java-将Map 转换为Map 如何将Map转换为Map？...votes 现在我们有了Java 8 / streams，我们可以在列表中添加一个可能的答案： 假设每个值实际上都是String对象，则强制转换为String应该是安全的。...) entry.getValue()); } } 如果不是每个Objects不是String，则可以将(String) entry.getValue()替换为entry.getValue().toString...2 votes 当您从Object转换为String时，我建议您捕获并报告(以某种方式，这里我只是打印一条消息，通常是不好的)异常。...转换为Map的方法。
通常而言，OO转FP会显得相对困难，这是两种根本不同的思维范式。张无忌学太极剑时，学会的是忘记，只取其神，我们学FP，还得尝试忘记OO。自然，学到后来，其实还是万法归一。...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。...转换时，通过foldLeft操作对前面List中tuple的Int值累加，所以得到的结果为： scala.collection.immutable.Map[String,Int] = Map(scala...-> 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。
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...然后返回这个新的集合 举个栗子： def getWords(lines: Seq[String]): Seq[String] = lines flatMap (line => line split "...a Set def lettersOf(words: Seq[String]) = words.toSet flatMap (word => word.toSeq) 2 .flatMap和Map的区别...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...] 而flatMap也可将Future[Future[T]]串接成Future[T] 这部分我也只知道一部分，等我更懂了再来发 OVER!
(_ => (values.next).asInstanceOf[Object] ).toList } def ccToMap(cc: Product): Map[String, Object]...= ctx.statements ++ heads } } val commands: Seq[(String, Seq[Object])] =...= ctx.statements ++ heads } } val commands: Seq[(String, Seq[Object])] = ctxstatements...(mapFunction: String, reduceFunction: String) extends MGOCommands case class Insert(newdocs: Seq...CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends
[String] = Nil, parameters: Seq[Seq[Any]] = Nil, fetchSize...[String], parameters: Seq[Seq[Object]] = Nil, consistency...[Boolean] = { val commands: Seq[(String,Seq[Object])] = ctx.statements zip ctx.parameters var...CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends...[T]] else coll.find().first().map(optConv.get).head().asInstanceOf[Future[T]] case Find
() Future(doc)}4、创建一个函数来爬取商品的信息：def crawl(url: String): Future[Elements] = { val doc = getHtml(url)...doc.map(doc => doc.select(".pdp-name").map(_.text))}5、创建一个函数来处理爬取到的商品信息：def process(crawlResult: Future...]): Unit = { val urls = Seq("item.jd/100005288533.html", "item.jd/100005288534.html"...然后，我们在主函数中定义了需要爬取的URL列表，并使用map函数将每个URL转换为一个爬取商品信息的Future。...然后，我们使用map函数将每个Future转换为一个处理爬取到的商品信息的Future。最后，我们使用map函数将每个Future转换为一个可视化处理后的Future。
DataFrame/DataSet 转 RDD 这个转换比较简单，直接调用 rdd 即可将 DataFrame/DataSet 转换为 RDD： val rdd1 = testDF.rdd val rdd2...RDD 转 DataSet 定义 case class，通过反射来设置 Schema，使用 toDS 进行转换： case class Person(name:String, age:Int) val...DataSet 转 DataFrame 直接调用 toDF，即可将 DataSet 转换为 DataFrame： val peopleDF4 = peopleDS.toDF peopleDF4.show...4.4 读取数据源，加载数据（RDD 转 DataFrame） 读取上传到 HDFS 中的广州二手房信息数据文件，分隔符为逗号，将数据加载到上面定义的 Schema 中，并转换为 DataFrame 数据集...RDD 转 DataSet 重新读取并加载广州二手房信息数据源文件，将其转换为 DataSet 数据集： val houseRdd = spark.sparkContext.textFile("hdfs
[(String,Seq[Object])] = ctx.statements zip params log.info(s"cqlExecute> multi-commands: ${...}.toList val futList = lstCmds.sequence.map(_ => true) //must map to execute */ /*...[String] = Nil, parameters: Seq[Seq[Any]] = Nil, fetchSize...}.toList val futList = lstCmds.sequence.map(_ => true) //must map to execute */ /*..., params: Seq[Object])( implicit session: Session, ec: ExecutionContext): Future[Boolean] =
(mapFunction: String, reduceFunction: String) extends MGOCommands case class Insert(newdocs: Seq...CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends...[T]] else next(coll.find[Document]()).map(Converter.get).toFuture().asInstanceOf[Future[T]]...targets: Seq[String] = Nil, only: Boolean = false, adminOptions...[ProtoMGOBson] else Seq(bsonToProto(filter.get))}, resultOptions = andThen.map(_.
：http://spark.apache.org/docs/latest/rdd-programming-guide.html#resilient-distributed-datasets-rdds 如何将数据封装到...{SparkConf, SparkContext} /** * Spark 采用并行化的方式构建Scala集合Seq中的数据为RDD * - 将Scala集合转换为RDD * sc.parallelize...(seq) * - 将RDD转换为Scala中集合 * rdd.collect() * rdd.collectAsMap() */ object SparkParallelizeTest...序列存储数据 val linesSeq: Seq[String] = Seq( "hello me you her", "hello you..., String)] = sc.wholeTextFiles("data/input/ratings10", minPartitions = 2) filesRDD.map(_._1).
： 可迭代类型工具 -functools ： 函数工具，尤其是装饰器 operator operator提供了一个函数与符号的相互转换，方便我们在编程时选择： examples （1）符号转函数...： 比如在一些需要某些符号功能，却需要提供该符号的函数表示时，尤其是在map reduce filter等的key 和cmp里面 from operator import add print reduce...(add,range(10)) （2）函数转符号： 这个例子有点特别，但是在类定义中常见，add->__add__这种方式差别跟python的变量有关。...without future.division) Division a / b truediv(a, b) (with future.division) Division a // b floordiv...del seq[i:j] delitem(seq, slice(i, j)) Slicing seq[i:j] getitem(seq, slice(i, j)) String Formatting
CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends...* * @since 3.0 */ public class BsonDocument extends BsonValue implements Map,...CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends..., coll: String) = new MGOContext(db, coll) } case class MGOBatContext(contexts: Seq[MGOContext],...collName: String = "", commandType: MGO_COMMAND_TYPE, bsonParam: Seq[Bson] = Nil,
[String] = Nil, parameters: Seq[Seq[Any]] = Nil, fetchSize...{ r => JDBCResult(marshal(r)) } } } jdbcExecuteDDL返回Future[String]，如下： def jdbcExecuteDDL...(ctx: JDBCContext)(implicit ec: ExecutionContextExecutor): Future[String] = { if (ctx.sqlType !...[String] = Nil, parameters: Seq[Seq[Any]] = Nil, fetchSize...(_statement: String, _parameters: Any*): JDBCContext = { ctx.copy( statements = Seq(_statement
targets: Seq[String] = Nil, only: Boolean = false, adminOptions...[ProtoMGOBson] else Seq(bsonToProto(filter.get))}, resultOptions = andThen.map(_....: String, reduceFunction: String) extends MGOCommands case class Insert(newdocs: Seq[Document],...CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends...[T]] else next(coll.find[Document]()).map(Converter.get).toFuture().asInstanceOf[Future[T]]
targets: Seq[String] = Nil, only: Boolean = false, adminOptions...[ProtoMGOBson] else Seq(bsonToProto(filter.get))}, resultOptions = andThen.map(_....(mapFunction: String, reduceFunction: String) extends MGOCommands case class Insert(newdocs: Seq...CreateView(viewName: String, viewOn: String, pipeline: Seq[Bson], options: Option[Any] = None) extends...[T]] else next(coll.find[Document]()).map(Converter.get).toFuture().asInstanceOf[Future[T]]
CQLContext( statements: Seq[String], parameters: Seq[Seq...[Object]): Seq[Object] = { params.map { obj => obj match { case CQLDate(yy, mm, dd)...[Boolean] = { val commands: Seq[(String,Seq[Object])] = ctx.statements zip ctx.parameters var...[String], parameters: Seq[Seq[Object]] = Nil, consistency...[Boolean] = { val commands: Seq[(String,Seq[Object])] = ctx.statements zip ctx.parameters var
[Option[M]] def getAll : Future[Seq[M]] def filter(expr: M => Boolean): Future[Seq[M]] def save...Seq[Person]] = Future.successful( Seq(Person("jonny lee",23),Person("candy wang",45),Person("jimmy...[Any]): Future[HttpResponse] = { obj.map { x => HttpResponse(status = StatusCodes.OK, entity...def filter(expr: Address => Boolean): Future[Seq[Address]] = ???...def saveAll(rows: Future[Seq[Address]]): Future[Int] = ???
[String], parameters: Seq[Seq[Object]] = Nil, consistency...[Boolean] = { val commands: Seq[(String,Seq[Object])] = ctx.statements zip ctx.parameters var...[String] = Nil, parameters: Seq[Seq[Any]] = Nil, fetchSize...): JDBCContext = ctx.copy(queryTags = ctx.queryTags :+ tag) def appendTags(tags: Seq[String]): JDBCContext...(_statement: String, _parameters: Any*): JDBCContext = { ctx.copy( statements = Seq(_statement
= [name: string, age: bigint] 3.2 RDD转换为DataSet SparkSQL能够自动将包含有case类的RDD转换成DataFrame，case类定义了..., age: Long) defined class Person 3）将RDD转化为DataSet scala> peopleRDD.map(line => {val para = line.split...= [name: string, age: bigint] 2）将DataSet转换为RDD scala> DS.rdd res11: org.apache.spark.rdd.RDD[Person]...转换为DataFrame 1）创建一个样例类 scala> case class Person(name: String, age: Long) defined class Person 2）创建DataSet...scala> val ds = Seq(Person("Andy", 32)).toDS() ds: org.apache.spark.sql.Dataset[Person] = [name: string
{ (pid, optDesc, optWid, optHgh) => val futCount: Future[Int] = repository.count(pid).value.value.runToFuture.map...[String] = repository.insert(doc).value.value.runToFuture.map { eoc =>...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...:Option[String],sort:Option[String],fields:Option[String],top:Option[Int]): DBOResult[Seq[R]] = {...]=None,fields:Option[String]=None,top:Option[Int]=None): DBOResult[Seq[R]] = { var res = Seq[ResultOptions
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