Java 8 Stream flatMap和group by code编译器错误

问题描述

// given a set of Item objects, group them by the managers of creator and owners
Map<String, List<Item>> managersItems =
    itemSet.parallelStream().flatMap(item -> {
        // get the list of the creator and owners
        List<String> users = new ArrayList();
        users.add(item.getCreator());
        users.addAll(item.getOwners());
        return Stream.of(users.toArray(new String[] {})).map(user -> {
            LdapUserInfo ldapUser = LdapUserInfoFactory.create(user);
            String manager = ldapUser.getManager();
            return new AbstractMap.SimpleImmutableEntry<String, Item(manager, item);
        });
    }).collect(
        Collectors.groupingBy(Map.Entry::getKey, Collectors.mapping(Map.Entry::getValue, Collectors.toList())));

此代码在Eclipse Mars中编译良好，但在Eclipse Luna中获得以下错误：

This code compiles fine in Eclipse Mars, but gets the following eror in Eclipse Luna:

如果我没有分配使用 Map< String，List< Item>>返回 Map 在Eclipse Luna中的managersItem = ，错误在 Map.Entry :: getKey 和 Map.Entry :: getValue 带消息的语句：

If I do not assign the returned to a Map with Map<String, List<Item>> managersItem = in Eclipse Luna, the error is at Map.Entry::getKey and Map.Entry::getValue statement with message:

什么我做错了吗？

推荐答案

你没有做错任何事.Eclipse编译器存在导致这些问题的类型推断问题。如果Luna兼容性很重要，则必须向lambda表达式添加显式类型。例如，尝试 Map.Entry ::< String，Item> getKey

You didn't do anything wrong. Eclipse compiler has problems with type inference that causes these issues. If Luna compatibility is important, you will have to add explicit types to lambda expressions. Try, for example, Map.Entry::<String,Item>getKey

另一方面，没有必要将 List 转换为数组来流式传输。你可以直接调用 users.stream（）。但是甚至不需要创建 List 。你可以使用 Stream.concat （Stream.of（item.getCreator（）），item.getOwners（）。stream（））而不是（g咆哮，这有点笨拙）。

On another note, it's not necessary to convert a List to array to stream it. You can directly call users.stream(). But even creating the List isn't necessary. You can use Stream.concat(Stream.of(item.getCreator()), item.getOwners().stream()) instead (granted, it's a bit unwieldy).

最后（最重要的是），避免使用带阻塞代码的 parallelStream ，例如在外部查找数据系统。并行流旨在处理CPU绑定的任务。

Finally (and most importantly), avoid using parallelStream with blocking code, such as looking up data in an external system. Parallel streams are designed to handle CPU-bound tasks.

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