berntan / quill

Compile-time Language Integrated Query for Scala

IMPORTANT: This is the documentation for the latest SNAPSHOT version. Please refer to the website at http://getquill.io for the lastest release's documentation.

Compile-time Language Integrated Query for Scala

Quill provides a Quoted Domain Specific Language (QDSL) to express queries in Scala and execute them in a target language. The library's core is designed to support multiple target languages, currently featuring specializations for Structured Query Language (SQL) and Cassandra Query Language (CQL).

1. Boilerplate-free mapping: The database schema is mapped using simple case classes.
2. Quoted DSL: Queries are defined inside a quote block. Quill parses each quoted block of code (quotation) at compile time and translates them to an internal Abstract Syntax Tree (AST)
3. Compile-time query generation: The db.run call reads the quotation's AST and translates it to the target language at compile time, emitting the query string as a compilation message. As the query string is known at compile time, the runtime overhead is very low and similar to using the database driver directly.
4. Compile-time query validation: If configured, the query is verified against the database at compile time and the compilation fails if it is not valid. The query validation does not alter the database state.

Quotation

Introduction

The QDSL allows the user to write plain Scala code, leveraging scala's syntax and type system. Quotations are created using the quote method and can contain any excerpt of code that uses supported operations. To create quotations, first import quote and some other auxiliary methods:

import io.getquill._

A quotation can be a simple value:

val pi = quote(3.14159)

And be used within another quotation:

case class Circle(radius: Float)

val areas = quote {
  query[Circle].map(c => pi * c.radius * c.radius)
}

Quotations can also contain high-order functions and inline values:

val area = quote {
  (c: Circle) => {
    val r2 = c.radius * c.radius
    pi * r2
  }
}

val areas = quote {
  query[Circle].map(c => area(c))
}

Quotations can contain values defined outside of the quotation:

val pi = 3.14159
val areas = quote {
  query[Circle].map(c => pi * c.radius * c.radius)
}

Quill's normalization engine applies reduction steps before translating the quotation to the target language. The correspondent normalized quotation for both versions of the areas query is:

val areas = quote {
  query[Circle].map(c => 3.14159 * c.radius * c.radius)
}

Scala doesn't have support for high-order functions with type parameters. Quill supports anonymous classes with an apply method for this purpose:

val existsAny = quote {
  new {
    def apply[T](xs: Query[T])(p: T => Boolean) =
    	xs.filter(p(_)).nonEmpty
  }
}

val q = quote {
  query[Circle].filter { c1 =>
    existsAny(query[Circle])(c2 => c2.radius > c1.radius)
  }
}

Mirror sources

Sources represent the database and provide an execution interface for queries. Quill provides mirror sources for test purposes. Please refer to sources for information on how to create normal sources.

Instead of running the query, mirror sources return a structure with the information that would be used to run the query. There are three mirror source configurations:

• io.getquill.MirrorSourceConfig: Mirrors the quotation AST
• io.getquill.SqlMirrorSourceConfig: Mirrors the SQL query
• io.getquill.CassandraMirrorSourceConfig: Mirrors the CQL query

This documentation uses the SQL mirror in its examples under the db name:

import io.getquill._

lazy val db = source(new SqlMirrorSourceConfig("testSource"))

Compile-time quotations

Quotations are both compile-time and runtime values. Quill uses a type refinement to store the quotation's AST as an annotation available at compile-time and the q.ast method exposes the AST as runtime value.

It is important to avoid giving explicit types to quotations when possible. For instance, this quotation can't be read at compile-time as the type refinement is lost:

// Avoid type widening (Quoted[Query[Circle]]), or else the quotation will be dynamic.
val q: Quoted[Query[Circle]] = quote {
  query[Circle].filter(c => c.radius > 10)
}

db.run(q) // Dynamic query

Quill falls back to runtime normalization and query generation if the quotation's AST can be read at compile-time. Please refer to dynamic queries for more information

Bindings

Quotations are designed to be self-contained, without references to runtime values outside their scope. There are two mechanisms to explicitly bind runtime values to a quotation execution.

Lifted values

A runtime value can be lifted to a quotation through the method lift:

def biggerThan(i: Float) = quote {
  query[Circle].filter(r => r.radius > lift(i))
}
db.run(biggerThan(10)) // SELECT r.radius FROM Circle r WHERE r.radius > ?

Parametrized quotations

A quotation can be defined as a function:

val biggerThan = quote {
  (i: Int) =>
    query[Circle].filter(r => r.radius > i)
}

And a runtime value can be specified when running it:

db.run(biggerThan)(10) // SELECT r.radius FROM Circle r WHERE r.radius > ?

Schema

The database schema is represented by case classes. By default, quill uses the class and field names as the database identifiers:

case class Circle(radius: Float)

val q = quote {
  query[Circle].filter(c => c.radius > 1)
}

db.run(q) // SELECT c.radius FROM Circle c WHERE c.radius > 1

Alternatively, the identifiers can be customized:

val circles = quote {
  query[Circle].schema(_.entity("circle_table").columns(_.radius -> "radius_column"))
}

val q = quote {
  circles.filter(c => c.radius > 1)
}

db.run(q)
// SELECT c.radius_column FROM circle_table c WHERE c.radius_column > 1

If multiple tables require custom identifiers, it is good practice to define a schema object with all table queries to be reused across multiple queries:

case class Circle(radius: Int)
case class Rectangle(length: Int, width: Int)
object schema {
  val circles = quote {
    query[Circle].schema(
        _.entity("circle_table")
        .columns(_.radius -> "radius_column"))
  }
  val rectangles = quote {
    query[Rectangle].schema(
        _.entity("rectangle_table")
        .columns(
          _.length -> "length_column",
          _.width -> "width_column"))
  }
}

It is possible to define a column that is a key generated by the database. It will be ignored during insertions and returned as the result. Note that it accepts only values that can be read as Long.

case class Product(id: Long, description: String, sku: Long)

val q = quote {
  query[Product].schema(_.generated(_.id)).insert
}

db.run(q)
// INSERT INTO Product (description,sku) VALUES (?, ?)

Queries

The overall abstraction of quill queries is use database tables as if they were in-memory collections. Scala for-comprehensions provide syntatic sugar to deal with this kind of monadic operations:

case class Person(id: Int, name: String, age: Int)
case class Contact(personId: Int, phone: String)

val q = quote {
  for {
    p <- query[Person] if(p.id == 999)
    c <- query[Contact] if(c.personId == p.id)
  } yield {
    (p.name, c.phone)
  }
}

db.run(q)
// SELECT p.name, c.phone FROM Person p, Contact c WHERE (p.id = 999) AND (c.personId = p.id)

Quill normalizes the quotation and translates the monadic joins to applicative joins, generating a database-friendly query that avoids nested queries.

Any of the following features can be used together with the others and/or within a for-comprehension:

filter

val q = quote {
  query[Person].filter(p => p.age > 18)
}

db.run(q)
// SELECT p.id, p.name, p.age FROM Person p WHERE p.age > 18

map

val q = quote {
  query[Person].map(p => p.name)
}

db.run(q)
// SELECT p.name FROM Person p

flatMap

val q = quote {
  query[Person].filter(p => p.age > 18).flatMap(p => query[Contact].filter(c => c.personId == p.id))
}

db.run(q)
// SELECT c.personId, c.phone FROM Person p, Contact c WHERE (p.age > 18) AND (c.personId = p.id)

sortBy

val q1 = quote {
  query[Person].sortBy(p => p.age)
}

db.run(q1)
// SELECT p.id, p.name, p.age FROM Person p ORDER BY p.age ASC NULLS FIRST

val q2 = quote {
  query[Person].sortBy(p => p.age)(Ord.descNullsLast)
}

db.run(q2)
// SELECT p.id, p.name, p.age FROM Person p ORDER BY p.age DESC NULLS LAST

val q3 = quote {
  query[Person].sortBy(p => (p.name, p.age))(Ord(Ord.asc, Ord.desc))
}

db.run(q3)
// SELECT p.id, p.name, p.age FROM Person p ORDER BY p.name ASC, p.age DESC

drop/take

val q = quote {
  query[Person].drop(2).take(1)
}

db.run(q)
// SELECT x.id, x.name, x.age FROM Person x LIMIT 1 OFFSET 2

groupBy

val q = quote {
  query[Person].groupBy(p => p.age).map {
    case (age, people) =>
      (age, people.size)
  }
}

db.run(q)
// SELECT p.age, COUNT(*) FROM Person p GROUP BY p.age

union

val q = quote {
  query[Person].filter(p => p.age > 18).union(query[Person].filter(p => p.age > 60))
}

db.run(q)
// SELECT x.id, x.name, x.age FROM (SELECT id, name, age FROM Person p WHERE p.age > 18
// UNION SELECT id, name, age FROM Person p1 WHERE p1.age > 60) x

unionAll/++

val q = quote {
  query[Person].filter(p => p.age > 18).unionAll(query[Person].filter(p => p.age > 60))
}

db.run(q)
// SELECT x.id, x.name, x.age FROM (SELECT id, name, age FROM Person p WHERE p.age > 18
// UNION ALL SELECT id, name, age FROM Person p1 WHERE p1.age > 60) x

val q2 = quote {
  query[Person].filter(p => p.age > 18) ++ query[Person].filter(p => p.age > 60)
}

db.run(q2)
// SELECT x.id, x.name, x.age FROM (SELECT id, name, age FROM Person p WHERE p.age > 18
// UNION ALL SELECT id, name, age FROM Person p1 WHERE p1.age > 60) x

aggregation

val r = quote {
  query[Person].map(p => p.age)
}

db.run(r.min) // SELECT MIN(p.age) FROM Person p
db.run(r.max) // SELECT MAX(p.age) FROM Person p
db.run(r.avg) // SELECT AVG(p.age) FROM Person p
db.run(r.sum) // SELECT SUM(p.age) FROM Person p
db.run(r.size) // SELECT COUNT(p.age) FROM Person p

isEmpty/nonEmpty

val q = quote {
  query[Person].filter{ p1 =>
    query[Person].filter(p2 => p2.id != p1.id && p2.age == p1.age).isEmpty
  }
}

db.run(q)
// SELECT p1.id, p1.name, p1.age FROM Person p1 WHERE
// NOT EXISTS (SELECT * FROM Person p2 WHERE (p2.id <> p1.id) AND (p2.age = p1.age))

val q2 = quote {
  query[Person].filter{ p1 =>
    query[Person].filter(p2 => p2.id != p1.id && p2.age == p1.age).nonEmpty
  }
}

db.run(q2)
// SELECT p1.id, p1.name, p1.age FROM Person p1 WHERE
// EXISTS (SELECT * FROM Person p2 WHERE (p2.id <> p1.id) AND (p2.age = p1.age))

contains

val q = quote {
  query[Person].filter(p => Set(1, 2).contains(p.id))
}

db.run(q)
// SELECT p.id, p.name, p.age FROM Person p WHERE p.id IN (1, 2)

val q1 = quote { (ids: Set[Int]) =>
  query[Person].filter(p => ids.contains(p.id))
}

db.run(q1)
// SELECT p.id, p.name, p.age FROM Person p WHERE p.id IN (?)

val peopleWithContacts = quote {
  query[Person].filter(p => query[Contact].filter(c => c.personId == p.id).nonEmpty)
}
val q2 = quote {
  query[Person].filter(p => peopleWithContacts.contains(p.id))
}

db.run(q2)
// SELECT p.id, p.name, p.age FROM Person p WHERE p.id IN (SELECT p1.* FROM Person p1 WHERE EXISTS (SELECT c.* FROM Contact c WHERE c.personId = p1.id))

distinct

val q = quote {
  query[Person].map(p => p.age).distinct
}

db.run(q)
// SELECT DISTINCT p.age FROM Person p

joins

In addition to applicative joins Quill also supports explicit joins (both inner and left/right/full outer joins).

val q = quote {
  query[Person].join(query[Contact]).on((p, c) => c.personId == p.id)
}

db.run(q)
// SELECT p.id, p.name, p.age, c.personId, c.phone•
// FROM Person p INNER JOIN Contact c ON c.personId = p.id

val q = quote {
  query[Person].leftJoin(query[Contact]).on((p, c) => c.personId == p.id)
}

db.run(q)
// SELECT p.id, p.name, p.age, c.personId, c.phone•
// FROM Person p LEFT JOIN Contact c ON c.personId = p.id

The example joins above cover the simple case. What do you do when a query requires joining more than 2 tables?

With Quill the following multi-join queries are equivalent, choose according to preference:

case class Employer(id: Int, personId: Int, name: String)

val qFlat = quote {
  for{
    (p,e) <- query[Person].join(query[Employer]).on(_.id == _.personId)
       c  <- query[Contact].leftJoin(_.personId == p.id)
  } yield(p, e, c)
}

val qNested = quote {
  for{
    ((p,e),c) <-
      query[Person].join(query[Employer]).on(_.id == _.personId)
      .leftJoin(query[Contact]).on(
        _._1.id == _.personId
      )
  } yield(p, e, c)
}

db.run(qFlat)
db.run(qNested)
// SELECT p.id, p.name, p.age, e.id, e.personId, e.name, c.id, c.phone•
// FROM Person p INNER JOIN Employer e ON p.id = e.personId LEFT JOIN Contact c ON c.personId = p.id

Query probing

Query probing is an experimental feature that validates queries against the database at compile time, failing the compilation if it is not valid. The query validation does not alter the database state.

This feature is disabled by default. To enable it, mix the QueryProbing trait to the database configuration:

lazy val db = source(new MySourceConfig("configKey") with QueryProbing)

The config configuration must be self-contained, not having references to variables outside its scope. This allows the macro load the source instance at compile-time.

The configurations correspondent to the config key must be available at compile time. You can achieve it by adding this line to your project settings:

unmanagedClasspath in Compile += baseDirectory.value / "src" / "main" / "resources"

If your project doesn't have a standard layout, e.g. a play project, you should configure the path to point to the folder that contains your config file.

Actions

Database actions are defined using quotations as well. These actions don't have a collection-like API but rather a custom DSL to express inserts, deletes and updates.

Note: Actions take either a List (in which case the query is batched) or a single value.

insert

val a = quote(query[Contact].insert)

db.run(a)(List(Contact(999, "[phone]")))
// INSERT INTO Contact (personId,phone) VALUES (?, ?)
db.run(a)(Contact(999, "[phone]"))
// insert single item

It is also possible to insert specific columns:

val a = quote {
  (personId: Int, phone: String) =>
    query[Contact].insert(_.personId -> personId, _.phone -> phone)
}

db.run(a)(List((999, "[phone]")))
// INSERT INTO Contact (personId,phone) VALUES (?, ?)

Or column queries:

val a = quote {
  (id: Int) =>
    query[Person].insert(_.id -> id, _.age -> query[Person].map(p => p.age).max)
}

db.run(a)(List(999))
// INSERT INTO Person (id,age) VALUES (?, (SELECT MAX(p.age) FROM Person p))

update

val a = quote {
  query[Person].filter(_.id == 999).update
}

db.run(a)(List(Person(999, "John", 22)))
// UPDATE Person SET id = ?, name = ?, age = ? WHERE id = 999
db.run(a)(Person(999, "John", 22))
// update single item

Using specific columns:

val a = quote {
  (id: Int, age: Int) =>
    query[Person].filter(p => p.id == id).update(_.age -> age)
}

db.run(a)(List((999, 18)))
// UPDATE Person SET age = ? WHERE id = ?

Using columns as part of the update:

val a = quote {
  (id: Int) =>
    query[Person].filter(p => p.id == id).update(p => p.age -> (p.age + 1))
}

db.run(a)(List(999))
// UPDATE Person SET age = (age + 1) WHERE id = ?

Using column a query:

val a = quote {
  (id: Int) =>
    query[Person].filter(p => p.id == id).update(_.age -> query[Person].map(p => p.age).max)
}

db.run(a)(List(999))
// UPDATE Person SET age = (SELECT MAX(p.age) FROM Person p) WHERE id = ?

delete

val a = quote {
  query[Person].filter(p => p.name == "").delete
}

db.run(a)
// DELETE FROM Person WHERE name = ''

Implicit query

Quill provides implicit conversions from case class companion objects to query[T] through an extra import:

import io.getquill.ImplicitQuery._

val q = quote {
  for {
    p <- Person if(p.id == 999)
    c <- Contact if(c.personId == p.id)
  } yield {
    (p.name, c.phone)
  }
}

db.run(q)
// SELECT p.name, c.phone FROM Person p, Contact c WHERE (p.id = 999) AND (c.personId = p.id)

Note the usage of Person and Contact instead of query[Person] and query[Contact].

SQL-specific operations

Some operations are sql-specific and not provided with the generic quotation mechanism. The io.getquill.sources.sql.ops package has some implicit classes for this kind of operations:

like

import io.getquill.sources.sql.ops._

val q = quote {
  query[Person].filter(p => p.name like "%John%")
}
db.run(q)
// SELECT p.id, p.name, p.age FROM Person p WHERE p.name like '%John%'

Cassandra-specific operations

The cql-specific operations are provided by the following import:

import io.getquill.sources.cassandra.ops._

The cassandra package also offers a mirror source:

import io.getquill._

lazy val db = source(new CassandraMirrorSourceConfig("testSource"))

Supported operations:

allowFiltering

val q = quote {
  query[Person].filter(p => p.age > 10).allowFiltering
}
db.run(q)
// SELECT id, name, age FROM Person WHERE age > 10 ALLOW FILTERING

ifNotExists

val q = quote {
  query[Person].insert(_.age -> 10, _.name -> "John").ifNotExists
}
db.run(q)
// INSERT INTO Person (age,name) VALUES (10, 'John') IF NOT EXISTS

ifExists

val q = quote {
  query[Person].filter(p => p.name == "John").delete.ifExists
}
db.run(q)
// DELETE FROM Person WHERE name = 'John' IF EXISTS

usingTimestamp

val q1 = quote {
  query[Person].insert(_.age -> 10, _.name -> "John").usingTimestamp(99)
}
db.run(q1)
// INSERT INTO Person (age,name) VALUES (10, 'John') USING TIMESTAMP 99

val q2 = quote {
  query[Person].usingTimestamp(99).update(_.age -> 10)
}
db.run(q2)
// UPDATE Person USING TIMESTAMP 99 SET age = 10

usingTtl

val q1 = quote {
  query[Person].insert(_.age -> 10, _.name -> "John").usingTtl(11)
}
db.run(q1)
// INSERT INTO Person (age,name) VALUES (10, 'John') USING TTL 11

val q2 = quote {
  query[Person].usingTtl(11).update(_.age -> 10)
}
db.run(q2)
// UPDATE Person USING TTL 11 SET age = 10

val q3 = quote {
  query[Person].usingTtl(11).filter(_.name == "John").delete
}
db.run(q3)  
// DELETE FROM Person USING TTL 11 WHERE name = 'John'

using

val q1 = quote {
  query[Person].insert(_.age -> 10, _.name -> "John").using(ts = 99, ttl = 11)
}
db.run(q1)
// INSERT INTO Person (age,name) VALUES (10, 'John') USING TIMESTAMP 99 AND TTL 11

val q2 = quote {
  query[Person].using(ts = 99, ttl = 11).update(_.age -> 10)
}
db.run(q2)
// UPDATE Person USING TIMESTAMP 99 AND TTL 11 SET age = 10

val q3 = quote {
  query[Person].using(ts = 99, ttl = 11).filter(_.name == "John").delete
}
db.run(q3)
// DELETE FROM Person USING TIMESTAMP 99 AND TTL 11 WHERE name = 'John'

ifCond

val q1 = quote {
  query[Person].update(_.age -> 10).ifCond(_.name == "John")
}
db.run(q1)
// UPDATE Person SET age = 10 IF name = 'John'

val q2 = quote {
  query[Person].filter(_.name == "John").delete.ifCond(_.age == 10)
}
db.run(q2)
// DELETE FROM Person WHERE name = 'John' IF age = 10

delete column

val q = quote {
  query[Person].map(p => p.age).delete
}
db.run(q)
// DELETE p.age FROM Person

Dynamic queries

Quill's default operation mode is compile-time, but there are queries that have their structure defined only at runtime. Quill automatically falls back to runtime normalization and query generation if the query's structure is not static. Example:

import io.getquill._

lazy val db = source(new SqlMirrorSourceConfig("testSource"))

sealed trait QueryType
case object Minor extends QueryType
case object Senior extends QueryType

def people(t: QueryType): Quoted[Query[Person]] =
  t match {
    case Minor => quote {
      query[Person].filter(p => p.age < 18)
    }
    case Senior => quote {
      query[Person].filter(p => p.age > 65)
    }
  }

db.run(people(Minor))
// SELECT p.id, p.name, p.age FROM Person p WHERE p.age < 18

db.run(people(Senior))
// SELECT p.id, p.name, p.age FROM Person p WHERE p.age > 65

Extending quill

Infix

Infix is a very flexible mechanism to use non-supported features without having to use plain queries in the target language. It allows insertion of arbitrary strings within quotations.

For instance, quill doesn't support the FOR UPDATE SQL feature. It can still be used through infix and implicit classes:

implicit class ForUpdate[T](q: Query[T]) {
  def forUpdate = quote(infix"$q FOR UPDATE".as[Query[T]])
}

val a = quote {
  query[Person].filter(p => p.age < 18).forUpdate
}

db.run(a)
// SELECT p.id, p.name, p.age FROM (SELECT * FROM Person p WHERE p.age < 18 FOR UPDATE) p

The forUpdate quotation can be reused for multiple queries.

The same approach can be used for RETURNING ID:

implicit class ReturningId[T](a: Action[T]) {
  def returningId = quote(infix"$a RETURNING ID".as[Action[T]])
}

val a = quote {
  query[Person].insert(_.name -> "John", _.age -> 21).returningId
}

db.run(a)
// INSERT INTO Person (name,age) VALUES ('John', 21) RETURNING ID

A custom database function can also be used through infix:

val myFunction = quote {
  (i: Int) => infix"MY_FUNCTION($i)".as[Int]
}

val q = quote {
  query[Person].map(p => myFunction(p.age))
}

db.run(q)
// SELECT MY_FUNCTION(p.age) FROM Person p

Custom encoding

Quill uses Encoders to encode query inputs and Decoders to read values returned by queries. The library provides a few built-in encodings and two mechanisms to define custom encodings: mapped encoding and raw encoding.

Mapped Encoding

If the correspondent database type is already supported, use mappedEncoding. In this example, String is already supported by Quill and the UUID encoding from/to String is defined through mapped encoding:

import java.util.UUID

implicit val encodeUUID = mappedEncoding[UUID, String](_.toString)
implicit val decodeUUID = mappedEncoding[String, UUID](UUID.fromString(_))

Raw Encoding

If the database type is not supported by Quill, it is possible to provide "raw" encoders and decoders:

import io.getquill.naming.Literal
import io.getquill.sources.jdbc.JdbcSource
import io.getquill.sources.sql.idiom.PostgresDialect

trait UUIDEncodingExample {
  val jdbcSource: JdbcSource[PostgresDialect, Literal] // your source should go here

  import jdbcSource._

  implicit val uuidDecoder: Decoder[UUID] =
    decoder[UUID] {
      row => index =>
        UUID.fromString(row.getObject(index).toString) // database-specific implementation
    }
  implicit val uuidEncoder: Encoder[UUID] =
    encoder[UUID] {
      row => (idx, uuid) =>
        row.setObject(idx, uuid, java.sql.Types.OTHER) // database-specific implementation
    }
}

Wrapped types

Quill also supports encoding of "wrapped types". Just extend the WrappedValue trait and Quill will automatically encode the underlying primitive type.

import io.getquill.sources._

case class UserId(value: Int) extends AnyVal with WrappedValue[Int]
case class User(id: UserId, name: String)

val q = quote {
  (id: UserId) => for {
    u <- query[User] if u.id == id
  } yield u
}
db.run(q)(UserId(1))

// SELECT u.id, u.name FROM User u WHERE (u.id = 1)

Sources

SQL Sources

Sources represent the database and provide an execution interface for queries. Example:

import io.getquill._
import io.getquill.naming.SnakeCase
import io.getquill.sources.sql.idiom.MySQLDialect

lazy val db = source(new JdbcSourceConfig[MySQLDialect, SnakeCase]("db"))

Dialect

The SQL dialect to be used by the source is defined by the first type parameter. Some source types are specific to a database and thus not require it.

Quill has three built-in dialects:

• io.getquill.sources.sql.idiom.H2Dialect
• io.getquill.sources.sql.idiom.MySQLDialect
• io.getquill.sources.sql.idiom.PostgresDialect

Naming strategy

The second type parameter defines the naming strategy to be used when translating identifiers (table and column names) to SQL.

strategy	example
io.getquill.naming.Literal	some_ident -> some_ident
io.getquill.naming.Escape	some_ident -> "some_ident"
io.getquill.naming.UpperCase	some_ident -> SOME_IDENT
io.getquill.naming.LowerCase	SOME_IDENT -> some_ident
io.getquill.naming.SnakeCase	someIdent -> some_ident
io.getquill.naming.CamelCase	some_ident -> someIdent
io.getquill.naming.MysqlEscape	some_ident -> `some_ident`
io.getquill.naming.PostgresEscape	$some_ident -> $some_ident

Multiple transformations can be defined using mixin. For instance, the naming strategy

SnakeCase with UpperCase

produces the following transformation:

someIdent -> SOME_IDENT

The transformations are applied from left to right.

Configuration

The string passed to the source configuration is used as the key to obtain configurations using the typesafe config library.

Additionally, any member of a source configuration can be overriden. Example:

import io.getquill._
import io.getquill.naming.SnakeCase
import io.getquill.sources.sql.idiom.MySQLDialect

lazy val db = source(new JdbcSourceConfig[MySQLDialect, SnakeCase]("db") {
  override def dataSource = ??? // create the datasource manually
})

quill-jdbc

Quill uses HikariCP for connection pooling. Please refer to HikariCP's documentation for a detailed explanation of the available configurations.

Note that there are dataSource configurations, that go under dataSource, like user and password, but some pool settings may go under the root config, like connectionTimeout.

MySQL

sbt dependencies

libraryDependencies ++= Seq(
  "mysql" % "mysql-connector-java" % "5.1.36",
  "io.getquill" %% "quill-jdbc" % "0.6.1-SNAPSHOT"
)

source definition

import io.getquill._
import io.getquill.naming.SnakeCase
import io.getquill.sources.sql.idiom.MySQLDialect

lazy val db = source(new JdbcSourceConfig[MySQLDialect, SnakeCase]("db"))

application.properties

db.dataSourceClassName=com.mysql.jdbc.jdbc2.optional.MysqlDataSource
db.dataSource.url=jdbc:mysql://host/database
db.dataSource.user=root
db.dataSource.password=root
db.dataSource.cachePrepStmts=true
db.dataSource.prepStmtCacheSize=250
db.dataSource.prepStmtCacheSqlLimit=2048
db.connectionTimeout=30000

Postgres

sbt dependencies

libraryDependencies ++= Seq(
  "org.postgresql" % "postgresql" % "9.4-1206-jdbc41",
  "io.getquill" %% "quill-jdbc" % "0.6.1-SNAPSHOT"
)

source definition

import io.getquill._
import io.getquill.naming.SnakeCase
import io.getquill.sources.sql.idiom.PostgresDialect

lazy val db = source(new JdbcSourceConfig[PostgresDialect, SnakeCase]("db"))

application.properties

db.dataSourceClassName=org.postgresql.ds.PGSimpleDataSource
db.dataSource.user=root
db.dataSource.password=root
db.dataSource.databaseName=database
db.dataSource.portNumber=5432
db.dataSource.serverName=host
db.connectionTimeout=30000

quill-async

MySQL Async

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-async" % "0.6.1-SNAPSHOT"
)

source definition

import io.getquill._
import io.getquill.naming.SnakeCase

lazy val db = source(new MysqlAsyncSourceConfig[SnakeCase]("db"))

application.properties

db.host=host
db.port=3306
db.user=root
db.password=root
db.database=database
db.poolMaxQueueSize=4
db.poolMaxObjects=4
db.poolMaxIdle=999999999
db.poolValidationInterval=100

Postgres Async

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-async" % "0.6.1-SNAPSHOT"
)

source definition

import io.getquill._
import io.getquill.naming.SnakeCase

lazy val db = source(new PostgresAsyncSourceConfig[SnakeCase]("db"))

application.properties

db.host=host
db.port=5432
db.user=root
db.password=root
db.database=database
db.poolMaxQueueSize=4
db.poolMaxObjects=4
db.poolMaxIdle=999999999
db.poolValidationInterval=100

quill-finagle-mysql

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-finagle-mysql" % "0.6.1-SNAPSHOT"
)

source definition

import io.getquill._
import io.getquill.naming.SnakeCase

lazy val db = source(new FinagleMysqlSourceConfig[SnakeCase]("db"))

application.properties

db.dest=localhost:3306
db.user=root
db.password=root
db.database=database
db.pool.watermark.low=0
db.pool.watermark.high=10
db.pool.idleTime=5 # seconds
db.pool.bufferSize=0
db.pool.maxWaiters=2147483647

Cassandra Sources

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-cassandra" % "0.6.1-SNAPSHOT"
)

synchronous source

import io.getquill._
import io.getquill.naming.SnakeCase

lazy val db = source(new CassandraSyncSourceConfig[SnakeCase]("db"))

asynchronous source

import io.getquill._
import io.getquill.naming.SnakeCase

lazy val db = source(new CassandraAsyncSourceConfig[SnakeCase]("db"))

stream source

import io.getquill._
import io.getquill.naming.SnakeCase

lazy val db = source(new CassandraStreamSourceConfig[SnakeCase]("db"))

The configurations are set using runtime reflection on the Cluster.builder instance. It is possible to set nested structures like queryOptions.consistencyLevel, use enum values like LOCAL_QUORUM, and set multiple parameters like in credentials.

application.properties

db.keyspace=quill_test
db.preparedStatementCacheSize=1000
db.session.contactPoint=127.0.0.1
db.session.queryOptions.consistencyLevel=LOCAL_QUORUM
db.session.withoutMetrics=true
db.session.withoutJMXReporting=false
db.session.credentials.0=root
db.session.credentials.1=pass
db.session.maxSchemaAgreementWaitSeconds=1
db.session.addressTranslater=com.datastax.driver.core.policies.IdentityTranslater

Additional resources

Templates

In order to quickly start with Quill, we have setup some template projects:

• Play Framework with Quill JDBC

Slick comparison

Please refer to SLICK.md for a detailed comparison between Quill and Slick.

Cassandra libraries comparison

Please refer to CASSANDRA.md for a detailed comparison between Quill and other main alternatives for interaction with Cassandra in Scala.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. See CODE_OF_CONDUCT.md for details.

License

See the LICENSE file for details.

Maintainers

• @fwbrasil
• @godenji
• @gustavoamigo
• @jilen
• @lvicentesanchez

You can notify all maintainers using the handle @getquill/maintainers.

Acknowledgments

The project was created having Philip Wadler's talk "A practical theory of language-integrated query" as its initial inspiration. The development was heavily influenced by the following papers:

• A Practical Theory of Language-Integrated Query
• Everything old is new again: Quoted Domain Specific Languages
• The Flatter, the Better