基本功能描述

Slick (Scala
language-integrated connection kit)是scala的一个FRM(Functional
Relational
Mapper),即函数式的关系数据库编程工具库。Slick的主要目的是使关系数据库能更容易、更自然的融入函数式编程模式,它可以使使用者像对待scala集合一样来处理关系数据库表。也就是说可以用scala集合的那些丰富的操作函数来处理库表数据。Slick把数据库编程融入到scala编程中,编程人员可以不需要编写SQL代码。我把Slick官方网站上Slick3.1.1文档的Slick介绍章节中的一些描述和例子拿过来帮助介绍Slick的功能。下面是Slick数据库和类对象关系对应的一个例子:

 1 import slick.driver.H2Driver.api._ 2 object slickIntro { 3   case class Coffee(id: Int,  4                     name: String, 5                     supID: Int = 0, 6                     price: Double , 7                     sales: Int = 0, 8                     total: Int = 0) 9 10   class Coffees extends Table[Coffee](tag, "COFFEES") {11     def id = column[Int]("COF_ID", O.PrimaryKey, O.AutoInc)12     def name = column[String]("COF_NAME")13     def supID = column[Int]("SUP_ID")14     def price = column[Double]("PRICE")15     def sales = column[Int]("SALES", O.Default(0))16     def total = column[Int]("TOTAL", O.Default(0))17     def * = (id, name, supID, price, sales, total) <> (Coffee.tupled, Coffee.unapply)18   }19   val coffees = TableQuery[Coffees]               20 //> coffees  : slick.lifted.TableQuery[worksheets.slickIntro.Coffees] = Rep(TableExpansion)21 }

我们把数据库中的COFFEES表与Coffees类做了对应,包括字段、索引、默认值、返回结果集字段等。现在这个coffees就是scala里的一个对象,但它代表了数据库表。现在我们可以用scala语言来编写数据存取程序了:

1 val limit = 10.0                                 //> limit  : Double = 10.02 // // 写Query时就像下面这样:3 ( for( c <- coffees; if c.price < limit ) yield c.name ).result4    //> res0: slick.driver.H2Driver.StreamingDriverAction[Seq[String],String,slick.dbio.Effect.Read] = slick.driver.JdbcActionComponent$QueryActionExtensionMethodsImpl$$anon$1@46cdf8bd5 // 相当于 SQL: select COF_NAME from COFFEES where PRICE < 10.0

或者下面这些不同的Query:

1 // 返回"name"字段的Query2 // 相当于 SQL: select NAME from COFFEES3 coffees.map                               4 //> res1: slick.lifted.Query[slick.lifted.Rep[String],String,Seq] = Rep5 // 选择 price < 10.0 的所有记录Query6 // 相当于 SQL: select * from COFFEES where PRICE < 10.07 coffees.filter(_.price < 10.0)                    8 //> res2: slick.lifted.Query[worksheets.slickIntro.Coffees,worksheets.slickIntro.Coffees#TableElementType,Seq] = Rep(Filter @1946988038)

我们可以这样表述:coffees.map
>>> coffees.map{row=>row.name},
coffees.filter(_.price<10.0) >>>
coffees.filter{row=>row.price<10.0),都是函数式集合操作语法。

Slick把Query编写与scala语言集成,这使编程人员可以用熟悉惯用的scala来表述SQL
Query,直接的好处是scalac在编译时就能够发现Query错误:

1 //coffees.map   2 //编译错误:value prices is not a member of worksheets.slickIntro.Coffees    

当然,嵌入scala的Query还可以获得运行效率的提升,因为在编译时可以进行前期优化。

最新版本的Slick最大的特点是采用了Functional
I/O技术,从而实现了安全的多线程无阻碍I/O操作。再就是实现了Query的函数组合(functional
composition),使Query编程更贴近函数式编程模式。通过函数组合实现代码重复利用,提高编程工作效率。具体实现方式是利用freemonad(DBIOAction类型就是个freemonad)的延迟运算模式,将DBIOAction的编程和实际运算分离,在DBIOAction编程过程中不会产生副作用(side-effect),从而实现纯代码的函数组合。我们来看看Query函数组合和DBIOAction运算示范:

 1 import scala.concurrent.ExecutionContext.Implicits.global 2 val qDelete = coffees.filter(_.price > 0.0).delete 3 //> qDelete  : slick.driver.H2Driver.DriverAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write] ... 4 val qAdd1 = (coffees returning coffees.map += Coffee(name="Columbia",price=128.0) 5 //> qAdd1  : slick.profile.FixedSqlAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write]... 6 val qAdd2 = (coffees returning coffees.map += Coffee(name="Blue Mountain",price=828.0) 7 //> qAdd2  : slick.profile.FixedSqlAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write]... 8 def getNameAndPrice = coffees.filter(_.id === n) 9     .map(r => (r.name,r.price)).result.head      10 //> getNameAndPrice: slick.profile.SqlAction[(String, Double),slick.dbio.NoStream,slick.dbio.Effect.Read]11 12 val actions = for {13   _ <- coffees.schema.create14   _ <- qDelete15   c1 <- qAdd116   c2 <- qAdd217    <- getNameAndPrice18    <- getNameAndPrice19 } yield (n1,p1,n2,p2)                             20 //> actions  : slick.dbio.DBIOAction[(String, Double, String, Double),..

我们可以放心的来组合这个actions,不用担心有任何副作用。actions的类型是:DBAction[String,Double,String,Double]。我们必须用Database.Run来真正开始运算,产生副作用:

 1 import java.sql.SQLException 2 import scala.concurrent.Await 3 import scala.concurrent.duration._ 4 val db = Database.forURL("jdbc:h2:mem:demo", driver="org.h2.Driver") 5      //> db  : slick.driver.H2Driver.backend.DatabaseDef = slick.jdbc.JdbcBackend$DatabaseDef@1a5b6f42 6 Await.result( 7       db.run(actions.transactionally).map { res => 8         println(s"Add coffee: ${res._1},${res._2} and ${res._3},${res._4}") 9       }.recover {10         case e: SQLException => println("Caught exception: " + e.getMessage)11       }, Duration.Inf)      //> Add coffee: Columbia,128.0 and Blue Mountain,828.0

在特殊的情况下我们也可以引用纯SQL语句:Slick提供了Plain
SQL API, 如下:

1 val limit = 10.02 sql"select COF_NAME from COFFEES where PRICE < $limit".as[String]3 // 用$来绑定变量: // select COF_NAME from COFFEES where PRICE < ?

下面是这篇讨论的示范代码:

 1 package worksheets 2 import slick.driver.H2Driver.api._ 3 object slickIntro { 4   case class Coffee(id: Int = 0, 5                     name: String, 6                     supID: Int = 0, 7                     price: Double, 8                     sales: Int = 0, 9                     total: Int = 0)10 11   class Coffees extends Table[Coffee](tag, "COFFEES") {12     def id = column[Int]("COF_ID", O.PrimaryKey, O.AutoInc)13     def name = column[String]("COF_NAME")14     def supID = column[Int]("SUP_ID")15     def price = column[Double]("PRICE")16     def sales = column[Int]("SALES", O.Default(0))17     def total = column[Int]("TOTAL", O.Default(0))18     def * = (id, name, supID, price, sales, total) <> (Coffee.tupled, Coffee.unapply)19   }20   val coffees = TableQuery[Coffees]21   22  val limit = 10.023 // // 写Query时就像下面这样:24 ( for( c <- coffees; if c.price < limit ) yield c.name ).result25 // 相当于 SQL: select COF_NAME from COFFEES where PRICE < 10.026 27 // 返回"name"字段的Query28 // 相当于 SQL: select NAME from COFFEES29 coffees.map30 // 选择 price < 10.0 的所有记录Query31 // 相当于 SQL: select * from COFFEES where PRICE < 10.032 coffees.filter(_.price < 10.0)33 //coffees.map34 //编译错误:value prices is not a member of worksheets.slickIntro.Coffees35 36 37 import scala.concurrent.ExecutionContext.Implicits.global38 val qDelete = coffees.filter(_.price > 0.0).delete39 val qAdd1 = (coffees returning coffees.map += Coffee(name="Columbia",price=128.0)40 val qAdd2 = (coffees returning coffees.map += Coffee(name="Blue Mountain",price=828.0)41 def getNameAndPrice = coffees.filter(_.id === n)42     .map(r => (r.name,r.price)).result.head43 44 val actions = for {45   _ <- coffees.schema.create46   _ <- qDelete47   c1 <- qAdd148   c2 <- qAdd249    <- getNameAndPrice50    <- getNameAndPrice51 } yield (n1,p1,n2,p2)52 import java.sql.SQLException53 import scala.concurrent.Await54 import scala.concurrent.duration._55 val db = Database.forURL("jdbc:h2:mem:demo", driver="org.h2.Driver")56 Await.result(57       db.run(actions.transactionally).map { res =>58         println(s"Add coffee: ${res._1},${res._2} and ${res._3},${res._4}")59       }.recover {60         case e: SQLException => println("Caught exception: " + e.getMessage)61       }, Duration.Inf)62       63 }

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