WARNING: This page is a guide for 1.x series.


What’s SQLInterpolation

SQLInterpolation is an SQL builder which uses String interpolation since Scala 2.10. Add scalikejbdc-interpolation to libraryDependencies.

libraryDependencies += "org.scalikejdbc" %% "scalikejdbc-interpolation" % "1.7.7"

The usage is pretty simple - just embedding values into sql"“ template without #bind or #bindByName. It looks really cool.

import scalikejdbc._, SQLInterpolation._

val id = 123
val member = sql"select id, name from members where id = ${id}"
    .map(rs => Member(rs)).single.apply()

// above code is same as follows:
// SQL("select id, name from members where id = {id}").bindByName('id -> id)
//   .map(rs => Member(rs)).single.apply()

Don’t worry, this code is safely protected from SQL injection attacks. ${id} will be a place holder.

select id, name from members where id = ?


SQLSyntax is not a binding parameter but a part of SQL. You can create a SQLSyntax object with sqls”“ String interpolation.

val ordering = if (isDesc) sqls"desc" else sqls"asc"
val members = sql"select id, name from members limit 10 order by id ${ordering}"
  .map(rs => Member(rs)).list.apply()

${ordering} will be directly converted to a part of SQL.

select id , name from members limit 10 order by id desc

Don’t worry again, this code is safely protected. sqls"" always treats external input values as binding parameters.


SQLSyntaxSupport is one step short of ORM. In other words, it’s a powerful DRY way to write SQL.

First of all, mix the SQLSyntaxSupport[A] trait to A companion object, and define tableName and #apply method to map values from ResultSet object in it. The unfamilier ResultName[A] are described later.

NOTICE: When you use case classes for entities as follows (Group, GroupMember) everything goes fine. But if you use normal classes for entities, be aware of entity equality. See the following page in detail: documentation/1.x/one-to-x.html

import scalikejdbc._, SQLInterpolation._

case class Group(id: Long, name: String)
case class GroupMember(id: Long, name: String, 
  groupId: Option[Long] = None, group: Option[Group] = None)

object Group extends SQLSyntaxSupport[Group] {

  // If you need to specify schema name, override this
  // def table will return sqls"public.groups" in this case
  // Of course, schemaName doesn't work with MySQL
  override val schemaName = Some("public")

  // If the table name is same as snake_case'd name of this companion object,
  // you don't need to specify tableName explicitly.
  override val tableName = "groups"

  // If you use NamedDB for this entity, override connectionPoolName
  //override val connectionPoolName = 'anotherdb

  def apply(g: ResultName[Group])(rs: WrappedResultSet) =
    new Group(rs.long(g.id), rs.string(g.name))

object GroupMember extends SQLSyntaxSupport[GroupMember] {
  def apply(m: ResultName[GroupMember])(rs: WrappedResultSet) =
    new GroupMember(rs.long(m.id), rs.string(m.name), rs.longOpt(m.groupId))

  def apply(m: ResultName[GroupMember], g: ResultName[Group])(rs: WrappedResultSet) =  {
    apply(m)(rs).copy(group = rs.longOpt(g.id).map(_ => Group(g)(rs)))

Use them as follows:

val id = 123

val (m, g) = (GroupMember.syntax("m"), Group.syntax("g"))
val groupMember: Option[GroupMember] = sql"""
    ${m.result.*}, ${g.result.*}
    ${GroupMember.as(m)} left join ${Group.as(g)} on ${m.groupId} = ${g.id}
    ${m.id} = ${id}
  .map(GroupMember(m.resultName, g.resultName)).single.apply()

Though the above code contains some ${...} parts, I believe that you can understand what it means. Actually, this code runs the following SQL statement.

   m.id as i_on_m, m.name as n_on_m, m.group_id as gi_on_m, g.id as i_on_g, g.name as n_on_g
  group_member m left join group g on m.group_id = g.id
  m.id = ?

It seems to be natural that ${m.result.*} is converted to listing all the columns, ${m.camelCase} is converted to the snake_case column name. However, since you may have questions, I’ll explain about some points.

Why is ${m.result.*} transformed to listing all the columns?

In spite of column names are not defined anywhere, ${m.result.*} is converted to listing all the column names. The secret is that laoding from metadata (and they will be cached) when first access to the table. It’s possible to obtain all column names as a Seq[String] value via columns.

If you need to define column names by yourself or need to access multi databases, please define column names as follows:

object GroupMember extends SQLSyntaxSupport[GroupMember] {
  override val tableName = "groups_members"
  override val columns = Seq("id", "name", "group_id")

Why can we use undefined methods such as m.groupId?

By Type Dynamic (SIP-17) since Scala 2.10.0, you can call undefined methods such as m.groupId. Type Dynamic is similar to Ruby’s method_missing.


When you call camel case fields, it will actually be transformed to column name as an underscore separated string. If column name doesn’t exist in columns, InvalidColumnNameException will be thrown.

Before that, the case case field name should be the same as any of the primary constructor arg names of type A of SQLSyntaxSupport[A]. The validation for this rule works in compilation phase with the power of Scala macros.


If you don’t want to use Type Dynamic, it’s also possible to call #field(String) or #column(String) with String value. For instance, the following four examples mean the same thing.


In some cases, you might want to avoid expose column names to application layer. If this is the case, override nameConverters. If you want to treat service_cd column as serviceCode, define regular expression and replaced name in columns as follows. Since the nameConverters works as partial match retrieval, it’s also possible to specify just Map("Code" -> "cd").

case class Event(id: Long, name: String, serviceCode: Long)

object Event extends SQLSyntaxSupport[Event] {
  override val tableName = "events"
  override val columns = Seq("id" "name", "service_cd")

  // specify regular expression to match
  override val nameConverters = Map("^serviceCode$" -> "service_cd")

What is the difference between m.id, m.result.id and m.resultName.id?

For instance, m’s APIs of val m = Member.syntax("mm") means as follows:

  • m.groupId will be converted to sqls"mm.group_id"
  • m.result.groupId will be converted to sqls"mm.group_id as gi_on_mm"
  • m.resultName returns a ResultName[Member] object
  • m.resultName.groupId will be converted to sqls"gi_on_mm"

  • Member.as(m) will be converted to members m in SQL

If you use Member.syntax(), tableName will be set. For example, m.result.groupId will be "members.group_id as gi_on_members". If you specified as Member.syntax("m"), m.result.groupId will be "members.group_id as gi_on_m".

That’s all the rules of SQLSyntaxSupport. I believed that now the following code will be easy to understand for you.

val ids: List[Long] = sql"select ${m.result.id} from ${Member.as(m)} where ${m.gourpId} = 1"
  .map(rs => rs.long(m.resultName.id)).list.apply()

Define #apply(ResultName[Member]) method and the method will make your #map operation pretty simple.

object Member extends SQLSyntaxSupport[Member] {
  override val tableName = "members"
  def apply(m: ResultName[User])(rs: WrappedResultSet) = {
    new Member(id = rs.long(m.id), name = rs.string(m.name))

Use the above code like this:

val m = Member.syntax("m")
val members = sql"select ${m.result.*} from ${Member.as(m)}".map(Member(m)).list.apply()
// select m.id as i_on_m, m.name as n_on_m from members m

If you need just column names for insert/update/delete queries, use #column.{name} like this:

val c = Member.column
sql"insert into ${Member.table} (${c.name}, ${c.birthday}) values (${name}, ${birthday})"

Since #column is a member of SQLSyntaxSupport[A], you can use #column in Member object:

object Member extends SQLSyntaxSupport[Member] {
  def create(name: String, birthday: LocalDate)(implicit s: DBSession = AutoSession): Member = {
    val id = sql"insert into ${table} (${column.name}, ${column.birthday}) values (${name}, ${birthday})"
    Member(id, name, birthday)

Use Case: SQLSyntaxSupport with table sharding

If you have multiple tables for same entity. For example, orders_2011, orders_2012 and orders_2013.

case class Order(id: Long, productId: Long, customerId: Option[Long], createdAt: DateTime)

class OrderTable(val year: Int) extends SQLSyntaxSupport[Order] {
  // Be careful if you build tableName with input values 
  // ScalikeJDBC cannot protect your app from SQL injection vulnerability
  override val tableName = s"orders_$year"

  def apply(o: ResultName[Order])(rs: WrappedResultSet) = new Order(
    id         = rs.long(o.id), 
    productId  = rs.long(o.productId), 
    customerId = rs.longOpt(o.customerId), 
    createdAt  = rs.dateTime(o.createdAt)
val (o2011, o2012, o2013) = (new OrderTable(2011), new OrderTable(2012), new OrderTable(2013))

val ordersIn2011 = DB readOnly { implicit s =>
  val o = o2011.syntax("o")
  sql"select ${o.result.*} from ${o2011 as o) where ${o.customerId} is not null"
DB readOnly { implicit s =>
  val o2 = o2012.syntax("o")
  val ordersIn2012 = 
    sql"select ${o.result.*} from ${o2012 as o2) where ${o.customerId} is not null"

  val o3 = o2013.syntax("o")
  val ordersIn2013 = 
    sql"select ${o.result.*} from ${o2013 as o3) where ${o.customerId} is not null"

Use Case: Working with 22+ columns table

Just use normal class and implement EntityEquality trait (for one-to-many relations).

import scalikekdbc._, SQLInterpolation._

class HugeTable(
  val column1: Long,
  val column2: Option[String],
  val column3: String,
  val column4: Int,
  val column5: DateTime,
  val column22: Int,
  val column23: DateTime) extends EntityEquality {

  override val entityIdentity = Seq(column1, columns2, .., columns23).mkString("\t")


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