细谈Slick(6)- Projection:ProvenShape,强类型的Query结果类型

2024-04-09 04:58

本文主要是介绍细谈Slick(6)- Projection:ProvenShape,强类型的Query结果类型,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

  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: Anyval 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 = vval 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 classcase class Pair[A, B](a: A, b: B)// A Shape implementation for Pairfinal 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(0), elems(1))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 definitionclass 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 {@inlineimplicit 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)@inlineimplicit 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.createAwait.ready(db.run(createSchemaAction),Duration.Inf)val initDataAction = DBIO.seq {tupleTypedPerson ++= Seq((Some(0),"Tiger Chan", 45, Some("Tiger_XC")),(Some(0),"Johnny Cox", 17, None),(Some(0),"Cathy Williams", 18, Some("Catty")),(Some(0),"David Wong", 43, None))}Await.ready(db.run(initDataAction),Duration.Inf)val queryAction = tupleTypedPerson.resultAwait.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(0),hl(1),hl(2),hl(3))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.createAwait.ready(db.run(createTitleAction),Duration.Inf)val initTitleData = DBIO.seq {personTitle ++= Seq(Title(1,"Manager"),Title(2,"Programmer"),Title(3,"Clerk"))}Await.ready(db.run(initTitleData),Duration.Inf)case class Titles(id: Int, name: String, title: String)val qPersonWithTitle = for {p <- hlistPersont <- 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.createAwait.ready(db.run(createSchemaAction),Duration.Inf)val initDataAction = DBIO.seq {tupleTypedPerson ++= Seq((Some(0),"Tiger Chan", 45, Some("Tiger_XC")),(Some(0),"Johnny Cox", 17, None),(Some(0),"Cathy Williams", 18, Some("Catty")),(Some(0),"David Wong", 43, None))}Await.ready(db.run(initDataAction),Duration.Inf)val queryAction = tupleTypedPerson.resultAwait.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("")}, ${row.age}")}def hlistToPerson(hl: Option[Int]::String::Int::(Option[String])::HNil) =new Person(hl(0),hl(1),hl(2),hl(3))def personToHList(p: Person) = Some(p.id::p.name::p.age::p.alias::HNil)class HListPerson(tag: Tag) extends Table[Person](tag,"PERSON") {def id = column[Int]("id",O.PrimaryKey,O.AutoInc)def name = column[String]("name")def age = column[Int]("age")def alias = column[Option[String]]("alias")def * = (id.?)::name::age::alias::HNil <> (hlistToPerson,personToHList)}val hlistPerson = TableQuery[HListPerson]Await.result(db.run(hlistPerson.result),Duration.Inf).foreach {row =>println(s"${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} ${row.yr}")}case class Title(id: Int, title: String)class PersonTitle(tag: Tag) extends Table[Title](tag,"TITLE") {def id = column[Int]("id")def title = column[String]("title")def * = (id,title) <> (Title.tupled,Title.unapply)}val personTitle = TableQuery[PersonTitle]val createTitleAction = personTitle.schema.createAwait.ready(db.run(createTitleAction),Duration.Inf)val initTitleData = DBIO.seq {personTitle ++= Seq(Title(1,"Manager"),Title(2,"Programmer"),Title(3,"Clerk"))}Await.ready(db.run(initTitleData),Duration.Inf)case class Titles(id: Int, name: String, title: String)val qPersonWithTitle = for {p <- hlistPersont <- personTitle if p.id === t.id} yield ((p.id,p.name,t.title) <> (Titles.tupled,Titles.unapply))Await.result(db.run(qPersonWithTitle.result),Duration.Inf).foreach {row =>println(s"${row.id} ${row.name}, ${row.title}")}}














这篇关于细谈Slick(6)- Projection:ProvenShape,强类型的Query结果类型的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/887228

相关文章

MySQL 中查询 VARCHAR 类型 JSON 数据的问题记录

《MySQL中查询VARCHAR类型JSON数据的问题记录》在数据库设计中,有时我们会将JSON数据存储在VARCHAR或TEXT类型字段中,本文将详细介绍如何在MySQL中有效查询存储为V... 目录一、问题背景二、mysql jsON 函数2.1 常用 JSON 函数三、查询示例3.1 基本查询3.2

Pydantic中Optional 和Union类型的使用

《Pydantic中Optional和Union类型的使用》本文主要介绍了Pydantic中Optional和Union类型的使用,这两者在处理可选字段和多类型字段时尤为重要,文中通过示例代码介绍的... 目录简介Optional 类型Union 类型Optional 和 Union 的组合总结简介Pyd

Oracle数据库常见字段类型大全以及超详细解析

《Oracle数据库常见字段类型大全以及超详细解析》在Oracle数据库中查询特定表的字段个数通常需要使用SQL语句来完成,:本文主要介绍Oracle数据库常见字段类型大全以及超详细解析,文中通过... 目录前言一、字符类型(Character)1、CHAR:定长字符数据类型2、VARCHAR2:变长字符数

Spring Boot 配置文件之类型、加载顺序与最佳实践记录

《SpringBoot配置文件之类型、加载顺序与最佳实践记录》SpringBoot的配置文件是灵活且强大的工具,通过合理的配置管理,可以让应用开发和部署更加高效,无论是简单的属性配置,还是复杂... 目录Spring Boot 配置文件详解一、Spring Boot 配置文件类型1.1 applicatio

Python如何查看数据的类型

《Python如何查看数据的类型》:本文主要介绍Python如何查看数据的类型方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录python查看数据的类型1. 使用 type()2. 使用 isinstance()3. 检查对象的 __class__ 属性4.

Python容器类型之列表/字典/元组/集合方式

《Python容器类型之列表/字典/元组/集合方式》:本文主要介绍Python容器类型之列表/字典/元组/集合方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录1. 列表(List) - 有序可变序列1.1 基本特性1.2 核心操作1.3 应用场景2. 字典(D

Python如何在Word中生成多种不同类型的图表

《Python如何在Word中生成多种不同类型的图表》Word文档中插入图表不仅能直观呈现数据,还能提升文档的可读性和专业性,本文将介绍如何使用Python在Word文档中创建和自定义各种图表,需要的... 目录在Word中创建柱形图在Word中创建条形图在Word中创建折线图在Word中创建饼图在Word

SpringBoot接收JSON类型的参数方式

《SpringBoot接收JSON类型的参数方式》:本文主要介绍SpringBoot接收JSON类型的参数方式,具有很好的参考价值,希望对大家有所帮助,如有错误或未考虑完全的地方,望不吝赐教... 目录一、jsON二、代码准备三、Apifox操作总结一、JSON在学习前端技术时,我们有讲到过JSON,而在

Rust中的BoxT之堆上的数据与递归类型详解

《Rust中的BoxT之堆上的数据与递归类型详解》本文介绍了Rust中的BoxT类型,包括其在堆与栈之间的内存分配,性能优势,以及如何利用BoxT来实现递归类型和处理大小未知类型,通过BoxT,Rus... 目录1. Box<T> 的基础知识1.1 堆与栈的分工1.2 性能优势2.1 递归类型的问题2.2

Python如何计算两个不同类型列表的相似度

《Python如何计算两个不同类型列表的相似度》在编程中,经常需要比较两个列表的相似度,尤其是当这两个列表包含不同类型的元素时,下面小编就来讲讲如何使用Python计算两个不同类型列表的相似度吧... 目录摘要引言数字类型相似度欧几里得距离曼哈顿距离字符串类型相似度Levenshtein距离Jaccard相