Custom Mappings

doobie provides two families of abstractions that define mappings between Scala types and schema types. These are used when we pass query arguments to the database, and when we interpret results that come back. Many such mappings are provided for free but it is sometimes necessary to define your own, and this is the subject of this chapter.

The most common kind of custom mapping operates on single column values, so we will examine this kind of mapping first. We will then talk about column vector mappings for “wide” structures.


In this chapter we’re importing the essentials from Cats and doobie, as well as some other odds and ends we’ll discuss below.

import cats._,, cats.implicits._
import doobie._, doobie.implicits._
import io.circe._, io.circe.jawn._, io.circe.syntax._
import java.awt.Point
import org.postgresql.util.PGobject

When do I need a custom type mapping?

Your first evidence that you need a new type mapping will likely be a type error. There are two common cases. The first case appears when you try to use an unmapped type as a statement parameter.

def nope(msg: String, ex: Exception): ConnectionIO[Int] =
  sql"INSERT INTO log (message, detail) VALUES ($msg, $ex)"
// error: type mismatch;
//  found   : Exception
//  required: doobie.syntax.SqlInterpolator.SingleFragment
//   sql"INSERT INTO log (message, detail) VALUES ($msg, $ex)"
//                                                        ^^

The second common case is when we try to read rows into a data type that includes an unmapped member type, such as this one.

case class LogEntry(msg: String, ex: Exception)

When we attept to define a Query0[LogEntry] we get a type error similar to the one above.

sql"SELECT message, detail FROM log".query[LogEntry]
// error:
// Cannot find or construct a Read instance for type:
//   repl.Session.App.LogEntry
// This can happen for a few reasons, but the most common case is that a data
// member somewhere within this type doesn't have a Get instance in scope. Here are
// some debugging hints:
// - For Option types, ensure that a Read instance is in scope for the non-Option
//   version.
// - For types you expect to map to a single column ensure that a Get instance is
//   in scope.
// - For case classes, HLists, and shapeless records ensure that each element
//   has a Read instance in scope.
// - Lather, rinse, repeat, recursively until you find the problematic bit.
// You can check that an instance exists for Read in the REPL or in your code:
//   scala> Read[Foo]
// and similarly with Get:
//   scala> Get[Foo]
// And find the missing instance and construct it as needed. Refer to Chapter 12
// of the book of doobie for more information.
// sql"SELECT message, detail FROM log".query[LogEntry]
// ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In both cases some hints are provided and refer you to this very chapter! So let’s keep on going and look at type mappings in detail.

Single-Column Type Mappings

JDBC defines mappings between JVM types like Int and String and standard schema types like INTEGER and VARCHAR. These suffice for most cases, and doobie provides them out of the box. We abstract over such mappings via the Get and Put typeclasses.

  • Get[A] describes a mapping from some non-nullable schema type to Scala type A, and from the equivalent nullable schema type to Option[A]. This lets us read column values and statement return values.
  • Put[A] describes a mapping from Scala type A to some non-nullable schema type, and from Option[A] to the equivalent nullable schema type. This lets us set statement parameters and update columns.

Instances are provided for the following Scala types:

  • JVM numeric types Byte, Short, Int, Long, Float, and Double;
  • BigDecimal (both Java and Scala versions);
  • Boolean, String, and Array[Byte];
  • Date, Time, and Timestamp from the java.sql package;
  • Date from the java.util package;
  • Instant, LocalDate, LocalTime, LocalDateTime, OffsetTime, OffsetDateTime and ZonedDateTime from the java.time package; and
  • single-element case classes wrapping one of the above types.

The above cases are defined by the JDBC specification. See later chapters on vendor-specific additions, which provide mappings for some non-standard types such as UUIDs and network addresses.

Deriving Get and Put from Existing Instances

If we don’t have the Get or Put instance we need, we can often one from an existing instance. Consider here a type Nat of natural numbers, along with a conversion to and from Int.

object NatModule {

  sealed trait Nat
  case object Zero extends Nat
  case class  Succ(n: Nat) extends Nat

  def toInt(n: Nat): Int = {
    def go(n: Nat, acc: Int): Int =
      n match {
        case Zero    => acc
        case Succ(n) => go(n, acc + 1)
    go(n, 0)

  def fromInt(n: Int): Nat = {
    def go(n: Int, acc: Nat): Nat =
      if (n <= 0) acc else go(n - 1, Succ(acc))
    go(n, Zero)

import NatModule._

There is no direct schema mapping for Nat, but there is a schema mapping for Int that we get out of the box, and we can use it to define our mapping for Nat.

// Bidirectional schema mapping for Nat, in terms of Int
implicit val natGet: Get[Nat] = Get[Int].map(fromInt)
implicit val natPut: Put[Nat] = Put[Int].contramap(toInt)

The .map and .contramap methods conform with the signatures of Functor and Contravariant, and indeed Get and Put are instances, respectively. However it’s best to use the tagged versions .tmap and .tcontramap when possible because it makes the name of the type (“Nat” in this case) available to the query checker by requiring a TypeTag for the mapped type. This isn’t always practical but it can result in better diagnostic messages so it should be preferred.

// Prefer .tmap and .tcontramap when possible.
implicit val natGet2: Get[Nat] = Get[Int].tmap(fromInt)
implicit val natPut2: Put[Nat] = Put[Int].tcontramap(toInt)

Deriving Get and Put from Meta

Because it is common to define bidirectional mappings there is also a Meta typeclass which serves to introduce both a Get and Put into implicit scope. If you have an implicit Meta[A] then you get an implicit Put[A] and a Get[A] for free.

Because a Meta instance exists for Int and other base types this is often the most convenient way to define a bidirectional mapping.

// Bidirectional schema mapping for Nat, in terms of Int
implicit val natMeta: Meta[Nat] = Meta[Int].imap(fromInt)(toInt)

And as above, prefer .timap when possible.

// Prefer .timap when possible.
implicit val natMeta2: Meta[Nat] = Meta[Int].timap(fromInt)(toInt)

Note: it is important to understand that Meta exists only to introduce Get/Put pairs into implicit scope. You should never demand Meta as evidence in user code: instead demand Get, Put, or both.

def foo[A: Meta](...)     // don't do this
def foo[A: Get: Put](...) // ok

Defining Get and Put for Exotic Types

In rare cases it is not possible to define a new mapping in terms of primitive JDBC types because the underlying schema type is vendor-specific or otherwise not part of the JDBC specification. In these cases it is necessary to define mappings explicitly.

In this example we will create a mapping for PostgreSQL’s json type, which is not part of the JDBC specification. On the Scala side we will use the Json type from Circe. The PostgreSQL JDBC driver transfers json values via the JDBC type OTHER, with an uwrapped payload type PGobject. The only way to know this is by experimentation. You can expect to get this kind of mapping wrong a few times before it starts working. In any case the OTHER type is commonly used for nonstandard types and doobie provides a way to construct such mappings.

implicit val showPGobject: Show[PGobject] =

implicit val jsonGet: Get[Json] =
  Get.Advanced.other[PGobject](NonEmptyList.of("json")).temap[Json] { o =>

In the instance above we read a value via JDBC’s getOther with schema type json, cast the result to PGobject, then parse its value (a String), returning the parse exception as a string on failure. Consider for a moment how comically optimistic this is. Many things have to work in order to get a Json value in hand, and if anything fails it’s an unrecoverable error. Effective testing is essential when defining new mappings like this.

The Put instance is less error-prone since we know the Json we start with is valid. Here we construct and return a new PGobject whose schema type and string value are filled in explicitly.

implicit val jsonPut: Put[Json] =
  Put.Advanced.other[PGobject](NonEmptyList.of("json")).tcontramap[Json] { j =>
      val o = new PGobject

As above, with bidirectional mappings it’s usually more convenient to use Meta, which provides an other constructor allowing the operations above to be combined.

implicit val jsonMeta: Meta[Json] =
    a => parse(a.getValue).leftMap[Json](e => throw e).merge)(
    a => {
      val o = new PGobject

There are similar constructors for array types and other possibilities, but other is by far the most common in user code and we won’t discuss the others here. See the Scaladoc for more information.

Column Vector Mappings

The Get and Put typeclasses described above define mappings between Scala types and single-column schema types, however in general we need more than this. Queries return heterogeneous vectors of values that we wish to map to composite Scala data types, and similarly we may wish to map a composite Scala data type to a heterogeneous vector of schema values (when setting a VALUES clause in an update, for instance). Mappings for these “wide” data types are provided by the Read and Write typeclasses.

  • Read[A] describes a mapping from some vector of schema types to Scala type A. This lets us read rows as composite values.
  • Write[A] describes a mapping from Scala type A to some vector of schema types. This lets us set multiple statement parameters.

As Read and Write instances are [logically] built from vectors of Get and Put instances we can construct them automatically in almost all cases. The base cases are:

  • We can Read and Write the zero-width types Unit and HNil.
  • We can Read or Write single-column types that have Get or Put instance, respectively; as well as Options thereof.

The inductive cases that build on the base cases above are:

  • We can Read or Write a shapeless HList if its elements can be read or written, respectively.
  • We can Read or Write a shapeless record if its values can be read or written, respectively.
  • We can Read or Write a product type (case class or tuple) if its shapeless Generic representation (i.e., its fields as an HList) can be read or written, respectively.

In addition, doobie provides Read[Option[A]] and Write[Option[A]] in the three cases above, mapping all columns to nullable schema types. This allows you to map the columns from an OUTER JOIN to an optional data type. For instance, reading parent/child pairs we might map output rows to the type (Parent, Option[Child]).

The above rules allow you to map between column/parameter vectors and [nested] tuples and case classes, which covers most use cases.

Deriving Read and Write from Existing Instances

Although automatic derivation will suffice in most cases, it does not work with traits and non-case classes. In these cases we must provide a mapping between the unruly data type and a type that has a defined mapping.

Consider the Point class from Java AWT, which is logically a pair of Ints but is not a case class and is thus not eligible for automatic derivation of Read and Write instances. We can define these by hand by mapping to and from the Scala type (Int, Int) which does have automatically-derived instances.

implicit val pointRead: Read[Point] =
  Read[(Int, Int)].map { case (x, y) => new Point(x, y) }
// pointRead: Read[Point] = doobie.util.Read@d52479c

implicit val pointWrite: Write[Point] =
  Write[(Int, Int)].contramap(p => (p.x, p.y))
// pointWrite: Write[Point] = doobie.util.Write@761c5042

There is no equivalent to Meta for bidirectional column vector mappings.

The source code for this page can be found here.