ldbc (Lepus Database Connectivity) is Pure functional JDBC layer with Cats Effect 3 and Scala 3.

ldbc is a Typelevel project. This means we embrace pure, typeful, functional programming, and provide a safe and friendly environment for teaching, learning, and contributing as described in the Scala Code of Conduct.

Note that ldbc is pre-1.0 software and is still undergoing active development. New versions are not binary compatible with prior versions, although in most cases user code will be source compatible.

Please drop a ⭐ if this project interests you. I need encouragement.

ldbc is available on the JVM, Scala.js, and ScalaNative

For people that want to skip the explanations and see it action, this is the place to start!

libraryDependencies += "io.github.takapi327" %% "ldbc-dsl" % "latest"

For Cross-Platform projects (JVM, JS, and/or Native):

libraryDependencies += "io.github.takapi327" %%% "ldbc-dsl" % "latest"

The dependency package used depends on whether the database connection is made via a connector using the Java API or a connector provided by ldbc.

Use jdbc connector

libraryDependencies += "io.github.takapi327" %% "jdbc-connector" % "latest"

Use ldbc connector

libraryDependencies += "io.github.takapi327" %% "ldbc-connector" % "latest"

For Cross-Platform projects (JVM, JS, and/or Native)

libraryDependencies += "io.github.takapi327" %%% "ldbc-connector" % "latest"

The difference in usage is that there are differences in the way connections are built between jdbc and ldbc.

jdbc connector

val ds = new com.mysql.cj.jdbc.MysqlDataSource()
ds.setServerName("127.0.0.1")
ds.setPortNumber(13306)
ds.setDatabaseName("world")
ds.setUser("ldbc")
ds.setPassword("password")

val datasource = jdbc.connector.MysqlDataSource[IO](ds)

val connection: Resource[IO, Connection[IO]] =
  Resource.make(datasource.getConnection)(_.close())

ldbc connector

val connection: Resource[IO, Connection[IO]] =
  ldbc.connector.Connection[IO](
    host     = "127.0.0.1",
    port     = 3306,
    user     = "ldbc",
    password = Some("password"),
    database = Some("ldbc"),
    ssl      = SSL.Trusted
  )

The connection process to the database can be carried out using the connections established by each of these methods.

val result: IO[(List[Int], Option[Int], Int)] = connection.use { conn =>
  (for
    result1 <- sql"SELECT 1".query[Int].to[List]
    result2 <- sql"SELECT 2".query[Int].to[Option]
    result3 <- sql"SELECT 3".query[Int].unsafe
  yield (result1, result2, result3)).readOnly(conn)
}

ldbc provides not only plain queries but also type-safe database connections using the query builder.

The first step is to set up dependencies.

libraryDependencies += "io.github.takapi327" %% "ldbc-query-builder" % "latest"

For Cross-Platform projects (JVM, JS, and/or Native):

libraryDependencies += "io.github.takapi327" %%% "ldbc-query-builder" % "latest"

ldbc uses classes to construct queries.

import ldbc.query.builder.Table

case class User(
  id: Long,
  name: String,
  age: Option[Int],
) derives Table

The next step is to create a Table using the classes you have created.

import ldbc.query.builder.TableQuery

val userTable = TableQuery[User]

Finally, you can use the query builder to create a query.

val result: IO[List[User]] = connection.use { conn =>
  userTable.selectAll.query.to[List].readOnly(conn)
  // "SELECT `id`, `name`, `age` FROM user"
}

ldbc also allows type-safe construction of schema information for tables.

The first step is to set up dependencies.

libraryDependencies += "io.github.takapi327" %% "ldbc-schema" % "latest"

For Cross-Platform projects (JVM, JS, and/or Native):

libraryDependencies += "io.github.takapi327" %%% "ldbc-schema" % "latest"

The next step is to create a schema for use by the query builder.

ldbc maintains a one-to-one mapping between Scala models and database table definitions. Implementers simply define columns and write mappings to the model, similar to Slick.

import ldbc.schema.*

case class User(
  id: Long,
  name: String,
  age: Option[Int],
)

class UserTable extends Table[User]("user"):
  def id: Column[Long] = column[Long]("id")
  def name: Column[String] = column[String]("name")
  def age: Column[Option[Int]] = column[Option[Int]]("age")

  override def * : Column[User] = (id *: name *: age).to[User]

Finally, you can use the query builder to create a query.

val userTable: TableQuery[UserTable] = TableQuery[UserTable]
val result: IO[List[User]] = connection.use { conn =>
  userTable.selectAll.query.to[List].readOnly(conn)
  // "SELECT `id`, `name`, `age` FROM user"
}

Full documentation can be found at Currently available in English and Japanese.

• English
• Japanese

Creating a MySQL connector project written in pure Scala3.

JVM, JS and Native platforms are all supported.

Most of the jdbc functionality used in other packages of ldbc at the moment could be implemented.

However, not all jdbc APIs could be supported. Nor can we guarantee that it is proven and stable enough to operate in a production environment.

We will continue to develop features and improve the stability of the ldbc connector to achieve the same level of stability and reliability as the jdbc connector.

• Failover Countermeasures

• Comparison with JDBC
• Comparison with other MySQL Scala libraries
• Verification of operation in AWS and other infrastructure environments

• Additional streaming implementation
• Integration with java.sql API
• etc...

Initially, ldbc was inspired by tapir to create a development system that could centralise Scala models, sql schemas and documentation by managing a single resource at the database level.

In addition, database connection, query construction and document generation were to be used in combination with retrofitted packages, as the aim was to be able to integrate with other database systems.

As a result, we feel that it has become difficult for users to use because of the various configurations required to build it.

What users originally wanted from a database connectivity library was something simpler, easier and more intuitive to use.

Initially, ldbc aimed to create documentation from the schema, so building the schema and query builder was not as simple as it could have been, as it required a complete description of the database data types and so on.

It was therefore decided to redesign it to make it simpler and easier to use.

All suggestions welcome :)!

If you’d like to contribute, see the list of issues and pick one! Or report your own. If you have an idea you’d like to discuss, that’s always a good option.

If you have any questions about why or how it works, feel free to ask on github. This probably means that the documentation, scaladocs, and code are unclear and can be improved for the benefit of all.

If you want to build and run the tests for yourself, you'll need a local MySQL database. The easiest way to do this is to run docker-compose up from the project root.