1. Home
2. Working with Play
3. Play for Scala developers
4. Main concepts
5. Accessing an SQL database

§Anorm, simple SQL data access

Play includes a simple data access layer called Anorm that uses plain SQL to interact with the database and provides an API to parse and transform the resulting datasets.

Anorm is Not an Object Relational Mapper

In the following documentation, we will use the MySQL world sample database.

If you want to enable it for your application, follow the MySQL website instructions, and configure it as explained on the Scala database page .

§Overview

It can feel strange to return to plain old SQL to access an SQL database these days, especially for Java developers accustomed to using a high-level Object Relational Mapper like Hibernate to completely hide this aspect.

Although we agree that these tools are almost required in Java, we think that they are not needed at all when you have the power of a higher-level programming language like Scala. On the contrary, they will quickly become counter-productive.

§Using JDBC is a pain, but we provide a better API

We agree that using the JDBC API directly is tedious, particularly in Java. You have to deal with checked exceptions everywhere and iterate over and over around the ResultSet to transform this raw dataset into your own data structure.

We provide a simpler API for JDBC; using Scala you don’t need to bother with exceptions, and transforming data is really easy with a functional language. In fact, the goal of the Play Scala SQL access layer is to provide several APIs to effectively transform JDBC data into other Scala structures.

§You don’t need another DSL to access relational databases

SQL is already the best DSL for accessing relational databases. We don’t need to invent something new. Moreover the SQL syntax and features can differ from one database vendor to another.

If you try to abstract this point with another proprietary SQL like DSL you will have to deal with several ‘dialects’ dedicated for each vendor (like Hibernate ones), and limit yourself by not using a particular database’s interesting features.

Play will sometimes provide you with pre-filled SQL statements, but the idea is not to hide the fact that we use SQL under the hood. Play just saves typing a bunch of characters for trivial queries, and you can always fall back to plain old SQL.

§A type safe DSL to generate SQL is a mistake

Some argue that a type safe DSL is better since all your queries are checked by the compiler. Unfortunately the compiler checks your queries based on a meta-model definition that you often write yourself by ‘mapping’ your data structure to the database schema.

There are no guarantees that this meta-model is correct. Even if the compiler says that your code and your queries are correctly typed, it can still miserably fail at runtime because of a mismatch in your actual database definition.

§Take Control of your SQL code

Object Relational Mapping works well for trivial cases, but when you have to deal with complex schemas or existing databases, you will spend most of your time fighting with your ORM to make it generate the SQL queries you want.

Writing SQL queries yourself can be tedious for a simple ‘Hello World’ application, but for any real-life application, you will eventually save time and simplify your code by taking full control of your SQL code.

§Add Anorm to your project

You will need to add Anorm and JDBC plugin to your dependencies :

libraryDependencies ++= Seq(
  jdbc,
  "com.typesafe.play" %% "anorm" % "2.4.0"
)

§Executing SQL queries

To start you need to learn how to execute SQL queries.

First, import anorm._, and then simply use the SQL object to create queries. You need a Connection to run a query, and you can retrieve one from the play.api.db.DB helper:

import anorm._
import play.api.db.DB

DB.withConnection { implicit c =>
  val result: Boolean = SQL("Select 1").execute()
}

The execute() method returns a Boolean value indicating whether the execution was successful.

To execute an update, you can use executeUpdate(), which returns the number of rows updated.

val result: Int = SQL("delete from City where id = 99").executeUpdate()

If you are inserting data that has an auto-generated Long primary key, you can call executeInsert().

val id: Option[Long] = 
  SQL("insert into City(name, country) values ({name}, {country})")
  .on('name -> "Cambridge", 'country -> "New Zealand").executeInsert()

When key generated on insertion is not a single Long, executeInsert can be passed a ResultSetParser to return the correct key.

import anorm.SqlParser.str

val id: List[String] = 
  SQL("insert into City(name, country) values ({name}, {country})")
  .on('name -> "Cambridge", 'country -> "New Zealand")
  .executeInsert(str.+) // insertion returns a list of at least one string keys

Since Scala supports multi-line strings, feel free to use them for complex SQL statements:

val sqlQuery = SQL(
  """
    select * from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where c.code = 'FRA';
  """
)

If your SQL query needs dynamic parameters, you can declare placeholders like {name} in the query string, and later assign a value to them:

SQL(
  """
    select * from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where c.code = {countryCode};
  """
).on("countryCode" -> "FRA")

You can also use string interpolation to pass parameters (see details thereafter).

In case several columns are found with same name in query result, for example columns named code in both Country and CountryLanguage tables, there can be ambiguity. By default a mapping like following one will use the last column:

import anorm.{ SQL, SqlParser }

val code: String = SQL(
  """
    select * from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where c.code = {countryCode}
  """)
  .on("countryCode" -> "FRA").as(SqlParser.str("code").single)

If Country.Code is ‘First’ and CountryLanguage is ‘Second’, then in previous example code value will be ‘Second’. Ambiguity can be resolved using qualified column name, with table name:

import anorm.{ SQL, SqlParser }

val code: String = SQL(
  """
    select * from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where c.code = {countryCode}
  """)
  .on("countryCode" -> "FRA").as(SqlParser.str("Country.code").single)
// code == "First"

When a column is aliased, typically using SQL AS, its value can also be resolved. Following example parses column with country_lang alias.

import anorm.{ SQL, SqlParser }

val lang: String = SQL(
  """
    select l.language AS country_lang from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where c.code = {countryCode}
  """).on("countryCode" -> "FRA").
    as(SqlParser.str("country_lang").single)

Columns can also be specified by position, rather than name:

import anorm.SqlParser.{ str, float }
// Parsing column by name or position
val parser = 
  str("name") ~ float(3) /* third column as float */ map {
    case name ~ f => (name -> f)
  }

val product: (String, Float) = SQL("SELECT * FROM prod WHERE id = {id}").
  on('id -> "p").as(parser.single)

If the columns are not strictly defined (e.g. with types that can vary), the SqlParser.folder can be used to fold each row in a custom way.

import anorm.{ RowParser, SqlParser }

val parser: RowParser[Map[String, Any]] = 
  SqlParser.folder(Map.empty[String, Any]) { (map, value, meta) => 
    Right(map + (meta.column.qualified -> value))
  }

val result: List[Map[String, Any]] = SQL"SELECT * FROM dyn_table".as(parser.*)

If the columns are not strictly defined (e.g. with types that can vary), the SqlParser.folder can be used to fold each row in a custom way.

import anorm.{ RowParser, SqlParser }

val parser: RowParser[Map[String, Any]] = 
  SqlParser.folder(Map.empty[String, Any]) { (map, value, meta) => 
    Right(map + (meta.column.qualified -> value))
  }

val result: List[Map[String, Any]] = SQL"SELECT * FROM dyn_table".as(parser.*)

§SQL queries using String Interpolation

Since Scala 2.10 supports custom String Interpolation there is also a 1-step alternative to SQL(queryString).on(params) seen before. You can abbreviate the code as:

val name = "Cambridge"
val country = "New Zealand"

SQL"insert into City(name, country) values ($name, $country)"

It also supports multi-line string and inline expresions:

val lang = "French"
val population = 10000000
val margin = 500000

val code: String = SQL"""
  select * from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where l.Language = $lang and c.Population >= ${population - margin}
    order by c.Population desc limit 1"""
  .as(SqlParser.str("Country.code").single)

This feature tries to make faster, more concise and easier to read the way to retrieve data in Anorm. Please, feel free to use it wherever you see a combination of SQL().on() functions (or even an only SQL() without parameters).

By using #$value instead of $value, interpolated value will be part of the prepared statement, rather being passed as a parameter when executing this SQL statement (e.g. #$cmd and #$table in example bellow).

val cmd = "SELECT"
val table = "Test"

SQL"""#$cmd * FROM #$table WHERE id = ${"id1"} AND code IN (${Seq(2, 5)})"""

// prepare the SQL statement, with 1 string and 2 integer parameters:
// SELECT * FROM Test WHERE id = ? AND code IN (?, ?)

§Generated parsers

The macro namedParser[T] can be used to create a RowParser[T] at compile-time, for any case class T.

import anorm.{ Macro, RowParser }

case class Info(name: String, year: Option[Int])

val parser: RowParser[Info] = Macro.namedParser[Info]
/* Generated as:
get[String]("name") ~ get[Option[Int]]("year") map {
  case name ~ year => Info(name, year)
}
*/

val result: List[Info] = SQL"SELECT * FROM list".as(parser.*)

A similar macro indexedParser[T] is available to get column values by positions instead of names.

import anorm.{ Macro, RowParser }

case class Info(name: String, year: Option[Int])

val parser: RowParser[Info] = Macro.indexedParser[Info]
/* Generated as:
get[String](1) ~ get[Option[Int]](2) map {
  case name ~ year => Info(name, year)
}
*/

val result: List[Info] = SQL"SELECT * FROM list".as(parser.*)

To indicate custom names for the columns to be parsed, the macro parser[T](names) can be used.

import anorm.{ Macro, RowParser }

case class Info(name: String, year: Option[Int])

val parser: RowParser[Info] = Macro.parser[Info]("a_name", "creation")
/* Generated as:
get[String]("a_name") ~ get[Option[Int]]("creation") map {
  case name ~ year => Info(name, year)
}
*/

val result: List[Info] = SQL"SELECT * FROM list".as(parser.*)

The anorm.macro.debug system property can be set to true (e.g. sbt -Danorm.macro.debug=true ...) to debug the generated parsers.

§Streaming results

Query results can be processed row per row, not having all loaded in memory.

In the following example we will count the number of country rows.

val countryCount: Either[List[Throwable], Long] = 
  SQL"Select count(*) as c from Country".fold(0L) { (c, _) => c + 1 }

In previous example, either it’s the successful Long result (right), or the list of errors (left).

Result can also be partially processed:

val books: Either[List[Throwable], List[String]] = 
  SQL("Select name from Books").foldWhile(List[String]()) { (list, row) => 
    if (list.size == 100) (list -> false) // stop with `list`
    else (list := row[String]("name")) -> true // continue with one more name
  }

It’s possible to use a custom streaming:

import anorm.{ Cursor, Row }

@annotation.tailrec
def go(c: Option[Cursor], l: List[String]): List[String] = c match {
  case Some(cursor) => {
    if (l.size == 100) l // custom limit, partial processing
    else {
      go(cursor.next, l :+ cursor.row[String]("name"))
    }
  }
  case _ => l
}

val books: Either[List[Throwable], List[String]] = 
  SQL("Select name from Books").withResult(go(_, List.empty[String]))

The parsing API can be used with streaming, using RowParser on each cursor .row. The previous example can be updated with row parser.

import scala.util.{ Try, Success => TrySuccess, Failure }

// bookParser: anorm.RowParser[Book]

@annotation.tailrec
def go(c: Option[Cursor], l: List[Book]): Try[List[Book]] = c match {
  case Some(cursor) => {
    if (l.size == 100) l // custom limit, partial processing
    else {
      val parsed: Try[Book] = cursor.row.as(bookParser)

      parsed match {
        case TrySuccess(book) => // book successfully parsed from row
          go(cursor.next, l :+ book)
        case Failure(f) => /* fails to parse a book */ Failure(f)
      }
    }
  }
  case _ => l
}

val books: Either[List[Throwable], Try[List[Book]]] = 
  SQL("Select name from Books").withResult(go(_, List.empty[Book]))

books match {
  case Left(streamingErrors) => ???
  case Right(Failure(parsingError)) => ???
  case Right(TrySuccess(listOfBooks)) => ???
}

§Multi-value support

Anorm parameter can be multi-value, like a sequence of string.
In such case, values will be prepared to be passed to JDBC.

// With default formatting (", " as separator)
SQL("SELECT * FROM Test WHERE cat IN ({categories})").
  on('categories -> Seq("a", "b", "c")
// -> SELECT * FROM Test WHERE cat IN ('a', 'b', 'c')

// With custom formatting
import anorm.SeqParameter
SQL("SELECT * FROM Test t WHERE {categories}").
  on('categories -> SeqParameter(
    values = Seq("a", "b", "c"), separator = " OR ", 
    pre = "EXISTS (SELECT NULL FROM j WHERE t.id=j.id AND name=",
    post = ")"))
/* ->
SELECT * FROM Test t WHERE 
EXISTS (SELECT NULL FROM j WHERE t.id=j.id AND name='a') 
OR EXISTS (SELECT NULL FROM j WHERE t.id=j.id AND name='b') 
OR EXISTS (SELECT NULL FROM j WHERE t.id=j.id AND name='c')
*/

On purpose multi-value parameter must strictly be declared with one of supported types (List, ’Seq,Set,SortedSet,Stream,VectorandSeqParameter`). Value of a subtype must be passed as parameter with supported:

val seq = IndexedSeq("a", "b", "c")
// seq is instance of Seq with inferred type IndexedSeq[String]

// Wrong
SQL"SELECT * FROM Test WHERE cat in ($seq)"
// Erroneous - No parameter conversion for IndexedSeq[T]

// Right
SQL"SELECT * FROM Test WHERE cat in (${seq: Seq[String]})"

// Right
val param: Seq[String] = seq
SQL"SELECT * FROM Test WHERE cat in ($param)"

In case parameter type is JDBC array (java.sql.Array), its value can be passed as Array[T], as long as element type T is a supported one.

val arr = Array("fr", "en", "ja")
SQL"UPDATE Test SET langs = $arr".execute()

A column can also be multi-value if its type is JDBC array (java.sql.Array), then it can be mapped to either array or list (Array[T] or List[T]), provided type of element (T) is also supported in column mapping.

import anorm.SQL
import anorm.SqlParser.{ scalar, * }

// array and element parser
import anorm.Column.{ columnToArray, stringToArray }

val res: List[Array[String]] =
  SQL("SELECT str_arr FROM tbl").as(scalar[Array[String]].*)

Convenient parsing functions is also provided for arrays with SqlParser.array[T](...) and SqlParser.list[T](...).

§Batch update

When you need to execute SQL statement several times with different arguments, batch query can be used (e.g. to execute a batch of insertions).

import anorm.BatchSql

val batch = BatchSql(
  "INSERT INTO books(title, author) VALUES({title}, {author})", 
  Seq[NamedParameter]("title" -> "Play 2 for Scala", 
    "author" -> "Peter Hilton"),
  Seq[NamedParameter]("title" -> "Learning Play! Framework 2", 
    "author" -> "Andy Petrella"))

val res: Array[Int] = batch.execute() // array of update count

Batch update must be called with at least one list of parameter. If a batch is executed with the mandatory first list of parameter being empty (e.g. Nil), only one statement will be executed (without parameter), which is equivalent to SQL(statement).executeUpdate().

§Edge cases

Type of parameter value should be visible, to be properly set on SQL statement.
Using value as Any, explicitly or due to erasure, leads to compilation error No implicit view available from Any => anorm.ParameterValue.

// Wrong #1
val p: Any = "strAsAny"
SQL("SELECT * FROM test WHERE id={id}").
  on('id -> p) // Erroneous - No conversion Any => ParameterValue

// Right #1
val p = "strAsString"
SQL("SELECT * FROM test WHERE id={id}").on('id -> p)

// Wrong #2
val ps = Seq("a", "b", 3) // inferred as Seq[Any]
SQL("SELECT * FROM test WHERE (a={a} AND b={b}) OR c={c}").
  on('a -> ps(0), // ps(0) - No conversion Any => ParameterValue
    'b -> ps(1), 
    'c -> ps(2))

// Right #2
val ps = Seq[anorm.ParameterValue]("a", "b", 3) // Seq[ParameterValue]
SQL("SELECT * FROM test WHERE (a={a} AND b={b}) OR c={c}").
  on('a -> ps(0), 'b -> ps(1), 'c -> ps(2))

// Wrong #3
val ts = Seq( // Seq[(String -> Any)] due to _2
  "a" -> "1", "b" -> "2", "c" -> 3)

val nps: Seq[NamedParameter] = ts map { t => 
  val p: NamedParameter = t; p
  // Erroneous - no conversion (String,Any) => NamedParameter
}

SQL("SELECT * FROM test WHERE (a={a} AND b={b}) OR c={c}").on(nps :_*) 

// Right #3
val nps = Seq[NamedParameter]( // Tuples as NamedParameter before Any
  "a" -> "1", "b" -> "2", "c" -> 3)
SQL("SELECT * FROM test WHERE (a={a} AND b={b}) OR c={c}").
  on(nps: _*) // Fail - no conversion (String,Any) => NamedParameter

In some cases, some JDBC drivers returns a result set positioned on the first row rather than before this first row (e.g. stored procedured with Oracle JDBC driver).
To handle such edge-case, .withResultSetOnFirstRow(true) can be used as following.

SQL("EXEC stored_proc {arg}").on("arg" -> "val").withResultSetOnFirstRow(true)
SQL"""EXEC stored_proc ${"val"}""".withResultSetOnFirstRow(true)

SQL"INSERT INTO dict(term, definition) VALUES ($term, $definition)".
  withResultSetOnFirstRow(true).executeInsert()
// Also needed on executeInsert for such driver, 
// as a ResultSet is returned in this case for the generated keys

§Using Pattern Matching

You can also use Pattern Matching to match and extract the Row content. In this case the column name doesn’t matter. Only the order and the type of the parameters is used to match.

The following example transforms each row to the correct Scala type:

import java.sql.Connection
import anorm._

trait Country
case class SmallCountry(name:String) extends Country
case class BigCountry(name:String) extends Country
case object France extends Country

val patternParser = RowParser[Country] {
  case Row("France", _) => Success(France)
  case Row(name:String, pop:Int) if (pop > 1000000) => Success(BigCountry(name))
  case Row(name:String, _) => Success(SmallCountry(name))
  case row => Error(TypeDoesNotMatch(s"unexpected: $row"))
}

def countries(implicit con: Connection): List[Country] =
  SQL("SELECT name,population FROM Country WHERE id = {i}").
    on("i" -> "id").as(patternParser.*)

§Using for-comprehension

Row parser can be defined as for-comprehension, working with SQL result type. It can be useful when working with lot of column, possibly to work around case class limit.

import anorm.SqlParser.{ str, int }

val parser = for {
  a <- str("colA")
  b <- int("colB")
} yield (a -> b)

val parsed: (String, Int) = SELECT("SELECT * FROM Test").as(parser.single)

§Retrieving data along with execution context

Moreover data, query execution involves context information like SQL warnings that may be raised (and may be fatal or not), especially when working with stored SQL procedure.

Way to get context information along with query data is to use executeQuery():

import anorm.SqlQueryResult

val res: SqlQueryResult = SQL("EXEC stored_proc {code}").
  on('code -> code).executeQuery()

// Check execution context (there warnings) before going on
val str: Option[String] =
  res.statementWarning match {
    case Some(warning) =>
      warning.printStackTrace()
      None

    case _ => res.as(scalar[String].singleOpt) // go on row parsing
  }

§Working with optional/nullable values

If a column in database can contain Null values, you need to parse it as an Option type.

For example, the indepYear of the Country table is nullable, so you need to match it as Option[Int]:

case class Info(name: String, year: Option[Int])

val parser = str("name") ~ get[Option[Int]]("indepYear") map {
  case n ~ y => Info(n, y)
}

val res: List[Info] = SQL("Select name,indepYear from Country").as(parser.*)

If you try to match this column as Int it won’t be able to parse Null values. Suppose you try to retrieve the column content as Int directly from the dictionary:

SQL("Select name,indepYear from Country")().map { row =>
  row[String]("name") -> row[Int]("indepYear")
}

This will produce an UnexpectedNullableFound(COUNTRY.INDEPYEAR) exception if it encounters a null value, so you need to map it properly to an Option[Int].

A nullable parameter is also passed as Option[T], T being parameter base type (see Parameters section thereafter).

Passing directly None for a NULL value is not supported, as inferred as Option[Nothing] (Nothing being unsafe for a parameter value). In this case, Option.empty[T] must be used.

// OK: 

SQL("INSERT INTO Test(title) VALUES({title})").on("title" -> Some("Title"))

val title1 = Some("Title1")
SQL("INSERT INTO Test(title) VALUES({title})").on("title" -> title1)

val title2: Option[String] = None
// None inferred as Option[String] on assignment
SQL("INSERT INTO Test(title) VALUES({title})").on("title" -> title2)

// Not OK:
SQL("INSERT INTO Test(title) VALUES({title})").on("title" -> None)

// OK:
SQL"INSERT INTO Test(title) VALUES(${Option.empty[String]})"

§Using the Parser API

You can use the parser API to create generic and reusable parsers that can parse the result of any select query.

Note: This is really useful, since most queries in a web application will return similar data sets. For example, if you have defined a parser able to parse a Country from a result set, and another Language parser, you can then easily compose them to parse both Country and Language from a join query.

First you need to import anorm.SqlParser._

§Getting a single result

First you need a RowParser, i.e. a parser able to parse one row to a Scala value. For example we can define a parser to transform a single column result set row, to a Scala Long:

val rowParser = scalar[Long]

Then we have to transform it into a ResultSetParser. Here we will create a parser that parse a single row:

val rsParser = scalar[Long].single

So this parser will parse a result set to return a Long. It is useful to parse to result produced by a simple SQL select count query:

val count: Long = 
  SQL("select count(*) from Country").as(scalar[Long].single)

If expected single result is optional (0 or 1 row), then scalar parser can be combined with singleOpt:

val name: Option[String] =
  SQL"SELECT name FROM Country WHERE code = $code" as scalar[String].singleOpt

§Getting a single optional result

Let’s say you want to retrieve the country_id from the country name, but the query might return null. We’ll use the singleOpt parser :

val countryId: Option[Long] = 
  SQL("SELECT country_id FROM Country C WHERE C.country='France'")
  .as(scalar[Long].singleOpt)

§Getting a more complex result

Let’s write a more complicated parser:

str("name") ~ int("population"), will create a RowParser able to parse a row containing a String name column and an Integer population column. Then we can create a ResultSetParser that will parse as many rows of this kind as it can, using *:

val populations: List[String ~ Int] = 
  SQL("SELECT * FROM Country").as((str("name") ~ int("population")).*) 

As you see, this query’s result type is List[String ~ Int] - a list of country name and population items.

You can also rewrite the same code as:

val result: List[String ~ Int] = SQL("SELECT * FROM Country").
  as((get[String]("name") ~ get[Int]("population")).*)

Now what about the String~Int type? This is an Anorm type that is not really convenient to use outside of your database access code. You would rather have a simple tuple (String, Int) instead. You can use the map function on a RowParser to transform its result to a more convenient type:

val parser = str("name") ~ int("population") map { case n ~ p => (n, p) }

Note: We created a tuple (String, Int) here, but there is nothing stopping you from transforming the RowParser result to any other type, such as a custom case class.

Now, because transforming A ~ B ~ C types to (A, B, C) is a common task, we provide a flatten function that does exactly that. So you finally write:

val result: List[(String, Int)] = 
  SQL("select * from Country").as(parser.flatten.*)

A RowParser can be combined with any function to applied it with extracted columns.

import anorm.SqlParser.{ int, str, to }

def display(name: String, population: Int): String = 
  s"The population in $name is of $population."

val parser = str("name") ~ int("population") map (to(display _))

Note: The mapping function must be partially applied (syntax fn _) when given to the parser (see SLS 6.26.2, 6.26.5 - Eta expansion).

If list should not be empty, parser.+ can be used instead of parser.*.

Anorm is providing parser combinators other than the most common ~ one: ~>, <~.

import anorm.{ SQL, SqlParser }, SqlParser.{ int, str }

// Combinator ~>
val String = SQL("SELECT * FROM test").as((int("id") ~> str("val")).single)
  // row has to have an int column 'id' and a string 'val' one,
  // keeping only 'val' in result

val Int = SQL("SELECT * FROM test").as((int("id") <~ str("val")).single)
  // row has to have an int column 'id' and a string 'val' one,
  // keeping only 'id' in result

§A more complicated example

Now let’s try with a more complicated example. How to parse the result of the following query to retrieve the country name and all spoken languages for a country code?

select c.name, l.language from Country c 
    join CountryLanguage l on l.CountryCode = c.Code 
    where c.code = 'FRA'

Let’s start by parsing all rows as a List[(String,String)] (a list of name,language tuple):

var p: ResultSetParser[List[(String,String)]] = {
  str("name") ~ str("language") map(flatten) *
}

Now we get this kind of result:

List(
  ("France", "Arabic"), 
  ("France", "French"), 
  ("France", "Italian"), 
  ("France", "Portuguese"), 
  ("France", "Spanish"), 
  ("France", "Turkish")
)

We can then use the Scala collection API, to transform it to the expected result:

case class SpokenLanguages(country:String, languages:Seq[String])

languages.headOption.map { f =>
  SpokenLanguages(f._1, languages.map(_._2))
}

Finally, we get this convenient function:

case class SpokenLanguages(country:String, languages:Seq[String])

def spokenLanguages(countryCode: String): Option[SpokenLanguages] = {
  val languages: List[(String, String)] = SQL(
    """
      select c.name, l.language from Country c 
      join CountryLanguage l on l.CountryCode = c.Code 
      where c.code = {code};
    """
  )
  .on("code" -> countryCode)
  .as(str("name") ~ str("language") map(flatten) *)

  languages.headOption.map { f =>
    SpokenLanguages(f._1, languages.map(_._2))
  }
}

To continue, let’s complicate our example to separate the official language from the others:

case class SpokenLanguages(
  country:String, 
  officialLanguage: Option[String], 
  otherLanguages:Seq[String]
)

def spokenLanguages(countryCode: String): Option[SpokenLanguages] = {
  val languages: List[(String, String, Boolean)] = SQL(
    """
      select * from Country c 
      join CountryLanguage l on l.CountryCode = c.Code 
      where c.code = {code};
    """
  )
  .on("code" -> countryCode)
  .as {
    str("name") ~ str("language") ~ str("isOfficial") map {
      case n~l~"T" => (n,l,true)
      case n~l~"F" => (n,l,false)
    } *
  }

  languages.headOption.map { f =>
    SpokenLanguages(
      f._1, 
      languages.find(_._3).map(_._2),
      languages.filterNot(_._3).map(_._2)
    )
  }
}

If you try this on the MySQL world sample database, you will get:

$ spokenLanguages("FRA")
> Some(
    SpokenLanguages(France,Some(French),List(
        Arabic, Italian, Portuguese, Spanish, Turkish
    ))
)

§JDBC mappings

As already seen in this documentation, Anorm provides builtins converters between JDBC and JVM types.

§Column parsers

Following table describes which JDBC numeric types (getters on java.sql.ResultSet, first column) can be parsed to which Java/Scala types (e.g. integer column can be read as double value).

• 1. Types java.math.BigDecimal and scala.math.BigDecimal.
• 1. Types java.math.BigInteger and scala.math.BigInt.

The second table shows mappings for the other supported types.

• 1. Array which type T of elements is supported.
• 1. Type java.util.UUID.
• 1. Type java.sql.Array.
• 1. Type java.lang.Iterable[_].

When an instance of java.util.UUID is used as a parameter, its string value is passed to statement. If the underlying database offers a specific SQL datatype for ID, it’s possible to cast the stringified value; e.g. Working with PostgreSQL uuid type: SQL"SELECT * FROM table WHERE anPostgreSQLuuid = ${aJavaUUID}::uuid"

Optional column can be parsed as Option[T], as soon as T is supported.

Binary data types are also supported.

• 1. Type java.io.InputStream.
• 1. Type java.sql.Blob.
• 1. Type java.sql.Clob.
• 1. Type java.io.Reader.

CLOBs/TEXTs can be extracted as so:

SQL("Select name,summary from Country")().map {
  case Row(name: String, summary: java.sql.Clob) => name -> summary
}

Here we specifically chose to use map, as we want an exception if the row isn’t in the format we expect.

Extracting binary data is similarly possible:

SQL("Select name,image from Country")().map {
  case Row(name: String, image: Array[Byte]) => name -> image
}

For types where column support is provided by Anorm, convenient functions are available to ease writing custom parsers. Each of these functions parses column either by name or index (> 1).

import anorm.SqlParser.str // String function

str("column")
str(1/* columnIndex)

• 1. Type java.io.InputStream.

The Joda and Java 8 temporal types are also supported.

• 1. Types java.util.Date, org.joda.time.LocalDate and java.time.LocalDate.
• 1. Types org.joda.time.DateTime, org.joda.time.LocalDateTime, java.time.LocalDateTime and java.time.ZonedDateTime.
• 1. Type org.joda.time.Instant and java.time.Instant (see Java 8).
• 1. Any type with a getter getTimestamp returning a java.sql.Timestamp.

It’s possible to add custom mapping, for example if underlying DB doesn’t support boolean datatype and returns integer instead. To do so, you have to provide a new implicit conversion for Column[T], where T is the target Scala type:

import anorm.Column

// Custom conversion from JDBC column to Boolean
implicit def columnToBoolean: Column[Boolean] = 
  Column.nonNull1 { (value, meta) =>
    val MetaDataItem(qualified, nullable, clazz) = meta
    value match {
      case bool: Boolean => Right(bool) // Provided-default case
      case bit: Int      => Right(bit == 1) // Custom conversion
      case _             => Left(TypeDoesNotMatch(s"Cannot convert $value: ${value.asInstanceOf[AnyRef].getClass} to Boolean for column $qualified"))
    }
  }

§Parameters

The following table indicates how JVM types are mapped to JDBC parameter types:

• 1. Type Scala Array[T].
• 1. Type java.sql.Array.
• 1. Types java.math.BigDecimal and scala.math.BigDecimal.
• 1. Types java.math.BigInteger and scala.math.BigInt.
• 1. Types Boolean and java.lang.Boolean.
• 1. Types Byte and java.lang.Byte.
• 1. Types Char and java.lang.Character.
• 1. Types compatible with java.util.Date, and any wrapper type with getTimestamp: java.sql.Timestamp.
• 1. Types Double and java.lang.Double.
• 1. Types Float and java.lang.Float.
• 1. Types Int and java.lang.Integer.
• 1. Types Long and java.lang.Long.
• 1. Type anorm.Object, wrapping opaque object.
• 1. Multi-value parameter, with one JDBC placeholder (?) added for each element.
• 1. Type scala.collection.immutable.Set.
• 1. Types Short and java.lang.Short.
• 1. Type scala.collection.immutable.SortedSet.
• 1. Not-null value extracted using .toString.

Passing None for a nullable parameter is deprecated, and typesafe Option.empty[T] must be use instead.

Large and stream parameters are also supported.

• 1. Type java.sql.Blob
• 1. Type java.io.InputStream
• 1. Type java.io.Reader

Joda and Java 8 temporal types are supported as parameters:

• 1. Type org.joda.time.DateTime.
• 1. Types org.joda.time.Instant and java.time.Instant.
• 1. Types org.joda.time.LocalDate and java.time.LocalDate.
• 1. Types org.joda.time.LocalDateTime, org.joda.time.LocalDate and java.time.LocalDateTime.
• 1. Type java.time.ZonedDateTime

To enable Joda types as parameter, the import anorm.JodaParameterMetaData._ must be used.

Custom or specific database conversion for parameter can also be provided:

import java.sql.PreparedStatement
import anorm.ToStatement

// Custom conversion to statement for type T
implicit def customToStatement: ToStatement[T] = new ToStatement[T] {
  def set(statement: PreparedStatement, i: Int, value: T): Unit =
    ??? // Sets |value| on |statement|
}

If involved type accept null value, it must be appropriately handled in conversion. The NotNullGuard trait can be used to explicitly refuse null values in parameter conversion: new ToStatement[T] with NotNullGuard { /* ... */ }.

DB specific parameter can be explicitly passed as opaque value.
In this case at your own risk, setObject will be used on statement.

val anyVal: Any = myVal
SQL("UPDATE t SET v = {opaque}").on('opaque -> anorm.Object(anyVal))

Next: Integrating with other database access libraries