The easiest way to get started is with a working sample application. The following samples are part of the official Slick distribution. You can either clone Slick from github or download pre-packaged zip files with an indiviual sample plus an sbt launcher.
The Hello Slick sample contains simple Scala application,
HelloSlick.scala, that does basic FRM operations with
Slick. You can run it out of the box with
sbt run. To make things simple this project uses an embedded in-memory
H2 database, so no database installation or configuration is required.
TableSuite.scala contains ScalaTest tests which perform some basic integration tests. You can run these
Note: The example code in this app has intentionally verbose type information. In normal applications type inference is used more extensively but to assist with learning the type information was included.
To include Slick in an existing project use the library published on Maven Central. Add the following to your
build definition (
build.sbt for sbt or
pom.xml for Maven):
Slick uses SLF4J for its own debug logging so you also need to add an SLF4J
implementation. Hello Slick uses
slf4j-nop to disable logging. You have
to replace this with a real logging framework like Logback if you want to see
The Reactive Streams API is pulled in automatically as a transitive dependency.
If you want to use Slick’s connection pool support for HikariCP, you need to add
slick-hikaricp module as a dependency as shown above. It will automatically
provide a compatible version of HikariCP as a transitive dependency. Otherwise, you
might need to disable connection pooling or specify a third-party connection pool.
To use Slick you first need to import the API for the database you will be using, like:
Since we are using H2 as our database system, we need to import features
H2Profile. A profile’s
api object contains all commonly
needed imports from the profile and other parts of Slick such as
Slick’s API is fully asynchronous and runs database calls in a separate thread pool. For running
user code in composition of
Future values, we import the global
ExecutionContext. When using Slick as part of a larger application (e.g. with Play or
Akka) the framework may provide a better alternative to this default
In the body of the application we create a
Database object which specifies how to connect to a
database. In most cases you will want to configure database connections with Typesafe Config in
application.conf, which is also used by Play and Akka for their configuration:
For the purpose of this example we disable the connection pool (there is no point in using one for an embedded in-memory database). When you use a real, external database server, the connection pool provides improved performance and resilience.
keepAliveConnection option (which is only available without a connection pool) keeps an extra connection
open for the lifetime of the
Database object in the application. This ensures that the
database does not get dropped while we are using it.
Hello Slick is a standalone command-line application, not running inside of a container which takes care of resource management, so we have to do it ourselves. Since all database calls in Slick are asynchronous, we are going to compose Futures throughout the app, but eventually we have to wait for the result. This gives us the following scaffolding:
Databaseobject usually manages a thread pool and a connection pool. You should always shut it down properly when it is no longer needed (unless the JVM process terminates anyway). Do not create a new
Databasefor every database operation. A single instance is meant to be kept alive for the entire lifetime your your application.
If you are not familiar with asynchronous, Future-based programming Scala, you can learn more about Futures and Promises in the Scala documentation.
Before we can write Slick queries, we need to describe a database schema with
Table row classes
TableQuery values for our tables. You can either use the code generator
to automatically create them for your database schema or you can write them by hand:
All columns get a name (usually in camel case for Scala and upper case with underscores for SQL) and a
Scala type (from which the SQL type can be derived automatically). The table object also needs a Scala
name, SQL name and type. The type argument of the table must match the type of the special
In simple cases this is a tuple of all columns but more complex mappings are possible.
foreignKey definition in the
coffees table ensures that the
supID field can only contain values
for which a corresponding
id exists in the
suppliers table, thus creating an n to one relationship:
Coffees row points to exactly one
Suppliers row but any number of coffees can point to the same
supplier. This constraint is enforced at the database level.
The connection to the embedded H2 database engine provides us with an empty database. Before we can
execute queries, we need to create the database schema (consisting of the
and insert some test data:
schema method creates
DDL (data definition language) objects with the database-specific
code for creating and dropping tables and other database entities. Multiple
DDL values can be combined with
++ to allow all entities to be created and dropped in the correct order, even when they have circular
dependencies on each other.
Inserting the tuples of data is done with the
++= methods, similar to how you add data to mutable
++= methods return database I/O actions (
DBIOAction) which can be executed on a database
at a later time to produce a result. If you do not care about more advanced features like streaming, effect tracking
or extension methods for certain actions, you can denote their type as
DBIO[T] (for an operation which will
eventually produce a value of type
There are several different combinators for combining multiple
DBIOActions into sequences, yielding another action.
Here we use the simplest one,
DBIO.seq, which can concatenate any number of actions, discarding the return values
(i.e. the resulting
DBIOAction produces a result of type
Unit). We then execute the setup action asynchronously
db.run, yielding a
Note: Database connections and transactions are managed automatically by Slick. By default connections are acquired and released on demand and used in auto-commit mode. In this mode we have to populate the
supplierstable first because the
coffeesdata can only refer to valid supplier IDs. We could also use an explicit transaction bracket encompassing all these statements (
db.run(setup.transactionally)). Then the order would not matter because the constraints are only enforced at the end when the transaction is committed.
When inserting data, the database usually returns the number of affected rows, therefore the return type is
Option[Int] as can be seen in this definition of
We can use the
map combinator to run some code and compute a new value from the value returned by the action
(or in this case run it only for its side effects and return
mapand all other combinators which run user code (e.g.
filter) take an implicit
ExecutionContexton which to run this code. Slick uses its own
ExecutionContextinternally for running blocking database I/O but it always maintains a clean separation and prevents you from running non-I/O code on it.
The simplest kind of query iterates over all the data in a table by calling
.result on the
TableQuery to get
This corresponds to a
SELECT * FROM COFFEES in SQL (except that the
* is the table’s
we defined earlier and not whatever the database sees as
*). The type of the values we get in the loop
is, unsurprisingly, the type parameter of
Let’s add a projection to this basic query. This is written in Scala with the
map method or a
The output will be the same: for each row of the table, all columns get converted to strings and concatenated
into one tab-separated string. The difference is that all of this now happens inside the database engine, and
only the resulting concatenated string is shipped to the client. Note that we avoid Scala’s
(which is already heavily overloaded) in favor of
++ (commonly used for sequence concatenation). Also,
there is no automatic conversion of other argument types to strings. This has to be done explicitly with the
type conversion method
This time we also use Reactive Streams to get a streaming result from the database and print the elements as they come in instead of materializing the whole result set upfront.
Joining and filtering tables is done the same way as when working with Scala collections:
Note the use of
==for comparing two values for equality and
!=for inequality. This is necessary because these operators are already defined (with unsuitable types and semantics) on the base type
Any, so they cannot be replaced by extension methods. The other comparison operators are the same as in standard Scala code:
The generator expression
suppliers if s.id === c.supID follows the relationship established by the foreign
Coffees.supplier. Instead of repeating the join condition here we can use the foreign key directly:
Aggregates values like minimum, maximum, summation, and average can be computed by the database using the query
This creates a new scalar query (
Rep) that can be run like a collection-valued
Query by calling
Sometimes writing SQL code manually is the easiest and best way to go but we don’t want to lose SQL injection
protection that Slick includes. SQL String Interpolation provides a nice API for doing this.
In Hello Slick we use the
This produces a database I/O action that can be run or streamed in the usual way.
CaseClassMapping.scala app provides an example which uses a case class instead of tupled values.
To use case classes instead of tuples setup a
def * projection which transforms the tuple values to and from the
case class. For example:
This uses the
mapTo macro to convert between
(Option[Int], String) and
User bidirectionally. Now all of the
queries can work with a
User object instead of the tuples.
See Mapped Tables for details.
Users table mapping in
CaseClassMapping.scala defines an
id column which uses an auto-incrementing
See Table Rows for more column options.
So far you have seen how to get a
Seq from a collection-valued query and how to stream individual elements.
There are several other useful methods which are shown in
QueryActions.scala. They are equally applicable to
Scala queries and Plain SQL queries.
Note the use of
Compiled in this app. It is used to define a pre-compiled query that can be executed with
different parameters without having to recompile the SQL statement each time. This is the preferred way of defining
queries in real-world applications. It prevents the (possibly expensive) compilation each time and leads to the
same SQL statement (or a small, fixed set of SQL statements) so that the database system can also reuse a previously
computed execution plan. As a side-effect, all parameters are automatically turned into bind variables:
See Compiled Queries for details.