14. 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._

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.Aux[M, A] closes over some source of connections and configuration information (A). Based on this, it provides several natural transformations from ConnectionIO to M, where M[_] is the target monad.

A Strategy, which represents the common setup, error-handling, and cleanup strategy associated with a SQL transaction, can also be configured for a Transactor, where sane defaults are provided. A Transactor uses a Strategy to wrap programs prior to execution.

These are the natural transformations that a Transactor provides:

So summarizing, once you have a Transactor[M, A] 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.

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[IOLite](
  "org.postgresql.Driver", // fully-qualified driver class name
  "jdbc:postgresql:world", // connect URL
  "jimmy",                 // user
  "coconut"                // password
)

Using a HikariCP Connection Pool

The doobie-hikari-cats 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.hikari.imports._

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

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

And running this IOLite gives us the desired result.

scala> p.unsafePerformIO
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[IOLite](ds)

val p: IOLite[Int] = for {
  _  <- xa.configure(ds => IOLite.primitive( /* 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.

Customizing Transactors

If the default Transactor behavior don’t meet your needs you can replace any member with one that does what you need. See the Scaladoc for Transactor and Strategy for details on the structure. Lenses are provided to make it straightforward to replace just the piece you’re interested in. For example, to create a transactor that is the same as xa but always rolls back (for testing perhaps) you can say:

scala> val testXa = Transactor.after.set(xa, HC.rollback)
testXa: doobie.util.transactor.Transactor[doobie.imports.IOLite] = doobie.util.transactor$Transactor$$anon$10@6ee0d976

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] yielded by the default interpreter.

val conn: java.sql.Connection = null     // Connection (pretending)
val prog = 42.pure[ConnectionIO]         // ConnectionIO[Int]
val int  = KleisliInterpreter[IOLite]    // KleisliInterpreter[IOLite]
val nat  = int.ConnectionInterpreter     // ConnectionIO ~> Kleisli[IOLite, Connection, ?]
val task = prog.foldMap(nat).run(conn)   // IOLite[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.pure[ResultSetIO]        // ResultSetIO[Int]
val nat  = int.ResultSetInterpreter    // ResultSetIO ~> Kleisli[IOLite, ResultSet, ?]
val task = prog.foldMap(nat).run(rs)   // IOLite[Int]

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