在对上一次3月份的scala-meetup里我曾分享了关于Future在函数组合中的问题及如何用Monix.Task来替代。具体分析可以查阅这篇博文。在上篇示范里我们使用了Future来实现某种non-blocking数据库操作，现在可以用Task替换Future部分：
class KVStore[K,V] {
private val kvs = new ConcurrentHashMap[K,V]()
def create(k: K, v: V): Task[Unit] = Task.delay(kvs.putIfAbsent(k,v))
def read(k: K): Task[Option[V]] = Task.delay(Option(kvs.get(k)))
def update(k: K, v: V): Task[Unit] = Task.delay(kvs.put(k,v))
def delete(k: K): Task[Boolean] = Task.delay(kvs.remove(k) != null)
}
Task是一个真正的Monad，我们可以放心的用来实现函数组合：
type FoodName = String
type Quantity = Int
type FoodStore = KVStore[String,Int]
def addFood(food: FoodName, qty: Quantity)(implicit fs: FoodStore): Task[Quantity] = for {
current <- fs.read(food)
newQty = current.map(cq => cq + qty).getOrElse(qty)
_ <- fs.update(food,newQty)
} yield newQty
def takeFood(food: FoodName, qty: Quantity)(implicit fs: FoodStore): Task[Quantity] = for {
current <- fs.read(food)
cq = current.getOrElse(0)
taken = Math.min(cq,qty)
left = cq - taken
_ <- if(left > 0) fs.update(food,left) else fs.delete(food)
} yield taken
def cookSauce(qty: Quantity)(get: (FoodName,Quantity) => Task[Quantity],
put: (FoodName,Quantity) => Task[Quantity]): Task[Quantity] = for {
tomato <- get("Tomato",qty)
vaggies <- get("Veggies",qty)
_ <- get("Galic",10)
sauceQ = tomato/2 + vaggies * 3 / 2
_ <- put("Sauce",sauceQ)
} yield sauceQ
def cookPasta(qty: Quantity)(get: (FoodName,Quantity) => Task[Quantity],
put: (FoodName,Quantity) => Task[Quantity]): Task[Quantity] = for {
pasta <- get("Pasta", qty)
sauce <- get("Sauce", qty)
_ <- get("Spice", 3)
portions = Math.min(pasta, sauce)
_ <- put("Meal", portions)
} yield portions
跟上次我们使用Future时的方式没有两样。值得研究的是如何获取Task运算结果，及如何更精确的控制Task运算如取消运行中的Task：
implicit val refridge = new FoodStore
val shopping: Task[Unit] = for {
_ <- addFood("Tomato",10)
_ <- addFood("Veggies",15)
_ <- addFood("Garlic", 42)
_ <- addFood("Spice", 100)
_ <- addFood("Pasta", 6)
} yield()
val cooking: Task[Quantity] = for {
_ <- shopping
sauce <- cookSauce(10)(takeFood(_,_),addFood(_,_))
meals <- cookPasta(10)(takeFood(_,_),addFood(_,_))
} yield meals
import scala.util._
import monix.execution.Scheduler.Implicits.global
val cancellableCooking = Cooking.runOnComplete { result =>
result match {
case Success(meals) => println(s"we have $meals pasta meals for the day.")
case Failure(err) => println(s"cooking trouble: ${err.getMessage}")
}
}
global.scheduleOnce(1 second) {
println(s"its taking too long, cancelling cooking ...")
cancellableCooking.cancel()
}
在上面例子里的addFood,takeFood函数中都有个fs:FoodStore参数。这样做可以使函数更加通用，可以对用不同方式实施的FoodStore进行操作。这里FoodStore就是函数的依赖，我们是通过函数参数来传递这个依赖的。重新组织一下代码使这种关系更明显：
class Refridge {
def addFood(food: FoodName, qty: Quantity): FoodStore => Task[Quantity] = { foodStore =>
for {
current <- foodStore.read(food)
newQty = current.map(c => c + qty).getOrElse(qty)
_ <- foodStore.update(food, newQty)
} yield newQty
}
def takeFood(food: FoodName, qty: Quantity): FoodStore => Task[Quantity] = { foodStore =>
for {
current <- foodStore.read(food)
cq = current.getOrElse(0)
taken = Math.min(cq, qty)
left = cq - taken
_ <- if (left > 0) foodStore.update(food, left) else foodStore.delete(food)
} yield taken
}
}现在我们用一个函数类型的结果来代表依赖注入。这样做的好处是简化了函数主体，彻底把依赖与函数进行了分割，使用函数时不必考虑依赖。
scala的函数式组件库cats提供了一个Kleisli类型，reader monad就是从它推导出来的：
final case class Kleisli[M[_], A, B](run: A => M[B]) { self =>
...
trait KleisliFunctions {
/**Construct a Kleisli from a Function1 */
def kleisli[M[_], A, B](f: A => M[B]): Kleisli[M, A, B] = Kleisli(f)
…
def >=>[C](k: Kleisli[M, B, C])(implicit b: Bind[M]): Kleisli[M, A, C] =
kleisli((a: A) => b.bind(this(a))(k.run))
…
Kleisli的用途就是进行函数的转换
// (A=>M[B]) >=> (B=>M[C]) >=> (C=>M[D]) = M[D]实际上Kleisli就是ReaderT：
type ReaderT[F[_], E, A] = Kleisli[F, E, A]
val ReaderT = Kleisli
val reader = ReaderT[F,B,A](A => F[B])
val readerTask = ReaderT[Task,B,A](A => Task[B])
val injection = ReaderT { foodStore => Task.delay { foodStore.takeFood } }
val food = injection.run(db) // run(kvs), run(dbConfig) …这段代码里我们也针对上面的例子示范了ReaderT的用法。现在我们可以把例子改成下面这样：
type FoodName = String
type Quantity = Int
type FoodStore = KVStore[String,Int]
class Refridge {
def addFood(food: FoodName, qty: Quantity): ReaderT[Task,FoodStore,Quantity] = ReaderT{ foodStore =>
for {
current <- foodStore.read(food)
newQty = current.map(c => c + qty).getOrElse(qty)
_ <- foodStore.update(food, newQty)
} yield newQty
}
def takeFood(food: FoodName, qty: Quantity): ReaderT[Task,FoodStore,Quantity] = ReaderT{ foodStore =>
for {
current <- foodStore.read(food)
cq = current.getOrElse(0)
taken = Math.min(cq, qty)
left = cq - taken
_ <- if (left > 0) foodStore.update(food, left) else foodStore.delete(food)
} yield taken
}
}ReaderT[F[_],E,A]就是ReaderT[Task,FoodStore,Quantity]. FoodStore是注入的依赖，ReaderT.run返回Task：
val cooking: ReaderT[Task,FoodStore,Quantity] = for {
_ <- shopping
sauce <- cooker.cookSauce(10)
pasta <- cooker.cookPasta(10)
} yield pasta
import scala.concurrent.duration._
import scala.util._
import monix.execution.Scheduler.Implicits.global
val timedCooking = cooking.run(foodStore).timeoutTo(1 seconds, Task.raiseError( new RuntimeException(
"oh no, take too long to cook ...")))
val cancellableCooking = timedCooking.runOnComplete { result =>
result match {
case Success(meals) => println(s"we have $meals specials for the day.")
case Failure(exception) => println(s"kitchen problem! ${exception.getMessage}")
}
}
global.scheduleOnce(3 seconds) {
println("3 seconds passed，cancelling ...")
cancellableCooking.cancel()
}
我们知道cooking是个ReaderT，用run(foodStore)来注入依赖foodStore。那么如果我们还有一个kvStore或者jdbcDB,mongoDB可以直接用run(kvStore), run(jdbcDB), run(mongoDB) ... 返回的结果都是Task。