怎么使用HashMap的循环
本篇内容介绍了“怎么使用HashMap的循环”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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先来看看每种遍历的方式：
在for循环中使用entries实现Map的遍历
public static void forEachEntries() { for (Map.Entry entry : map.entrySet()) { String mapKey = entry.getKey(); String mapValue = entry.getValue(); } } 在for循环中遍历key
public static void forEachKey() { for (String key : map.keySet()) { String mapKey = key; String mapValue = map.get(mapKey); } }在for循环中遍历value
public static void forEachValues() { for (String key : map.values()) { String val = key; } }Iterator遍历
public static void forEachIterator() { Iterator> entries = map.entrySet().iterator(); while (entries.hasNext()) { Entry entry = entries.next(); String key = entry.getKey(); String value = entry.getValue(); } } forEach jdk1.8遍历
public static void forEach() { map.forEach((key, val) -> { String key1 = key; String value = val; }); }Stream jdk1.8遍历
map.entrySet().stream().forEach((entry) -> { String key = entry.getKey(); String value = entry.getValue(); });Streamparallel jdk1.8遍历
public static void forEachStreamparallel() { map.entrySet().parallelStream().forEach((entry) -> { String key = entry.getKey(); String value = entry.getValue(); }); }以上就是常见的对于map的一些遍历的方式，下面我们来写个测试用例来看下这些遍历方式，哪些是效率最好的。下面测试用例是基于JMH来测试的 首先引入pom
org.openjdk.jmh jmh-core 1.23 org.openjdk.jmh jmh-generator-annprocess 1.23 provided
关于jmh测试如可能会影响结果的一些因素这里就不详细介绍了，可以参考文末的第一个链接写的非常详细。以及测试用例为什么要这么写(都是为了消除JIT对测试代码的影响)这是参照官网的链接：编写测试代码如下：
package com.workit.autoconfigure.autoconfigure.controller; import org.openjdk.jmh.annotations.*; import org.openjdk.jmh.infra.Blackhole; import org.openjdk.jmh.results.format.ResultFormatType; import org.openjdk.jmh.runner.Runner; import org.openjdk.jmh.runner.RunnerException; import org.openjdk.jmh.runner.options.Options; import org.openjdk.jmh.runner.options.OptionsBuilder; import java.util.HashMap; import java.util.Iterator; import java.util.Map; import java.util.Map.Entry; import java.util.UUID; import java.util.concurrent.TimeUnit; /** * @author:公众号：java金融 * @Date: * @Description:微信搜一搜【java金融】回复666 */ @State(Scope.Thread) @Warmup(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS) @Measurement(iterations = 5, time = 1, timeUnit = TimeUnit.SECONDS) @Fork(1) @BenchmarkMode(Mode.AverageTime) @OutputTimeUnit(TimeUnit.NANOSECONDS) public class InstructionsBenchmark { public static void main(String[] args) throws RunnerException { Options opt = new OptionsBuilder().include(InstructionsBenchmark.class.getSimpleName()).result("result.json").resultFormat(ResultFormatType.JSON).build(); new Runner(opt).run(); } static final int BASE = 42; static int add(int key,int val) { return BASE + key +val; } @Param({"1", "10", "100", "1000","10000","100000"}) int size; private static Map map; // 初始化方法，在全部Benchmark运行之前进行 @Setup(Level.Trial) public void init() { map = new HashMap<>(size); for (int i = 0; i < size; i++) { map.put(i, i); } } /** * 在for循环中使用entries实现Map的遍历： */ @Benchmark public static void forEachEntries(Blackhole blackhole) { for (Map.Entry entry : map.entrySet()) { Integer mapKey = entry.getKey(); Integer mapValue = entry.getValue(); blackhole.consume(add(mapKey,mapValue)); } } /** * 在for循环中遍历key */ @Benchmark public static StringBuffer forEachKey(Blackhole blackhole) { StringBuffer stringBuffer = new StringBuffer(); for (Integer key : map.keySet()) { // Integer mapValue = map.get(key); blackhole.consume(add(key,key)); } return stringBuffer; } /** * 在for循环中遍历value */ @Benchmark public static void forEachValues(Blackhole blackhole) { for (Integer key : map.values()) { blackhole.consume(add(key,key)); } } /** * Iterator遍历； */ @Benchmark public static void forEachIterator(Blackhole blackhole) { Iterator> entries = map.entrySet().iterator(); while (entries.hasNext()) { Entry entry = entries.next(); Integer key = entry.getKey(); Integer value = entry.getValue(); blackhole.consume(add(key,value)); } } /** * forEach jdk1.8遍历 */ @Benchmark public static void forEachLamada(Blackhole blackhole) { map.forEach((key, value) -> { blackhole.consume(add(key,value)); }); } /** * forEach jdk1.8遍历 */ @Benchmark public static void forEachStream(Blackhole blackhole) { map.entrySet().stream().forEach((entry) -> { Integer key = entry.getKey(); Integer value = entry.getValue(); blackhole.consume(add(key,value)); }); } @Benchmark public static void forEachStreamparallel(Blackhole blackhole) { map.entrySet().parallelStream().forEach((entry) -> { Integer key = entry.getKey(); Integer value = entry.getValue(); blackhole.consume(add(key,value)); }); } } 运行结果如下：「注：运行环境idea 2019.3，jdk1.8，windows7 64位。」
Benchmark (size) Mode Cnt Score Error Units InstructionsBenchmark.forEachEntries 1 avgt 5 10.021 ± 0.224 ns/op InstructionsBenchmark.forEachEntries 10 avgt 5 71.709 ± 2.537 ns/op InstructionsBenchmark.forEachEntries 100 avgt 5 738.873 ± 12.132 ns/op InstructionsBenchmark.forEachEntries 1000 avgt 5 7804.431 ± 136.635 ns/op InstructionsBenchmark.forEachEntries 10000 avgt 5 88540.345 ± 14915.682 ns/op InstructionsBenchmark.forEachEntries 100000 avgt 5 1083347.001 ± 136865.960 ns/op InstructionsBenchmark.forEachIterator 1 avgt 5 10.675 ± 2.532 ns/op InstructionsBenchmark.forEachIterator 10 avgt 5 73.934 ± 4.517 ns/op InstructionsBenchmark.forEachIterator 100 avgt 5 775.847 ± 198.806 ns/op InstructionsBenchmark.forEachIterator 1000 avgt 5 8905.041 ± 1294.618 ns/op InstructionsBenchmark.forEachIterator 10000 avgt 5 98686.478 ± 10944.570 ns/op InstructionsBenchmark.forEachIterator 100000 avgt 5 1045309.216 ± 36957.608 ns/op InstructionsBenchmark.forEachKey 1 avgt 5 18.478 ± 1.344 ns/op InstructionsBenchmark.forEachKey 10 avgt 5 76.398 ± 12.179 ns/op InstructionsBenchmark.forEachKey 100 avgt 5 768.507 ± 23.892 ns/op InstructionsBenchmark.forEachKey 1000 avgt 5 11117.896 ± 1665.021 ns/op InstructionsBenchmark.forEachKey 10000 avgt 5 84871.880 ± 12056.592 ns/op InstructionsBenchmark.forEachKey 100000 avgt 5 1114948.566 ± 65582.709 ns/op InstructionsBenchmark.forEachLamada 1 avgt 5 9.444 ± 0.607 ns/op InstructionsBenchmark.forEachLamada 10 avgt 5 76.125 ± 5.640 ns/op InstructionsBenchmark.forEachLamada 100 avgt 5 861.601 ± 98.045 ns/op InstructionsBenchmark.forEachLamada 1000 avgt 5 7769.714 ± 1663.914 ns/op InstructionsBenchmark.forEachLamada 10000 avgt 5 73250.238 ± 6032.161 ns/op InstructionsBenchmark.forEachLamada 100000 avgt 5 836781.987 ± 72125.745 ns/op InstructionsBenchmark.forEachStream 1 avgt 5 29.113 ± 3.275 ns/op InstructionsBenchmark.forEachStream 10 avgt 5 117.951 ± 13.755 ns/op InstructionsBenchmark.forEachStream 100 avgt 5 1064.767 ± 66.869 ns/op InstructionsBenchmark.forEachStream 1000 avgt 5 9969.549 ± 342.483 ns/op InstructionsBenchmark.forEachStream 10000 avgt 5 93154.061 ± 7638.122 ns/op InstructionsBenchmark.forEachStream 100000 avgt 5 1113961.590 ± 218662.668 ns/op InstructionsBenchmark.forEachStreamparallel 1 avgt 5 65.466 ± 5.519 ns/op InstructionsBenchmark.forEachStreamparallel 10 avgt 5 2298.999 ± 721.455 ns/op InstructionsBenchmark.forEachStreamparallel 100 avgt 5 8270.759 ± 1801.082 ns/op InstructionsBenchmark.forEachStreamparallel 1000 avgt 5 16049.564 ± 1972.856 ns/op InstructionsBenchmark.forEachStreamparallel 10000 avgt 5 69230.849 ± 12169.260 ns/op InstructionsBenchmark.forEachStreamparallel 100000 avgt 5 638129.559 ± 14885.962 ns/op InstructionsBenchmark.forEachValues 1 avgt 5 9.743 ± 2.770 ns/op InstructionsBenchmark.forEachValues 10 avgt 5 70.761 ± 16.574 ns/op InstructionsBenchmark.forEachValues 100 avgt 5 745.069 ± 329.548 ns/op InstructionsBenchmark.forEachValues 1000 avgt 5 7772.584 ± 1702.295 ns/op InstructionsBenchmark.forEachValues 10000 avgt 5 74063.468 ± 23752.678 ns/op InstructionsBenchmark.forEachValues 100000 avgt 5 994057.370 ± 279310.867 ns/op
我们可以发现，数据量较小的时候forEachEntries和forEachIterator、以及lamada循环效率都差不多forEachStreamarallel的效率反而较低，只有当数据量达到10000以上parallelStream的优势就体现出来了。所以平时选择使用哪种循环方式的时候没必要太纠结哪一种方式，其实每种方式之间的效率还是微乎其微的。选择适合自己的就好。
“怎么使用HashMap的循环”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注创新互联网站，小编将为大家输出更多高质量的实用文章！
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