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Penkala

Clojars Project

Penkala is a composable query builder for PostgreSQL written in Clojure.

Motivation

Clojure has a number of libraries to interact with the database layer. So, why write another one? For my needs, I find them all operating on a wrong level of abstraction. Libraries like HugSQL and HoneySQL allow you to use full extent of SQL, but still require you to write a lot of boilerplate, while libraries like Walkable and seql operate on a very high level, but are eschewing SQL and instead use EQL to write the queries.

In the previous version of the readme, Walkable was mentioned as one of the libraries that operate on EQL level vs. SQL level. Walkable does have an EQL layer, but it also offers a lower level API that allows you to express SQL in a more direct manner (https://walkable.gitlab.io/walkable/1.3.0/s-expressions.html).

I wanted something in the middle:

  • Support for SQL semantics
  • High(er) level API than HugSQL or HoneySQL have
  • Implementation that is not "taking over" - you should be able to use Penkala in combination with any other library if you need to
  • Composability
  • API that is as pure as possible
  • First class support for PostgreSQL features

The idea is to get as close to the features offered by ORM libraries in other languages, without taking on all the complexity that ORMs bring with them.

Relation as a first class citizen

The API is inspired by many libraries (Ecto, Massive.js, rasql), but the main inspiration comes from a library called bmg - written in Ruby. You can read more about its design decisions here. While Penkala is not implementing relational algebra, and is instead staying close to the SQL semantics, the API is designed around the same principles - composition and reuse are built in.

Every operation on a relation is returning a new relation that you can use and reuse in other queries.

Usage

(require '[com.verybigthings.penkala.relation :as r])

(def users-spec {:name "users"
                 :columns ["id" "username" "is_admin"]
                 :pk ["id"]
                 :schema "public"})

(def users-rel (r/spec->relation users-spec))

(r/get-select-query users-rel {})

=> ["SELECT \"users\".\"id\" AS \"id\", \"users\".\"is_admin\" AS \"is-admin\", \"users\".\"username\" AS \"username\" FROM \"public\".\"users\" AS \"users\""]

To create a relation, you need a relation spec. This describes the table (or view) columns, its name, primary key and schema. The spec can be used to create a relation, which can then be used to generate queries.

r/get-select-query function returns a "sqlvec" where the first element in the vector is the SQL query, and the remaining elements are the query parameters.

(def admins-rel (r/where users-rel [:is-true :is-admin]))
(r/get-select-query admins-rel {})

=> ["SELECT \"users\".\"id\" AS \"id\", \"users\".\"is_admin\" AS \"is-admin\", \"users\".\"username\" AS \"username\" FROM \"public\".\"users\" AS \"users\" WHERE \"users\".\"is_admin\" IS TRUE"]

In this case, we've created a new relation - admins-rel which is representing only the admin users.

Let's introduce a new relation - posts and load posts only written by admins

(def posts-spec {:name "posts"
                 :columns ["id" "user_id" "body"]
                 :pk ["id"]
                 :schema "public"})
  
(def posts-rel (r/spec->relation users-spec))

(r/get-select-query (r/where posts-rel [:in :user-id (r/select admins-rel [:id])]) {})
=> ["SELECT \"posts\".\"body\" AS \"body\", \"posts\".\"id\" AS \"id\", \"posts\".\"user_id\" AS \"user-id\" FROM \"public\".\"posts\" AS \"posts\" WHERE \"posts\".\"user_id\" IN (SELECT \"sq_7758__users\".\"id\" AS \"id\" FROM \"public\".\"users\" AS \"sq_7758__users\" WHERE \"sq_7758__users\".\"is_admin\" IS TRUE)"]

Or maybe, we want only posts posted by admins joined with their author:

(r/get-select-query (r/join posts-rel :left admins-rel :author [:= :user-id :author/id]) {})
=> ["SELECT \"posts\".\"body\" AS \"body\", \"posts\".\"id\" AS \"id\", \"posts\".\"user_id\" AS \"user-id\", \"author\".\"id\" AS \"author__id\", \"author\".\"is-admin\" AS \"author__is-admin\", \"author\".\"username\" AS \"author__username\" FROM \"public\".\"posts\" AS \"posts\" LEFT OUTER JOIN (SELECT \"users\".\"id\" AS \"id\", \"users\".\"is_admin\" AS \"is-admin\", \"users\".\"username\" AS \"username\" FROM \"public\".\"users\" AS \"users\" WHERE \"users\".\"is_admin\" IS TRUE) \"author\" ON \"posts\".\"user_id\" = \"author\".\"id\""]

Notice that you're using namespaced keywords when you're referencing columns in joined relations, where the namespace is the alias used to join the relation.

As you can see, Penkala takes care of all the nitty-gritty details around sub-selects, joins and aliasing. Relation API functions are implemented, documented and spec'ed in the com.verybigthings.penkala.relation namespace.

Penkala provides the integration with the next.jdbc library, which will query the DB, get the information about the tables and views and return a map with relations based on this information.

(require '[com.verybigthings.penkala.next-jdbc :refer [get-env]])
(get-env "db-url-or-spec")

Value expressions

A lot of the relation API functions accept value expressions. Value expressions are used to write where clauses, extend relations with computed columns, write having clauses, etc.. Value expressions API is heavily inspired by the HoneySQ library, and value expressions are written as vectors:

[:= :id 1]

In this case Penkala will look for a column named :id and compare it to the value 1. Value expressions can be arbitrarily nested to any depth, and allow you to write almost any SQL you might need.

Some examples:

;; Complex where predicate
(-> *env* :products (r/where [:and [:= :id 1] [:= :price 12.0] [:is-null :tags]]))

;; Querying a JSON field
(-> *env* :products (r/where [:= ["#>>" :specs ["weight"]] [:cast 30 "text"]]))

;; Use a POSIX regex in a where clause
(-> *env* :products (r/where ["!~*" :name "product[ ]*[2-4](?!otherstuff)"]))

;; Use the "overlap" operator
(-> *env* :products (r/where ["&&" :tags ["tag3" "tag4" "tag5"]]))

When writing a value expression, first element of a vector will be used to determine the type of the expression (operator, function, etc.) and the rest will be sent as arguments. Keywords have a special meaning in value expressions, and when encountering them (anywhere except in the first position), Penkala will check if there is a column with that name in the current relation. If there is, it will treat it as a column name, and if it is not, it will treat it as a value. If you need to explicitly treat something as a value or param or column... com.verybigthings.penkala.helpers provides wrapper functions which will allow you to explicitly mark something as a value or as a column.

One of the cases where you'll need to explicitly wrap keywords is when you want to provide named params to the query. So, far all examples inlined the params in the value expression, but Penkala provides a better way, which will allow you to write reusable value expressions:

(require '[com.verybigthings.penkala.helpers :refer [param]])
(r/get-select-query (r/where posts-rel [:= :user-id (param :user/id )]) {} {:user/id 1})
=> ["SELECT \"posts\".\"body\" AS \"body\", \"posts\".\"id\" AS \"id\", \"posts\".\"user_id\" AS \"user-id\" FROM \"public\".\"posts\" AS \"posts\" WHERE \"posts\".\"user_id\" = ?" 1]

Decomposition

If the first half of boilerplate you have to write when you generate SQL queries is composition, then the second one is decomposition of results into a graph form (nested maps and vectors). Penkala provides a decomposition layer which allows you to get the results in the format that's usable. Penkala can infer the decomposition schema from the relation, so in most cases you won't have to do anything to get it working. In other cases, you can override the behavior. This allows you to query the DB with a convenience of an ORM without having to use limited concepts like has many, has one, many to many, you get the same results while being able to use the rest of the Penkala API.

Here's an example from the test suite:

(ns com.verybigthings.penkala.relation.join-test
  (:require [clojure.test :refer :all]
            [com.verybigthings.penkala.next-jdbc :refer [select!]]
            [com.verybigthings.penkala.relation :as r]
            [com.verybigthings.penkala.test-helpers :as th :refer [*env*]]
            [com.verybigthings.penkala.decomposition :refer [map->DecompositionSchema]]))

(deftest it-joins-multiple-tables-at-multiple-levels
  (let [alpha (:alpha *env*)
        beta (:beta *env*)
        gamma (:gamma *env*)
        sch-delta (:sch/delta *env*)
        sch-epsilon (:sch/epsilon *env*)
        joined (-> alpha
                 (r/join :inner
                   (-> beta
                     (r/join :inner gamma :gamma [:= :id :gamma/beta-id])
                     (r/join :inner sch-delta :delta [:= :id :delta/beta-id]))
                   :beta
                   [:= :id :beta/alpha-id])
                 (r/join :inner sch-epsilon :epsilon [:= :id :epsilon/alpha-id]))
        res (select! *env* joined)]
    (is (= [#:alpha{:val "one"
                    :id 1
                    :beta
                    [#:beta{:val "alpha one"
                            :j nil
                            :id 1
                            :alpha-id 1
                            :gamma
                            [#:gamma{:beta-id 1
                                     :alpha-id-one 1
                                     :alpha-id-two 1
                                     :val "alpha one alpha one beta one"
                                     :j nil
                                     :id 1}]
                            :delta
                            [#:delta{:beta-id 1 :val "beta one" :id 1}]}],
                    :epsilon [#:epsilon{:val "alpha one" :id 1 :alpha-id 1}]}]
          res))))

In this case, multiple relations were joined (on multiple levels) and then transformed into a graph form. Decomposition schema was inferred from the relation joins structure.

Insert, Update and Delete

From version 0.0.3 Penkala supports insert, update and delete statements. To insert, update or delete data you need to create an "Insertable", "Updatable" or "Deletable" (commonly called "Writeable") record from the base relation. If you create a writeable record from a relation that has joins, it will use only the topmost relation. Insertable and updatable will filter the data passed to them and only use the keys that relate to the columns in the target table.

Insert

If we have a posts relation:

(def posts-spec {:name "posts"
                 :columns ["id" "user_id" "body"]
                 :pk ["id"]
                 :schema "public"})
  
(def posts-rel (r/spec->relation posts-spec))

We can create an insertable record:

(def posts-ins (r/->insertable posts-rel))

This insertable record will by default use the returning statement, and will return all columns from the created record(s). You can change the returned columns by using the returning function

(r/returning posts-ins nil)

In this case, default next.jdbc data will be returned which will include the count of the updated rows:

#:next.jdbc{:update-count 1}
(r/returning posts-ins [:id])

In this case, only the id column will be returned.

To actually insert the data, use com.verybigthings.penkala.next-jdbc/insert! function:

(insert! *env* posts-ins {:user-id 1 :body "This is my first post"})
=> #:posts{:id 1
           :user-id 1
           :body "This is my first post"}

If you pass a vector of maps to the insert! function, multiple records will be created, and a vector of results will be returned

(insert! *env* posts-ins [{:user-id 1 :body "This is my first post"} {:user-id 1 :body "This is my second post"}])
=> [#:posts{:id 1
           :user-id 1
           :body "This is my first post"}
    #:posts{:id 2
            :user-id 1
            :body "This is my second post"}]

Upsert

Penkala supports upserts ("INSERT ... ON CONFLICT"). There are two functions - on-conflict-do-nothing and on-conflict-do-update - exposed:

(insert! *env* 
 (-> posts-ins
  (r/on-conflict-do-nothing))
 {:user-id 1 :body "This is my first post"})
=> nil

You can pass explicit "conflict target" to the on-conflict-do-... functions:

(insert! *env* 
 (-> posts-ins
  (r/on-conflict-do-nothing [:body]))
 {:user-id 1 :body "This is my first post"})
=> nil

or

(insert! *env* 
 (-> posts-ins
  (r/on-conflict-do-nothing [:on-constraint "posts_pkey"]))
 {:id 1 :user-id 1 :body "This is my first post"})
=> nil

If you use constraint inference (and you're not passing explicit constraint name through the :on-constraint form), you can add the "WHERE" clause to the on-conflict-do-... functions:

(insert! *env* 
 (-> posts-ins
  (r/on-conflict-do-nothing [:body] [:= :id 1]))
 {:user-id 1 :body "This is my first post"})
=> nil

When using on-conflict-do-update function, you must pass the update map:

(insert! *env*
  (-> posts-ins
    (r/on-conflict-do-update
      [:body]
      {:body [:concat :excluded/body " (1)"]}))
  {:user-id 1
   :body "This is my first post"})
=> #:posts{:id 1
           :user-id 1
           :body "This is my first post (1)"}

In the update map, keys are names of the columns and values are "value expressions". When using on-conflict-do-update function, you get access to the implicit "EXCLUDED" table (https://www.postgresql.org/docs/12/sql-insert.html).

Update

If we have a posts relation:

(def posts-spec {:name "posts"
                 :columns ["id" "user_id" "body"]
                 :pk ["id"]
                 :schema "public"})
  
(def posts-rel (r/spec->relation users-spec))

We can create an updatable record:

(def posts-upd (r/->updatable posts-rel))

Now we can perform updates by using the com.verybigthings.penkala.next-jdbc/update! function:

(update! *env*
 (-> posts-upd
  (r/where [:= :id 1]))
 {:body "This is my updated title"})
=> [#:posts{:id 1
            :user-id 1
            :body "This is my updated title"}]

Where function in updatables supports value expressions (like selects).

Updatables implement the returning function which can be used to select the returned columns (like inserts):

(update! *env*
 (-> posts-upd
  (r/where [:= :id 1])
  (r/returning nil))
 {:body "This is my updated title"})
=> #:next.jdbc{:update-count 1}

Penkala supports the FROM clause in updates, which allows you to join tables and reference them from the WHERE clause or from the update value expressions:

Example from the tests:

(deftest it-updates-with-from-tables
  (let [normal-pk (:normal-pk *env*)
        normal-pk-id-1 (select-one! *env* (-> normal-pk
                                            (r/where [:= :id 1])))
        upd-normal-pk (-> (r/->updatable normal-pk)
                        (r/from normal-pk :normal-pk-2)
                        (r/from normal-pk :normal-pk-3)
                        (r/where [:and [:= :id 1]
                                  [:= :id :normal-pk-2/id]
                                  [:= :id :normal-pk-3/id]]))
        res (update! *env* upd-normal-pk {:field-1 [:concat "from-outside" "<->" :normal-pk-2/field-1 "<->" :normal-pk-3/field-1]
                                          :field-2 "foo"})]
    (is (= [#:normal-pk{:field-1 (str "from-outside<->" (:normal-pk/field-1 normal-pk-id-1) "<->" (:normal-pk/field-1 normal-pk-id-1))
                        :json-field nil
                        :field-2 "foo"
                        :array-of-json nil
                        :array-field nil
                        :id 1}]
          res))))

In this example normal-pk table is self joined twice, under :normal-pk-2 and :normal-pk-3 aliases and then referenced both in the WHERE clause and in the update map.

In update map, keys are names of columns, and values are value expressions that update the column. Only the keys that match column names will be selected from the update map.

Delete

If we have a posts relation:

(def posts-spec {:name "posts"
                 :columns ["id" "user_id" "body"]
                 :pk ["id"]
                 :schema "public"})
  
(def posts-rel (r/spec->relation users-spec))

We can create a deletable record:

(def posts-del (r/->deletable posts-rel))

Now we can perform deletes by using the com.verybigthings.penkala.next-jdbc/delete! function:

(delete! *env*
 (-> posts-del
  (r/where [:= :id 1])))
=> [#:posts{:id 1
            :user-id 1
            :body "This is my updated title"}]

Where function in deletables supports value expressions (like selects).

Deletables implement the returning function which can be used to select the returned columns (like inserts):

(delete! *env*
 (-> posts-upd
  (r/where [:= :id 1])
  (r/returning nil)))
=> #:next.jdbc{:update-count 1}

Penkala supports the USING clause in deletes, which allows you to join tables and reference them from the WHERE clause:

Example from the tests:

(deftest it-deletes-with-using-multiple
  (let [products (:products *env*)
        del-products (-> (r/->deletable products)
                       (r/using (r/where products [:= :id 3]) :other-products-1)
                       (r/using (r/where products [:= :id 3]) :other-products-2)
                       (r/where [:and
                                 [:= :id :other-products-1/id]
                                 [:= :id :other-products-2/id]]))
        res (delete! *env* del-products)]
    (is (= [#:products{:description nil,
                       :tags nil,
                       :string "three",
                       :id 3,
                       :specs nil,
                       :case-name nil,
                       :price 0.00M}]
          (mapv #(dissoc % :products/uuid) res)))))

In this example products table is joined twice under :other-products-1 and :other-products-2 alias and then referenced in the WHERE clause.

Credits

I've spent a lot of time researching other libraries that are doing the similar thing, but two of them affected Penkala the most

  • Massive.js - Penkala started as a port of this lib, and I'm thankful I was able to study its source code to learn how to approach the architecture. Also, the tests and SQL queries to get the DB structure are copied from the Massive.js codebase.
  • bmg - this library helped me to design the API in a way that's composable and reusable

Next.jdbc integration code is taken from Luminus, so I want to thank the Lumius team for that effort.

Other libraries I've researched:

Penkala development is kindly sponsored by Very Big Things

License

Copyright © 2020 Mihael Konjevic (https://verybigthings.com)

Distributed under the MIT License.