This chapter describes how to write type-safe queries for selecting, inserting, updating and deleting data with the Lifted Embedding API.


Scalar (non-record, non-collection) values are representend by type Column[T] (a sub-type of Rep[T]) where a TypedType[T] must exist. Only some special methods for internal use are defined directly in the Column class.

The operators and other methods which are commonly used in the lifted embedding are added through implicit conversions defined in ExtensionMethodConversions. The actual methods can be found in the classes AnyExtensionMethods, ColumnExtensionMethods, NumericColumnExtensionMethods, BooleanColumnExtensionMethods and StringColumnExtensionMethods (cf. ExtensionMethods).

Collection values are represented by the Query class (a Rep[Seq[T]]) which contains many standard collection methods like flatMap, filter, take and groupBy. Due to the two different component types of a Query (lifted and plain), the signatures for these methods are very complex but the semantics are essentially the same as for Scala collections.

Additional methods for queries of scalar values are added via an implicit conversion to SingleColumnQueryExtensionMethods.

Sorting and Filtering

There are various methods with sorting/filtering semantics (i.e. they take a Query and return a new Query of the same type), for example:

val q1 = coffees.filter(_.supID === 101)
// compiles to SQL (simplified):
//   select "COF_NAME", "SUP_ID", "PRICE", "SALES", "TOTAL"
//     from "COFFEES"
//     where "SUP_ID" = 101

val q2 = coffees.drop(10).take(5)
// compiles to SQL (simplified):
//   select "COF_NAME", "SUP_ID", "PRICE", "SALES", "TOTAL"
//     from "COFFEES"
//     limit 5 offset 10

val q3 = coffees.sortBy(_.name.desc.nullsFirst)
// compiles to SQL (simplified):
//   select "COF_NAME", "SUP_ID", "PRICE", "SALES", "TOTAL"
//     from "COFFEES"
//     order by "COF_NAME" desc nulls first

Joining and Zipping

Joins are used to combine two different tables or queries into a single query.

There are two different ways of writing joins: Explicit joins are performed by calling a method that joins two queries into a single query of a tuple of the individual results. Implicit joins arise from a specific shape of a query without calling a special method.

An implicit cross-join is created with a flatMap operation on a Query (i.e. by introducing more than one generator in a for-comprehension):

val implicitCrossJoin = for {
  c <- coffees
  s <- suppliers
} yield (c.name, s.name)
// compiles to SQL:
//   select x2."COF_NAME", x3."SUP_NAME"
//     from "COFFEES" x2, "SUPPLIERS" x3

If you add a filter expression, it becomes an implicit inner join:

val implicitInnerJoin = for {
  c <- coffees
  s <- suppliers if c.supID === s.id
} yield (c.name, s.name)
// compiles to SQL:
//   select x2."COF_NAME", x3."SUP_NAME"
//     from "COFFEES" x2, "SUPPLIERS" x3
//     where x2."SUP_ID" = x3."SUP_ID"

The semantics of these implicit joins are the same as when you are using flatMap on Scala collections.

Explicit joins are created by calling one of the available join methods:

val explicitCrossJoin = for {
  (c, s) <- coffees innerJoin suppliers
} yield (c.name, s.name)
// compiles to SQL (simplified):
//   select x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2
//     inner join "SUPPLIERS" x3

val explicitInnerJoin = for {
  (c, s) <- coffees innerJoin suppliers on (_.supID === _.id)
} yield (c.name, s.name)
// compiles to SQL (simplified):
//   select x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2
//     inner join "SUPPLIERS" x3
//     on x2."SUP_ID" = x3."SUP_ID"

val explicitLeftOuterJoin = for {
  (c, s) <- coffees leftJoin suppliers on (_.supID === _.id)
} yield (c.name, s.name.?)
// compiles to SQL (simplified):
//   select x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2
//     left outer join "SUPPLIERS" x3
//     on x2."SUP_ID" = x3."SUP_ID"

val explicitRightOuterJoin = for {
  (c, s) <- coffees rightJoin suppliers on (_.supID === _.id)
} yield (c.name.?, s.name)
// compiles to SQL (simplified):
//   select x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2
//     right outer join "SUPPLIERS" x3
//     on x2."SUP_ID" = x3."SUP_ID"

val explicitFullOuterJoin = for {
  (c, s) <- coffees outerJoin suppliers on (_.supID === _.id)
} yield (c.name.?, s.name.?)
// compiles to SQL (simplified):
//   select x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2
//     full outer join "SUPPLIERS" x3
//     on x2."SUP_ID" = x3."SUP_ID"

Note the use of .? in the outer joins. Since these joins can introduce additional NULL values (on the right-hand side for a left outer join, on the left-hand sides for a right outer join, and on both sides for a full outer join), you have to make sure to retrieve Option values from them.

In addition to the usual join operators supported by relational databases (which are based off a cross join or outer join), Slick also has zip joins which create a pairwise join of two queries. The semantics are again the same as for Scala collections, using the zip and zipWith methods:

val zipJoinQuery = for {
  (c, s) <- coffees zip suppliers
} yield (c.name, s.name)

val zipWithJoin = for {
  res <- coffees.zipWith(suppliers, (c: Coffees, s: Suppliers) => (c.name, s.name))
} yield res

A particular kind of zip join is provided by zipWithIndex. It zips a query result with an infinite sequence starting at 0. Such a sequence cannot be represented by an SQL database and Slick does not currently support it, either. The resulting zipped query, however, can be represented in SQL with the use of a row number function, so zipWithIndex is supported as a primitive operator:

val zipWithIndexJoin = for {
  (c, idx) <- coffees.zipWithIndex
} yield (c.name, idx)


Two queries can be concatenated with the ++ (or unionAll) and union operators if they have compatible types:

val q1 = coffees.filter(_.price < 8.0)
val q2 = coffees.filter(_.price > 9.0)

val unionQuery = q1 union q2
// compiles to SQL (simplified):
//   select x8."COF_NAME", x8."SUP_ID", x8."PRICE", x8."SALES", x8."TOTAL"
//     from "COFFEES" x8
//     where x8."PRICE" < 8.0
//   union select x9."COF_NAME", x9."SUP_ID", x9."PRICE", x9."SALES", x9."TOTAL"
//     from "COFFEES" x9
//     where x9."PRICE" > 9.0

val unionAllQuery = q1 ++ q2
// compiles to SQL (simplified):
//   select x8."COF_NAME", x8."SUP_ID", x8."PRICE", x8."SALES", x8."TOTAL"
//     from "COFFEES" x8
//     where x8."PRICE" < 8.0
//   union all select x9."COF_NAME", x9."SUP_ID", x9."PRICE", x9."SALES", x9."TOTAL"
//     from "COFFEES" x9
//     where x9."PRICE" > 9.0

Unlike union which filters out duplicate values, ++ simply concatenates the results of the individual queries, which is usually more efficient.


The simplest form of aggregation consists of computing a primitive value from a Query that returns a single column, usually with a numeric type, e.g.:

val q = coffees.map(_.price)

val q1 = q.min
// compiles to SQL (simplified):
//   select min(x4."PRICE") from "COFFEES" x4

val q2 = q.max
// compiles to SQL (simplified):
//   select max(x4."PRICE") from "COFFEES" x4

val q3 = q.sum
// compiles to SQL (simplified):
//   select sum(x4."PRICE") from "COFFEES" x4

val q4 = q.avg
// compiles to SQL (simplified):
//   select avg(x4."PRICE") from "COFFEES" x4

Note that these aggregate queries return a scalar result, not a collection. Some aggregation functions are defined for arbitrary queries (of more than one column):

val q1 = coffees.length
// compiles to SQL (simplified):
//   select count(1) from "COFFEES"

val q2 = coffees.exists
// compiles to SQL (simplified):
//   select exists(select * from "COFFEES")

Grouping is done with the groupBy method. It has the same semantics as for Scala collections:

val q = (for {
  c <- coffees
  s <- c.supplier
} yield (c, s)).groupBy(_._1.supID)

val q2 = q.map { case (supID, css) =>
  (supID, css.length, css.map(_._1.price).avg)
// compiles to SQL:
//   select x2."SUP_ID", count(1), avg(x2."PRICE")
//     from "COFFEES" x2, "SUPPLIERS" x3
//     where x3."SUP_ID" = x2."SUP_ID"
//     group by x2."SUP_ID"

Note that the intermediate query q contains nested values of type Query. These would turn into nested collections when executing the query, which is not supported at the moment. Therefore it is necessary to flatten the nested queries immediately by aggregating their values (or individual columns) as done in q2.


Queries are executed using methods defined in the Invoker trait (or UnitInvoker for the parameterless versions). There is an implicit conversion from Query, so you can execute any Query directly. The most common usage scenario is reading a complete result set into a strict collection with a specialized method such as list or the generic method to which can build any kind of collection:

val l = q.list
val v = q.buildColl[Vector]
val invoker = q.invoker
val statement = q.selectStatement

This snippet also shows how you can get a reference to the invoker without having to call the implicit conversion method manually.

All methods that execute a query take an implicit Session value. Of course, you can also pass a session explicitly if you prefer:

val l = q.list()(session)

If you only want a single result value, you can use first or firstOption. The methods foreach, foldLeft and elements can be used to iterate over the result set without first copying all data into a Scala collection.


Deleting works very similarly to querying. You write a query which selects the rows to delete and then call the delete method on it. There is again an implicit conversion from Query to the special DeleteInvoker which provides the delete method and a self-reference deleteInvoker:

val affectedRowsCount = q.delete
val invoker = q.deleteInvoker
val statement = q.deleteStatement

A query for deleting must only select from a single table. Any projection is ignored (it always deletes full rows).


Inserts are done based on a projection of columns from a single table. When you use the table directly, the insert is performed against its * projection. Omitting some of a table’s columns when inserting causes the database to use the default values specified in the table definition, or a type-specific default in case no explicit default was given. All methods for inserting are defined in InsertInvoker and FullInsertInvoker.

coffees += ("Colombian", 101, 7.99, 0, 0)

coffees ++= Seq(
  ("French_Roast", 49, 8.99, 0, 0),
  ("Espresso",    150, 9.99, 0, 0)

// "sales" and "total" will use the default value 0:
coffees.map(c => (c.name, c.supID, c.price)) += ("Colombian_Decaf", 101, 8.99)

val statement = coffees.insertStatement
val invoker = coffees.insertInvoker

// compiles to SQL:

When you include an AutoInc column in an insert operation, it is silently ignored, so that the database can generate the proper value. In this case you usually want to get back the auto-generated primary key column. By default, += gives you a count of the number of affected rows (which will usually be 1) and ++= gives you an accumulated count in an Option (which can be None if the database system does not provide counts for all rows). This can be changed with the returning method where you specify the columns to be returned (as a single value or tuple from += and a Seq of such values from ++=):

val userId =
  (users returning users.map(_.id)) += User(None, "Stefan", "Zeiger")

Note that many database systems only allow a single column to be returned which must be the table’s auto-incrementing primary key. If you ask for other columns a SlickException is thrown at runtime (unless the database actually supports it).

You can follow the returning method with the into method to map the inserted values and the generated keys (specified in returning) to a desired value. Here is an example of using this feature to return an object with an updated id:

val userWithId =
  (users returning users.map(_.id)
         into ((user,id) => user.copy(id=Some(id)))
  ) += User(None, "Stefan", "Zeiger")

Instead of inserting data from the client side you can also insert data created by a Query or a scalar expression that is executed in the database server:

class Users2(tag: Tag) extends Table[(Int, String)](tag, "users2") {
  def id = column[Int]("id", O.PrimaryKey)
  def name = column[String]("name")
  def * = (id, name)
val users2 = TableQuery[Users2]


users2 insert (users.map { u => (u.id, u.first ++ " " ++ u.last) })

users2 insertExpr (users.length + 1, "admin")

In these cases, AutoInc columns are not ignored.


Updates are performed by writing a query that selects the data to update and then replacing it with new data. The query must only return raw columns (no computed values) selected from a single table. The relevant methods for updating are defined in UpdateInvoker.

val q = for { c <- coffees if c.name === "Espresso" } yield c.price

val statement = q.updateStatement
val invoker = q.updateInvoker

// compiles to SQL:
//   update "COFFEES" set "PRICE" = ? where "COFFEES"."COF_NAME" = 'Espresso'

There is currently no way to use scalar expressions or transformations of the existing data in the database for updates.

Compiled Queries

Database queries typically depend on some parameters, e.g. an ID for which you want to retrieve a matching database row. You can write a regular Scala function to create a parameterized Query object each time you need to execute that query but this will incur the cost of recompiling the query in Slick (and possibly also on the database if you don’t use bind variables for all parameters). It is more efficient to pre-compile such parameterized query functions:

def userNameByIDRange(min: Column[Int], max: Column[Int]) =
  for {
    u <- users if u.id >= min && u.id < max
  } yield u.first

val userNameByIDRangeCompiled = Compiled(userNameByIDRange _)

// The query will be compiled only once:
val names1 = userNameByIDRangeCompiled(2, 5).run
val names2 = userNameByIDRangeCompiled(1, 3).run
// Also works for .update and .delete

This works for all functions that take Column parameters (or records of Columns) and return a Query object or a scalar query. See the API documentation for Compiled and its subclasses for details on composing compiled queries.

You can use a compiled query for querying, updating and deleting data. (For inserts, you can cache the InsertInvoker and re-use it instead. To get it, call a query’s insertInvoker method, which is added by the createInsertInvoker implicit conversion.)

For backwards-compatibility with Slick 1.0 you can still create a compiled query by calling flatMap on a Parameters object. In many cases this enables you to write a single for comprehension for a compiled query:

val userNameByID = for {
  id <- Parameters[Int]
  u <- users if u.id is id
} yield u.first

val name = userNameByID(2).first

val userNameByIDRange = for {
  (min, max) <- Parameters[(Int, Int)]
  u <- users if u.id >= min && u.id < max
} yield u.first

val names = userNameByIDRange(2, 5).list