val res9: String = scala.collection.immutable.$colon$colon
时间: 2024-04-21 22:26:44 浏览: 32
这样的输出结果通常是由于在Scala REPL中执行了一些操作,例如创建了一个列表或者调用了某个函数。`res9`是REPL中自动生成的一个变量名,表示上一个表达式的值,类型为`String`。`scala.collection.immutable.$colon$colon`表示Scala标准库中的一个不可变列表类型,类似于其他编程语言中的数组或链表。`$colon$colon`是Scala中用于表示列表头和尾的符号,它由两个冒号组成,其中一个在左边,一个在右边。
如果你想了解更多关于Scala中的列表类型的信息,可以查看[Scala官方文档中关于列表的介绍](https://docs.scala-lang.org/zh-cn/overviews/collections/lists.html)。
相关问题
override fun fillCompletionVariants(parameters: CompletionParameters, result: CompletionResultSet) { // 获取当前文件 val file: PsiFile = parameters.originalFile // 获取当前光标位置 val offset: Int = parameters.offset // 获取当前光标所在的元素 val element: PsiElement? = file.findElementAt(offset) // 如果当前光标所在的元素是字符串字面量 if (element is PsiLiteralExpression && element.value is String) { val text: String = element.value as String val prefix: String = text.substring(0, offset - element.textOffset) // 异步添加代码补全项 val future: CompletableFuture<List<String>> = CompletableFuture.supplyAsync { // 在这里使用异步网络搜索预测代码 searchPredictions(prefix) } // 当异步操作完成后,将结果添加到结果集中 future.thenAccept { predictions -> predictions.forEach { prediction -> result.addElement(LookupElementBuilder.create("$prefix $prediction")) } } } } // 使用异步网络搜索预测代码 private fun searchPredictions(prefix: String): List<String> { // 在这里实现异步网络搜索预测代码的逻辑 // 返回预测的代码列表 return listOf("Hello", "World") }这段代码转换成java代码
Here is the Java code equivalent of the given Kotlin code:
```
@Override
public void fillCompletionVariants(@NotNull CompletionParameters parameters, @NotNull CompletionResultSet result) {
// Get the current file
PsiFile file = parameters.getOriginalFile();
// Get the current cursor position
int offset = parameters.getOffset();
// Get the element at the current cursor position
PsiElement element = file.findElementAt(offset);
// If the element at the current cursor position is a string literal
if (element instanceof PsiLiteralExpression && element.getValue() instanceof String) {
String text = (String) element.getValue();
String prefix = text.substring(0, offset - element.getTextOffset());
// Asynchronously add code completion items
CompletableFuture<List<String>> future = CompletableFuture.supplyAsync(() -> {
// Implement the logic to search for predicted code asynchronously over the network here
// Return the list of predicted code
return searchPredictions(prefix);
});
// When the asynchronous operation is complete, add the results to the result set
future.thenAccept(predictions -> {
predictions.forEach(prediction -> {
result.addElement(LookupElementBuilder.create(prefix + " " + prediction));
});
});
}
}
// Implement the logic to search for predicted code asynchronously over the network here
// Return the list of predicted code
private List<String> searchPredictions(String prefix) {
// Implement the logic to search for predicted code asynchronously over the network here
// Return the list of predicted code
return Arrays.asList("Hello", "World");
}
```
完善如下代码:import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame,SparkSession} object First_Question { case class Student(name:String,age:String,sex:String) def main(args: Array[String]): Unit = { val spark: SparkSession = SparkSession .builder() .appName("First_Question") .master("local[*]") .getOrCreate() val rdd: RDD[String] = spark.sparkContext.parallelize(List("张三,20,男", "李四,22,男", "李婷,23,女","赵六,21,男")) val temp: RDD[Student] = rdd.map(s => { val split_rdd: Array[String] = s.split(",") Student(split_rdd(0), split_rdd(1), split_rdd(2)) }) import spark.implicits._ // DataFrame 源数据 val dataFrame: DataFrame = temp.toDF() spark.stop() } }
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, SparkSession}
object First_Question {
case class Student(name: String, age: Int, sex: String)
def main(args: Array[String]): Unit = {
val spark: SparkSession = SparkSession
.builder()
.appName("First_Question")
.master("local[*]")
.getOrCreate()
val rdd: RDD[String] = spark.sparkContext.parallelize(List("张三,20,男", "李四,22,男", "李婷,23,女", "赵六,21,男"))
val temp: RDD[Student] = rdd.map(s => {
val split_rdd: Array[String] = s.split(",")
Student(split_rdd(0), split_rdd(1).toInt, split_rdd(2))
})
import spark.implicits._
// DataFrame 源数据
val dataFrame: DataFrame = temp.toDF()
spark.stop()
}
}
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