12. Managing Connections

In this chapter we discuss several ways to manage connections in applications that use doobie, including managed/pooled connections and re-use of existing connections. For this chapter we have a few imports and no other setup.

import doobie.imports._
import scalaz._, Scalaz._
import scalaz.concurrent.Task

About Transactors

Most doobie programs are values of type ConnectionIO[A] or Process[ConnnectionIO, A] that describe computations requiring a database connection. By providing a means of acquiring a connection we can transform these programs into computations that can actually be executed. The most common way of performing this transformation is via a Transactor.

A Transactor is a parameterized by some target monad M and closes over some source of connections (and configuration information, as needed) yielding a pair of natural transformations:

ConnectionIO ~> M
Process[ConnectionIO, ?] ~> Process[M, ?]

So once you have a Transactor[M] you have a way of discharging ConnectionIO and replacing it with some effectful M like Task or IO. In effect this turns a doobie program into a “real” program value that you can integrate with the rest of your application; all doobieness is left behind.

In addition to simply supplying a connection, a Transactor (by default) wraps the transformed ConnectionIO as follows:

doobie provides several implementations, described below.

Using the JDBC DriverManager

JDBC provides a bare-bones connection provider via DriverManager.getConnection, which has the advantage of being extremely simple: there is no connection pooling and thus no configuration required. The disadvantage is that it is quite a bit slower than pooling connection managers, and provides no upper bound on the number of concurrent connections.

However, for experimentation as described in this book (and for situations where you really do want to ensure that you get a truly fresh connection right away) the DriverManager is ideal. Support in doobie is via DriverManagerTransactor. To construct one you must pass the name of the driver class and a connect URL. Normally you will also pass a user/password (the API provides several variants matching the DriverManager static API).

val xa = DriverManagerTransactor[Task](
  "org.postgresql.Driver", // fully-qualified driver class name
  "jdbc:postgresql:world", // connect URL
  "jimmy",                 // user
  "coconut"                // password

Using a HikariCP Connection Pool

The doobie-contrib-hikari add-on provides a Transactor implementation backed by a HikariCP connection pool. The connnection pool has internal state so constructing one is an effect:

import doobie.contrib.hikari.hikaritransactor._

val q = sql"select 42".query[Int].unique

val p: Task[Int] = for {
  xa <- HikariTransactor[Task]("org.postgresql.Driver", "jdbc:postgresql:world", "postgres", "")
  _  <- xa.configure(hx => Task.delay( /* do something with hx */ ()))
  a  <- q.transact(xa) ensuring xa.shutdown
} yield a

And running this Task gives us the desired result.

scala> p.unsafePerformSync
res2: Int = 42

The returned instance is of type HikariTransactor, which provides a shutdown method, as well as a configure method that provides access to the underlying HikariDataSource if additional configuration is required.

Using an existing DataSource

If your application exposes an existing javax.sql.DataSource you can use it directly by wrapping it in a DataSourceTransactor.

val ds: javax.sql.DataSource = null // pretending

val xa = DataSourceTransactor[Task](ds)

val p: Task[Int] = for {
  _  <- xa.configure(ds => Task.delay( /* do something with ds */ ()))
  a  <- q.transact(xa)
} yield a

The configure method on DataSourceTransactor provides access to the underlying DataSource if additional configuration is required.

Building your own Transactor

If the provided Transactor implementations don’t meet your needs, it is straightforward to build your own using any connection provider. At a minimum all you need to do is implement the connect method, which returns a [logically] fresh connection lifted into a target monad. See the source for existing implementations; it’s likely that you can copy/paste your way to a custom Transactor without much trouble.

Using an Existing JDBC Connection

If you have an existing Connection you can transform a ConnectionIO[A] to an M[A] for any target monad M that has Catchable and Capture instances by running the Kleisli[M, Connection, A] provided by the transK method.

val conn: java.sql.Connection = null   // Connection (pretending)
val prog = 42.point[ConnectionIO]      // ConnectionIO[Int]
val task = prog.transK[Task].run(conn) // Task[Int]

As an aside, this technique works for programs written in any of the provided contexts. For example, here we run a program in ResultSetIO.

val rs: java.sql.ResultSet = null    // ResultSet (pretending)
val prog = 42.point[ResultSetIO]     // ResultSetIO[Int]
val task = prog.transK[Task].run(rs) // Task[Int]

This facility allows you to mix doobie programs into existing JDBC applications in a fine-grained manner if this meets your needs.