9. SQL Arrays

This chapter shows how we can map Scala sequence types to SQL ARRAY types, for vendors that support it. Note that although SQL array mappings are part of the JDBC specification, their behavior is vendor-specific and requires an add-on library; the code in this chapter requires doobie-contrib-postgresql.

Setting Up

Again we set up a transactor and pull in YOLO mode. We also need an import to get PostgreSQL-specific type mappings.

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

val xa = DriverManagerTransactor[Task](
  "org.postgresql.Driver", "jdbc:postgresql:world", "postgres", ""

import xa.yolo._, doobie.contrib.postgresql.pgtypes._

Reading and Writing Arrays

Let’s create a new table with a SQL array column. Note that this is likely to work only for PostgreSQL; the syntax for arrays differs significantly from vendor to vendor.

val drop = sql"DROP TABLE IF EXISTS person".update.quick

val create = 
    CREATE TABLE person (
      id   SERIAL,
      pets VARCHAR[] NOT NULL
scala> (drop *> create).run
  0 row(s) updated
  0 row(s) updated

doobie maps SQL array columns to Array, List, and Vector by default. No special handling is required, other than importing the vendor-specific array support above.

case class Person(id: Long, name: String, pets: List[String])

def insert(name: String, pets: List[String]): ConnectionIO[Person] = {
  sql"insert into person (name, pets) values ($name, $pets)"
    .update.withUniqueGeneratedKeys("id", "name", "pets")

Insert works fine, as does reading the result. No surprises.

scala> insert("Bob", List("Nixon", "Slappy")).quick.run
  Person(1,Bob,List(Nixon, Slappy))

scala> insert("Alice", Nil).quick.run

Lamentations of NULL

doobie maps nullable columns via Option, so null is never observed in programs that use the high-level API, and the typechecking feature discussed earlier will find mismatches. So this means if you have a nullable SQL varchar[] then you will be chided grimly if you don’t map it as Option[List[String]] (or some other supported sequence type).

However there is another axis of variation here: the array cells themselves may contain null values. And the query checker can’t save you here because this is not reflected in the metadata provided by PostgreSQL or H2, and probably not by anyone.

So there are actually four ways to map an array, and you should carefully consider which is appropriate for your schema. In the first two cases reading a NULL cell would result in a NullableCellRead exception.

scala> sql"select array['foo','bar','baz']".query[List[String]].quick.run
  List(foo, bar, baz)

scala> sql"select array['foo','bar','baz']".query[Option[List[String]]].quick.run
  Some(List(foo, bar, baz))

scala> sql"select array['foo',NULL,'baz']".query[List[Option[String]]].quick.run
  List(Some(foo), None, Some(baz))

scala> sql"select array['foo',NULL,'baz']".query[Option[List[Option[String]]]].quick.run
  Some(List(Some(foo), None, Some(baz)))

Diving Deep

We can easily add support for other sequence types like scalaz.IList by invariant mapping. The nxmap method is a variant of xmap that ensures null values read from the database are never observed. The TypeTag is required to provide better feedback when type mismatches are detected.

import scala.reflect.runtime.universe.TypeTag

implicit def IListMeta[A: TypeTag](implicit ev: Meta[List[A]]): Meta[IList[A]] =
  ev.nxmap[IList[A]](IList.fromList, _.toList)

Once this mapping is in scope we can map columns directly to IList.

scala> sql"select pets from person where name = 'Bob'".query[IList[String]].quick.run