clojure.spec/keys :req-un [:toy/a :toy/b :toy/c])))

The sequence of specs that you get out of infer-spec is technically correct, but not very useful for pasting into your code. Luckily, you can do:

> (sp/pprint-specs inferred-specs 'toy 's)

(s/def ::c (s/or :keyword keyword? :string string?))
(s/def ::b string?)
(s/def ::a integer?)
(s/def ::small-map (s/keys :req-un [::a ::b ::c]))

Passing 'toy to pprint-specs signals that we intend to paste this code into the toy namespace, so spec names are printed using the :: syntax.

Passing 's signals that we are going to require clojure.spec as s, so the calls to clojure.spec/def become s/def etc.

Nested data structures

spec-provider will walk nested data structures in your sample data and attempt to infer specs for everything.

Let's use clojure.spec to generate a larger sample of data with nested structures.

(s/def ::id (s/or :numeric pos-int? :string string?))
(s/def ::codes (s/coll-of keyword? :max-gen 5))
(s/def ::first-name string?)
(s/def ::surname string?)
(s/def ::k (nilable keyword?))
(s/def ::age (s/with-gen
               (s/and integer? pos? #(<= % 130))
               #(gen/int 130)))
(s/def :person/role #{:programmer :designer})
(s/def ::phone-number string?)

(s/def ::street string?)
(s/def ::city string?)
(s/def ::country string?)
(s/def ::street-number pos-int?)

(s/def ::address
  (s/keys :req-un [::street ::city ::country]
          :opt-un [::street-number]))

(s/def ::person
  (s/keys :req-un [::id ::first-name ::surname ::k ::age ::address]
          :opt-un [::phone-number ::codes]
          :req    [:person/role]))

This spec can be used to generate a reasonably large random sample of persons:

(def persons (gen/sample (s/gen ::person) 100))

Which generates structures like:

{:id "d7FMcH52",
 :first-name "6",
 :surname "haFsA",
 :k :a-*?DZ/a,
 :age 5,
 :person/role :designer,
 :address {:street "Yrx963uDy", :city "b", :country "51w5NQ6", :street-number 53},
 :codes
 [:*.?m_o-9_j?b.N?_!a+IgUE._coE.S4l4_8_.MhN!5_!x.axztfh.x-/?*
  :*-DA?+zU-.T0u5R.evD8._r_D!*K0Q.WY-F4--.O*/**O+_Qg+
  :Bh8-A?t-f]}

Now watch what happens when we infer the spec of persons:

> (sp/pprint-specs
   (sp/infer-specs persons :person/person)
   'person 's)

(s/def ::codes (s/coll-of keyword?))
(s/def ::phone-number string?)
(s/def ::street-number integer?)
(s/def ::country string?)
(s/def ::city string?)
(s/def ::street string?)
(s/def
 ::address
 (s/keys :req-un [::street ::city ::country] :opt-un [::street-number]))
(s/def ::age integer?)
(s/def ::k (s/nilable keyword?))
(s/def ::surname string?)
(s/def ::first-name string?)
(s/def ::id (s/or :string string? :integer integer?))
(s/def ::role #{:programmer :designer})
(s/def
 ::person
 (s/keys
  :req [::role]
  :req-un [::id ::first-name ::surname ::k ::age ::address]
  :opt-un [::phone-number ::codes]))

Which is very close to the original spec. We are going to break down this result to bring attention to specific features in the following sections.

Nilable

If the sample data contain any nil values, this is detected and reflected in the inferred spec:

(s/def ::k (s/nilable keyword?))

Optional detection

Things like ::street-number, ::codes and ::phone-number did not appear consistently in the sampled data, so they are correctly identified as optional in the inferred spec.

(s/def
 ::address
 (s/keys :req-un [::street ::city ::country] :opt-un [::street-number]))

Qualified vs unqualified keys

Most of the keys in the sample data are not qualified, and they are detected as such in the inferred spec. The :person/role key is identified as fully qualified.

(s/def
 ::person
 (s/keys
  :req [::role]
  :req-un [::id ::first-name ::surname ::k ::age ::address]
  :opt-un [::phone-number ::codes]))

Note that the s/def for role is pretty printed as ::role because when calling pprint-specs we indicated that we are going to paste this into the person namespace.

> (sp/pprint-specs
   (sp/infer-specs persons :person/person)
   'person 's)

...

(s/def ::role #{:programmer :designer})

Enumerations

You may have also noticed that role has been identified as an enumeration of :programmer and :designer. To see how it's decided whether a field is an enumeration or not, we have to look under the hood. Let's generate a small sample of roles:

> (gen/sample (s/gen ::role) 5)

(:designer :designer :designer :designer :programmer)

spec-provider collects statistics about all the sample data before deciding on the spec:

> (require '[spec-provider.stats :as stats])
> (stats/collect-stats (gen/sample (s/gen ::role) 5) {})

#:spec-provider.stats{:distinct-values #{:programmer :designer},
                      :sample-count 5,
                      :pred-map {#function[clojure.core/keyword?] #:spec-provider.stats{:sample-count 5}}}

The stats include a set of distinct values observed (up to a certain limit), the sample count for each field, and counts on each of the predicates that the field matches -- in this case just keyword?. Based on these statistics, the spec is inferred and a decision is made on whether the value is an enumeration or not.

If the following statement is true, then the value is considered an enumeration:

(>= 0.1
    (/ (count distinct-values)
       sample-count))

In other words, if the number of distinct values found is less that 10% of the total recorded values, then the value is an enumeration. This threshold is configurable.

Looking at the actual numbers can make this logic easier to understand. For the small sample above:

> (sp/infer-specs (gen/sample (s/gen ::role) 5) ::role)

((clojure.spec/def :spec-provider.person-spec/role keyword?))

We have 2 distinct values in a sample of 5, which is 40% of the values being distinct. Imagine this percentage in a larger sample, say distinct 400 values in a sample of size 2000. That doesn't sound likely to be an enumeration, so it's interpreted as a normal value.

If you increase the sample:

> (sp/infer-specs (gen/sample (s/gen ::role) 100) ::role)

((clojure.spec/def :spec-provider.person-spec/role #{:programmer :designer}))

We have 2 distinct values in a sample of 100, which is 2%, which means that the same values appear again and again in the sample, so it must be an enumeration.

Merging

clojure-spec makes the same assumption as clojure.spec that keys that have same name also have the same data shape as their value, even when they appear in different maps. This means that the specs from different maps are merged by key.

To demonstrate this we need to "spike" the generated persons with an id field that's inconsistent with the existing (s/or :numeric pos-int? :string string?):

(defn add-inconsistent-id [person]
  (if (:address person)
    (assoc-in person [:address :id]