[Solved] Equivalent Scala code for Java code
To cast to a different type in Scala use: session.openChannel(“sftp”).asInstanceOf[ChannelSftp] 2 solved Equivalent Scala code for Java code
To cast to a different type in Scala use: session.openChannel(“sftp”).asInstanceOf[ChannelSftp] 2 solved Equivalent Scala code for Java code
list.zipWithIndex.foldLeft(0) { (sum, pair) => val (elem, index) = pair sum + elem * elem * (length – index) } 5 solved minimum or no use of loops in scala for Rotated Hyper-Ellipsoid Function
What I think is happening here is that Spark serializes functions to send them over the wire. And that because your function (the one you’re passing to map) calls the accessor param_user_minimal_rating_count of object odbscan, the entire object odbscan will need to get serialized and sent along with it. Deserializing and then using that deserialized … Read more
You’re looking for the transpose method: scala> val in = List(List(1, 2, 3), List(4, 5, 6), List(7, 8, 9)) in: List[List[Int]] = List(List(1, 2, 3), List(4, 5, 6), List(7, 8, 9)) scala> in.transpose res0: List[List[Int]] = List(List(1, 4, 7), List(2, 5, 8), List(3, 6, 9)) scala> in.transpose.map(_.sum) res1: List[Int] = List(12, 15, 18) solved How … Read more
Your method should return an Int instead of Unit. To print elements iteratively, just insert a println underneath case head :: tail: def multiply(list: List[Int]): Int = list match { case Nil => 1 case n :: rest => println(n) n * multiply(rest) } multiply(List(1,2,3,4)) // 1 // 2 // 3 // 4 // res1: … Read more
It is really simple: yourMap.mapValues(_.toMap) scala> :paste // Entering paste mode (ctrl-D to finish) Map( 0 -> Array((1,1), (2,1)), 1 -> Array((2,1), (3,1), (0,1)), 2 -> Array((4,1), (0,1), (1,1)), 3 -> Array((1,1)), 4 -> Array((5,1), (2,1)), 5 -> Array((4,1)) ) // Exiting paste mode, now interpreting. res0: scala.collection.immutable.Map[Int,Array[(Int, Int)]] = Map(0 -> Array((1,1), (2,1)), 5 … Read more
Take a look at the full signature for flatten: def flatten[B](implicit asTraversable: (A) ⇒ GenTraversableOnce[B]): List[B] As you can see, flatten takes an implicit parameter. That parameter provides the rules for how to flatten the given collection types. If the compiler can’t find an implicit in scope then it can be provided explicitly. flatten can … Read more
Shown here is a PySpark solution. You can use conditional aggregation with max(when…)) to get the necessary difference of ranks with the first ‘PD’ row. After getting the difference, use a when… to null out rows with negative ranks as they all occur after the first ‘PD’ row. # necessary imports w1 = Window.partitionBy(df.id).orderBy(df.svc_dt) df … Read more
Demonstrating what the commenter said: scala> import collection.JavaConverters._ import collection.JavaConverters._ scala> val js = (1 to 10).toSet.asJava js: java.util.Set[Int] = [5, 10, 1, 6, 9, 2, 7, 3, 8, 4] scala> def f(is: collection.mutable.Set[Int]) = is.size f: (is: scala.collection.mutable.Set[Int])Int scala> def g(js: java.util.Set[Int]) = f(js.asScala) g: (js: java.util.Set[Int])Int scala> g(js) res0: Int = 10 3 … Read more
Spark – java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.List$SerializationProxy solved Spark – java.lang.ClassCastException: cannot assign instance of scala.collection.immutable.List$SerializationProxy
You basically have two steps here: First is exploding the arrays (using the explode functions) to get a row for each value in the array, then fixing each element. You do not have the schema here so the internal structure of each element in the array is not clear, however, I would assume it is … Read more
Check below code. scala> val vowels= Set(‘a’,’e’,’i’,’o’,’u’) // List Of vowels oval: Seq[Char] = List(a, e, i, o, u) scala> val data = “BangaLore” // Value data: String = BangaLore scala> data.toLowerCase.map(c => if(vowels(c)) ‘.’ else c) res17: String = b.ng.l.r. 4 solved searching for list of vowels and replacing the vowels with dot (.) … Read more
The time difference (in milliseconds) can be obtained using: ChronoUnit.MILLIS .between(Instant.parse(“2018-07-26T16:00:17.163Z”), Instant.now()) Instant.now() will be the current UTC time, and your input date is UTC time, which makes the diff sensible. Regardless of the current time zone, the difference will be consistent. You can change the time unit accordingly (such as ChronoUnit.HOURS). 0 solved How … Read more
if you are looking for swapping column 3rd with 4th, split with , construct new List with swapped columns concat List to get string example, scala> val list = List(“banana,QS,1,0,0”, “apple,EE,1,2,1”, “peas,US,1,4,4”) list: List[String] = List(banana,QS,1,0,0, apple,EE,1,2,1, peas,US,1,4,4) scala> list.map(_.split(“,”)).map(elem => List(elem(0), elem(1), elem(3), elem(2), elem(4)).mkString(“,”)) res0: List[String] = List(banana,QS,0,1,0, apple,EE,2,1,1, peas,US,4,1,4) solved change a … Read more
Turns out it’s easier to do df1.except(df2) and then join the results with df1 to get all the duplicates. Full code: def exceptAllCustom(df1: DataFrame, df2: DataFrame): DataFrame = { val except = df1.except(df2) val columns = df1.columns val colExpr: Column = df1(columns.head) <=> except(columns.head) val joinExpression = columns.tail.foldLeft(colExpr) { (colExpr, p) => colExpr && df1(p) … Read more