首先應該知道這個單詞怎麼讀
使用partition()方法先引入jar包
<dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>25.0-jre</version>
</dependency>
1、將list(當然,也可以是其他集合)拆分成多份,常見的場景,比如批量執行sql、分批推送消息等。
(1)guava 實現(按照固定大小分片)
public static void main(String[] args) {
List<Integer> numLists = Lists.newArrayList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 1, 2, 3, 4, 4, 5, 6, 7, 78, 8, 9, 0,45,67,87);
Collections.shuffle(numLists);
List<List<Integer>> lists = Lists.partition(numLists, 6);
Random random = new Random();
lists.parallelStream().forEach(x -> {
System.out.println(x.toString());
});
(2)使用apache.commons.collection實現
import org.apache.commons.collections4.ListUtils;
public static void main(String[] args) {
List<Integer> intList = Lists.newArrayList(1, 2, 3, 4, 5, 6, 7, 8);
List<List<Integer>> lists = ListUtils.partition(intList, 3);
lists.parallelStream().forEach(x-> System.out.println(x.toString()));
}
2、平均切分集合(借鑑文章地址 :https://www.jianshu.com/p/d0b6d686677d)
/**
* 將一個list均分成n個list,主要通過偏移量來實現的
*
* @param source
* @return
*/
public static <T> List<List<T>> averageAssignList(List<T> source, int n) {
List<List<T>> result = new ArrayList<>();
int remaider = source.size() % n; //(先計算出餘數)
int number = source.size() / n; //然後是商
int offset = 0;//偏移量
for (int i = 0; i < n; i++) {
List<T> value = null;
if (remaider > 0) {
value = source.subList(i * number + offset, (i + 1) * number + offset + 1);
remaider--;
offset++;
} else {
value = source.subList(i * number + offset, (i + 1) * number + offset);
}
result.add(value);
}
return result;
}
pubic static void main(String[] args){
System.out.println("平均分list集合....");
List<List<Integer>> assignList = averageAssignList(numLists, 5);
assignList.parallelStream().forEach(x-> System.out.println(x.toString()));
}
3、使用Java8 stream流 partition by , partitioningBy是一種特殊的分組,只會分成兩組
System.out.println("使用Java8 stream流 partition by , partitioningBy是一種特殊的分組,只會分成兩組");
List<Integer> nums = Lists.newArrayList(1,1,8,2,3,4,5,6,7,9,10);
Map<Boolean,List<Integer>> numMap= nums.stream().collect(Collectors.partitioningBy(num -> num > 5));
System.out.println(numMap);