Java 8的流:为什么并行流较慢?(Java 8's streams: why parallel stream is slower?)
我在玩Java 8的流,不能理解我获得的性能结果。
我有2个核心CPU(Intel i73520M),Windows 8 x64和64位Java 8更新5.我正在做简单的映射流/并行流Strings,发现并行版本有点慢。
当我运行这个代码:
String[] array = new String[1000000]; Arrays.fill(array, "AbabagalamagA"); Stream<String> stream = Arrays.stream(array); long time1 = System.nanoTime(); List<String> list = stream.map((x) -> x.toLowerCase()).collect(Collectors.toList()); long time2 = System.nanoTime(); System.out.println((time2 - time1) / 1000000f);
...我得到了600左右的结果。这个版本使用并行流:
String[] array = new String[1000000]; Arrays.fill(array, "AbabagalamagA"); Stream<String> stream = Arrays.stream(array).parallel(); long time1 = System.nanoTime(); List<String> list = stream.map((x) -> x.toLowerCase()).collect(Collectors.toList()); long time2 = System.nanoTime(); System.out.println((time2 - time1) / 1000000f);
给我一些约900个。
考虑到我有2个CPU内核,并行版本不应该更快, 有人可以给我一个提示,为什么并行版本较慢?
I am playing with Java 8's streams and cannot understand the performance results I am getting. I have 2 core CPU (Intel i73520M), Windows 8 x64, and 64-bit Java 8 update 5. I am doing simple map over stream/parallel stream of Strings and found that parallel version is somewhat slower.
Function<Stream<String>, Long> timeOperation = (Stream<String> stream) -> { long time1 = System.nanoTime(); final List<String> list = stream .map(String::toLowerCase) .collect(Collectors.toList()); long time2 = System.nanoTime(); return time2 - time1; }; Consumer<Stream<String>> printTime = stream -> System.out.println(timeOperation.apply(stream) / 1000000f); String[] array = new String[1000000]; Arrays.fill(array, "AbabagalamagA"); printTime.accept(Arrays.stream(array)); // prints around 600 printTime.accept(Arrays.stream(array).parallel()); // prints around 900
Shouldn't the parallel version be faster, considering the fact that I have 2 CPU cores? Could someone give me a hint why parallel version is slower?
原文:https://stackoverflow.com/questions/23170832
最满意答案
切勿在一列中存储多个值。 规范化数据库中的所有列值都应该是原子的(单值)。
articles
和categories
之间的关系是多对多的 。 这意味着您应该有一个单独的表,例如article_category (article_id, category_id)
,每个文章所包含的每个类别都包含一行。Never store multiple values in one column. All column values in a normalized database should be atomic (single-valued).
The relationship between
articles
andcategories
is many-to-many. That means you should have a separate table, such asarticle_category (article_id, category_id)
containing one row for each category each article is in.
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