浅谈Slick(3)- Slick201:从fp角度了解Slick
我在上期讨论里已经成功的创建了一个简单的Slick项目,然后又尝试使用了一些最基本的功能。Slick是一个FRM(Functional Relational Mapper),是为fp编程提供的scala SQL Query集成环境,可以让编程人员在scala编程语言里用函数式编程模式来实现对数据库操作的编程。在这篇讨论里我想以函数式思考模式来加深了解Slick。我对fp编程模式印象最深的就是类型匹配:从参数类型和返回结果类型来了解函数功能。所以上面我所指的函数式思考方式主要是从Slick函数的类型匹配角度来分析函数所起的作用和具体使用方式。
我们先了解一下建表过程:
1 import slick.driver.H2Driver.api._ 2 object slick201 { 3 //projection case classes 表列模版 4 case class Coffee( 5 id: Option[Long] 6 ,name: String 7 ,sup_ID: Int 8 ,price: Double 9 ,grade: Grade 10 ,total: Int 11 ) 12 case class Supplier( 13 id: Option[Int] 14 ,name: String 15 ,address: String 16 ,website: Option[String] 17 ) 18 //自定义字段 19 abstract class Grade(points: Int) 20 object Grade { 21 case object Premium extends Grade(2) 22 case object Quality extends Grade(1) 23 case object Bestbuy extends Grade(0) 24 25 def fromInt(p: Int) = p match { 26 case 2 => Premium 27 case 1 => Quality 28 case 0 => Bestbuy 29 } 30 def toInt(g: Grade) = g match { 31 case Premium => 2 32 case Quality => 1 33 case Bestbuy => 0 34 } 35 implicit val customColumn: BaseColumnType[Grade] = 36 MappedColumnType.base[Grade,Int](Grade.toInt, Grade.fromInt) 37 } 38 //schema 表行结构定义 39 class Coffees(tag: Tag) extends Table[Coffee](tag, "COFFEES") { 40 def id = column[Long]("COF_ID", O.AutoInc, O.PrimaryKey) 41 def name = column[String]("COF_NAME") 42 def price = column[Double]("COF_PRICE") 43 def supID = column[Int]("COF_SUP") 44 def grade = column[Grade]("COF_GRADE", O.Default(Grade.Bestbuy)) 45 def total = column[Int]("COF_TOTAL", O.Default(0)) 46 47 def * = (id.?,name,supID,price,grade,total) <> (Coffee.tupled, Coffee.unapply) 48 49 def supplier = foreignKey("SUP_FK",supID,suppliers)(_.id,onUpdate = ForeignKeyAction.Restrict, onDelete = ForeignKeyAction.Cascade) 50 def nameidx = index("NM_IX",name,unique = true) 51 } 52 val coffees = TableQuery[Coffees] 53 54 class Suppliers(tag: Tag) extends Table[Supplier](tag, "SUPPLIERS") { 55 def id = column[Int]("SUP_ID", O.PrimaryKey, O.AutoInc) 56 def name = column[String]("SUP_NAME") 57 def address = column[String]("SUP_ADDR", O.Default("-")) 58 def website = column[Option[String]]("SUP_WEB") 59 60 def * = (id.?, name, address, website) <> (Supplier.tupled, Supplier.unapply) 61 def addidx = index("ADDR_IX",(name,address),unique = true) 62 } 63 val suppliers = TableQuery[Suppliers] 64 65 }
我尽量把经常会遇到的情况如:定义字段、建索引、默认值、自定义字段等都作了尝试。coffees和suppliers代表了最终的数据表Query,def * 定义了这个Query的默认返回结果字段。
所有的定义都是围绕着表行(Table Row)结构进行的,包括:表属性及操作(Table member methods)、字段(Column)、字段属性(ColumnOptions)。表行定义操作方法基本都在slick.lifted.AbstractTable里、表属性定义在slick.model命名空间里、而大部分的帮助支持函数都在slick.lifted命名空间的其它对象里。
表行的实际类型如下:
abstract class Table[T](_tableTag: Tag, _schemaName: Option[String], _tableName: String) extends AbstractTable[T](_tableTag, _schemaName, _tableName) { table => ...} /** The profile-independent superclass of all table row objects. * @tparam T Row type for this table. Make sure it matches the type of your `*` projection. */ abstract class AbstractTable[T](val tableTag: Tag, val schemaName: Option[String], val tableName: String) extends Rep[T] {...}
如上所示,Table[T] extends AbstractTable[T]。现在所有表行定义操作函数应该在slick.profile.relationalTableComponent.Table里可以找得到。值得注意的是表行的最终类型是Rep[T],T可能是case class或者Tuple,被升格(lift)到Rep[T]。所以大部分表行定义的支持函数都是在slick.lifted命名空间内的。
上面我们使用了模版对应表行定义方式,所有列都能和模版case class对应。那么在定义projection def * 时就需要使用<>函数:
def <>[R : ClassTag](f: (U => R), g: (R => Option[U])) = new MappedProjection[R, U](shape.toNode(value), MappedScalaType.Mapper(g.andThen(_.get).asInstanceOf[Any => Any], f.asInstanceOf[Any => Any], None), implicitly[ClassTag[R]])
f,g是两个case class <> Tuple转换函数。在上面的例子里我们提供的是tupled和unapply,效果就是这样的:
Coffee.tupled //res2: ((Option[Long], String, Int, Double, Grade, Int)) => Coffee = <function1> Coffee.unapply _ //res3: Coffee => Option[(Option[Long], String, Int, Double, Grade, Int)] = <function1>
res2 >>> 把tuple: (...)转成coffee,res2 >>> 把coffee转成Option[(...)]
TableQuery[T]继承了Query[T]:slick.lifted.Query.scala
/** Represents a database table. Profiles add extension methods to TableQuery * for operations that can be performed on tables but not on arbitrary * queries, e.g. getting the table DDL. */ class TableQuery[E <: AbstractTable[_]](cons: Tag => E) extends Query[E, E#TableElementType, Seq] {...} ... sealed trait QueryBase[T] extends Rep[T] /** An instance of Query represents a query or view, i.e. a computation of a * collection type (Rep[Seq[T]]). It is parameterized with both, the mixed * type (the type of values you see e.g. when you call map()) and the unpacked * type (the type of values that you get back when you run the query). * * Additional extension methods for queries containing a single column are * defined in [[slick.lifted.SingleColumnQueryExtensionMethods]]. */ sealed abstract class Query[+E, U, C[_]] extends QueryBase[C[U]] { self =>...}
所有Query对象里提供的函数TableQuery类都可以调用。上面例子里coffees,suppliers实际是数据库表COFFEES,SUPPLIERS的Query实例,它们的默认字段集如:coffees.result是通过def * 定义的(除非用map或yield改变默认projection)。在slick.profile.RelationalProfile.TableQueryExtensionMethods里还有专门针对TableQuery类型的函数如schema等。
好了,来到了Query才算真正进入主题。Query可以说是Slick最核心的类型了。所有针对数据库的读写操作都是通过Query产生SQL语句发送到数据库实现的。Query是个函数式类型,即高阶类型Query[A]。A代表生成SQL语句的元素,通过转变A可以实现不同的SQL语句构建。不同功能的Query包括读取(retreive)、插入(insert)、更新(update)、删除(delete)都是通过Query变形(transformation)实现的。所有Query操作函数的款式:Query[A] => Query[B],是典型的函数式编程方式,也是scala集合操作函数款式。我们先从数据读取Query开始,因为上面我们曾经提到过可以通过map来决定新的结果集结构(projection):
val q1 = coffees.result q1.statements.head //res0: String = select "COF_ID", "COF_NAME", "COF_SUP", "COF_PRICE", "COF_GRADE", "COF_TOTAL" from "COFFEES" val q2 = coffees.map(r => (r.id, r.name)).result q2.statements.head //res1: String = select "COF_ID", "COF_NAME" from "COFFEES" val q3 = (for (c <- coffees) yield(c.id,c.name)).result q3.statements.head //res2: String = select "COF_ID", "COF_NAME" from "COFFEES"
因为map和flatMap的函数款式是:
map[A,B](Q[A])(A=>B]):Q[B], flatMap[A,B](Q[A])(A => Q[B]):Q[B]
所以不同的SQL语句基本上是通过Query[A] => Query[B]这种对高阶类型内嵌元素进行转变的函数式操作方式实现的。下面是一个带筛选条件的Query:
1 val q = coffees.filter(_.price > 100.0).map(r => (r.id,r.name)).result 2 q.statements.head 3 //res3: String = select "COF_ID", "COF_NAME" from "COFFEES" where "COF_PRICE" > 100.0 4 5 val q4 = coffees.filter(_.price > 100.0).take(4).map(_.name).result 6 q4.statements.head 7 //res4: String = select "COF_NAME" from "COFFEES" where "COF_PRICE" > 100.0 limit 4 8 9 val q5 = coffees.sortBy(_.id.desc.nullsFirst).map(_.name).drop(3).result 10 q5.statements.head 11 //res5: String = select "COF_NAME" from "COFFEES" order by "COF_ID" desc nulls first limit -1 offset 3
再复杂一点的Query,比如说join两个表:
val q6 = for { (c,s) <- coffees join suppliers on (_.supID === _.id) } yield(c.id,c.name,s.name) q6.result.statements.head //res6: String = select x2."COF_ID", x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2, "SUPPLIERS" x3 where x2."COF_SUP" = x3."SUP_ID" val q7 = for { c <- coffees s <- suppliers.filter(c.supID === _.id) } yield(c.id,c.name,s.name) q7.result.statements.head //res7: String = select x2."COF_ID", x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2, "SUPPLIERS" x3 where x2."COF_SUP" = x3."SUP_ID"
还有汇总类型的Query:
coffees.map(_.price).max.result.statements.head //res10: String = select max("COF_PRICE") from "COFFEES" coffees.map(_.total).sum.result.statements.head //res11: String = select sum("COF_TOTAL") from "COFFEES" coffees.length.result.statements.head //res12: String = select count(1) from "COFFEES" coffees.filter(_.price > 100.0).exists.result.statements.head //res13: String = select exists(select "COF_TOTAL", "COF_NAME", "COF_SUP", "COF_ID", "COF_PRICE", "COF_GRADE" from "COFFEES" where "COF_PRICE" > 100.0)
Query是个monad,它可以实现函数组合(functional composition)。如上所示:所有Query操作函数都是Query[A]=>Query[B]形式的。由于Query[A]里面的A类型是Rep[T]类型,是SQL语句组件类型。典型函数如flatMap的调用方式是:flatMap{a => MakeQuery(a ...)},可以看到下一个Query的构成可能依赖a值,而a的类型是表行或列定义。所以Query的函数组合就是SQL语句的组合,最终结果是产生目标SQL语句。
Slick处理数据的方式是通过组合相应的SQL语句后发送给数据库去运算的,相关SQL语句的产生当然是通过Query来实现的:
1 val qInsert = coffees += Coffee(Some(0),"American",101,56.0,Grade.Bestbuy,0) 2 qInsert.statements.head 3 //res10: String = insert into "COFFEES" ("COF_NAME","COF_SUP","COF_PRICE","COF_GRADE","COF_TOTAL") values (?,?,?,?,?) 4 val qInsert2 = coffees.map{r => (r.name, r.supID, r.price)} += ("Columbia",101,102.0) 5 qInsert2.statements.head 6 //res11: String = insert into "COFFEES" ("COF_NAME","COF_SUP","COF_PRICE") values (?,?,?) 7 val qInsert3 = (suppliers.map{r => (r.id,r.name)}). 8 returning(suppliers.map(_.id)) += (101,"The Coffee Co.,") 9 qInsert3.statements.head 10 //res12: String = insert into "SUPPLIERS" ("SUP_NAME") values (?)
从qInsert3产生的SQL语句来看:jdbc返回数据后还必须由Slick进一步处理后才能返回用户要求的结果值。下面是一些其它更改数据的Query示范:
val qDelete = coffees.filter(_.price === 0.0).delete qDelete.statements.head //res17: String = delete from "COFFEES" where "COFFEES"."COF_PRICE" = 0.0 val qUpdate = for (c <- coffees if (c.name === "American")) yield c.price qUpdate.update(10.0).statements.head //res18: String = update "COFFEES" set "COF_PRICE" = ? where "COFFEES"."COF_NAME" = 'American'
update query必须通过for-comprehension的yield来确定更新字段。Slick3.x最大的改进就是采用了functional I/O技术。具体做法就是引进DBIOAction类型,这是一个free monad。通过采用free monad的延迟运算模式来实现数据库操作动作的可组合性(composablility)及多线程运算(concurrency)。
DBIOAction类型款式如下:
sealed trait DBIOAction[+R, +S <: NoStream, -E <: Effect] extends Dumpable { ...} package object dbio { /** Simplified type for a streaming [[DBIOAction]] without effect tracking */ type StreamingDBIO[+R, +T] = DBIOAction[R, Streaming[T], Effect.All] /** Simplified type for a [[DBIOAction]] without streaming or effect tracking */ type DBIO[+R] = DBIOAction[R, NoStream, Effect.All] val DBIO = DBIOAction }
DBIO[+R]和StreamingDBIO[+R,+T]分别是固定类型参数S和E的类型别名,用它们来简化代码。所有的数据库操作函数包括result、insert、delete、update等都返回DBIOAction类型结果:
def result: DriverAction[R, S, Effect.Read] = {...} def delete: DriverAction[Int, NoStream, Effect.Write] = {...} def update(value: T): DriverAction[Int, NoStream, Effect.Write] = {...} def += (value: U): DriverAction[SingleInsertResult, NoStream, Effect.Write] = {...}
上面的DriverAction是DBIOAction的子类。因为DBIOAction是个free monad,所以多个DBIOAction可以进行组合,而在过程中是不会立即产生DBIO副作用的。我们只能通过DBIOAction类型的运算器来对DBIOAction的组合进行运算才会正真进行数据库数据读写。DBIOAction运算函数款式如下:
/** Run an Action asynchronously and return the result as a Future. */ final def run[R](a: DBIOAction[R, NoStream, Nothing]): Future[R] = runInternal(a, false)
run函数返回Future[R],代表在异步线程运算完成后返回R类型值。一般来讲Query.result返回R类型为Seq[?]。
DBIOAction只是对数据库操作动作的描述,不是实际的读写,所以DBIOAction可以进行组合。所谓组合的意思实际上就是把几个动作连续起来。DBIOAction的函数组件除monad通用的map、flatMap、sequence等,还包括了andThen、zip等合并操作函数,andThen可以返回最后一个动作结果、zip在一个pair里返回两个动作的结果。因为DBIOAction是monad,所以for-comprehension应该是最灵活、最强大的组合方式了。我们来试试用上面Query产生的动作来进行一些组合示范:
val initSupAction = suppliers.schema.create andThen qInsert3 val createCoffeeAction = coffees.schema.create val insertCoffeeAction = qInsert zip qInsert2 val initSupAndCoffee = for { _ <- initSupAction _ <- createCoffeeAction (i1,i2) <- insertCoffeeAction } yield (i1,i2)
我们可以任意组合这些操作步骤,因为它们的返回结果类型都是DBIOAction[R]:一个free monad。大多数时间这些动作都是按照一定的流程顺序组合的。可能有些时候下一个动作需要依赖上一个动作产生的结果,这个时候用for-comprehension是最适合的了:
//先选出所有ESPRESSO开头的coffee名称,然后逐个删除 val delESAction = (for { ns <- coffees.filter(_.name.startsWith("ESPRESSO")).map(_.name).result _ <- DBIO.seq(ns.map(n => coffees.filter(_.name === n).delete): _*) } yield ()).transactionally //delESAction: slick.dbio.DBIOAction[Unit,slick.dbio.NoStream,slick.dbio.Effect.Read ... //对一个品种价格升10% def raisePriceAction(i: Long, np: Double, pc: Double) = (for(c <- coffees if (c.id === i)) yield c.price).update(np * pc) //raisePriceAction: raisePriceAction[](val i: Long,val np: Double,val pc: Double) => slick.driver.H2Driver.DriverAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write] //对所有价格<100的coffee加价 val updatePriceAction = (for { ips <- coffees.filter(_.price < 100.0).map(r => (r.id, r.price)).result _ <- DBIO.seq{ips.map { ip => raisePriceAction(ip._1, ip._2, 110.0)}: _* } } yield()).transactionally //updatePriceAction: slick.dbio.DBIOAction[Unit,slick.dbio.NoStream,slick.dbio.Effect.Read ...
另外,像monad的point:successful(R)可以把R升格成DBIOAction,failed(T)可以把T升格成DBIOAction[T]:
1 DBIO.successful(Supplier(Some(102),"Coffee Company","",None)) 2 //res19: slick.dbio.DBIOAction[Supplier,slick.dbio.NoStream,slick.dbio.Effect] = SuccessAction(Supplier(Some(102),Coffee Company,,None)) 3 4 DBIO.failed(new Exception("oh my god...")) 5 //res20: slick.dbio.DBIOAction[Nothing,slick.dbio.NoStream,slick.dbio.Effect] = FailureAction(java.lang.Exception: oh my god...)
DBIOAction还有比较完善的事后处理和异常处理机制:
1 //主要示范事后处理机制用法,不必理会功能的具体目的是否有任何意义 2 qInsert.andFinally(qDelete) 3 //res21: slick.dbio.DBIOAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write with slick.dbio.Effect.Write] = slick.dbio.SynchronousDatabaseAction$$anon$6@1d46b337 4 5 updatePriceAction.cleanUp ( 6 { case Some(e) => initSupAction; DBIO.failed(new Exception("oh my...")) 7 case _ => qInsert3 8 } 9 ,true 10 ) 11 //res22: slick.dbio.DBIOAction[Unit,slick.dbio.NoStream,slick.dbio.Effect.Read ... 12 13 raisePriceAction(101,10.0,110.0).asTry 14 //res23: slick.dbio.DBIOAction[scala.util.Try[Int],slick.dbio.NoStream,slick.dbio.Effect.Write] = slick.dbio.SynchronousDatabaseAction$$anon$9@60304a44
从上面的这些示范例子我们认识到DBIOAction的函数组合就是数据库操作步骤组合、实际上就是程序的组合或者是功能组合:把一些简单的程序组合成功能更全面的程序,然后才运算这个组合而成的程序。DBIOAction的运算函数run的函数款式如下:
/** Run an Action asynchronously and return the result as a Future. */ final def run[R](a: DBIOAction[R, NoStream, Nothing]): Future[R] = runInternal(a, false)
对DBIOAction进行运算后的结果是个Future类型,也是一个高阶类型,同样可以用map、flatMap、sequence、andThen等泛函组件进行函数组合。可以参考下面的这个示范:
import slick.jdbc.meta.MTable import scala.concurrent.ExecutionContext.Implicits.global import scala.concurrent.duration.Duration import scala.concurrent.{Await, Future} import scala.util.{Success,Failure} val db = Database.forURL("jdbc:h2:mem:test1;DB_CLOSE_DELAY=-1", driver="org.h2.Driver") def recreateCoffeeTable: Future[Unit] = { db.run(MTable.getTables("Coffees")).flatMap { case tables if tables.isEmpty => db.run(coffees.schema.create).andThen { case Success(_) => println("coffee table created") case Failure(e) => println(s"failed to create! ${e.getMessage}") } case _ => db.run((coffees.schema.drop andThen coffees.schema.create)).andThen { case Success(_) => println("coffee table recreated") case Failure(e) => println(s"failed to recreate! ${e.getMessage}") } } }
好了,下面是这次讨论的示范代码:
1 import slick.driver.H2Driver.api._ 2 3 object slick201 { 4 //projection case classes 表列模版 5 case class Coffee( 6 id: Option[Long] 7 ,name: String 8 ,sup_ID: Int 9 ,price: Double 10 ,grade: Grade 11 ,total: Int 12 ) 13 case class Supplier( 14 id: Option[Int] 15 ,name: String 16 ,address: String 17 ,website: Option[String] 18 ) 19 //自定义字段 20 abstract class Grade(points: Int) 21 object Grade { 22 case object Premium extends Grade(2) 23 case object Quality extends Grade(1) 24 case object Bestbuy extends Grade(0) 25 26 def fromInt(p: Int) = p match { 27 case 2 => Premium 28 case 1 => Quality 29 case 0 => Bestbuy 30 } 31 def toInt(g: Grade) = g match { 32 case Premium => 2 33 case Quality => 1 34 case Bestbuy => 0 35 } 36 implicit val customColumn: BaseColumnType[Grade] = 37 MappedColumnType.base[Grade,Int](Grade.toInt, Grade.fromInt) 38 } 39 //schema 表行结构定义 40 class Coffees(tag: Tag) extends Table[Coffee](tag, "COFFEES") { 41 def id = column[Long]("COF_ID", O.AutoInc, O.PrimaryKey) 42 def name = column[String]("COF_NAME") 43 def price = column[Double]("COF_PRICE") 44 def supID = column[Int]("COF_SUP") 45 def grade = column[Grade]("COF_GRADE", O.Default(Grade.Bestbuy)) 46 def total = column[Int]("COF_TOTAL", O.Default(0)) 47 48 def * = (id.?,name,supID,price,grade,total) <> (Coffee.tupled, Coffee.unapply) 49 50 def supplier = foreignKey("SUP_FK",supID,suppliers)(_.id,onUpdate = ForeignKeyAction.Restrict, onDelete = ForeignKeyAction.Cascade) 51 def nameidx = index("NM_IX",name,unique = true) 52 } 53 val coffees = TableQuery[Coffees] 54 55 class Suppliers(tag: Tag) extends Table[Supplier](tag, "SUPPLIERS") { 56 def id = column[Int]("SUP_ID", O.PrimaryKey, O.AutoInc) 57 def name = column[String]("SUP_NAME") 58 def address = column[String]("SUP_ADDR", O.Default("-")) 59 def website = column[Option[String]]("SUP_WEB") 60 61 def * = (id.?, name, address, website) <> (Supplier.tupled, Supplier.unapply) 62 def addidx = index("ADDR_IX",(name,address),unique = true) 63 } 64 val suppliers = TableQuery[Suppliers] 65 66 class Bars(tag: Tag) extends Table[(Int,String)](tag,"BARS") { 67 def id = column[Int]("BAR_ID",O.AutoInc,O.PrimaryKey) 68 def name = column[String]("BAR_NAME") 69 def * = (id, name) 70 } 71 val bars = TableQuery[Bars] 72 73 Coffee.tupled 74 //res2: ((Option[Long], String, Int, Double, Grade, Int)) => Coffee = <function1> 75 Coffee.unapply _ 76 //res3: Coffee => Option[(Option[Long], String, Int, Double, Grade, Int)] = <function1> 77 78 79 val q1 = coffees.result 80 q1.statements.head 81 //res0: String = select "COF_ID", "COF_NAME", "COF_SUP", "COF_PRICE", "COF_GRADE", "COF_TOTAL" from "COFFEES" 82 83 val q2 = coffees.map(r => (r.id, r.name)).result 84 q2.statements.head 85 //res1: String = select "COF_ID", "COF_NAME" from "COFFEES" 86 87 val q3 = (for (c <- coffees) yield(c.id,c.name)).result 88 q3.statements.head 89 //res2: String = select "COF_ID", "COF_NAME" from "COFFEES" 90 91 92 val q = coffees.filter(_.price > 100.0).map(r => (r.id,r.name)).result 93 q.statements.head 94 //res3: String = select "COF_ID", "COF_NAME" from "COFFEES" where "COF_PRICE" > 100.0 95 96 val q4 = coffees.filter(_.price > 100.0).take(4).map(_.name).result 97 q4.statements.head 98 //res4: String = select "COF_NAME" from "COFFEES" where "COF_PRICE" > 100.0 limit 4 99 100 val q5 = coffees.sortBy(_.id.desc.nullsFirst).map(_.name).drop(3).result 101 q5.statements.head 102 //res5: String = select "COF_NAME" from "COFFEES" order by "COF_ID" desc nulls first limit -1 offset 3 103 104 val q6 = for { 105 (c,s) <- coffees join suppliers on (_.supID === _.id) 106 } yield(c.id,c.name,s.name) 107 q6.result.statements.head 108 //res6: String = select x2."COF_ID", x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2, "SUPPLIERS" x3 where x2."COF_SUP" = x3."SUP_ID" 109 110 val q7 = for { 111 c <- coffees 112 s <- suppliers.filter(c.supID === _.id) 113 } yield(c.id,c.name,s.name) 114 q7.result.statements.head 115 //res7: String = select x2."COF_ID", x2."COF_NAME", x3."SUP_NAME" from "COFFEES" x2, "SUPPLIERS" x3 where x2."COF_SUP" = x3."SUP_ID" 116 117 coffees.map(_.price).max.result.statements.head 118 //res10: String = select max("COF_PRICE") from "COFFEES" 119 coffees.map(_.total).sum.result.statements.head 120 //res11: String = select sum("COF_TOTAL") from "COFFEES" 121 coffees.length.result.statements.head 122 //res12: String = select count(1) from "COFFEES" 123 coffees.filter(_.price > 100.0).exists.result.statements.head 124 //res13: String = select exists(select "COF_TOTAL", "COF_NAME", "COF_SUP", "COF_ID", "COF_PRICE", "COF_GRADE" from "COFFEES" where "COF_PRICE" > 100.0) 125 val qInsert = coffees += Coffee(Some(0),"American",101,56.0,Grade.Bestbuy,0) 126 qInsert.statements.head 127 //res14: String = insert into "COFFEES" ("COF_NAME","COF_SUP","COF_PRICE","COF_GRADE","COF_TOTAL") values (?,?,?,?,?) 128 val qInsert2 = coffees.map{r => (r.name, r.supID, r.price)} += ("Columbia",101,102.0) 129 qInsert2.statements.head 130 //res15: String = insert into "COFFEES" ("COF_NAME","COF_SUP","COF_PRICE") values (?,?,?) 131 val qInsert3 = (suppliers.map{r => (r.id,r.name)}). 132 returning(suppliers.map(_.id)) += (101,"The Coffee Co.,") 133 qInsert3.statements.head 134 //res16: String = insert into "SUPPLIERS" ("SUP_NAME") values (?) 135 136 val qDelete = coffees.filter(_.price === 0.0).delete 137 qDelete.statements.head 138 //res17: String = delete from "COFFEES" where "COFFEES"."COF_PRICE" = 0.0 139 val qUpdate = for (c <- coffees if (c.name === "American")) yield c.price 140 qUpdate.update(10.0).statements.head 141 //res18: String = update "COFFEES" set "COF_PRICE" = ? where "COFFEES"."COF_NAME" = 'American' 142 143 val initSupAction = suppliers.schema.create andThen qInsert3 144 val createCoffeeAction = coffees.schema.create 145 val insertCoffeeAction = qInsert zip qInsert2 146 val initSupAndCoffee = for { 147 _ <- initSupAction 148 _ <- createCoffeeAction 149 (i1,i2) <- insertCoffeeAction 150 } yield (i1,i2) 151 152 //先选出所有ESPRESSO开头的coffee名称,然后逐个删除 153 val delESAction = (for { 154 ns <- coffees.filter(_.name.startsWith("ESPRESSO")).map(_.name).result 155 _ <- DBIO.seq(ns.map(n => coffees.filter(_.name === n).delete): _*) 156 } yield ()).transactionally 157 //delESAction: slick.dbio.DBIOAction[Unit,slick.dbio.NoStream,slick.dbio.Effect.Read with slick.dbio.Effect.Write with slick.dbio.Effect.Transactional] = CleanUpAction(AndThenAction(Vector(slick.driver.JdbcActionComponent$StartTransaction$@6e76c850, FlatMapAction(slick.driver.JdbcActionComponent$QueryActionExtensionMethodsImpl$$anon$1@2005bce5,<function1>,scala.concurrent.impl.ExecutionContextImpl@245036ad))),<function1>,true,slick.dbio.DBIOAction$sameThreadExecutionContext$@294c4c1d) 158 159 //对一个品种价格升10% 160 def raisePriceAction(i: Long, np: Double, pc: Double) = 161 (for(c <- coffees if (c.id === i)) yield c.price).update(np * pc) 162 //raisePriceAction: raisePriceAction[](val i: Long,val np: Double,val pc: Double) => slick.driver.H2Driver.DriverAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write] 163 //对所有价格<100的coffee加价 164 val updatePriceAction = (for { 165 ips <- coffees.filter(_.price < 100.0).map(r => (r.id, r.price)).result 166 _ <- DBIO.seq{ips.map { ip => raisePriceAction(ip._1, ip._2, 110.0)}: _* } 167 } yield()).transactionally 168 //updatePriceAction: slick.dbio.DBIOAction[Unit,slick.dbio.NoStream,slick.dbio.Effect.Read with slick.dbio.Effect.Write with slick.dbio.Effect.Transactional] = CleanUpAction(AndThenAction(Vector(slick.driver.JdbcActionComponent$StartTransaction$@6e76c850, FlatMapAction(slick.driver.JdbcActionComponent$QueryActionExtensionMethodsImpl$$anon$1@49c8a41f,<function1>,scala.concurrent.impl.ExecutionContextImpl@245036ad))),<function1>,true,slick.dbio.DBIOAction$sameThreadExecutionContext$@294c4c1d) 169 170 DBIO.successful(Supplier(Some(102),"Coffee Company","",None)) 171 //res19: slick.dbio.DBIOAction[Supplier,slick.dbio.NoStream,slick.dbio.Effect] = SuccessAction(Supplier(Some(102),Coffee Company,,None)) 172 173 DBIO.failed(new Exception("oh my god...")) 174 //res20: slick.dbio.DBIOAction[Nothing,slick.dbio.NoStream,slick.dbio.Effect] = FailureAction(java.lang.Exception: oh my god...) 175 176 //示范事后处理机制,不必理会功能的具体目的 177 qInsert.andFinally(qDelete) 178 //res21: slick.dbio.DBIOAction[Int,slick.dbio.NoStream,slick.dbio.Effect.Write with slick.dbio.Effect.Write] = slick.dbio.SynchronousDatabaseAction$$anon$6@1d46b337 179 180 updatePriceAction.cleanUp ( 181 { case Some(e) => initSupAction; DBIO.failed(new Exception("oh my...")) 182 case _ => qInsert3 183 } 184 ,true 185 ) 186 //res22: slick.dbio.DBIOAction[Unit,slick.dbio.NoStream,slick.dbio.Effect.Read with slick.dbio.Effect.Write with slick.dbio.Effect.Transactional with slick.dbio.Effect.Write] = CleanUpAction(CleanUpAction(AndThenAction(Vector(slick.driver.JdbcActionComponent$StartTransaction$@6e76c850, FlatMapAction(slick.driver.JdbcActionComponent$QueryActionExtensionMethodsImpl$$anon$1@1f7aad00,<function1>,scala.concurrent.impl.ExecutionContextImpl@245036ad))),<function1>,true,slick.dbio.DBIOAction$sameThreadExecutionContext$@294c4c1d),<function1>,true,scala.concurrent.impl.ExecutionContextImpl@245036ad) 187 188 raisePriceAction(101,10.0,110.0).asTry 189 //res23: slick.dbio.DBIOAction[scala.util.Try[Int],slick.dbio.NoStream,slick.dbio.Effect.Write] = slick.dbio.SynchronousDatabaseAction$$anon$9@60304a44 190 191 192 import slick.jdbc.meta.MTable 193 import scala.concurrent.ExecutionContext.Implicits.global 194 import scala.concurrent.duration.Duration 195 import scala.concurrent.{Await, Future} 196 import scala.util.{Success,Failure} 197 198 val db = Database.forURL("jdbc:h2:mem:test1;DB_CLOSE_DELAY=-1", driver="org.h2.Driver") 199 200 def recreateCoffeeTable: Future[Unit] = { 201 db.run(MTable.getTables("Coffees")).flatMap { 202 case tables if tables.isEmpty => db.run(coffees.schema.create).andThen { 203 case Success(_) => println("coffee table created") 204 case Failure(e) => println(s"failed to create! ${e.getMessage}") 205 } 206 case _ => db.run((coffees.schema.drop andThen coffees.schema.create)).andThen { 207 case Success(_) => println("coffee table recreated") 208 case Failure(e) => println(s"failed to recreate! ${e.getMessage}") 209 } 210 } 211 } 212 213 }