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 ScalaDatabase]].


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(
  "org.playframework.anorm" %% "anorm" % "2.7.0"

Table of contents:

See release notes

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 with the help of DI:

import anorm._

database.withConnection { implicit c =>
  val _: 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 a key generated is on insertion is not a single Long, executeInsert can be passed a ResultSetParser to return the correct key.

import anorm._

val id: List[String] = 
  SQL("insert into City(name, country) values ({name}, {country})")
  .on("name" -> "Cambridge", "country" -> "New Zealand")
  .executeInsert(str(1).+) // 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:

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

The curly braces can be escaped using \: SQL("SELECT * FROM test WHERE code = '\{foo\}'").

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._

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._

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._

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").

The columns can also be specified by position, rather than name:

import anorm._

// 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._

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.*)

Table alias:

With some databases, it’s possible to define aliases for table (or for sub-query), as in the following example.

=> SELECT * FROM test t1 JOIN (SELECT * FROM test WHERE parent_id ISNULL) t2 ON WHERE'bar';
 id  | value  | parent_id | id  | value  | parent_id 
 bar | value2 | foo       | foo | value1 | 
(1 row)

Unfortunately, such aliases are not supported in JDBC, so Anorm introduces the ColumnAliaser to be able to define user aliases over columns.

import anorm._

val parser: RowParser[(String, String, String, Option[String])] = SqlParser.str("id") ~ SqlParser.str("value") ~ SqlParser.str("parent.value") ~ SqlParser.str("parent.parent_id").? map(SqlParser.flatten)

val aliaser: ColumnAliaser = ColumnAliaser.withPattern((3 to 6).toSet, "parent.")

val res: Try[(String, String, String, Option[String])] = SQL"""SELECT * FROM test t1 JOIN (SELECT * FROM test WHERE parent_id ISNULL) t2 ON WHERE${"bar"}""".asTry(parser.single, aliaser)

res.foreach {
  case (id, value, parentVal, grandPaId) => ???

Passing parameters

Values can be easily bound as query parameters using Anorm.

With placeholders in the query statement, the .on(..) binding can be used.

SQL("SELECT * FROM a_table WHERE col = {placeholder1}").
  on("placeholder1" -> "paramValue")

Otherwise, the Anorm Interpolation is available.

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"""

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 (?, ?)

Multi-value parameter

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

import anorm._

// 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
SQL("SELECT * FROM Test t WHERE {categories}").
  on("categories" -> SeqParameter(
    values = Seq("a", "b", "c"), separator = " OR ", 
    post = ")"))
/* ->

On purpose multi-value parameter must strictly be declared with one of supported types (List, Seq, Set, SortedSet, Stream, Vector and SeqParameter). 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._, 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 a same update several times with different arguments, a batch query can be used (e.g. to execute a batch of insertions).

import anorm._

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

A 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().

Custom parameter conversions

It’s possible to define custom or database specific conversion for parameters.

import java.sql.PreparedStatement

import anorm._

// 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|

// Metadata about the custom parameter type
implicit def customParamMeta: ParameterMetaData[T] = new ParameterMetaData[T] {
  val sqlType = "VARCHAR"
  def jdbcType = java.sql.Types.VARCHAR

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))

Generated parameter conversions

Anorm also provides utility to generate parameter conversions for case classes.

import anorm._

case class Bar(v: Int)

val bar1 = Bar(1)

// Convert all supported properties as parameters
val toParams1: ToParameterList[Bar] = Macro.toParameters[Bar]

val params1: List[NamedParameter] = toParams1(bar1)
// --> List(NamedParameter(v,ParameterValue(1)))

val names1: List[String] =
// --> List(v)

val placeholders = { n => s"{$n}" } mkString ", "
// --> "{v}"

val generatedStmt = s"""INSERT INTO bar(${names1 mkString ", "}) VALUES ($placeholders)"""
val generatedSql1 = SQL(generatedStmt).on(params1: _*)

Using Macro.ParameterProjection is possible to customize the parameter names, instead of using the names of the class properties by default.

// Convert only `v` property as `w`
implicit val toParams2: ToParameterList[Bar] = Macro.toParameters(
  Macro.ParameterProjection("v", "w"))

// --> List(NamedParameter(w,ParameterValue(1)))

val insert1 = SQL("INSERT INTO table(col_w) VALUES ({w})").
  bind(bar1) // bind bar1 as params implicit toParams2

For a property bar of a case class Foo which whose type is itself a case class Bar, the appropriate instance of ToParameterList[Bar] will be resolved from the implicit scope to be able to generate ToParameterList[Foo].

case class Foo(n: Int, bar: Bar)

val foo1 = Foo(2, bar1)

// For Nested case class
val toParams3: ToParameterList[Foo] =
  Macro.toParameters() // uses `toParams2` from implicit scope for `bar` property

// --> List(NamedParameter(n,ParameterValue(2)), NamedParameter(bar_w,ParameterValue(1)))
// * bar_w = Bar.{v=>w} with Bar instance itself as `bar` property of Foo

By default, the nested properties (e.g. Bar.w) are represented using _ as separator, as for bar_w in the previous example. A custom separator can be specified.

// With parameter projection (aliases) and custom separator # instead of the default _
val toParams4: ToParameterList[Foo] =
  Macro.toParameters("#", Macro.ParameterProjection("bar", "lorem"))

// --> List(NamedParameter(lorem#w,ParameterValue(1)))

A sealed trait with some known subclasses can also be supported.

sealed trait Family
case class Sub1(v: Int) extends Family
case object Sub2 extends Family

val sealedToParams: ToParameterList[Family] = {
  // the instances for the subclasses need to be in the implicit scope first
  implicit val sub1ToParams = Macro.toParameters[Sub1]
  implicit val sub2ToParams = ToParameterList.empty[Sub2.type]


The value classes can be supported, if the underlying value itself <: Any.

final class ValueClassType(val underlying: Double) extends AnyVal

object Parameters2 {
  import anorm._

  val valueClassToStatement: ToStatement[ValueClassType] =

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

A type which is provided a ToParameterList instance can be used to bind a value as parameters.

import anorm._

val query = SQL("INSERT INTO foo(id, bar) VALUES ({n}, {bar_v})").
  bind(Foo(1, Bar(2)))

Also see the additional module for Enumeratum.

Parsing rows

Anorm provides several ways to handle and parse the row retrieved by the database queries.

Generated row parsers

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

import anorm._

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.*)

The similar macros indexedParser[T] and offsetParser[T] are available to get column values by positions instead of names.

import anorm._

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

val parser1: RowParser[Info] = Macro.indexedParser[Info]
/* Generated as:
get[String](1) ~ get[Option[Int]](2) map {
  case name ~ year => Info(name, year)

val result1: List[Info] = SQL"SELECT * FROM list".as(parser1.*)

// With offset
val parser2: RowParser[Info] = Macro.offsetParser[Info](2)
/* Generated as:
get[String](2 + 1) ~ get[Option[Int]](2 + 2) map {
  case name ~ year => Info(name, year)

val result2: List[Info] = SQL"SELECT * FROM list".as(parser2.*)

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

import anorm._

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.*)

It’s also possible to configure the named parsers using a naming strategy for the corresponding columns.

import anorm._

case class Info(name: String, lastModified: Long)

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

A custom column naming can be defined using ColumnNaming(String => String).

The RowParser exposed in the implicit scope can be used as nested one generated by the macros.

case class Bar(lorem: Float, ipsum: Long)
case class Foo(name: String, bar: Bar, age: Int)

import anorm._

// nested parser
implicit val barParser = Macro.parser[Bar]("bar_lorem", "bar_ipsum")

val fooBar = Macro.namedParser[Foo] /* generated as:
  get[String]("name") ~ barParser ~ get[Int]("age") map {
    case name ~ bar ~ age => Foo(name, bar, age)

val result: Foo = SQL"""SELECT, age, bar_lorem, bar_ipsum 
  FROM foo f JOIN bar b ON WHERE${"Foo"}""".

A row parser for sealed trait can be generated by the macro sealedParser.

import anorm._

sealed trait Family
case class Bar(v: Int) extends Family
case object Lorem extends Family

// First, RowParser instances for all the subtypes must be provided,
// either by macros or by custom parsers
implicit val barParser = Macro.namedParser[Bar]
implicit val loremParser = RowParser[Lorem.type] {
  _ /*anyRowDiscriminatedAsLorem*/ => Success(Lorem)

val familyParser = Macro.sealedParser[Family]

// Generate a parser as following...
val generated: RowParser[Family] =
  SqlParser.str("classname").flatMap { discriminator: String =>
    discriminator match {
      case "scalaguide.sql.MacroParsers.Bar" =>

      case "scalaguide.sql.MacroParsers.Lorem" =>

      case (d) => RowParser.failed[Family](Error(SqlMappingError(
        "unexpected row type \'%s\'; expected: %s".format(d, "scalaguide.sql.MacroParsers.Bar, scalaguide.sql.MacroParsers.Lorem"))))

As it can be seen in the previous example with the generated code working with the discriminator value, a column named “classname” is expected to specify the fully qualified name of a subtype (e.g. scalaguide.sql.MacroFixtures.Bar for the child case class Bar).

The discriminator strategy can be customized.

import anorm._

// Defines the name of the discriminator column according the family name;
// There use "foo" as column name for any family
val naming = DiscriminatorNaming(_ => "foo")

// How to use the name of each family type as a discriminator value;
// There "type:<type-name>"
val discriminate = Discriminate { t => s"type:$t" }

val familyParser = Macro.sealedParser[Family](naming, discriminate)

The value classes are also supported, by generated instances of anorm.Column.

import anorm._

final class ValueClassType(underlying: Double) extends AnyVal

implicit val valueClassColumn: Column[ValueClassType] =

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

Parser API

You can use the parser API to create custom parsers that can handle the result of the queries.

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._

Parsing 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

Parsing 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'France'")

Parsing a more complex result

Let’s write a more complete parser:

str("name") ~ int("population")

It 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._, 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

Complete example

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

select, 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:

  ("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]) { f =>

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, 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) *) { f =>

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

case class SpokenLanguages(
  officialLanguage: Option[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)
    } *
  } { f =>

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

$ spokenLanguages("FRA")
> Some(
        Arabic, Italian, Portuguese, Spanish, Turkish

Handling 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 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

A 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) = SQL("SELECT * FROM Test").as(parser.single)

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._

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(, 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 }

import anorm._

// bookParser: anorm.RowParser[Book]

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] =

      parsed match {
        case TrySuccess(book) => // book successfully parsed from row
          go(, 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)) => ???

Akka Stream

The query result from Anorm can be processed as Source with Akka Stream.

To do so, the Anorm Akka module must be used.

libraryDependencies ++= Seq(
  "org.playframework.anorm" %% "anorm-akka" % "ANORM_VERSION",
  "com.typesafe.akka" %% "akka-stream" % "2.4.12")

This module is tested with Akka Stream 2.4.12.

Once this library is available, the query can be used as streaming source.

import java.sql.Connection

import scala.concurrent.Future

import{ Sink, Source }

import anorm._

def resultSource(implicit m: Materializer, con: Connection): Source[String, Future[Int]] = AkkaStream.source(SQL"SELECT * FROM Test", SqlParser.scalar[String], ColumnAliaser.empty)

def countStrings()(implicit m: Materializer, con: Connection): Future[Int] =
    Sink.fold[Int, String](0) { (count, str) => count + str.length })

It materializes a Future containing either the number of read rows from the source if successful, or the exception if row parsing failed. This could be useful to actually close the connection afterwards.

import java.sql.Connection

import scala.concurrent.Future

import{ Sink, Source }

import anorm._

def source(implicit m: Materializer, connection: Connection): Source[String, Future[Int]]#ReprMat[String, Unit] = 
  AkkaStream.source(SQL"SELECT * FROM Test", SqlParser.scalar[String], ColumnAliaser.empty)
    .mapMaterializedValue(_.onComplete { _ =>


It’s possible to use Anorm along with Play Iteratees, using the following dependencies.

libraryDependencies ++= Seq(
  "org.playframework.anorm" %% "anorm-iteratee" % "ANORM_VERSION",
  "" %% "play-iteratees" % "ITERATEES_VERSION")

For a Play application, as play-iteratees is provided there is no need to add this dependency.

Since Scala 2.13, play-iteratees is no longer available.

Then the parsed results from Anorm can be turned into Enumerator.

import java.sql.Connection
import anorm._
import play.api.libs.iteratee._

def resultAsEnumerator(implicit con: Connection): Enumerator[String] =
  Iteratees.from(SQL"SELECT * FROM Test", SqlParser.scalar[String])

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._

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) =>

    case _ =>[String].singleOpt) // go on row parsing

JDBC mappings

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

Also see the additional modules:

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).

↓JDBC / JVM➞ BigDecimal1 BigInteger2 Boolean Byte Double Float Int Long Short
BigDecimal1 Yes Yes No No Yes No Yes Yes No
BigInteger2 Yes Yes No No Yes Yes Yes Yes No
Boolean No No Yes Yes No No Yes Yes Yes
Byte Yes Yes No Yes Yes Yes Yes Yes Yes
Double Yes No No No Yes No No No No
Float Yes No No No Yes Yes No No No
Int Yes Yes No No Yes Yes Yes Yes No
Long Yes Yes No No No No Yes Yes No
Short Yes Yes No Yes Yes Yes Yes Yes Yes
  1. Types java.math.BigDecimal and scala.math.BigDecimal.
  2. Types java.math.BigInteger and scala.math.BigInt.

The second table shows mappings for the other supported types.

↓JDBC / JVM➞ Array[T]3 Char List3 String UUID4
Array5 Yes No Yes No No
Clob No Yes No Yes No
Iterable6 Yes No Yes No No
Long No No No No No
String No Yes No Yes Yes
UUID No No No No Yes

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

Binary data types are also supported.

↓JDBC / JVM➞ Array[Byte] InputStream1
Array[Byte] Yes Yes
Blob2 Yes Yes
Clob3 No No
InputStream4 Yes Yes
Reader5 No No
  1. Type
  2. Type java.sql.Blob.
  3. Type java.sql.Clob.
  4. Type

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(1/* columnIndex)
Type Function
Array[Byte] byteArray
Boolean bool
Byte byte
Date date
Double double
Float float
InputStream1 binaryStream
Int int
Long long
Short short
String str
  1. Type

The Joda and Java 8 temporal types are also supported.

↓JDBC / JVM➞ Date1 DateTime2 Instant3 Long
Date Yes Yes Yes Yes
Long Yes Yes Yes Yes
Timestamp Yes Yes Yes Yes
Timestamp wrapper5 Yes Yes Yes Yes
  1. Types java.util.Date, org.joda.time.LocalDate and java.time.LocalDate.
  2. Types org.joda.time.DateTime, org.joda.time.LocalDateTime, java.time.LocalDateTime and java.time.ZonedDateTime.
  3. Type org.joda.time.Instant and java.time.Instant (see Java 8).
  4. 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.nonNull { (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 bindings

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

JVM JDBC Nullable
Array[T]1 Array2 with T mapping for each element Yes
BigDecimal3 BigDecimal Yes
BigInteger4 BigDecimal Yes
Boolean5 Boolean Yes
Byte6 Byte Yes
Char7/String String Yes
Date/Timestamp Timestamp Yes
Double8 Double Yes
Float9 Float Yes
Int10 Int Yes
List[T] Multi-value11, with T mapping for each element No
Long12 Long Yes
Object13 Object Yes
Option[T] T being type if some defined value No
Seq[T] Multi-value, with T mapping for each element No
Set[T]14 Multi-value, with T mapping for each element No
Short15 Short Yes
SortedSet[T]16 Multi-value, with T mapping for each element No
Stream[T] Multi-value, with T mapping for each element No
UUID String17 No
Vector Multi-value, with T mapping for each element No
  1. Type Scala Array[T].
  2. Type java.sql.Array.
  3. Types java.math.BigDecimal and scala.math.BigDecimal.
  4. Types java.math.BigInteger and scala.math.BigInt.
  5. Types Boolean and java.lang.Boolean.
  6. Types Byte and java.lang.Byte.
  7. Types Char and java.lang.Character.
  8. Types compatible with java.util.Date, and any wrapper type with getTimestamp: java.sql.Timestamp.
  9. Types Double and java.lang.Double.
  10. Types Float and java.lang.Float.
  11. Types Int and java.lang.Integer.
  12. Types Long and java.lang.Long.
  13. Type anorm.Object, wrapping opaque object.
  14. Multi-value parameter, with one JDBC placeholder (?) added for each element.
  15. Type scala.collection.immutable.Set.
  16. Types Short and java.lang.Short.
  17. Type scala.collection.immutable.SortedSet.
  18. 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.

Array[Byte] Long varbinary
Blob1 Blob
InputStream2 Long varbinary
Reader3 Long varchar
  1. Type java.sql.Blob
  2. Type
  3. Type

Joda and Java 8 temporal types are supported as parameters:

DateTime1 Timestamp
Instant2 Timestamp
LocalDate3 Timestamp
LocalDateTime4 Timestamp
ZonedDateTime5 Timestamp
  1. Type org.joda.time.DateTime.
  2. Types org.joda.time.Instant and java.time.Instant.
  3. Types org.joda.time.LocalDate and java.time.LocalDate.
  4. Types org.joda.time.LocalDateTime, org.joda.time.LocalDate and java.time.LocalDateTime.
  5. Type java.time.ZonedDateTime

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


This section gathers some errors/warnings you can encounter when using Anorm.

value SQL is not a member of StringContext; This compilation error is raised when using the Anorm interpolation without the appropriate import. It can be fixed by adding the package import: import anorm._

type mismatch; found : T; required : anorm.ParameterValue; This compilation error occurs when a value of type T is passed as parameter, whereas this T type is not supported. You need to ensure that a anorm.ToStatement[T] and a anorm.ParameterMetaData[T] can be found in the implicit scope (see parameter conversions).

On .executeInsert(), you can get the error TypeDoesNotMatch(Cannot convert <value>: class <T> to Long for column ColumnName(<C>). This occurs when the key returned by the database on insertion is not compatible with Long (the default key parser). It can be fixed by providing the appropriate key parser; e.g. if the database returns a text key: SQL"...".executeInsert(scalar[String].singleOpt) (get an Option[String] as insertion key).

Edge cases

The type of a parameter 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.

import anorm._

// 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)".
// Also needed on executeInsert for such driver, 
// as a ResultSet is returned in this case for the generated keys