Contexts

Contexts represent the database and provide an execution interface for queries.

Mirror context

Quill provides a mirror context for testing purposes. Instead of running the query, the mirror context returns a structure with the information that would be used to run the query. There are three mirror context instances:

• io.getquill.MirrorContext: Mirrors the quotation AST
• io.getquill.SqlMirrorContext: Mirrors the SQL query
• io.getquill.CassandraMirrorContext: Mirrors the CQL query

Dependent contexts

The context instance provides all methods and types to interact with quotations and the database. Contexts can be imported and passed around normally in constructors and function arguments.

class MyContext extends SqlMirrorContext(MirrorSqlDialect, Literal)
case class MySchema(c: MyContext) {
  import c._
  val people = quote {
    querySchema[Person]("people")
  }
}
case class MyDao(c: MyContext, schema: MySchema) {
  def allPeople = c.run(schema.people)
}

Context Traits

One way to compose applications with this kind of context is to use traits with an abstract context variable:

class MyContext extends SqlMirrorContext(MirrorSqlDialect, Literal)

trait MySchema {

  val c: MyContext
  import c._

  val people = quote {
    querySchema[Person]("people")
  }
}

case class MyDao(c: MyContext) extends MySchema {
  import c._

  def allPeople =
    c.run(people)
}

Modular Contexts

Another simple way to modularize Quill code is by extending Context as a self-type and applying mixins. Using this strategy, it is possible to create functionality that is fully portable across databases and even different types of databases (e.g. creating common queries for both Postgres and Spark).

For example, create the following abstract context:

trait ModularContext[I <: Idiom, N <: NamingStrategy] { this: Context[I, N] =>
  def peopleOlderThan = quote {
    (age:Int, q:Query[Person]) => q.filter(p => p.age > age)
  }
}

Let's see how this can be used across different kinds of databases and Quill contexts.

Use ModularContext in a mirror context:

// Note: In some cases need to explicitly specify [MirrorSqlDialect, Literal].
val ctx =
  new SqlMirrorContext[MirrorSqlDialect, Literal](MirrorSqlDialect, Literal)
    with ModularContext[MirrorSqlDialect, Literal]

import ctx._
println( run(peopleOlderThan(22, query[Person])).string )

Use ModularContext to query a Postgres Database

val ctx =
  new PostgresJdbcContext[Literal](Literal, ds)
    with ModularContext[PostgresDialect, Literal]

import ctx._
val results = run(peopleOlderThan(22, query[Person]))

Use ModularContext to query a Spark Dataset

object CustomQuillSparkContext extends QuillSparkContext
  with ModularContext[SparkDialect, Literal]

val results = run(peopleOlderThan(22, liftQuery(dataset)))

Spark Integration

Quill provides a fully type-safe way to use Spark's highly-optimized SQL engine. It's an alternative to Dataset's weakly-typed API.

Importing Quill Spark

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-spark" % "4.8.5"
)

Usage

Unlike the other modules, the Spark context is a companion object. Also, it does not depend on a spark session. To use it, add the following import:

import org.apache.spark.sql.SparkSession

// Create your Spark Context
val session =
  SparkSession.builder()
    .master("local")
    .appName("spark test")
    .getOrCreate()

// The Spark SQL Context must be provided by the user through an implicit value:
implicit val sqlContext = session.sqlContext
import sqlContext.implicits._      // Also needed...

// Import the Quill Spark Context
import io.getquill.QuillSparkContext._

Note Unlike the other modules, the Spark context is a companion object. Also, it does not depend on a spark session.

Also Note: Quill decoders and meta instances are not used by the quill-spark module, Spark's Encoders are used instead.

Using Quill-Spark

The run method returns a Dataset transformed by the Quill query using the SQL engine.

// Typically you start with some type dataset.
val peopleDS: Dataset[Person] = spark.read.parquet("path/to/people").as[Person]
val addressesDS: Dataset[Address] = spark.read.parquet("path/to/addresses").as[Address]

// The liftQuery method converts Datasets to Quill queries:
val people = quote { liftQuery(peopleDS) }
val addresses = quote { liftQuery(addressesDS) }

val peopleAndAddresses = quote {
  (people join addresses).on((p, a) => p.id == a.ownerFk)
}

val peopleAndAddressesDS: Dataset[(Person, Address)] = run(peopleAndAddresses)

Simplify it

Since the run method allows for Quill queries to be specified directly, and liftQuery can be used inside of any Quoted block, you can shorten various steps of the above workflow:

val peopleDS: Dataset[Person] = spark.read.parquet("path/to/people")
val addressesDS: Dataset[Address] = spark.read.parquet("path/to/addresses")

val peopleAndAddressesDS: Dataset[(Person, Address)] = run {
  liftQuery(peopleDS)
    .join(liftQuery(addressesDS))
    .on((p, a) => p.id == a.ownerFk)
}

Here is an example of a Dataset being converted into Quill, filtered, and then written back out.

import org.apache.spark.sql.Dataset

def filter(myDataset: Dataset[Person], name: String): Dataset[Int] =
  run {
    liftQuery(myDataset).filter(_.name == lift(name)).map(_.age)
  }
// SELECT x1.age _1 FROM (?) x1 WHERE x1.name = ?

Workflow

Due to the design of Quill-Spark, it can be used interchangeably throughout your Spark workflow:

• Lift a Dataset to Query to do some filtering and sub-selecting (with Predicate and Filter Pushdown!).
• Then covert it back to a Dataset to do Spark-Specific operations.
• Then convert it back to a Query to use Quills great Join DSL...
• Then convert it back to a Dataset to write it to a file or do something else with it...

Custom Functions

TODO UDFs and UDAFs

Restrictions

Top Level Classes

Spark only supports using top-level classes as record types. That means that when using quill-spark you can only use a top-level case class for T in Query[T].

TODO Get the specific error

Lifted Variable Interpolation

The queries printed from run(myQuery) during compile time escape question marks via a backslash them in order to be able to substitute liftings properly. They are then returned back to their original form before running.

import org.apache.spark.sql.Dataset

def filter(myDataset: Dataset[Person]): Dataset[Int] =
  run {
    liftQuery(myDataset).filter(_.name == "?").map(_.age)
  }
// This is generated during compile time:
// SELECT x1.age _1 FROM (?) x1 WHERE x1.name = '\?'
// It is reverted upon run-time:
// SELECT x1.age _1 FROM (ds1) x1 WHERE x1.name = '?'

SQL Contexts

Example:

lazy val ctx = new MysqlJdbcContext(SnakeCase, "ctx")

Dialect

The SQL dialect parameter defines the specific database dialect to be used. Some context types are specific to a database and thus not require it.

Quill has five built-in dialects:

• io.getquill.H2Dialect
• io.getquill.MySQLDialect
• io.getquill.PostgresDialect
• io.getquill.SqliteDialect
• io.getquill.SQLServerDialect
• io.getquill.OracleDialect

Naming strategy

The naming strategy parameter defines the behavior 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 NamingStrategy(). For instance, the naming strategy

NamingStrategy(SnakeCase, UpperCase)

produces the following transformation:

someIdent -> SOME_IDENT

The transformations are applied from left to right.

Configuration

The string passed to the context is used as the key in order to obtain configurations using the typesafe config library.

Additionally, the contexts provide multiple constructors. For instance, with JdbcContext it's possible to specify a DataSource directly, without using the configuration:

def createDataSource: javax.sql.DataSource with java.io.Closeable = ???

lazy val ctx = new MysqlJdbcContext(SnakeCase, createDataSource)

quill-jdbc

The quill-jdbc module provides a simple blocking JDBC context for standard use-cases. For transactions, the JDBC connection is kept in a thread-local variable.

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.

transactions

The JdbcContext provides thread-local transaction support:

ctx.transaction {
  ctx.run(query[Person].delete)
  // other transactional code
}

The body of transaction can contain calls to other methods and multiple run calls since the transaction is propagated through a thread-local.

MySQL (quill-jdbc)

sbt dependencies

libraryDependencies ++= Seq(
  "mysql" % "mysql-connector-java" % "8.0.17",
  "io.getquill" %% "quill-jdbc" % "4.8.5"
)

context definition

lazy val ctx = new MysqlJdbcContext(SnakeCase, "ctx")

application.properties

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

Postgres (quill-jdbc)

sbt dependencies

libraryDependencies ++= Seq(
  "org.postgresql" % "postgresql" % "42.2.8",
  "io.getquill" %% "quill-jdbc" % "4.8.5"
)

context definition

lazy val ctx = new PostgresJdbcContext(SnakeCase, "ctx")

application.properties

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

Sqlite (quill-jdbc)

sbt dependencies

libraryDependencies ++= Seq(
  "org.xerial" % "sqlite-jdbc" % "3.28.0",
  "io.getquill" %% "quill-jdbc" % "4.8.5"
)

context definition

lazy val ctx = new SqliteJdbcContext(SnakeCase, "ctx")

application.properties

ctx.driverClassName=org.sqlite.JDBC
ctx.jdbcUrl=jdbc:sqlite:/path/to/db/file.db

H2 (quill-jdbc)

sbt dependencies

libraryDependencies ++= Seq(
  "com.h2database" % "h2" % "1.4.199",
  "io.getquill" %% "quill-jdbc" % "4.8.5"
)

context definition

lazy val ctx = new H2JdbcContext(SnakeCase, "ctx")

application.properties

ctx.dataSourceClassName=org.h2.jdbcx.JdbcDataSource
ctx.dataSource.url=jdbc:h2:mem:yourdbname
ctx.dataSource.user=sa

SQL Server (quill-jdbc)

sbt dependencies

libraryDependencies ++= Seq(
  "com.microsoft.sqlserver" % "mssql-jdbc" % "7.4.1.jre8",
  "io.getquill" %% "quill-jdbc" % "4.8.5"
)

context definition

lazy val ctx = new SqlServerJdbcContext(SnakeCase, "ctx")

Oracle (quill-jdbc)

Quill supports Oracle version 12c and up although due to licensing restrictions, version 18c XE is used for testing.

Note that the latest Oracle JDBC drivers are not publicly available. In order to get them, you will need to connect to Oracle's private maven repository as instructed here. Unfortunately, this procedure currently does not work for SBT. There are various workarounds available for this situation here.

sbt dependencies

libraryDependencies ++= Seq(
  "com.oracle.jdbc" % "ojdbc8" % "18.3.0.0.0",
  "io.getquill" %% "quill-jdbc" % "4.8.5"
)

context definition

lazy val ctx = new OracleJdbcContext(SnakeCase, "ctx")

application.properties

ctx.dataSourceClassName=com.microsoft.sqlserver.jdbc.SQLServerDataSource
ctx.dataSource.user=user
ctx.dataSource.password=YourStrongPassword
ctx.dataSource.databaseName=database
ctx.dataSource.portNumber=1433
ctx.dataSource.serverName=host

ZIO (quill-jdbc-zio)

Quill context that executes JDBC queries inside of ZIO. Unlike most other contexts that require passing in a java.sql.DataSource when the context is created, this context's run methods return a ZIO that has a DataSource resource dependency. Naturally, this should be provided later on in your application (see ZioJdbc for helper methods that assist in doing this).

Since resource dependency is DataSource the result of a run call is ZIO[DataSource, SQLException, T]. This means that if you have a DataSource object, you can just provide it!

def ds: DataSource = _
run(people).provide(Has(ds))

Since most quill-zio methods return ZIO[DataSource, SQLException, T] the type QIO[T] i.e. Quill-IO has been defined as an alias.

For underlying-contexts (see below) that depend on Connection, the alias QCIO[T] (i.e. Quill-Connection-IO) has been defined for ZIO[Connection, SQLException, T].

Since in most JDBC use-cases, a connection-pool datasource (e.g. Hikari) is used, constructor-methods fromPrefix, fromConfig, fromJdbcConfig are available on DataSourceLayer to construct instances of a ZLayer[Any, SQLException, DataSource] which can be easily used to provide a DataSource dependency. You can use them like this:

import ZioJdbc._
val zioDs = DataSourceLayer.fromPrefix("testPostgresDB")
MyZioContext.run(query[Person]).provideCustomLayer(zioDS)

If in some rare cases, you wish to provide a java.sql.Connection to a run method directly, you can delegate to the underlying-context. This is a more low-level context whose run methods have a Connection resource. Here is an example of how this can be done.

def conn: Connection = _ // If you are starting with a connection object

import io.getquill.context.ZioJdbc._
// Import encoders/decoders of the underlying context. Do not import quote/run/prepare methods to avoid conflicts.
import MyZioContext.underlying.{ quote => _, run => _, prepare => _,  _ }

MyZioContext.underlying.run(people).provide(Has(conn))

If you are working with an underlying-context and want to provide a DataSource instead of a connection, you can use the onDataSource method. Note however that this is only needed when working with an underlying-context. When working with a normal context, onDataSource is not available or necessary (since for a normal contexts R will be DataSource).

val ds: DataSource = _

import io.getquill.context.ZioJdbc._
// Import encoders/decoders of the underlying context. Do not import quote/run/prepare methods to avoid conflicts.
import MyZioContext.underlying.{ quote => _, run => _, prepare => _,  _ }

MyZioContext.underlying.run(people).onDataSource.provide(Has(ds))

Also note that if you are using a Plain Scala app however, you will need to manually run it i.e. using zio.Runtime

Runtime.default.unsafeRun(MyZioContext.run(query[Person]).provideLayer(zioDS))

DAO helper

One additional useful pattern is to use import io.getquill.context.qzio.ImplicitSyntax.Implicit to provide an implicit DataSource to one or multiple run(qry) calls in a context. This is very useful when creating DAO patterns that will reuse a DataSource many times:

case class MyQueryService(ds: DataSource with Closeable) { // I.e. our DAO
 import Ctx._
 implicit val env = Implicit(Has(ds)) // This will be looked up in each `.implicitDS` call

 val joes = Ctx.run(query[Person].filter(p => p.name == "Joe")).implicitDS
 val jills = Ctx.run(query[Person].filter(p => p.name == "Jill")).implicitDS
 val alexes = Ctx.run(query[Person].filter(p => p.name == "Alex")).implicitDS
}

More examples of a Quill-JDBC-ZIO app quill-jdbc-zio/src/test/scala/io/getquill/examples.

streaming

The ZioJdbcContext can stream using zio.ZStream:

ctx.stream(query[Person])             // returns: ZStream[Connection, Throwable, Person]
  .run(Sink.collectAll).map(_.toList) // returns: ZIO[Connection, Throwable, List[T]]

transactions

The ZioJdbcContexts provide support for transactions without needing thread-local storage or similar because they propagate the resource dependency in the ZIO effect itself (i.e. the Connection in Zio[Connection, _, _]). As with the other contexts, if an exception is thrown anywhere inside a task or sub-task within a transaction block, the entire block will be rolled back by the database.

Basic syntax:

val trans =
  ctx.transaction {
    for {
      _ <- ctx.run(query[Person].delete)
      _ <- ctx.run(query[Person].insertValue(Person("Joe", 123)))
      p <- ctx.run(query[Person])
    } yield p
  } //returns: ZIO[Connection, Throwable, List[Person]]

val result = Runtime.default.unsafeRun(trans.onDataSource.provide(ds)) //returns: List[Person]

json

The Zio Quill Postgres supports JSON encoding/decoding via the zio-json library. Just wrap your object in a JsonValue instance and then define encoders/decoders via zio-json's JsonEncoder/JsonDecoder modules.

import context._
case class Person(name: String, age: Int)
case class MyTable(name: String, value: JsonValue[Person])

val joe = Person("Joe", 123)
val joeRow = MyTable("SomeJoe", JsonValue(joe))

// Declare an encoder/decoder for `Person` via zio-json
implicit val personEncoder: JsonEncoder[Person] = DeriveJsonEncoder.gen[Person]
implicit val personDecoder: JsonDecoder[Person] = DeriveJsonDecoder.gen[Person]

val myApp: ZIO[Any, SQLException, List[MyTable]] =
  for {
    // You can then insert the value:
    _ <- context.run(query[MyTable].insertValue(lift(joeRow)))
    // As well read it:
    value <- context.run(query[MyTable])
  } yield (value)

You can also encode/decode objects that have the type zio.json.ast.Json directly.

import context._
case class MyTable(name: String, value: JsonValue[Json])

// i.e. {name:"Joe", age:123}
val jsonJoe = Json.Obj(Chunk("name" -> Json.Str("Joe"), "age" -> Json.Num(123)))
val joeRow = MyTable("SomeJoe", JsonValue(jsonJoe))

testContext.run(jsonAstQuery.insertValue(lift(joeRow)))

MySQL (quill-jdbc-zio)

sbt dependencies

libraryDependencies ++= Seq(
  "mysql" % "mysql-connector-java" % "8.0.17",
  "io.getquill" %% "quill-jdbc-zio" % "4.8.5"
)

context definition

val ctx = new MysqlZioJdbcContext(SnakeCase)
// Also can be static:
object MyContext extends MysqlZioJdbcContext(SnakeCase)

application.properties

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

Postgres (quill-jdbc-zio)

sbt dependencies

libraryDependencies ++= Seq(
  "org.postgresql" % "postgresql" % "42.2.8",
  "io.getquill" %% "quill-jdbc-zio" % "4.8.5"
)

context definition

val ctx = new PostgresZioJdbcContext(SnakeCase)
// Also can be static:
object MyContext extends PostgresZioJdbcContext(SnakeCase)

application.properties

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

Sqlite (quill-jdbc-zio)

sbt dependencies

libraryDependencies ++= Seq(
  "org.xerial" % "sqlite-jdbc" % "3.28.0",
  "io.getquill" %% "quill-jdbc-zio" % "4.8.5"
)

context definition

val ctx = new SqlitezioJdbcContext(SnakeCase)
// Also can be static:
object MyContext extends SqlitezioJdbcContext(SnakeCase)

application.properties

ctx.driverClassName=org.sqlite.JDBC
ctx.jdbcUrl=jdbc:sqlite:/path/to/db/file.db

H2 (quill-jdbc-zio)

sbt dependencies

libraryDependencies ++= Seq(
  "com.h2database" % "h2" % "1.4.199",
  "io.getquill" %% "quill-jdbc-zio" % "4.8.5"
)

context definition

val ctx = new H2ZioJdbcContext(SnakeCase)
// Also can be static:
object MyContext extends H2ZioJdbcContext(SnakeCase)

application.properties

ctx.dataSourceClassName=org.h2.jdbcx.JdbcDataSource
ctx.dataSource.url=jdbc:h2:mem:yourdbname
ctx.dataSource.user=sa

SQL Server (quill-jdbc-zio)

sbt dependencies

libraryDependencies ++= Seq(
  "com.microsoft.sqlserver" % "mssql-jdbc" % "7.4.1.jre8",
  "io.getquill" %% "quill-jdbc-zio" % "4.8.5"
)

context definition

val ctx = new SqlServerZioJdbcContext(SnakeCase)
// Also can be static:
object MyContext extends SqlServerZioJdbcContext(SnakeCase)

application.properties

ctx.dataSourceClassName=com.microsoft.sqlserver.jdbc.SQLServerDataSource
ctx.dataSource.user=user
ctx.dataSource.password=YourStrongPassword
ctx.dataSource.databaseName=database
ctx.dataSource.portNumber=1433
ctx.dataSource.serverName=host

Oracle (quill-jdbc-zio)

Quill supports Oracle version 12c and up although due to licensing restrictions, version 18c XE is used for testing.

sbt dependencies

libraryDependencies ++= Seq(
  "com.oracle.jdbc" % "ojdbc8" % "18.3.0.0.0",
  "io.getquill" %% "quill-jdbc-zio" % "4.8.5"
)

context definition

val ctx = new OracleZioJdbcContext(SnakeCase)
// Also can be static:
object MyContext extends OracleZioJdbcContext(SnakeCase)

application.properties

ctx.dataSourceClassName=oracle.jdbc.xa.client.OracleXADataSource
ctx.dataSource.databaseName=xe
ctx.dataSource.user=database
ctx.dataSource.password=YourStrongPassword
ctx.dataSource.driverType=thin
ctx.dataSource.portNumber=1521
ctx.dataSource.serverName=host

quill-doobie

Quill 3.16.5 and above supports Doobie starting 1.0.0-RC1. You can use quill quotes to construct ConnectionIO programs. Quill provides statement construction and type mapping, and doobie takes care of statement execution.

Note that if you are migrating from the original doobie-quill integration (e.g. here) just add the below dependency and replace the doobie.quill package with io.getquill.doobie. (If you are using the package provided by kubukoz (i.e. here), then replace org.polyvariant with io.getquill.doobie.)

In order to use this feature, add the following dependency.

libraryDependencies += "io.getquill" %% "quill-doobie" % "4.6.1-SNAPSHOT"

The examples below require the following imports.

import io.getquill.{ idiom => _, _ }
import io.getquill.DoobieContext

We can now construct a DoobieContext for our back-end database and import its members, as we would with a traditional Quill context. The options are H2, MySQL, Oracle, Postgres, SQLite, and SQLServer.

val dc = new DoobieContext.Postgres(Literal) // Literal naming scheme
import dc.{ SqlInfixInterpolator => _, _ }   // Quill's `sql` interpolator conflicts with doobie so don't import it
import dc.compat._                           // Import the qsql interpolator instead

Instead of using Quill's sql"MyUDF(${something})" interpolator, use qsql"MyUDF(${something})" since we have excluded it.

We will be using the country table from our test database, so we need a data type of that name, with fields whose names and types line up with the table definition.

case class Country(code: String, name: String, population: Int)

We're now ready to construct doobie programs using Quill quotes. Note the return types from run, which are normal doobie types. You can freely mix Quill quotes into existing doobie programs.

running and streaming

val q1 = quote { query[Country].filter(_.code == "GBR") }

// Select all at once
run(q1)

// Stream in chunks of 16
stream(q1, 16)

actions

A simple update.

val u1 = quote { query[Country].filter(_.name like "U%").update(_.name -> "foo") }

// Update yielding count of affected rows
run(u1)

A batch update.

val u2 = quote {
  liftQuery(List("U%", "A%")).foreach { pat =>
    query[Country].filter(_.name like pat).update(_.name -> "foo")
  }
}

// Update yielding list of counts of affected rows
run(u2)

Now we will look at batch updates with generated keys. For this we will create a new table.

CREATE TABLE Foo (
  id    SERIAL,
  value VARCHAR(42)
)

And a related data type.

case class Foo(id: Int, value: String)

We can now write an update returning generated keys.

val u3 = quote {
  query[Foo].insert(lift(Foo(0, "Joe"))).returning(_.id)
}

// Update yielding a single id
run(u3)

And a batch update returning generated keys.

val u4 = quote {
  liftQuery(List(Foo(0, "Joe"), Foo(0, "Bob"))).foreach { a =>
    query[Foo].insert(a).returning(_.id)
  }
}

// Update yielding a list of ids
run(u4)

Finagle Contexts

Support for the Twitter Finagle library is available with MySQL and Postgres databases.

quill-finagle-mysql

transactions

The finagle context provides transaction support through a Local value. See twitter util's scaladoc for more details.

ctx.transaction {
  ctx.run(query[Person].delete)
  // other transactional code
}

streaming

The finagle context allows streaming a query response, returning an AsyncStream value.

ctx.stream(query[Person]) // returns: Future[AsyncStream[Person]]
  .flatMap(_.toSeq())

The body of transaction can contain calls to other methods and multiple run calls since the transaction is automatically propagated through the Local value.

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-finagle-mysql" % "4.8.5"
)

context definition

lazy val ctx = new FinagleMysqlContext(SnakeCase, "ctx")

application.properties

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

quill-finagle-postgres

transactions

The finagle context provides transaction support through a Local value. See twitter util's scaladoc for more details.

ctx.transaction {
  ctx.run(query[Person].delete)
  // other transactional code
}

The body of transaction can contain calls to other methods and multiple run calls since the transaction is automatically propagated through the Local value.

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-finagle-postgres" % "4.8.5"
)

context definition

lazy val ctx = new FinaglePostgresContext(SnakeCase, "ctx")

application.properties

ctx.host=localhost:3306
ctx.user=root
ctx.password=root
ctx.database=database
ctx.useSsl=false
ctx.hostConnectionLimit=1
ctx.numRetries=4
ctx.binaryResults=false
ctx.binaryParams=false

quill-cassandra

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-cassandra" % "4.8.5"
)

synchronous context

lazy val ctx = new CassandraSyncContext(SnakeCase, "ctx")

asynchronous context

lazy val ctx = new CassandraAsyncContext(SnakeCase, "ctx")

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

ctx.keyspace=quill_test
ctx.preparedStatementCacheSize=1000
ctx.session.contactPoint=127.0.0.1
ctx.session.withPort=9042
ctx.session.queryOptions.consistencyLevel=LOCAL_QUORUM
ctx.session.withoutMetrics=true
ctx.session.withoutJMXReporting=false
ctx.session.credentials.0=root
ctx.session.credentials.1=pass
ctx.session.maxSchemaAgreementWaitSeconds=1
ctx.session.addressTranslator=com.datastax.driver.core.policies.IdentityTranslator

quill-cassandra-zio

Quill context that executes Cassandra queries inside of ZIO. Unlike most other contexts that require passing in a Data Source, this context takes in a CassandraZioSession as a resource dependency which can be provided later (see the CassandraZioSession object for helper methods that assist in doing this).

The resource dependency itself is just a CassandraZioSession hence run(qry) and other methods in this context will return ZIO[CassandraZioSession, Throwable, T]. The type CIO[T] i.e. Cassandra-IO is an alias for this. Providing a CassandraZioSession dependency is now very simple:

val session: CassandraZioSession = _
run(people)
  .provide(Has(session))

Various methods in the io.getquill.CassandraZioSession can assist in simplifying it's creation, for example, you can provide a Config object instead of a CassandraZioSession like this:

 val zioSessionLayer: ZLayer[Any, Throwable, CassandraZioSession] =
   CassandraZioSession.fromPrefix("testStreamDB")
run(query[Person])
  .provideCustomLayer(zioSessionLayer)

(Note that the resulting CassandraZioSession has a closing bracket)

If you are using a Plain Scala app, you will need to manually run it e.g. using zio.Runtime

 Runtime.default.unsafeRun(MyZioContext.run(query[Person]).provideCustomLayer(zioSessionLayer))

DAO helper

One additional useful pattern is to use import io.getquill.context.qzio.ImplicitSyntax.Implicit to provide an implicit CassandraZioSession to one or multiple run(qry) calls in a context. This is very useful when creating DAO patterns that will reuse a CassandraZioSession many times:

case class MyQueryService(cs: CassandraZioSession) {
  import Ctx._
  implicit val env = Implicit(Has(cs))

  def joes = Ctx.run { query[Person].filter(p => p.name == "Joe") }.implicitly
  def jills = Ctx.run { query[Person].filter(p => p.name == "Jill") }.implicitly
  def alexes = Ctx.run { query[Person].filter(p => p.name == "Alex") }.implicitly
}

More examples of a Quill-Cassandra-ZIO app quill-cassandra-zio/src/test/scala/io/getquill/context/cassandra/zio/examples.

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-cassandra-zio" % "4.8.5"
)

quill-cassandra-pekko

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-cassandra-pekko" % "4.8.5"
)

See Pekko Cassandra documentation page for more information.

context

import org.apache.pekko.actor.ActorSystem
import org.apache.pekko.stream.pekko.cassandra.CassandraSessionSettings
import org.apache.pekko.stream.connectors.cassandra.scaladsl.{CassandraSession, CassandraSessionRegistry}
import io.getquill.CassandraAlpakkaContext

val system: ActorSystem = ???
val pekkoSessionSettings = CassandraSessionSettings("quill-test.pekko.cassandra")
val pekkoSession: CassandraSession = CassandraSessionRegistry.get(system).sessionFor(pekkoSessionSettings)

lazy val ctx = new CassandraAlpakkaContext(SnakeCase, pekkoSession, preparedStatementCacheSize = 100)

application.properties

// pekko cassandra session with keyspace
quill-test.pekko.cassandra: ${pekko.cassandra} { // inheritance of pekko.cassandra session configuration
  // custom datastax driver setup
  datastax-java-driver-config = quill-test-datastax-java-driver
}

quill-test-datastax-java-driver {
  basic {
    // keyspace at datastax driver setup, as there is not different option now
    session-keyspace = "quill_test"
  }
}

OrientDB Contexts

sbt dependencies

libraryDependencies ++= Seq(
  "io.getquill" %% "quill-orientdb" % "4.8.5"
)

synchronous context

lazy val ctx = new OrientDBSyncContext(SnakeCase, "ctx")

The configurations are set using OPartitionedDatabasePool which creates a pool of DB connections from which an instance of connection can be acquired. It is possible to set DB credentials using the parameter called username and password.

application.properties

ctx.dbUrl=remote:127.0.0.1:2424/GratefulDeadConcerts
ctx.username=root
ctx.password=root