Getting Started
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.
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To learn the basics of Slick, start with the Hello Slick sample (github, zip). This is the one we are using in this chapter.
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The Plain SQL Queries sample (github, zip) shows you how to do SQL queries with Slick. See Plain SQL Queries for details.
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The Multi-DB Patterns sample (github, zip) shows you how to write Slick applications that can use different database systems and how to use custom database functions in Slick queries.
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The TestKit sample (github, zip) shows you how to use Slick TestKit to test your own database profiles.
Hello Slick
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.
The file TableSuite.scala
contains ScalaTest tests which perform some basic integration tests. You can run these
tests with sbt test
.
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.
Adding Slick to Your Project
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
log output.
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
the 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.
Quick Introduction
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
from Slick‘s H2Profile
. A profile’s api
object contains all commonly
needed imports from the profile and other parts of Slick such as
database handling.
Slick’s API is fully asynchronous and runs database calls in a separate thread pool. For running
user code in composition of DBIOAction
and 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 ExecutionContext
.
Database Configuration
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
your 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.
The 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:
Note: A
Database
object 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 newDatabase
for 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.
Schema
Before we can write Slick queries, we need to describe a database schema with Table
row classes
and 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 *
projection.
In simple cases this is a tuple of all columns but more complex mappings are possible.
The 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:
A 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.
Populating the Database
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 coffees
and suppliers
tables)
and insert some test data:
The TableQuery
’s 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 +=
and ++=
methods, similar to how you add data to mutable
Scala collections.
The create
, +=
and ++=
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 T
).
There are several different combinators for combining multiple DBIOAction
s 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
with db.run
, yielding a Future[Unit]
.
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
suppliers
table first because thecoffees
data 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 insertAction
:
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 Unit
).
Note that
map
and all other combinators which run user code (e.g.flatMap
,cleanup
,filter
) take an implicitExecutionContext
on which to run this code. Slick uses its ownExecutionContext
internally for running blocking database I/O but it always maintains a clean separation and prevents you from running non-I/O code on it.
Querying
The simplest kind of query iterates over all the data in a table by calling .result
on the TableQuery
to get
a DBIOAction
:
This corresponds to a SELECT * FROM COFFEES
in SQL (except that the *
is the table’s *
projection
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 Coffees
.
Let’s add a projection to this basic query. This is written in Scala with the map
method or a
for comprehension:
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 +
operator
(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 asColumnOf
.
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
===
instead of==
for comparing two values for equality and=!=
instead of!=
for inequality. This is necessary because these operators are already defined (with unsuitable types and semantics) on the base typeAny
, 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
key Coffees.supplier
. Instead of repeating the join condition here we can use the foreign key directly:
Aggregations
Aggregates values like minimum, maximum, summation, and average can be computed by the database using the query
functions min
, max
, sum
and avg
like:
This creates a new scalar query (Rep
) that can be run like a collection-valued Query
by calling .result
.
Plain SQL / String Interpolation
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 sql
interpolator:
This produces a database I/O action that can be run or streamed in the usual way.
Case Class Mapping
The 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.
Auto-Generated Primary Keys
The Users
table mapping in CaseClassMapping.scala
defines an id
column which uses an auto-incrementing
primary key:
See Table Rows for more column options.
Running Queries
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.