java-8

问题 I wrote some simple code like below. This class works fine without any errors. public class Test { public static void main(String[] args) { List<Integer> intList = IntStream.of(1,2,3,4,5,6,7,8,9,10).boxed().collect(Collectors.toList()); int value = intList.stream().max(Integer::compareTo).get(); //int value = intList.stream().max(<Comparator<? super T> comparator type should pass here>).get(); System.out.println("value :"+value); } } As the code comment shows the max() method should pass an

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 4 years ago . I have to convert Java 8 code to JavaScript (one way, once in a lifetime). To speed things up I want to automate as much as possible and use the test suite afterwards to fix all remaining issues. I wonder what the difference between Java 8 lambdas and JavaScript (functions) are?

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 4 years ago . I have to convert Java 8 code to JavaScript (one way, once in a lifetime). To speed things up I want to automate as much as possible and use the test suite afterwards to fix all remaining issues. I wonder what the difference between Java 8 lambdas and JavaScript (functions) are?

问题 I'm using spark-sql-2.4.1v, and I'm trying to do find quantiles, i.e. percentile 0, percentile 25, etc, on each column of my given data. My dataframe df : +----+---------+-------------+----------+-----------+--------+ | id| date| revenue|con_dist_1| con_dist_2| state | +----+---------+-------------+----------+-----------+--------+ | 10|1/15/2018| 0.010680705| 6|0.019875458| TX | | 10|1/15/2018| 0.006628853| 4|0.816039063| AZ | | 10|1/15/2018| 0.01378215| 4|0.082049528| TX | | 10|1/15/2018| 0

问题 I'm using spark-sql-2.4.1v, and I'm trying to do find quantiles, i.e. percentile 0, percentile 25, etc, on each column of my given data. My dataframe df : +----+---------+-------------+----------+-----------+--------+ | id| date| revenue|con_dist_1| con_dist_2| state | +----+---------+-------------+----------+-----------+--------+ | 10|1/15/2018| 0.010680705| 6|0.019875458| TX | | 10|1/15/2018| 0.006628853| 4|0.816039063| AZ | | 10|1/15/2018| 0.01378215| 4|0.082049528| TX | | 10|1/15/2018| 0

问题 I have a scenario in spark. Have to partition data frame. resultant should be processed by each partition at a time. List<String> data = Arrays.asList("con_dist_1", "con_dist_2", "con_dist_3", "con_dist_4", "con_dist_5", "con_dist_6"); Dataset<Row> codes = sparkSession.createDataset(data, Encoders.STRING()); Dataset<Row> partitioned_codes = codes.repartition(col("codes")); // I need to paritition it dues to functional requirement partitioned_codes.foreachPartition(itr -> { if (itr.hasNext())

问题 I have a scenario in spark. Have to partition data frame. resultant should be processed by each partition at a time. List<String> data = Arrays.asList("con_dist_1", "con_dist_2", "con_dist_3", "con_dist_4", "con_dist_5", "con_dist_6"); Dataset<Row> codes = sparkSession.createDataset(data, Encoders.STRING()); Dataset<Row> partitioned_codes = codes.repartition(col("codes")); // I need to paritition it dues to functional requirement partitioned_codes.foreachPartition(itr -> { if (itr.hasNext())

问题 HI I have a class below which has functions to pull data from DataServices. I want to always call a method which prints a time at which the method was called and a method which prints time after the method was executed . Basically after every method is executed i want to know how much time it took to execute each method. Is there a generic way to do it instead of adding time at start and end of every function? class ABC { void getAllProducts(){ // some logic } void getAllClients(){ // some

问题 I love Java 8 streams. They are intuitive, powerful and elegant. But they do have one major drawback IMO: they make debugging much harder (unless you can solve your problem by just debugging lambda expressions, which is answered here). Consider the following two equivalent fragments: int smallElementBitCount = intList.stream() .filter(n -> n < 50) .mapToInt(Integer::bitCount) .sum(); and int smallElementBitCount = 0; for (int n: intList) { if (n < 50) { smallElementBitCount += Integer

问题 Here's what I've got so far: Optional<Foo> firstChoice = firstChoice(); Optional<Foo> secondChoice = secondChoice(); return Optional.ofNullable(firstChoice.orElse(secondChoice.orElse(null))); This strikes me as both hideous and wasteful. If firstChoice is present I am needlessly computing secondChoice. There's also a more efficient version: Optional<Foo> firstChoice = firstChoice(); if(firstChoice.isPresent()) { return firstChoice; } else { return secondChoice(); } Here I can't chain some