scala

问题 I would like to wrap all the user defined functions in a scala project that return a certain type T , into a function that accepts a T and the function name as parameters. eg. given this function is in scope: def withMetrics[T](functionName: String)(f: => Try[T]): Try[T] = { f match { case _: Success[T] => println(s"send metric: success for $functionName") case _: Failure[T] => println(s"send metric: failure for $functionName") } f } the user can send metrics for their functions which return

问题 I would like to wrap all the user defined functions in a scala project that return a certain type T , into a function that accepts a T and the function name as parameters. eg. given this function is in scope: def withMetrics[T](functionName: String)(f: => Try[T]): Try[T] = { f match { case _: Success[T] => println(s"send metric: success for $functionName") case _: Failure[T] => println(s"send metric: failure for $functionName") } f } the user can send metrics for their functions which return

问题 I am trying to create a function, which takes a tuple of higher-kinded types and applies a function to the types within the higher-kinded types. In the example below, there is a trait Get[A] which is our higher-kinded type. There is also a tuple of Get's: (Get[String],Get[Int]) as well as function from (String,Int) => Person . Scala-3 has a Match-Type called InverseMap which converts the type (Get[String], Get[Int]) into what is essentially the type (String,Int). So the ultimate goal is to

问题 Suppose I have a trait Foo with several methods. I want to create a new trait which extends Foo but "wraps" each method call, for example with some print statement (in reality this will be something more complicated / I have a couple of distinct use cases in mind). trait Foo { def bar(x: Int) = 2 * x def baz(y: Int) = 3 * y } I can do this manually, by overriding each method. But this seems unnecessarily verbose (and all too easy to call the wrong super method): object FooWrapped extends

问题 The following code seems obvious enough to compile and run case class Pair(a: String, b: Int) val pairGen = Generic[Pair] object size extends Poly1 { implicit def caseInt = at[Int](x => 1) implicit def caseString = at[String](_.length) } def funrun(p: Pair) = { val hp: HList = pairGen.to(p) hp.map(size) } but the compiler says "could not find implicit value for parameter mapper". In my use case, I want to map over an HList to get and HList of String(s) and then convert the HList of String(s)

问题 The following code seems obvious enough to compile and run case class Pair(a: String, b: Int) val pairGen = Generic[Pair] object size extends Poly1 { implicit def caseInt = at[Int](x => 1) implicit def caseString = at[String](_.length) } def funrun(p: Pair) = { val hp: HList = pairGen.to(p) hp.map(size) } but the compiler says "could not find implicit value for parameter mapper". In my use case, I want to map over an HList to get and HList of String(s) and then convert the HList of String(s)

问题 I want my Spark application to read a table from DynamoDB, do stuff, then write the result in DynamoDB. Read the table into a DataFrame Right now, I can read the table from DynamoDB into Spark as a hadoopRDD and convert it to a DataFrame. However, I had to use a regular expression to extract the value from AttributeValue . Is there a better/more elegant way? Couldn't find anything in the AWS API. package main.scala.util import org.apache.spark.sql.SparkSession import org.apache.spark

问题 I want my Spark application to read a table from DynamoDB, do stuff, then write the result in DynamoDB. Read the table into a DataFrame Right now, I can read the table from DynamoDB into Spark as a hadoopRDD and convert it to a DataFrame. However, I had to use a regular expression to extract the value from AttributeValue . Is there a better/more elegant way? Couldn't find anything in the AWS API. package main.scala.util import org.apache.spark.sql.SparkSession import org.apache.spark

问题 I want my Spark application to read a table from DynamoDB, do stuff, then write the result in DynamoDB. Read the table into a DataFrame Right now, I can read the table from DynamoDB into Spark as a hadoopRDD and convert it to a DataFrame. However, I had to use a regular expression to extract the value from AttributeValue . Is there a better/more elegant way? Couldn't find anything in the AWS API. package main.scala.util import org.apache.spark.sql.SparkSession import org.apache.spark

问题 This question already has answers here : how to make saveAsTextFile NOT split output into multiple file? (9 answers) Closed 4 years ago . I am using Spark and Scala on my laptop at this moment. When I write an RDD to a file, the output is written to two files "part-00000" and "part-00001". How can I force Spark / Scala to write to one file ? My code is currently: myRDD.map(x => x._1 + "," + x._2).saveAsTextFile("/path/to/output") where I am removing the parentheses to write out key,value