scala

问题 I am having a hard time understanding what this piece of code does exactly: import scala.language.implicitConversions implicit def list2ordered[A](x: List[A]) (implicit elem2ordered: A => Ordered[A]): Ordered[List[A]] = new Ordered[List[A]] { //replace with a more useful implementation def compare(that: List[A]): Int = 1 } It comes from the Scala Tour and it is in the section "Implicit Conversions". I understand that list2ordered takes a List[A] that comes from the left hand side of List(1, 2

问题 I am trying to solve the reconstruct itinerary problem (https://leetcode.com/problems/reconstruct-itinerary/) in Scala using functional approach. Java solution works but Scala doesn't. One reason I found out was the hashmap is being updated and every iteration has the latest hashmap (even when popping from recursion) which is weird. Here is the solution in Java: import java.util.ArrayList; import java.util.HashMap; import java.util.LinkedList; import java.util.List; import java.util.Map;

问题 I am trying to update a json value present within a json using Scala play framework.Instead of updating the value it is appending the value. val newJsonString = """{"P123": 25}""" val jsonStringAsJsValue = Json.parse("""{"counter_holders": {"Peter": 25}}""") //jsonStringAsJsValue: play.api.libs.json.JsValue = {"counter_holders":{"Peter":25}} val jsonTransformer = (__ \"counter_holders" ).json.update(__.read[JsValue].map{o => Json.parse(newJsonString)}) jsonStringAsJsValue.transform

问题 I am trying to update a json value present within a json using Scala play framework.Instead of updating the value it is appending the value. val newJsonString = """{"P123": 25}""" val jsonStringAsJsValue = Json.parse("""{"counter_holders": {"Peter": 25}}""") //jsonStringAsJsValue: play.api.libs.json.JsValue = {"counter_holders":{"Peter":25}} val jsonTransformer = (__ \"counter_holders" ).json.update(__.read[JsValue].map{o => Json.parse(newJsonString)}) jsonStringAsJsValue.transform

问题 I am new to hive and spark and am trying to figure out a way to access tables in hive to manipulate and access the data. How can it be done? 回答1: in spark < 2.0 val sc = new SparkContext(conf) val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc) val myDataFrame = sqlContext.sql("select * from mydb.mytable") in later versions of spark, use SparkSession: SparkSession is now the new entry point of Spark that replaces the old SQLContext and HiveContext. Note that the old SQLContext and

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 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 trait JsonOps[J] { type ObjectFields def partitionObjectFields(fields: ObjectFields, fieldNames: List[String]): (ObjectFields, ObjectFields) } def compilerNoLikey[J](stuff: ops.ObjectFields)(implicit ops:JsonOps[J]) = {} def compilerLikey[J](stuff: Any)(implicit ops:JsonOps[J]) = { val stuff2 = stuff.asInstanceOf[ops.ObjectFields] } You can see my intent here. I define a type in JsonOps to encapsulate a structure dependant on J. Then later when I want to use this, I have a function that

问题 What is the semantic difference between size and sizeIs? For example, List(1,2,3).sizeIs > 1 // true List(1,2,3).size > 1 // true Luis mentions in a comment that ...on 2.13+ one can use sizeIs > 1 which will be more efficient than size > 1 as the first one does not compute all the size before returning Add size comparison methods to IterableOps #6950 seems to be the pull request that introduced it. Reading the scaladoc Returns a value class containing operations for comparing the size of this

问题 While running my spark-submit code, I get this error when I execute. Scala file which performs joins. I am just curious to know what is this TreeNodeException error. Why do we have this error? Please share your ideas on this TreeNodeException error: Exception in thread “main” org.apache.spark.sql.catalyst.errors.package$TreeNodeException: execute, tree: 回答1: I encountered this exception when joining dataframes too Exception in thread “main” org.apache.spark.sql.catalyst.errors.package