Introduction¶

What is Slick?¶

Slick is Typesafe‘s modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. You can also use SQL directly.

val limit = 10.0

// Your query could look like this:
( for( c <- coffees; if c.price < limit ) yield c.name ).list

// Or using more plain SQL String Interpolation:
sql"select COF_NAME from COFFEES where PRICE < $limit".as[String].list

// Both queries result in SQL equivalent to:
// select COF_NAME from COFFEES where PRICE < 10.0

When using Scala instead of raw SQL for your queries you benefit from compile-time safety and compositionality. Slick can generate queries for different back-end databases including your own, using its extensible query compiler.

Get started learning Slick in minutes using the Hello Slick template in Typesafe Activator.

Features¶

Scala¶

Queries, Table & Column Mappings, and types are plain Scala

class Coffees(tag: Tag) extends Table[(String, Double)](tag, "COFFEES") {
  def name = column[String]("COF_NAME", O.PrimaryKey)
  def price = column[Double]("PRICE")
  def * = (name, price)
}
val coffees = TableQuery[Coffees]

Data access APIs similar to Scala collections

// Query that only returns the "name" column
coffees.map(_.name)

// Query that does a "where price < 10.0"
coffees.filter(_.price < 10.0)

Type Safe¶

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Find problems at compile-time instead of at runtime

// The result of "select PRICE from COFFEES" is a Seq of Double
// because of the type safe column definitions
val coffeeNames: Seq[Double] = coffees.map(_.price).list

// Query builders are type safe:
coffees.filter(_.price < 10.0)
// Using a string in the filter would result in a compilation error

Composable¶

Queries are functions that can be composed and reused

// Create a query for coffee names with a price less than 10, sorted by name
coffees.filter(_.price < 10.0).sortBy(_.name).map(_.name)
// The generated SQL is equivalent to:
// select name from COFFEES where PRICE < 10.0 order by NAME

Compatibility¶

Slick requires Scala 2.10. (For Scala 2.9 please use ScalaQuery, the predecessor of Slick).

Supported database systems¶

DB2 (via slick-extensions)
Derby/JavaDB
H2
HSQLDB/HyperSQL
Microsoft Access
Microsoft SQL Server (via slick-extensions)
MySQL
Oracle (via slick-extensions)
PostgreSQL
SQLite

Other SQL databases can be accessed right away with a reduced feature set. Writing a fully featured plugin for your own SQL-based backend can be achieved with a reasonable amount of work. Support for other backends (like NoSQL) is under development but not yet available.

License¶

Slick is released under a BSD-Style free and open source software license. See the chapter on the commercial Slick Extensions add-on package for details on licensing the Slick drivers for the big commercial database systems.

Query APIs¶

The Lifted Embedding is the standard API for type-safe queries and updates in Slick. Please see Getting Started for an introduction. Most of this user manual focuses on the Lifted Embedding.

For writing your own SQL statements you can use the Plain SQL API.

The experimental Direct Embedding is available as an alternative to the Lifted Embedding.

Lifted Embedding¶

The name Lifted Embedding refers to the fact that you are not working with standard Scala types (as in the direct embedding) but with types that are lifted into a Rep type constructor. This becomes clear when you compare the types of a simple Scala collections example

case class Coffee(name: String, price: Double)
val coffees: List[Coffee] = //...

val l = coffees.filter(_.price > 8.0).map(_.name)
//                       ^       ^          ^
//                       Double  Double     String

... with the types of similar code using the lifted embedding:

class Coffees(tag: Tag) extends Table[(String, Double)](tag, "COFFEES") {
  def name = column[String]("COF_NAME")
  def price = column[Double]("PRICE")
  def * = (name, price)
}
val coffees = TableQuery[Coffees]

val q = coffees.filter(_.price > 8.0).map(_.name)
//                       ^       ^          ^
//               Rep[Double]  Rep[Double]  Rep[String]

All plain types are lifted into Rep. The same is true for the table row type Coffees which is a subtype of Rep[(String, Double)]. Even the literal 8.0 is automatically lifted to a Rep[Double] by an implicit conversion because that is what the > operator on Rep[Double] expects for the right-hand side. This lifting is necessary because the lifted types allow us to generate a syntax tree that captures the query computations. Getting plain Scala functions and values would not give us enough information for translating those computations to SQL.