An introduction to Slick

19 minute read

This article is brought to you by Yadu Krishnan, a new contributor to Rock the JVM. He’s a senior developer and constantly shares his passion for new languages, libraries and technologies. He also loves writing Scala articles, especially for newcomers.

This is a beginner-friendly article to get started with Slick, a popular database library in Scala. After following this post, you will be able to write Scala code to communicate with a database using SQL.

This guide will explain

  • what Slick is and how to use Slick for basic CRUD operations
  • how to apply advanced concepts like join, transaction etc. using Slick
  • how to use Postgres specific data types using slick-pg
  • how to auto-generate Slick schema from database

For this blog, we will build a basic database for movies and related entities. We will use Slick to save and fetch rows from multiple tables related to the movies database. For explaining different features of Slick, we will make use of tables with different types of columns.

1. Introduction

Slick is a functional relational library in Scala which makes working with relational databases easier. We can interact with the database almost in the same way as we do with Scala collections. Additionally, Slick uses asynchronous programming using Scala Futures. It also supports the usage of plain SQL queries which might come in handy if we want to exactly control the way the queries are built. Apart from that, Slick provides compile time safety by mapping the database columns to Scala data types. This ensures that it is less likely to get runtime errors for database queries.

We assume the readers have basic knowledge of Scala and PostgreSQL for this post.

2. Setup

For this blog, we will be using Slick with PostgreSQL and Hikari connection pool. Also we will be using slick-pg library for advanced postgres features. We will be using Scala 2 version as support for Scala 3 is still in progress for Slick. Let’s add all the necessary dependencies together in the build.sbt:

libraryDependencies ++= Seq(
  "com.typesafe.slick" %% "slick" % "3.3.3",
  "org.postgresql" % "postgresql" % "42.3.4",
  "com.typesafe.slick" %% "slick-hikaricp" % "3.3.3",
  "com.github.tminglei" %% "slick-pg" % "0.20.3",
  "com.github.tminglei" %% "slick-pg_play-json" % "0.20.3"

We need to have a working PostgreSQL database for testing the application. Some options are:

  • Installing a PostgreSQL database locally
  • Use a dockerized PostgreSQL instance (locally). In this tutorial, you can just run docker-compose up to set up the database tables.
  • Use any free online services such as ElephantSQL

A sample docker-compose.yaml file looks like this:

version: '3.8'
    image: postgres
    restart: always
      - POSTGRES_USER=postgres
      - '5432:5432'
      - db:/var/lib/postgresql11/data
      - ./db/init-scripts.sql:/docker-entrypoint-initdb.d/scripts.sql

    driver: local

Next, we can add the database configurations to the config file such as application.conf.

postgres = {
  connectionPool = "HikariCP" 
  dataSourceClass = "org.postgresql.ds.PGSimpleDataSource" 
  properties = {
    serverName = "localhost"
    portNumber = "5432"
    databaseName = "postgres"
    user = "postgres"
    password = "admin"
  numThreads = 10

Within the postgres node, we will be providing all the necessary configurations to connect to the database. The key connectionPoll lets us use a connection pool to improve the database querying performance. If we don’t provide that property, it will connect without using the connection pooling feature. Slick will be using the JDBC connection implementation from the PGSimpleDataSource to connect to the database. This class is provided by the JDBC library we added, in our case postgresql:42.3.4. The node properties are the information required to connect to the correct database. Alternatively, we can also use direct URL and connect to the database. For example, for connecting to the elephantsql service, we can use the configuration as:

databaseUrl {
  dataSourceClass = "slick.jdbc.DatabaseUrlDataSource"
  properties = {
    driver = "org.postgresql.Driver"
    url = "postgres://"

In this case, we are using a different dataSource as DatabaseUrlDataSource which is provided by Slick itself. We then provide the connection information under the node properties.

There are many other ways to connect to the database, which can be found in the docs.

Slick uses a combination of case classes and Slick Table classes to model the database schema. For that, we need to provide a JDBC Profile. Since we are using PostgreSQL, we can use the PostgresProfile.

As next step, we can create a connection instance using the PostgresProfile and previously defined configuration.

import PostgresProfile.profile.api._
object Connection {
  val db = Database.forConfig("postgres")

3. Models

As mentioned in the introduction, Slick provides compile-time type safety by mapping the database fields to Scala case classes. So, we need to create a case class corresponding to the database table for us to use in the queries.

final case class Movie(
    id: Long,
    name: String,
    releaseDate: LocalDate,
    lengthInMin: Int

Next, we need to create a Slick Table class. Slick Table maps the table fields to the case class. We need to import the PostgresProfile api to get the relevant methods in scope.

class SlickTablesGeneric(val profile: PostgresProfile) {
  import profile.api._
  class MovieTable(tag: Tag) extends Table[Movie](tag, Some("movies"), "Movie") {
    def id = column[Long]("movie_id", O.PrimaryKey, O.AutoInc)
    def name = column[String]("name")
    def releaseDate = column[LocalDate]("release_date")
    def lengthInMin = column[Int]("length_in_min")
    override def * = (id, name, releaseDate, lengthInMin) <> (Movie.tupled, Movie.unapply)

We need to first extends the class with Table provided by Slick. It takes tag as the first parameter. This is used by Slick to mark the class as a Table class. We can give an optional parameter for the database schema and then the table name. If schema is not provided, it will use the default schema, public in the case of postgres. For each of the column in the table, we need to define a def with the corresponding column type of the Slick. We can use the method column with the correct data type. We also need to provide the column name and any other properties that are needed. For example, let’s look at the primary key movie_id in the Movie table.

The method column[Long] defines the column type. It takes the string parameter “movie_id” which is the actual column name in the database table. After that, we can provide multiple properties as vararg field. O.PrimaryKey informs Slick that this column is the primary key. O.AutoInc informs that this column is an auto-increment field and postgres will take care of incrementing the value.

In the same way, we define the other fields.

Now, we have to override a method * which does the mapping between the column field and the case class field. The <> operator maps a tuple to the case class fields.

Now, we can create an instance for the MovieTable. We will be using this instance to write Slick queries to communicate with database.

lazy val movieTable = TableQuery[MovieTable]

We can also create a Singleton object for the SlickTablesGeneric by providing the Profile. The Profile class contains all the necessary methods for Slick to connect to the database and execute the queries. For now, we will be using the profile provided by Slick for the Postgres(slick.jdbc.PostgresProfile). Later, we can see how to write custom profile.

object SlickTables extends SlickTablesGeneric(PostgresProfile)

4. Basic CRUD Operations

Now that we have completed the initial setup, we can look at performing basic CRUD operations using Slick. We will be using the movieTable we created for all the operations. But before that, we need to manually create the database in Postgres and also create the table.

create extension hstore;
create schema movies;
create table if not exists movies."Movie" ("movie_id" BIGSERIAL NOT NULL PRIMARY KEY,"name" VARCHAR NOT NULL,"release_date" DATE NOT NULL,"length_in_min" INTEGER NOT NULL);
create table if not exists movies."Actor" ("actor_id" BIGSERIAL NOT NULL PRIMARY KEY,"name" VARCHAR NOT NULL);
create table if not exists movies."MovieActorMapping" ("movie_actor_id" BIGSERIAL NOT NULL PRIMARY KEY,"movie_id" BIGINT NOT NULL,"actor_id" BIGINT NOT NULL);
create table if not exists movies."StreamingProviderMapping" ("id" BIGSERIAL NOT NULL PRIMARY KEY,"movie_id" BIGINT NOT NULL,"streaming_provider" VARCHAR NOT NULL);
create table if not exists movies."MovieLocations" ("movie_location_id" BIGSERIAL NOT NULL PRIMARY KEY,"movie_id" BIGINT NOT NULL,"locations" text [] NOT NULL);
create table if not exists movies."MovieProperties" ("id" bigserial NOT NULL PRIMARY KEY,"movie_id" BIGINT NOT NULL,"properties" hstore NOT NULL);
create table if not exists movies."ActorDetails" ("id" bigserial NOT NULL PRIMARY KEY,"actor_id" BIGINT NOT NULL,"personal_info" jsonb NOT NULL);

The above scripts are for all the tables we will be using as part of this blog.

4.1. Insert Rows

Firstly, we can insert a record into the database.

val shawshank = Movie(1L, "Shawshank Redemption", LocalDate.of(1994, 4, 2), 162)

def insertMovie(movie: Movie): Future[Int] = {
  val insertQuery = SlickTables.movieTable += movie

The method takes an instance of DBIOAction and execute the action on database. DBIOAction is the most important type in Slick as all the queries in Slick are an instance of DBIOAction. DBIOAction takes 3 parameters as DBIOAction[R,T,E], where R is the result type of the query, T specifies if streaming is supported or not and E is the effect type(Read/Write/Transaction/DDL).

Now we can invoke insertMovie(shawshank) and it will asynchronously insert the record into the database. Since we have used the BIGSERIAL for the movie_id, postgres will automatically assign a numeric value. If we want to copy the generated id and return it along with the inserted object, we can use theee method returning as:

val insertQueryWithReturn = SlickTables.movieTable.returning(SlickTables.movieTable) += movie
val movieFuture: Future[Movie] =

If we want to ensure that the provided movie_id is used instead of the autogenerated one, we can use the method forceInsert instead of +=.

val forceInsertQuery = SlickTables.movieTable forceInsert movie

We can also insert multiple movies at a time.

val insertBatchQuery = SlickTables.movieTable ++= Seq(movie)
val forceInsertBatchQuery = SlickTables.movieTable forceInsertAll Seq(movie)

4.2. Querying Rows from Database

Now, let’s see how to execute select queries and fetch the rows from the table. To get all the movies, we can use the movieTable as:

val movies: Future[Seq[Movie]] =

The method .result on the movieTable will create an executable action. We can filter the rows in the table using the filter method just like on a collection. However, we need to use === method instead. This method will compare the value provided with that in the database column. For this method to be available, we need to import api from the profile.

import profile.api._

where profile is PostgresProfile we used before.

def findMovieByName(name: String): Future[Option[Movie]] = { === name).result.headOption)

4.3. Update a Row

We can use the method update to modify a record after applying the filter.

def updateMovie(movieId: Long, movie: Movie): Future[Int] = {
    val updateQuery = SlickTables.movieTable.filter( === movieId).update(movie)

In the above sample, if the filter by id is not applied, it will update all the records with the same value.

If we want to update only a particular field, we can use the map to get the field and then update it. For example, to update the movie name, we can do as:

val updateMovieNameQuery = SlickTables.movieTable.filter( === movieId).map("newName")

4.4. Delete a Row

We can also delete a record from the database by using the method delete. To delete a movie we can do:

val deleteQuery = SlickTables.movieTable.filter( === movieId).delete

5. Advanced Options

Since we are become familiar with the basic CRUD operations, let’s look at some more advanced concepts.

5.1 Executing Plain Query

Sometimes we might need to run plain SQL queries to get the results in a better way. Slick provides multiple ways to run the plain query. Let’s look at a simple way to run the query to select rows. However, since we are using the plain query, we need to provide some additional information to Slick to make the queries typesafe. For that, we need to provide an implicit value with the relevant mappings.

def getAllMoviesByPlainQuery: Future[Seq[Movie]] = {
  implicit val getResultMovie =
    GetResult(r => Movie(r.<<, r.<<, LocalDate.parse(r.nextString()), r.<<))
  val moviesQuery = sql"""SELECT * FROM public."Movie" """.as[Movie]

The implicit GetResult informs Slick on how to map the results of a plain query to required case class. GetResult takes a lambda, which has the ResultSet from the query. This implicit is used when the as method is applied to convert the ResultSet to case class. Here, we need to provide the data types of the result fields so that Slick can apply the proper type handling. We can retrieve the values from result set using r.nextInt, r.nextString and so on. But if we need to just map the columns to case class fields without any transformation, we can simply use the method r.<< on the result set. The method << may be considered as a placeholder for the datatype, with Slick automatically inferring the correct type. In the above example, we are explicitly parsing the date to LocalDate format. We can apply any other transformations on the column result before setting the value on the case class.

5.2. Transactional Queries

When we have multiple queries that modifies the database table, it is always advisable to use transactions. It will ensure that the modifications happen atomically. When we use transaction, if one of the queries in the transaction fails, all the queries in the same transaction will be rolledback.

def saveWithTransaction(movie: Movie, actor: Actor): Future[Unit] = {
  val saveMovieQuery = SlickTables.movieTable += movie
  val saveActorQuery = SlickTables.actorTable += actor
  val combinedQuery = DBIO.seq(saveMovieQuery, saveActorQuery)

The method transactionally on the combinedQuery will make both the insert queries in a single transaction. So, if one of the fails, both the inserts will be rolled back. We can combine multiple queries in Slick using DBIO.seq. The seq method takes a vararg of DBIOAction which will then execute all the actions sequentially. If we don’t add the method .transactionally at the end, it will run all the queries, but without transaction.

5.3. Joining Tables

Slick also provides methods to write join queries. Let’s try to join Actor table and MovieActorMapping table to fetch the results.

def getActorsByMovie(movieId: Long): Future[Seq[Actor]] = {
  val joinQuery: Query[(SlickTables.MovieActorMappingTable, SlickTables.ActorTable), (MovieActorMapping, Actor), Seq] = movieActorMappingTable
      .filter(_.movieId === movieId)
      .on(_.actorId ===

The above join operation returns a tuple of both the table results as a Query type. We can then transform the query in the way we want before exeucting it. Her we are applying map and returning only the Actor table results and discarding the other one.

Apart from this, Slick also provide methods for left and right outer joins as well using joinLeft and joinRight combinators.

5.4. Mapping Enumeration Field to Column

In all the above samples, we used standard data types such as Int, String, Date, etc for the case classes. If we want to use a custom type, we need to provide an implicit converter to convert between the Scala type and relevant column type. Let’s try to use an enumeration field in case class.

object StreamingProvider extends Enumeration {
  type StreamingProviders = Value
  val Netflix = Value("Netflix")
  val Hulu = Value("Hulu")
final case class StreamingProviderMapping(
  id: Long,
  movieId: Long,
  streamingProvider: StreamingProvider.StreamingProviders

We created enums for Streaming providers. We can then provide the enum in the case class field. We are using Scala 2 enums as there is no support yet for Scala 3.

Now, let’s create the mapping table for Slick. We are going to use the same format for creating the Slick table by extending with Table and implementing the * method for mapping.

class StreamingProviderMappingTable(tag: Tag)
    extends Table[StreamingProviderMapping](tag, Some("movies"), "StreamingProviderMapping") {

  implicit val providerMapper =
    MappedColumnType.base[StreamingProvider.StreamingProviders, String](
      e => e.toString,
      s => StreamingProvider.withName(s)

  def id = column[Long]("id", O.PrimaryKey, O.AutoInc)
  def movieId = column[Long]("movie_id")
  def streamingProvider = column[StreamingProvider.StreamingProviders]("streaming_provider")
  override def * =
    ) <> (StreamingProviderMapping.tupled, StreamingProviderMapping.unapply)
lazy val streamingProviderMappingTable = TableQuery[StreamingProviderMappingTable]

Here, we defined an implicit converter for StreamingProvider enum. We will be saving the enum value as a string in the column. When the record is fetched, Slick will convert it to the relevant enum type using the implicit. Slick will use the providerMapper to convert between case class and database column for the enum field.

5.5. Generating DDL Scripts from Slick Tables

Slick also provides a way to generate Data Definition Language(DDL) scripts from the Slick tables. DDL scripts explains the structure of the database using CREATE, DROP, ALTER queries and provides additional information for relationship between tables. This way, we can generate the table scripts and track the versions easily. This will also make sure that we can easily set up an empty database.

To generate the DDL scripts, we need to first collect all the Slick tables in a collection.

val tables = Seq(movieTable, actorTable, movieActorMappingTable, streamingProviderMappingTable)

Then we can combine them into a Slick DDL schema using:

val ddl: profile.DDL = ++ _)

Now, we can invoke the method to generate the scripts:


This will generate SQL scripts for creating all the tables we have used in our application. If we make any changes to the Slick tables, we can then again generate the DDL scripts. We can write the results to a .sql file and keep in the resource directory within the project. This will make sure that we always have the correct database structure and can create an empty database easily.

6. Slick-Pg for Postgres

PostgreSQL has additional powerful data types and features. But by default, Slick doesn’t have support for all the advanced features of Postgres. Some of these features are not available in many of the relational databases. However, We can use a third-party library slick-pg to utilise all those amazing features of Postgres in Slick with ease. We have already added the necessary dependencies in the build.sbt.

To use it, we need to write a custom Postgres Profile and use it instead of the Slick provided PostgresProfile. We can mix-in the traits from slik-pg based on the required features of postgres. Let’s add the support for JSON, HStore and Array data types. HStore is a special datatype available in PostgreSQL database. It is used for storing key-value pair of data similar to Map type in Scala.

Now, let’s create a new custom profile with HStore support.

trait CustomPostgresProfile
    extends ExPostgresProfile with PgHStoreSupport {

  override val api = CustomPGAPI
  object CustomPGAPI
      extends API
      with HStoreImplicits 
object CustomPostgresProfile extends CustomPostgresProfile

To create a custom profile, we need to extend with ExPostgresProfile provided by Slick-PG that is a wrapper on Slick’s PostgresProfile. To use HStore features, we need to mix-in with PgHStoreSupport trait from slick-pg.

For get the implicit methods, we were importing PostgresProfile.api.__. To get the Hstore implicit methods, we need to extend the Slick’s API with _HStoreImplicits provided by slick-pg. Then we can create a companion object for our custom profile, so that we can import it when we need to build the queries.

In the same way, we can add the support for other data types such as JSON, Array etc.

trait CustomPostgresProfile
    extends ExPostgresProfile with PgJsonSupport with PgPlayJsonSupport 
    with PgArraySupport with PgHStoreSupport with PgDate2Support {
  override def pgjson = "jsonb"
  override protected def computeCapabilities: Set[slick.basic.Capability] =
    super.computeCapabilities + slick.jdbc.JdbcCapabilities.insertOrUpdate

  override val api = CustomPGAPI
  object CustomPGAPI
      extends API
      with JsonImplicits
      with HStoreImplicits
      with ArrayImplicits
      with DateTimeImplicits {
    implicit val strListTypeMapper = new SimpleArrayJdbcType[String]("text").to(_.toList)
    implicit val playJsonArrayTypeMapper =
      new AdvancedArrayJdbcType[JsValue](
        (s) => utils.SimpleArrayUtils.fromString[JsValue](Json.parse(_))(s).orNull,
        (v) => utils.SimpleArrayUtils.mkString[JsValue](_.toString())(v)
object CustomPostgresProfile extends CustomPostgresProfile

In the advanced profile, we have implemented the following features:

  • Support for HStore, JSON, Array
  • Support for jsonb type which is an improved and better way to serialise JSON data in postgres
  • Implicit parameters to support Array types for conversion between Scala class and postgres json array types
  • Support for insertOrUpdate feature from postgres

Now, we can use CustomPostgresProfile instead of the PostgresProfile to make use of these features.

6.1. Querying from an Array Column

Let’s see how we can use filter query on an postgres array column. We have created a MovieLocationsTable which has a movieLocations array field. If we want to filter movies which was shot on any of the input locations, we can do that as:

val locations: List[String] = List("USA", "Germany")
val query = SpecialTables.movieLocationsTable.filter(_.locations @& locations.bind)

The operator @& will return true if there is an overlap between the input list and the database column. That means, if there is atleast one common item between the column value and input list, it will return true. For example, assume that the database column has the countries as [USA, Canada, Mexico]. The above query will match and return true since USA is a common value in the input [USA, Germany]. The method bind will convert the literal value into a database bind type. Bind parameters helps the database to improve the query performance by using pre-compiled prepared queries instead of re-creating separate queries each time.

Some of the other popular array operators in slick-pg are:

  • @> will check for contains. For example, [USA, Mexico, Canada, Germany] @> [USA, Germany] returns true since all elements of rightside array is present in left
  • ++ will concatenate two arrays
  • length returns the length of the array

6.2. Querying from an HStore Column

Postgres has an extension called as hstore. Once it is installed, we can use the datatype hstore for the columns. It is a type which stores the data as key and value pair, equivalent to an Map type in Scala/Java. We can see how to filter a hstore column using slick-pg:

def getMoviesByDistributor(distributor: String): Future[Seq[MovieProperties]] = {
  val condition = Map("distributor" -> distributor)
  val query = SpecialTables.moviePropertiesTable.filter( @> condition.bind)

The operator @> filters the hstore column for the key distributor and the input value. Similar to array, there are many other operators in hstore as well.

Some of the other available operations on HStore are:

  • @+ concatenates two hstore columns
  • ?? checks if the provided key exists in the hstore field
  • +> returns the value for the key provided ('a=>x, b=>y' -> 'a' returns x as the value for a)

6.3. Querying from a JSON Column

Postgres also supports JSON datatype by default. Slick-Pg also has support for querying the json columns. Let’s look at it with an example:

def getActorsBornOn(year: String): Future[Seq[ActorDetails]] = {
    SpecialTables.actorDetailsTable.filter(_.personal.+>>("birthYear") === year.bind).result

In the above code, we are filtering the personal column which is a JSON type in ActorDetails table. The method +>> will get the json key birthYear and compares it with the value provided.

Some of the other JSON Operators are:

  • || combines two json fields
  • - removes a field from json for the matching key

7. Code Generation

So far, we have written the Slick tables manually. If we are following a database first approach, Slick provides a way to generate the mapping tables easily. For now, we are ignoring the slick-pg types and trying to generate the Slick mappings for basic data types.

We can use an sbt plugin to do that. For that, let’s add the following lines to the plugins.sbt file.

addSbtPlugin("com.github.tototoshi" % "sbt-slick-codegen" % "1.4.0")
libraryDependencies += "org.postgresql" % "postgresql" % "42.3.4"

Once the sbt is refreshed, we can add the configurations in build.sbt:

slickCodegenDatabaseUrl := "jdbc:postgresql://localhost:5432/postgres"
slickCodegenDatabaseUser := "postgres"
slickCodegenDatabasePassword := "admin"
slickCodegenDriver := slick.jdbc.PostgresProfile
slickCodegenJdbcDriver := "org.postgresql.Driver"
slickCodegenOutputPackage := "com.rockethejvm.generated.models"
slickCodegenCodeGenerator := { (slickModel: model.Model) => new SourceCodeGenerator(slickModel) }

Now, we can use the sbt command slickCodegen. This will generate the case classes and Slick tables. By default, the code generator will generate the file under the path target/scala-2.13/src_managed. It will generate both case classes and the Slick tables. It will also generate all the special relationships like primary key, foreign keys, sequences etc based on the database structure. It will also generate all the implicit GetResult parameters for the plain SQL execution. This way, we can avoid manually writing most of the necessary code for mapping the database to Scala classes.

We can also customise the code generator class to use advanced features like slick-pg, but we will not be looking at this as part of this blog.

8. Conclusion

In this article, we looked at Slick and how we can execute different types of queries using it. We also introduced some advanced features using slick-pg. The sample code used in this article is available in GitHub.