Option[Bson]) extends MGOCommands /* org.mongodb.scala.FindObservable import com.mongodb.async.client.FindIterable...Option[Bson]) extends MGOCommands /* org.mongodb.scala.FindObservable import com.mongodb.async.client.FindIterable...[T]] else coll.find(filter).map(Converter.get).toFuture().asInstanceOf[Future[T]] case...{b => sdp.grpc.services.MGOBson(marshal(b))}, objName = this.objName, options = this.options.map...) = new MGOAdminCtx( tarName = msg.tarName, bsonParam = msg.bsonParam.map(b => unmarshal[
ActorSystem("PubSubSystem", config) system.actorOf(props, "publisher") } } publisher构造指令：将一个...{b => sdp.grpc.services.ProtoMGOBson(marshal(b))}, objName = this.objName, options = this.options.map...) = new AdminContext( tarName = msg.tarName, bsonParam = msg.bsonParam.map(b => unmarshal...[Bson](b.bson)), objName = msg.objName, options = msg.options.map(b => unmarshal[Any](b.value...: Option[Any] = None, documents: Seq[Document] = Nil, targets
[Seq[Object]] = txns.foldRight(Seq[Seq[Object]]()) { (txnitem,b) => (Seq(pid.asInstanceOf[Object...command effect val lstCmds: List[Future[Boolean]] = commands.map { case (stmt,param)...{b => sdp.grpc.services.ProtoMGOBson(marshal(b))}, objName = this.objName, options = this.options.map...[Bson](b.bson)), objName = msg.objName, options = msg.options.map(b => unmarshal[Any](b.value...: Option[Any] = None, documents: Seq[Document] = Nil, targets
{ (pid, optDesc, optWid, optHgh) => val futCount: Future[Int] = repository.count(pid).value.value.runToFuture.map...=> doc = doc + ("pic" -> b.toArray) val futmsg: Future[String...现在整个futCount算式可以简化成下面这样： val futCount: Future[Int] = repository.count(pid).value.value.runToFuture.map...],fields:Option[String],top:Option[Int]): DBOResult[Seq[R]] = { var res = Seq[ResultOptions]()...]=None,fields:Option[String]=None,top:Option[Int]=None): DBOResult[Seq[R]] = { var res = Seq[ResultOptions
[C[A]]) else optionToDBOResult(Some(coll): Option[C[A]]) } filestream/FileStreaming.scala package...{b => sdp.grpc.services.ProtoMGOBson(marshal(b))}, objName = this.objName, options = this.options.map...) = new AdminContext( tarName = msg.tarName, bsonParam = msg.bsonParam.map(b => unmarshal...[Bson](b.bson)), objName = msg.objName, options = msg.options.map(b => unmarshal[Any](b.value...: Option[Any] = None, documents: Seq[Document] = Nil, targets
{b => sdp.grpc.services.ProtoMGOBson(marshal(b))}, objName = this.objName, options = this.options.map...) = new AdminContext( tarName = msg.tarName, bsonParam = msg.bsonParam.map(b => unmarshal...[Bson](b.bson)), objName = msg.objName, options = msg.options.map(b => unmarshal[Any](b.value...: Option[Any] = None, documents: Seq[Document] = Nil, targets...[ProtoMGOBson] else Seq(bsonToProto(filter.get))}, resultOptions = andThen.map(_.
对于管理者而言，最关键的一点是明白Scala与Java的优劣对比，然后根据项目情况和团队情况，明智地进行技术决策。我们不能完全脱离上下文去说A优于B。世上哪有绝对呢？...分组后得到一个Map[String, Seq[(Stirng, Int)]]类型： scala.collection.immutable.Map[String,Seq[(String, Int)]] =...10))) 然后将这个类型转换为一个Map。...-> 12, java -> 4, python -> 10) 之后，将Map转换为Seq，然后按照统计的数值降序排列，接着反转顺序即可。...JVM的编译与纯粹的静态编译不同，Java和Scala编译器都是将源代码转换为JVM字节码，而在运行时，JVM会根据当前运行机器的硬件架构，将JVM字节码转换为机器码。
batch: Boolean = false, returnGeneratedKey: Seq[Option[Any]] = Nil,...[C[Long]] = { val keys: Seq[Option[Any]] = ctx.returnGeneratedKey match { case Nil => Seq.fill...{Await, Future} import scala.concurrent.duration._ def fetchResultPage[C[_] <: TraversableOnce[_...= new Array[Byte](fis.available + 1) val length = b.length fis.read(b) ByteBuffer.wrap(b)...=> a + unmarshal[Int](b.result)} fut.onComplete { case scala.util.Success(cnt) => println(s"done
我们也可以通过akka的Flow[A,B]来以stream里的A元素为变量对MongoDB数据进行更新操作： object MongoActionStream { import MGOContext...[T]] else coll.find().map(optConv.get).toFuture().asInstanceOf[Future[T]] case Find(None...[T]] else coll.find().first().map(optConv.get).head().asInstanceOf[Future[T]] case Find...[T]] else coll.find(filter).first().map(optConv.get).head().asInstanceOf[Future[T]] case...[T]] else next(coll.find[Document](filter)).map(optConv.get).toFuture().asInstanceOf[Future[T
[T]] else coll.find().map(Converter.get).toFuture().asInstanceOf[Future[T]] case Find...[T]] else coll.find().first().map(Converter.get).head().asInstanceOf[Future[T]] case...[T]] else coll.find(filter).map(Converter.get).toFuture().asInstanceOf[Future[T]] case...[T]] else next(coll.find[Document]()).map(Converter.get).toFuture().asInstanceOf[Future[T]]...[T]] else next(coll.find[Document](filter)).map(Converter.get).toFuture().asInstanceOf[Future
我们可以用隐式转换（implicit conversion）把它转换成scala-future来使用： implicit def listenableFutureToFuture[T](...[Object]): Seq[Object] = { params.map { obj => obj match { case CQLDate(yy, mm, dd)...= new Array[Byte](fis.available + 1) val length = b.length fis.read(b) ByteBuffer.wrap(b)...[Object]): Seq[Object] = { params.map { obj => obj match { case CQLDate(yy, mm, dd)...= new Array[Byte](fis.available + 1) val length = b.length fis.read(b) ByteBuffer.wrap(b)
[String],top:Option[Int]): DBOResult[Seq[R]] = { var res = Seq[ResultOptions]() next.foreach...) } def query(filtr: Bson, next:Option[String],sort:Option[String],fields:Option[String],top...:Option[Int]): DBOResult[Seq[R]] = { var res = Seq[ResultOptions]() next.foreach {b =>...],fields:Option[String],top:Option[Int]): DBOResult[Seq[R]] = { var res = Seq[ResultOptions]()...:Option[String],top:Option[Int]): DBOResult[Seq[R]] = { var res = Seq[ResultOptions]()
Future. */ def fromFuture(futStmt: Future[Statement])(implicit session: Session): Source[Row, NotUsed...= new Array[Byte](fis.available + 1) val length = b.length fis.read(b) ByteBuffer.wrap(b)...batch: Boolean = false, returnGeneratedKey: Seq[Option[Any]] = Nil,...[Option[Any]] = ctx.returnGeneratedKey match { case Nil => Seq.fill(ctx.statements.size)(None...package configdbs import scala.collection.mutable import scala.concurrent.duration.Duration import scala.language.implicitConversions
由于本人也是初学者，如果内容有误，欢迎大家指出错误 flatMap 文章目录 flatMap的常见用法 flatMap和Map的区别 flatMap与Future 1 . flatMap常见用法 首先看看...scala中Seq 的flatMap的函数定义 def flatMap[B](f: (A) ⇒ GenTraversableOnce[B]): Seq[B] Builds a new collection...意思大概就是将f这个函数应用到Seq里的所有元素，并将函数产生的集合里的元素取出来，组成一个新的集合。...先将e中的两个元素：”I love”,”coding scala”，变成Seq（”I”,”love”）和Seq(“coding”,”scala”)，然后从那两个Seq中取得元素，组成一个新的Seq(“I...在知乎中看到的，觉得很有道理： flatMap=map + flatten 3 .flatMap与Future 在1中我们讲到flatMap是将函数产生的List[List[T]]串接成List[T
[Option[M]] def getAll : Future[Seq[M]] def filter(expr: M => Boolean): Future[Seq[M]] def save...[Option[Person]] = Future.successful(Some(Person("johnny lee",23))) override def getAll: Future[...[Any]): Future[HttpResponse] = { obj.map { x => HttpResponse(status = StatusCodes.OK, entity...[Option[Address]] = ???...def saveAll(rows: Future[Seq[Address]]): Future[Int] = ???
[T]] else coll.find().map(optConv.get).toFuture().asInstanceOf[Future[T]] case Find(None...] else coll.find().first().map(optConv.get).head().asInstanceOf[Future[T]] case Find(Some...[T]] else coll.find(filter).map(optConv.get).toFuture().asInstanceOf[Future[T]] case Find...[T]] else coll.find(filter).first().map(optConv.get).head().asInstanceOf[Future[T]] case...[T]] else next(coll.find[Document](filter)).map(optConv.get).toFuture().asInstanceOf[Future[T
方法转函数 上文中提到任何方法都是在声明一个表达式，所以将方法转为函数也就非常简单了，相当于是把方法指向的表达式，又重新赋给了一个函数变量，这就是显式转化。...class OptionDemo { val map = Map (("a",18),("b",81)) //get方法返回的类型就是Option[Int] map.get("b") match...第一种操作类型将集合转换为另一个集合，第二种操作类型返回某些类型的值。 1）最大值和最小值 先从行动函数开始。在序列中查找最大或最小值是一个极常见的需求。 先看一下简单的例子。...case class Book(title: String, pages: Int) val books = Seq( Book("Future of Scala developers", 85),...'a', 'b', 'c', 'd') //返回List(A, B, C, D)chars.map(ch => ch.toUpper) 6）flatMap 它将map & flatten组合起来，请看下面的操作
{ (pid, optDesc, optWid, optHgh) => val futCount: Future[Int] = repository.count(pid).value.value.runToFuture.map...=> doc = doc + ("pic" -> b.toArray) val futmsg: Future[String...],fields:Option[String],top:Option[Int]): DBOResult[Seq[R]] = { var res = Seq[ResultOptions]()...]=None,fields:Option[String]=None,top:Option[Int]=None): DBOResult[Seq[R]] = { var res = Seq[ResultOptions...=> doc = doc + ("pic" -> b.toArray) val futmsg: Future[String
val respToken = for { resp <- futToken jstr s + b.utf8String} } yield jstr val jstr = Await.result[String](respToken,2 seconds)...{Config, ConfigFactory} import scala.concurrent.Future object TestMultipartFileUpload extends App {...[(String, Option[String], Long)])(_ :+ _).map(_.mkString(", ")) complete { fileNamesFuture...=> s + b.utf8String} } yield jstr val jstr = Await.result[String](respToken,2 seconds)
因为我们已经明确选择了在akka-cluster集群环境里实施gRPC服务模式，通过akka-stream的流控制方式实现数据库操作的程序控制，所以在本次讨论里我们将示范说明gRPC-JDBC-Streaming...batch: Boolean = false, returnGeneratedKey: Seq[Option[Any]] = Nil,...(ctx.fetchSize) ctx.queryTimeout.foreach(session.queryTimeout(_)) val keys: Seq[Option...[Option[Any]] = Seq(Some(1),Some("id"),None,Some(2)) println(s"original value: ${orgval}") val marval...= marshal(orgval) println(s"marshal value: ${marval}") val unmval = unmarshal[Seq[Option[Any]]
领取专属 10元无门槛券
手把手带您无忧上云
云服务器
即时通信 IM
ICP备案
对象存储
实时音视频
