scala-collections

问题 As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. Closed 6 years ago . I have just started to look at the Scala collections library re-implementation which is coming in the imminent 2.8 release. Those

问题 Is there a more idiomatic and maybe faster way to check if there are duplicates in a Seq , than this: mySeq.size == mySeq.toSet.size 回答1: This will be faster, because it can terminate early: def allUnique[A](to: TraversableOnce[A]) = { val set = scala.collection.mutable.Set[A]() to.forall { x => if (set(x)) false else { set += x true } } } 来源： https://stackoverflow.com/questions/23752677/scala-how-to-check-if-all-items-are-unique-in-a-seq

问题 To use guava Table and Multiset in scala? are there already different concenpts in scala instead of importing guava library for this usage? 回答1: You could use Map[(R, C), V] instead of Table<R, C, V> and Map[T, Int] instead of Multiset<T> . You could also add helper methods to Map[T, Int] like this: implicit class Multiset[T](val m: Map[T, Int]) extends AnyVal { def setAdd(e: T, i: Int = 1) = { val cnt = m.getOrElse(e, 0) + i if (cnt <= 0) m - e else m.updated(e, cnt) } def setRemove(e: T, i:

问题 I Have two functions like below def method1(ip:String,r:Double,op:String)={ val data = spark.read.option("header", true).csv(ip).toDF() val r3= data.select("c", "S").dropDuplicates("C", "S").withColumn("R", lit(r)) r3.coalesce(1).write.format("com.databricks.spark.csv").option("header", "true").save(op) } def method2(ip:String,op:String)={ val data = spark.read.option("header", true).csv(ip).toDF() val r3= data.select("c", "S").dropDuplicates("C", "StockCode") r3.coalesce(1).write.format("com

问题 I have a dataframe called DF1 like below. DF1: srcColumnZ|srcCoulmnY|srcCoulmnR| +---------+----------+----------+ |John |Non Hf |New york | |Steav |Non Hf |Mumbai | |Ram |HF |Boston | And also having one list of map with source to target column mapping like below. List(Map(targetColumn -> columnNameX, sourceColumn -> List(srcColumnX, srcColumnY, srcColumnZ, srcColumnP, srcColumnQ, srcColumnR)), Map(targetColumn -> columnNameY, sourceColumn -> List(srcColumnY)), Map(targetColumn ->

问题 I have a Spark Dataframe with the below columns. C1 | C2 | C3| C4 1 | 2 | 3 | S1 2 | 3 | 3 | S2 4 | 5 | 3 | S2 I want to generate another column C5 by taking distinct values from column C4 like C5 [S1,S2] [S1,S2] [S1,S2] Can somebody help me how to achieve this in Spark data frame using Scala? 回答1: You might want to collect the distinct items from column 4 and put them in a List firstly, and then use withColumn to create a new column C5 by creating a udf that always return a constant list:

问题 I am getting started with Scala and I am replacing the deprecated JavaConversions library with JavaConverters . I have the following code: import scala.collection.JavaConversions._ new AMQP.BasicProperties.Builder() .contentType(message.contentType.map(_.toString).orNull) .contentEncoding(message.contentEncoding.orNull) .headers(message.headers) //<<<<--------------- I SEE THE ERROR ON THIS LINE (datatype of message.heads is Map[String, String] .deliveryMode(toDeliveryMode(message.mode))

问题 I'd like to extend scala.Stream . When I try, it tells me I can't, because I don't have the required method tailDefined . class S[T](s:Stream[T]) extends Stream[T] { } When I try this, it tells me tailDefined is protected: class S[T](s:Stream[T]) extends Stream[T] { def tailDefined = s.tailDefined } How do I get around this limitation and implement an extension of Stream ? 回答1: If you want to "add new methods" to Stream , use implicit classes: implicit class S[T](s:Stream[T]) { def method1 =

问题 val descrList = cursorReal.interfaceInfo.interfaces.map { case values => (values.ifIndex , values.ifName , values.ifType) } val ipAddressList = cursorReal.interfaceIpAndIndex.filter(x=> (!x.ifIpAddress.equalsIgnoreCase("0"))).map { case values => (values.ifIndex,values.ifIpAddress) } For instance, val descrList = List((12,"VoIP-Null0",1), (8,"FastEthernet6",6), (19,"Vlan11",53), (4,"FastEthernet2",6), (15,"Vlan1",53), (11,"GigabitEthernet0",6), (9,"FastEthernet7",6), (22,"Vlan20",53), (13,

问题 trait Account[T <: Account[T]] case class BrokerAccount(total:BigDecimal) extends Account[BrokerAccount] case class SavingsAccount(total:BigDecimal) extends Account[SavingsAccount] Below function declaration and invocation works fine. def foo1( xs: Array[T forSome { type T <: Account[T] }]):Array[T forSome { type T <: Account[T] }] = xs foo1(Array(BrokerAccount(100),SavingsAccount(50))) But below invocation gives compilation error. def foo2( xs: List[T forSome { type T <: Account[T] }]):List