業務邏輯是相同的：

給定一個由訪問次數/時間組成的String表達式，例如：“ 1 / 24h，1..3 / 3h，5 / *”

然后，結果應為以下字符串列表：

“ 1 / 24h”，
“ 1 / 3h”，“ 2 / 3h”，“ 3 / 3h”，
“ 5 / 1h”，“ 5 / 2h”，“ 5 / 3h”，“ 5 / 4h”，“ 5 / 5h”，直到“ 24 / 1h”

因此，需要解決2件事，即訪問點和時間點和星星。

我將使用Java 8，但我將向您展示使用Functional實現此邏輯與使用循環和ifs以傳統方式實現此邏輯之間的區別。

package com.marco; import java.util.ArrayList; import java.util.List; import java.util.function.Function; import java.util.stream.Collectors; import com.google.common.collect.ImmutableList; import com.google.common.collect.Lists; public class ExpressionConverter {private static final String COMMA = ",";private static final String SEPARATOR = "/";private final ImmutableList<Function<String, List<String>>> visitFunctions;private final ImmutableList<Function<String, List<String>>> timeFunctions;public ExpressionConverter(DotsVisitFunction dotsVisitFunction, StarVisitFunction starVisitFunction, StandardFunction standardFunction,StarTimeFunction starTimeFunction) {this.visitFunctions = ImmutableList.of(dotsVisitFunction, starVisitFunction, standardFunction);this.timeFunctions = ImmutableList.of(starTimeFunction, standardFunction);}public List<String> convertVisitTimeExpressionFunctional(String visitTimeExpression) {return Lists.newArrayList(visitTimeExpression.split(COMMA)).parallelStream().filter(it -> !it.isEmpty()).map(it -> interpretSingleExpressionFunctional(it)).flatMap(it -> it.parallelStream()).collect(Collectors.toList());}public List<String> convertVisitTimeExpressionTraditional(String visitTimeExpression) {List<String> result = new ArrayList<String>();for (String singleVisitTime : visitTimeExpression.split(COMMA)) {if (!singleVisitTime.isEmpty()) {result.addAll(interpretSingleVisitTimeExpressionTraditional(singleVisitTime));}}return result;}private List<String> interpretSingleExpressionFunctional(String singleExpression) {String visit = singleExpression.split(SEPARATOR)[0];String time = singleExpression.split(SEPARATOR)[1];List<String> result = Lists.newArrayList();visitFunctions.stream().map(it -> it.apply(visit)).flatMap(it -> it.stream()).forEach(visitIt -> {timeFunctions.stream().map(it -> it.apply(time)).flatMap(it -> it.stream()).forEach(timeIt -> {result.add(visitIt + SEPARATOR + timeIt);});});return result;}private List<String> interpretSingleVisitTimeExpressionTraditional(String singleExpression) {String visit = singleExpression.split(SEPARATOR)[0];String time = singleExpression.split(SEPARATOR)[1];List<String> result = Lists.newArrayList();List<String> visists = Lists.newArrayList();List<String> times = Lists.newArrayList();for (Function<String, List<String>> visitFunction : visitFunctions) {visists.addAll(visitFunction.apply(visit));}for (Function<String, List<String>> timeFunction : timeFunctions) {times.addAll(timeFunction.apply(time));}for (String visitIt : visists) {for (String timeIt : times) {result.add(visitIt + SEPARATOR + timeIt);}}return result;} }

如您所見，我們有2種公共方法和2種私有方法（功能方法和傳統方法）。

這是一個針對這兩種樣式的性能的簡單測試：

package com.marco; import static org.mockito.Matchers.any; import static org.mockito.Matchers.anyInt; import static org.mockito.Matchers.anyLong; import static org.mockito.Mockito.mock; import static org.mockito.Mockito.when; import java.math.BigDecimal; import java.util.Set; import org.junit.Before; import org.junit.Test; import com.google.common.collect.ImmutableSet; import com.google.common.collect.Sets; public class ExpressionConverterTest {private static final String EXPRESSIONS = 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Cache cache;private ExpressionConverter expressionConverter;@Beforepublic void init() {cache = mock(Cache.class);expressionConverter = new ExpressionConverter(new DotsVisitFunction(cache), new StarVisitFunction(cache), new StandardFunction(),new StarTimeFunction(cache));Set<Integer> totVisists = Sets.newHashSet();for (int i = 1; i <= 100; i++) {totVisists.add(i);}when(cache.getVisits(anyLong(), (BigDecimal) any(), anyInt())).thenReturn(ImmutableSet.copyOf(totVisists));}@Testpublic void testPerf() {long averageTraditional = 0l;long averageFuntional = 0l;for (int a = 0; a < 10; a++) {long start = System.currentTimeMillis();for (int i = 0; i < 1000; i++) {expressionConverter.convertVisitTimeExpressionTraditional(EXPRESSIONS);}System.out.println("Traditional java " + (System.currentTimeMillis() - start) + " ms");averageTraditional += (System.currentTimeMillis() - start);long start2 = System.currentTimeMillis();for (int i = 0; i < 1000; i++) {expressionConverter.convertVisitTimeExpressionFunctional(EXPRESSIONS);}System.out.println("Functional java " + (System.currentTimeMillis() - start2) + " ms");averageFuntional += (System.currentTimeMillis() - start2);}System.out.println("Average Traditional java : " + (averageTraditional / 10) + " ms");System.out.println("Average Functional java : " + (averageFuntional / 10) + " ms");} }

這是輸出：

Traditional java 1274 msFunctional java 773 msTraditional java 1054 msFunctional java 531 msTraditional java 961 msFunctional java 493 msTraditional java 948 msFunctional java 492 msTraditional java 949 msFunctional java 491 msTraditional java 958 msFunctional java 481 msTraditional java 1004 msFunctional java 474 msTraditional java 949 msFunctional java 471 msTraditional java 947 msFunctional java 475 msTraditional java 942 msFunctional java 472 msAverage Traditional java : 998 msAverage Functional java : 515 ms

功能是傳統的2倍。 為什么？

因此：parallelStream（）

Parallel Stream將利用您的多核系統將工作分解為多個任務。

因此，您可以將parallelStream（）放在任何地方，并用parallelStream（）替換所有stream（）方法嗎？

沒有！

如果在錯誤的地方使用parallelStream（）實際上會導致性能大大降低。

在使用此功能之前，應閱讀有關此參數的大量文章，但請記住，如果要使用parallelStream（），請確保在測量前后的性能！！！！

翻譯自: https://www.javacodegeeks.com/2014/08/java-8-functional-vs-traditional.html

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