在Slick官方文档中描述：连接后台数据库后，需要通过定义Projection，即def * 来进行具体库表列column的选择和排序。通过Projection我们可以选择库表中部分列、也可以增加一些自定义列computed column。具体来说Projection提供了数据库表列与Scala值的对应。例如def * = (column1,column2)把库表的column1和column2与(Int,String)对应，column1[Int]，column2[String]。也可以说是与定义column的类参数进行对应。从Slick源代码中我们可以找到Projection定义：
abstract class AbstractTable[T](val tableTag: Tag, val schemaName: Option[String], val tableName: String) extends Rep[T] {
/** The client-side type of the table as defined by its * projection */
type TableElementType
...
/** The * projection of the table used as default for queries and inserts.
* Should include all columns as a tuple, HList or custom shape and optionally
* map them to a custom entity type using the <> operator.
* The `ProvenShape` return type ensures that
* there is a `Shape` available for translating between the `Column`-based
* type in * and the client-side type without `Column` in the table's type
* parameter. */
def * : ProvenShape[T]
...
}
我们看到Projection是个ProvenShape[T]类。再看看ProvenShape是怎么定义的：
/** A limited version of ShapedValue which can be constructed for every type
* that has a valid shape. We use it to enforce that a table's * projection
* has a valid shape. A ProvenShape has itself a Shape so it can be used in
* place of the value that it wraps for purposes of packing and unpacking. */
trait ProvenShape[U] {
def value: Any
val shape: Shape[_ <: FlatShapeLevel, _, U, _]
def packedValue[R](implicit ev: Shape[_ <: FlatShapeLevel, _, U, R]): ShapedValue[R, U]
def toNode = packedValue(shape).toNode
} object ProvenShape {
/** Convert an appropriately shaped value to a ProvenShape */
implicit def proveShapeOf[T, U](v: T)(implicit sh: Shape[_ <: FlatShapeLevel, T, U, _]): ProvenShape[U] =
new ProvenShape[U] {
def value = v
val shape: Shape[_ <: FlatShapeLevel, _, U, _] = sh.asInstanceOf[Shape[FlatShapeLevel, _, U, _]]
def packedValue[R](implicit ev: Shape[_ <: FlatShapeLevel, _, U, R]): ShapedValue[R, U] = ShapedValue(sh.pack(value).asInstanceOf[R], sh.packedShape.asInstanceOf[Shape[FlatShapeLevel, R, U, _]])
} /** The Shape for a ProvenShape */
implicit def provenShapeShape[T, P](implicit shape: Shape[_ <: FlatShapeLevel, T, T, P]): Shape[FlatShapeLevel, ProvenShape[T], T, P] = new Shape[FlatShapeLevel, ProvenShape[T], T, P] {
def pack(value: Mixed): Packed =
value.shape.pack(value.value.asInstanceOf[value.shape.Mixed]).asInstanceOf[Packed]
def packedShape: Shape[FlatShapeLevel, Packed, Unpacked, Packed] =
shape.packedShape.asInstanceOf[Shape[FlatShapeLevel, Packed, Unpacked, Packed]]
def buildParams(extract: Any => Unpacked): Packed =
shape.buildParams(extract.asInstanceOf[Any => shape.Unpacked])
def encodeRef(value: Mixed, path: Node) =
value.shape.encodeRef(value.value.asInstanceOf[value.shape.Mixed], path)
def toNode(value: Mixed): Node =
value.shape.toNode(value.value.asInstanceOf[value.shape.Mixed])
}
}
从implicit def proveShapeOf[T,U](v：T)：ProvenShape[U]可以得出对于任何T，如果能提供Shape[_,_,T,U,_]的隐式实例implicit instance的话就能构建出ProvenShape[U]。我们再看看什么是Shape：
/** A type class that encodes the unpacking `Mixed => Unpacked` of a
* `Query[Mixed]` to its result element type `Unpacked` and the packing to a
* fully packed type `Packed`, i.e. a type where everything which is not a
* transparent container is wrapped in a `Column[_]`.
*
* =Example:=
* - Mixed: (Column[Int], Column[(Int, String)], (Int, Option[Double]))
* - Unpacked: (Int, (Int, String), (Int, Option[Double]))
* - Packed: (Column[Int], Column[(Int, String)], (Column[Int], Column[Option[Double]]))
* - Linearized: (Int, Int, String, Int, Option[Double])
*/
abstract class Shape[Level <: ShapeLevel, -Mixed, Unpacked_, Packed_] {...}
上面的Mixed就是ProvenShape的T，Unpacked就是U。如此看来T代表Query[T]的T，而U就是返回结果类型了。如果我们能提供T的Shape隐式实例就能把U升格成ProvenShape[U]。我们来看看Slick官方文件上的例子：
import scala.reflect.ClassTag
// A custom record class
case class Pair[A, B](a: A, b: B) // A Shape implementation for Pair
final class PairShape[Level <: ShapeLevel, M <: Pair[_,_], U <: Pair[_,_] : ClassTag, P <: Pair[_,_]](
val shapes: Seq[Shape[_, _, _, _]])
extends MappedScalaProductShape[Level, Pair[_,_], M, U, P] {
def buildValue(elems: IndexedSeq[Any]) = Pair(elems(), elems())
def copy(shapes: Seq[Shape[_ <: ShapeLevel, _, _, _]]) = new PairShape(shapes)
} implicit def pairShape[Level <: ShapeLevel, M1, M2, U1, U2, P1, P2](
implicit s1: Shape[_ <: Level, M1, U1, P1], s2: Shape[_ <: Level, M2, U2, P2]
) = new PairShape[Level, Pair[M1, M2], Pair[U1, U2], Pair[P1, P2]](Seq(s1, s2)) // Use it in a table definition
class A(tag: Tag) extends Table[Pair[Int, String]](tag, "shape_a") {
def id = column[Int]("id", O.PrimaryKey)
def s = column[String]("s")
def * = Pair(id, s)
}
val as = TableQuery[A]
现在Projection可以写成Pair(id,s)。也就是说因为有了implicit def pairShape[...](...)：PairShape所以Pair(id,s)被升格成ProvenShape[Pair]。这样Query的返回类型就是Seq[Pair]了。实际上Slick本身提供了Tuple、Case Class、HList等类型的默认Shape隐式实例，所以我们可以把Projection直接写成 def * = (...) 或 Person(...) 或 Int::String::HNil。下面是Tuple的默认Shape：
trait TupleShapeImplicits {
@inline
implicit final def tuple1Shape[Level <: ShapeLevel, M1, U1, P1](implicit u1: Shape[_ <: Level, M1, U1, P1]): Shape[Level, Tuple1[M1], Tuple1[U1], Tuple1[P1]] =
new TupleShape[Level, Tuple1[M1], Tuple1[U1], Tuple1[P1]](u1)
@inline
implicit final def tuple2Shape[Level <: ShapeLevel, M1,M2, U1,U2, P1,P2](implicit u1: Shape[_ <: Level, M1, U1, P1], u2: Shape[_ <: Level, M2, U2, P2]): Shape[Level, (M1,M2), (U1,U2), (P1,P2)] =
new TupleShape[Level, (M1,M2), (U1,U2), (P1,P2)](u1,u2)
...
回到主题，下面是一个典型的Slick数据库表读取例子：
class TupleTypedPerson(tag: Tag) extends Table[(
Option[Int],String,Int,Option[String])](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?,name,age,alias)
}
val tupleTypedPerson = TableQuery[TupleTypedPerson] val db = Database.forURL("jdbc:h2:mem:test1;DB_CLOSE_DELAY=-1", driver = "org.h2.Driver")
val createSchemaAction = tupleTypedPerson.schema.create
Await.ready(db.run(createSchemaAction),Duration.Inf)
val initDataAction = DBIO.seq {
tupleTypedPerson ++= Seq(
(Some(),"Tiger Chan", , Some("Tiger_XC")),
(Some(),"Johnny Cox", , None),
(Some(),"Cathy Williams", , Some("Catty")),
(Some(),"David Wong", , None)
)
}
Await.ready(db.run(initDataAction),Duration.Inf)
val queryAction = tupleTypedPerson.result Await.result(db.run(queryAction),Duration.Inf).foreach {row =>
println(s"${row._1.get} ${row._2} ${row._4.getOrElse("")}, ${row._3}")
}
在这个例子的表结构定义里默认的Projection是个Tuple。造成的后果是返回的结果行不含字段名，只有字段位置。使用这样的行数据很容易错误对应，或者重复确认正确的列值会影响工作效率。如果返回的结果类型是Seq[Person]这样的话：Person是个带属性的对象如case class，那么我们就可以通过IDE提示的字段名称来选择字段了。上面提过返回结果类型可以通过ProvenShape来确定，如果能实现ProvenShape[A] => ProvenShape[B]这样的转换处理，那么我们就可以把返回结果行类型从Tuple变成有字段名的类型了：
class Person(val id: Option[Int],
val name: String, val age: Int, val alias: Option[String])
def toPerson(t: (Option[Int],String,Int,Option[String])) = new Person (
t._1,t._2,t._3,t._4
)
def fromPerson(p: Person) = Some((p.id,p.name,p.age,p.alias))
class TupleMappedPerson(tag: Tag) extends Table[
Person](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?,name,age,alias) <> (toPerson,fromPerson)
}
val tupleMappedPerson = TableQuery[TupleMappedPerson] Await.result(db.run(tupleMappedPerson.result),Duration.Inf).foreach {row =>
println(s"${row.id.get} ${row.name} ${row.alias.getOrElse("")}, ${row.age}")
}
我们用<>函数进行了Tuple=>Person转换。注意toPerson和fromPerson这两个相互转换函数。如果Person是个case class，那么Person.tupled和Person.unapply就是它自备的转换函数，我们可以用case class来构建MappedProjection：
case class Person(id: Option[Int]=None, name: String, age: Int, alias: Option[String])
class MappedTypePerson(tag: Tag) extends Table[Person](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?,name,age,alias) <> (Person.tupled,Person.unapply)
}
val mappedPeople = TableQuery[MappedTypePerson]
从上面两个例子里我们似乎可以得出ProvenShape[T]的T类型就是Table[T]的T，也就是返回结果行的类型了。我们可以用同样方式来进行HList与Person转换：
def hlistToPerson(hl: Option[Int]::String::Int::(Option[String])::HNil) =
new Person(hl(),hl(),hl(),hl())
def personToHList(p: Person) = Some(p.id::p.name::p.age::p.alias::HNil)
class HListPerson(tag: Tag) extends Table[Person](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?)::name::age::alias::HNil <> (hlistToPerson,personToHList)
}
val hlistPerson = TableQuery[HListPerson]
Await.result(db.run(hlistPerson.result),Duration.Inf).foreach {row =>
println(s"${row.id.get} ${row.name} ${row.alias.getOrElse("")}, ${row.age}")
}
同样，必须首先实现hlistToPerson和personToHList转换函数。现在Table的类型参数必须是Person。上面的Projection都是对Table默认Projection的示范。实际上我们可以针对每个Query来自定义Projection，如下：
case class YR(name: String, yr: Int)
val qYear = for {
p <- hlistPerson
} yield ((p.name, p.age) <> (YR.tupled,YR.unapply))
Await.result(db.run(qYear.result),Duration.Inf).foreach {row =>
println(s"${row.name} ${row.yr}")
}
上面这个例子里我们构建了基于case class YR的projection。在join table query情况下只能通过这种方式来构建Projection，看看下面这个例子：
case class Title(id: Int, title: String)
class PersonTitle(tag: Tag) extends Table[Title](tag,"TITLE") {
def id = column[Int]("id")
def title = column[String]("title")
def * = (id,title) <> (Title.tupled,Title.unapply)
}
val personTitle = TableQuery[PersonTitle]
val createTitleAction = personTitle.schema.create
Await.ready(db.run(createTitleAction),Duration.Inf)
val initTitleData = DBIO.seq {
personTitle ++= Seq(
Title(,"Manager"),
Title(,"Programmer"),
Title(,"Clerk")
)
}
Await.ready(db.run(initTitleData),Duration.Inf) case class Titles(id: Int, name: String, title: String)
val qPersonWithTitle = for {
p <- hlistPerson
t <- personTitle if p.id === t.id
} yield ((p.id,p.name,t.title) <> (Titles.tupled,Titles.unapply))
Await.result(db.run(qPersonWithTitle.result),Duration.Inf).foreach {row =>
println(s"${row.id} ${row.name}, ${row.title}")
}
现在对任何形式的Query结果我们都能使用强类型（strong typed）的字段名称来进行操作了。
下面是本次示范的源代码：
import slick.collection.heterogeneous.{ HList, HCons, HNil }
import slick.collection.heterogeneous.syntax._
import slick.driver.H2Driver.api._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
import scala.concurrent.{Await, Future}
object chkProjection {
class TupleTypedPerson(tag: Tag) extends Table[(
Option[Int],String,Int,Option[String])](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?,name,age,alias)
}
val tupleTypedPerson = TableQuery[TupleTypedPerson]
val db = Database.forURL("jdbc:h2:mem:test1;DB_CLOSE_DELAY=-1", driver = "org.h2.Driver")
val createSchemaAction = tupleTypedPerson.schema.create
Await.ready(db.run(createSchemaAction),Duration.Inf)
val initDataAction = DBIO.seq {
tupleTypedPerson ++= Seq(
(Some(),"Tiger Chan", , Some("Tiger_XC")),
(Some(),"Johnny Cox", , None),
(Some(),"Cathy Williams", , Some("Catty")),
(Some(),"David Wong", , None)
)
}
Await.ready(db.run(initDataAction),Duration.Inf)
val queryAction = tupleTypedPerson.result
Await.result(db.run(queryAction),Duration.Inf).foreach {row =>
println(s"${row._1.get} ${row._2} ${row._4.getOrElse("")}, ${row._3}")
}
class Person(val id: Option[Int],
val name: String, val age: Int, val alias: Option[String])
def toPerson(t: (Option[Int],String,Int,Option[String])) = new Person (
t._1,t._2,t._3,t._4
)
def fromPerson(p: Person) = Some((p.id,p.name,p.age,p.alias))
class TupleMappedPerson(tag: Tag) extends Table[
Person](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?,name,age,alias) <> (toPerson,fromPerson)
}
val tupleMappedPerson = TableQuery[TupleMappedPerson]
Await.result(db.run(tupleMappedPerson.result),Duration.Inf).foreach {row =>
println(s"${row.id.get} ${row.name} ${row.alias.getOrElse("")}, ${row.age}")
}
def hlistToPerson(hl: Option[Int]::String::Int::(Option[String])::HNil) =
new Person(hl(),hl(),hl(),hl())
def personToHList(p: Person) = Some(p.id::p.name::p.age::p.alias::HNil)
class HListPerson(tag: Tag) extends Table[Person](tag,"PERSON") {
def id = column[Int]("id",O.PrimaryKey,O.AutoInc)
def name = column[String]("name")
def age = column[Int]("age")
def alias = column[Option[String]]("alias")
def * = (id.?)::name::age::alias::HNil <> (hlistToPerson,personToHList)
}
val hlistPerson = TableQuery[HListPerson]
Await.result(db.run(hlistPerson.result),Duration.Inf).foreach {row =>
println(s"${row.id.get} ${row.name} ${row.alias.getOrElse("")}, ${row.age}")
}
case class YR(name: String, yr: Int)
val qYear = for {
p <- hlistPerson
} yield ((p.name, p.age) <> (YR.tupled,YR.unapply))
Await.result(db.run(qYear.result),Duration.Inf).foreach {row =>
println(s"${row.name} ${row.yr}")
}
case class Title(id: Int, title: String)
class PersonTitle(tag: Tag) extends Table[Title](tag,"TITLE") {
def id = column[Int]("id")
def title = column[String]("title")
def * = (id,title) <> (Title.tupled,Title.unapply)
}
val personTitle = TableQuery[PersonTitle]
val createTitleAction = personTitle.schema.create
Await.ready(db.run(createTitleAction),Duration.Inf)
val initTitleData = DBIO.seq {
personTitle ++= Seq(
Title(,"Manager"),
Title(,"Programmer"),
Title(,"Clerk")
)
}
Await.ready(db.run(initTitleData),Duration.Inf)
case class Titles(id: Int, name: String, title: String)
val qPersonWithTitle = for {
p <- hlistPerson
t <- personTitle if p.id === t.id
} yield ((p.id,p.name,t.title) <> (Titles.tupled,Titles.unapply))
Await.result(db.run(qPersonWithTitle.result),Duration.Inf).foreach {row =>
println(s"${row.id} ${row.name}, ${row.title}")
}
}