淺談Slick(1)- 基本功能描述

分類:IT技術 時間:2016-10-18

   Slick (Scala language-integrated connection kit)是scala的一個FRM(Functional Relational Mapper),即函數式的關系數據庫編程工具庫。Slick的主要目的是使關系數據庫能更容易、更自然的融入函數式編程模式,它可以使使用者像對待scala集合一樣來處理關系數據庫表。也就是說可以用scala集合的那些豐富的操作函數來處理庫表數據。Slick?把數據庫編程融入到scala編程中,編程人員可以不需要編寫SQL代碼。我把Slick官方網站上Slick3.1.1文檔的Slick介紹章節中的一些描述和例子拿過來幫助介紹Slick的功能。下面是Slick數據庫和類對象關系對應的一個例子:

 1 import slick.driver.H2Driver.api._
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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(tag: Tag) extends Table[Coffee](tag, "COFFEES") { 11 def id = column[Int]("
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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)
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18 } 19 val coffees = TableQuery[Coffees] 20 //> coffees : slick.lifted.TableQuery[worksheets.slickIntro.Coffees] = Rep(TableExpansion) 21 }

我們把數據庫中的COFFEES表與Coffees類做了對應,包括字段、索引、默認值、返回結果集字段等。現在這個coffees就是scala裏的一個對象,但它代表了數據庫表。現在我們可以用scala語言來編寫數據存取程序了:

1 val limit = 10.0                                 //> limit  : Double = 10.0
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2 // // 寫Query時就像下面這樣: 3 ( for( c <- coffees; if c.price < limit ) yield c.name ).result 4 //> res0: slick.driver.H2Driver.StreamingDriverAction[Seq[String],String,slick.dbio.Effect.Read] = slick.driver.JdbcActionComponent$QueryActionExtensionMethodsImpl$$anon$1@46cdf8bd 5 // 相當於 SQL: select COF_NAME from COFFEES where PRICE < 10.0

或者下面這些不同的Query:

1 // 返回"name"字段的Query
2 // 相當於 SQL: select NAME from COFFEES
3 coffees.map(_.name)                               
4 //> res1: slick.lifted.Query[slick.lifted.Rep[String],String,Seq] = Rep(Bind)
5 // 選擇 price < 10.0 的所有記錄Query
6 // 相當於 SQL: select * from COFFEES where PRICE < 10.0
7 coffees.filter(_.price < 10.0)                    
8 //> res2: slick.lifted.Query[worksheets.slickIntro.Coffees,worksheets.slickIntro.Coffees#TableElementType,Seq] = Rep(Filter @1946988038)

我們可以這樣表述:coffees.map(_.name) >>> coffees.map{row=>row.name}, coffees.filter(_.price<10.0) >>> coffees.filter{row=>row.price<10.0),都是函數式集合操作語法。

Slick把Query編寫與scala語言集成,這使編程人員可以用熟悉慣用的scala來表述SQL Query,直接的好處是scalac在編譯時就能夠發現Query錯誤:

1 //coffees.map(_.prices)   
2 //編譯錯誤:value prices is not a member of worksheets.slickIntro.Coffees    

當然,嵌入scala的Query還可以獲得運行效率的提升,因為在編譯時可以進行前期優化。

最新版本的Slick最大的特點是采用了Functional I/O技術,從而實現了安全的多線程無阻礙I/O操作。再就是實現了Query的函數組合(functional composition),使Query編程更貼近函數式編程模式。通過函數組合實現代碼重復利用,提高編程工作效率。具體實現方式是利用freemonad(DBIOAction類型就是個freemonad)的延遲運算模式,將DBIOAction的編程和實際運算分離,在DBIOAction編程過程中不會產生副作用(side-effect),從而實現純代碼的函數組合。我們來看看Query函數組合和DBIOAction運算示範:

 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(_.id)) += 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(_.id)) += Coffee(name="Blue Mountain",price=828.0)
 7 //> qAdd2  : slick.profile.FixedSqlAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write]...
 8 def getNameAndPrice(n: Int) = coffees.filter(_.id === n)
 9     .map(r => (r.name,r.price)).result.head      
10 //> getNameAndPrice: (n: Int)slick.profile.SqlAction[(String, Double),slick.dbio.NoStream,slick.dbio.Effect.Read]
11 
12 val actions = for {
13   _ <- coffees.schema.create
14   _ <- qDelete
15   c1 <- qAdd1
16   c2 <- qAdd2
17   (n1,p1) <- getNameAndPrice(c1)
18   (n2,p2) <- getNameAndPrice(c2)
19 } yield (n1,p1,n2,p2)                             
20 //> actions  : slick.dbio.DBIOAction[(String, Double, String, Double),..

我們可以放心的來組合這個actions,不用擔心有任何副作用。actions的類型是:DBAction[String,Double,String,Double]。我們必須用database.Run來真正開始運算,產生副作用:

 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

在特殊的情況下我們也可以引用純SQL語句:Slick提供了Plain SQL API, 如下:

1 val limit = 10.0
2 sql"select COF_NAME from COFFEES where PRICE < $limit".as[String]
3 // 用$來綁定變量: // select COF_NAME from COFFEES where PRICE < ?

下面是這篇討論的示範代碼:

 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(tag: Tag) 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 = 10.0
23 // // 寫Query時就像下面這樣:
24 ( for( c <- coffees; if c.price < limit ) yield c.name ).result
25 // 相當於 SQL: select COF_NAME from COFFEES where PRICE < 10.0
26 
27 // 返回"name"字段的Query
28 // 相當於 SQL: select NAME from COFFEES
29 coffees.map(_.name)
30 // 選擇 price < 10.0 的所有記錄Query
31 // 相當於 SQL: select * from COFFEES where PRICE < 10.0
32 coffees.filter(_.price < 10.0)
33 //coffees.map(_.prices)
34 //編譯錯誤:value prices is not a member of worksheets.slickIntro.Coffees
35 
36 
37 import scala.concurrent.ExecutionContext.Implicits.global
38 val qDelete = coffees.filter(_.price > 0.0).delete
39 val qAdd1 = (coffees returning coffees.map(_.id)) += Coffee(name="Columbia",price=128.0)
40 val qAdd2 = (coffees returning coffees.map(_.id)) += Coffee(name="Blue Mountain",price=828.0)
41 def getNameAndPrice(n: Int) = coffees.filter(_.id === n)
42     .map(r => (r.name,r.price)).result.head
43 
44 val actions = for {
45   _ <- coffees.schema.create
46   _ <- qDelete
47   c1 <- qAdd1
48   c2 <- qAdd2
49   (n1,p1) <- getNameAndPrice(c1)
50   (n2,p2) <- getNameAndPrice(c2)
51 } yield (n1,p1,n2,p2)
52 import java.sql.SQLException
53 import scala.concurrent.Await
54 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 }

 

 

 

 

 

 

 

 

 

 


Tags: 關系數據庫 數據庫編程 數據庫表 官方網站 import

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