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Getting Started with next.jdbc

The next.jdbc library provides a simpler, faster alternative to the Contrib library and is the next step in the evolution of that library.

It is designed to work with Clojure 1.10 or later, supports datafy/nav, and by default produces hash maps with automatically qualified keywords, indicating source tables and column names (labels).


You can add next.jdbc to your project with either:

{seancorfield/next.jdbc {:mvn/version "1.0.0-beta1"}}

for deps.edn or:

[seancorfield/next.jdbc "1.0.0-beta1"]

for project.clj or build.boot.

In addition, you will need to add dependencies for the JDBC drivers you wish to use for whatever databases you are using. You can see the drivers and versions that next.jdbc is tested against in the project's deps.edn file, but many other JDBC drivers for other databases should also work (e.g., Oracle, Red Shift).

An Example REPL Session

To start using next.jdbc, you need to create a datasource (an instance of javax.sql.DataSource). You can use next.jdbc/get-datasource with either a "db-spec" -- a hash map describing the database you wish to connect to -- or a JDBC URI string. Or you can construct a datasource from one of the connection pooling libraries out there, such as HikariCP or c3p0.

For the examples in this documentation, we will use a local H2 database on disk, and we'll use the Clojure CLI tools and deps.edn:

;; deps.edn
{:deps {org.clojure/clojure {:mvn/version "1.10.0"}
        seancorfield/next.jdbc {:mvn/version "1.0.0-beta1"}
        com.h2database/h2 {:mvn/version "1.4.197"}}}

In this REPL session, we'll define an H2 datasource, create a database with a simple table, and then add some data and query it:

> clj
Clojure 1.10.0
user=> (require '[next.jdbc :as jdbc])
user=> (def db {:dbtype "h2" :dbname "example"})
user=> (def ds (jdbc/get-datasource db))
user=> (jdbc/execute! ds ["
create table address (
  id int auto_increment primary key,
  name varchar(32),
  email varchar(255)
[#:next.jdbc{:update-count 0}]
user=> (jdbc/execute! ds ["
insert into address(name,email)
  values('Sean Corfield','')"])
[#:next.jdbc{:update-count 1}]
user=> (jdbc/execute! ds ["select * from address"])
[#:ADDRESS{:ID 1, :NAME "Sean Corfield", :EMAIL ""}]

We described the database with just :dbtype and :dbname because it is created as a local file and needs no authentication. For most databases, you would need :user and :password for authentication, and if the database is running on a remote machine you would need :host and possibly :port (next.jdbc tries to guess the correct port based on the :dbtype).

Note: You can see the full list of :dbtype values supported in next.jdbc/get-datasource.

We used execute! to create the address table, to insert a new row into it, and to query it. In all three cases, execute! returns a vector of hash maps with namespace-qualified keys, representing the result set from the operation, if available. When no result set is produced, next.jdbc returns a "result set" containing the "update count" from the operation (which is usually the number of rows affected). By default, H2 uses uppercase names and next.jdbc returns these as-is.

If you only want a single row back -- the first row of any result set, generated keys, or update counts -- you can use execute-one! instead. Continuing the REPL session, we'll insert another address and ask for the generated keys to be returned, and then we'll query for a single row:

user=> (jdbc/execute-one! ds ["
insert into address(name,email)
  values('Someone Else','')
"] {:return-keys true})
user=> (jdbc/execute-one! ds ["select * from address where id = ?" 2])
#:ADDRESS{:ID 2, :NAME "Someone Else", :EMAIL ""}

Since we used execute-one!, we get just one row back (a hash map). This also shows how you provide parameters to SQL statements -- with ? in the SQL and then the corresponding parameter values in the vector after the SQL string.

While these functions are fine for retrieving result sets as data, most of the time you want to process that data efficiently, so next.jdbc provides a SQL execution function that works with reduce and with transducers to consume the result set without the intermediate overhead of creating Clojure data structures for every row:

user=> (into #{}
             (map :ADDRESS/NAME)
             (jdbc/plan ds ["select * from address"]))
#{"Sean Corfield" "Someone Else"}

This produces a set of all the unique names in the address table, directly from the java.sql.ResultSet object returned by the JDBC driver, without creating any Clojure hash maps. That means you can use either the qualified keyword that would be produced by execute! or execute-one! or you can use a simple keyword that mirrors the column name directly:

user=> (into #{}
             (map :name)
             (jdbc/plan ds ["select * from address"]))
#{"Sean Corfield" "Someone Else"}

Any operation that can perform key-based lookup can be used here without creating hash maps: get, contains?, find (returns a MapEntry of whatever key you requested and the corresponding column value), or direct keyword access as shown above. Any operation that would require a Clojure hash map, such as assoc or anything that invokes seq (keys, vals), will cause the full row to be expanded into a hash map, such as produced by execute! or execute-one!.

Datasources, Connections & Transactions

In the examples above, we created a datasource and then passed it into each function call. When next.jdbc is given a datasource, it creates a java.sql.Connection from it, uses it for the SQL operation, and then closes it. If you're not using a connection pooling datasource, that can be quite an overhead: setting up database connections to remote servers is not cheap!

If you want to run multiple SQL operations without that overhead each time, you can create the connection yourself and reuse it across several operations using with-open and next.jdbc/get-connection:

(with-open [con (jdbc/get-connection ds)]
  (jdbc/execute! con ...)
  (jdbc/execute! con ...)
  (into [] (map :column) (jdbc/plan con ...)))

If any of these operations throws an exception, the connection will still be closed but operations prior to the exception will have already been committed to the database. If you want to reuse a connection across multiple operations but have them all rollback if an exception occurs, you can use next.jdbc/with-transaction:

(jdbc/with-transaction [tx ds]
  (jdbc/execute! tx ...)
  (jdbc/execute! tx ...)
  (into [] (map :column) (jdbc/plan tx ...)))

If with-transaction is given a datasource, it will create and close the connection for you. If you pass in an existing connection, with-transaction will set up a transaction on that connection and, after either committing or rolling back the transaction, will restore the state of the connection and leave it open:

(with-open [con (jdbc/get-connection ds)]
  (jdbc/execute! con ...) ; committed
  (jdbc/with-transaction [tx con] ; will commit or rollback this group:
    (jdbc/execute! tx ...)
    (jdbc/execute! tx ...)
    (into [] (map :column) (jdbc/plan tx ...)))
  (jdbc/execute! con ...)) ; committed

Support from Specs

As you are developing with next.jdbc, it can be useful to have assistance from clojure.spec in checking calls to next.jdbc's functions, to provide explicit argument checking and/or better error messages for some common mistakes, e.g., trying to pass a plain SQL string where a vector (containing a SQL string, and no parameters) is expected.

You can enable argument checking for functions in both next.jdbc and next.jdbc.sql by requiring the next.jdbc.specs namespace and instrumenting the functions. A convenience function is provided:

(require '[next.jdbc.specs :as specs])
(specs/instrument) ; instruments all next.jdbc functions

(jdbc/execute! ds "SELECT * FROM fruit")
Call to #'next.jdbc/execute! did not conform to spec.

In the :problems output, you'll see the :path [:sql :sql-params] and :pred vector? for the :val "SELECT * FROM fruit". Without the specs' assistance, this mistake would produce a more cryptic error, a ClassCastException, that a Character cannot be cast to a String, from inside next.jdbc.prepare.

Friendly SQL Functions :>

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