Java8的groupingBy实现集合的分组，类似Mysql的group by分组功能，注意得到的是一个map
对集合按照单个属性分组、分组计数、排序
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List<String> items =
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Arrays.asList(
"apple",
"apple",
"banana",
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"apple",
"orange",
"banana",
"papaya");
-
-
// 分组
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Map<String, List<String>> result1 = items.stream().collect(
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Collectors.groupingBy(
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Function.identity()
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)
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);
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//{papaya=[papaya], orange=[orange], banana=[banana, banana], apple=[apple, apple, apple]}
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System.out.println(result1);
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-
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// 分组计数
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Map<String, Long> result2 = items.stream().collect(
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Collectors.groupingBy(
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Function.identity(), Collectors.counting()
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)
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);
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// {papaya=1, orange=1, banana=2, apple=3}
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System.out.println(result2);
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Map<String, Long> finalMap = new LinkedHashMap<>();
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-
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//分组, 计数和排序
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result2.entrySet().stream()
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.sorted(Map.Entry.<String, Long>comparingByValue().reversed())
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.forEachOrdered(e -> finalMap.put(e.getKey(), e.getValue()));
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// {apple=3, banana=2, papaya=1, orange=1}
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System.out.println(finalMap);
集合按照多个属性分组
1.多个属性拼接出一个组合属性
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public static void main(String[] args) {
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User user1 =
new User(
"zhangsan",
"beijing",
10);
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User user2 =
new User(
"zhangsan",
"beijing",
20);
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User user3 =
new User(
"lisi",
"shanghai",
30);
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List<User> list =
new ArrayList<User>();
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list.add(user1);
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list.add(user2);
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list.add(user3);
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Map<String, List<User>> collect = list.stream().collect(Collectors.groupingBy(e -> fetchGroupKey(e)));
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//{zhangsan#beijing=[User{age=10, name='zhangsan', address='beijing'}, User{age=20, name='zhangsan', address='beijing'}],
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// lisi#shanghai=[User{age=30, name='lisi', address='shanghai'}]}
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System.out.println(collect);
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}
-
-
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private static String fetchGroupKey(User user){
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return user.getName() +
"#"+ user.getAddress();
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}
2.嵌套调用groupBy
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User user1 =
new User(
"zhangsan",
"beijing",
10);
-
User user2 =
new User(
"zhangsan",
"beijing",
20);
-
User user3 =
new User(
"lisi",
"shanghai",
30);
-
List<User> list =
new ArrayList<User>();
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list.add(user1);
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list.add(user2);
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list.add(user3);
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Map<String, Map<String, List<User>>> collect
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= list.stream().collect(
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Collectors.groupingBy(
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User::getAddress, Collectors.groupingBy(User::getName)
-
)
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);
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System.out.println(collect);
3. 使用Arrays.asList
我有一个与Web访问记录相关的域对象列表。这些域对象可以扩展到数千个。
我没有资源或需求将它们以原始格式存储在数据库中，因此我希望预先计算聚合并将聚合的数据放在数据库中。
我需要聚合在5分钟窗口中传输的总字节数，如下面的sql查询
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select
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round(request_timestamp,
'5')
as window,
--round timestamp to the nearest 5 minute
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cdn,
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isp,
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http_result_code,
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transaction_time,
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sum(bytes_transferred)
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from web_records
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group
by
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round(request_timestamp,
'5'),
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cdn,
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isp,
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http_result_code,
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transaction_time
在java 8中，我当前的第一次尝试是这样的，我知道这个解决方案类似于Group by multiple field names in java 8
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Map<Date, Map<String, Map<String, Map<String, Map<String, Integer>>>>>>> aggregatedData =
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webRecords
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.stream()
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.collect(Collectors.groupingBy(WebRecord::getFiveMinuteWindow,
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Collectors.groupingBy(WebRecord::getCdn,
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Collectors.groupingBy(WebRecord::getIsp,
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Collectors.groupingBy(WebRecord::getResultCode,
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Collectors.groupingBy(WebRecord::getTxnTime,
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Collectors.reducing(
0,
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WebRecord::getReqBytes(),
-
Integer::sum)))))));
这是可行的，但它是丑陋的，所有这些嵌套的地图是一个噩梦！要将地图“展平”或“展开”成行，我必须这样做
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for (Date window : aggregatedData.keySet()) {
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for (String cdn : aggregatedData.get(window).keySet()) {
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for (String isp : aggregatedData.get(window).get(cdn).keySet()) {
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for (String resultCode : aggregatedData.get(window).get(cdn).get(isp).keySet()) {
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for (String txnTime : aggregatedData.get(window).get(cdn).get(isp).get(resultCode).keySet()) {
-
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Integer bytesTransferred = aggregatedData.get(window).get(cdn).get(distId).get(isp).get(resultCode).get(txnTime);
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AggregatedRow row =
new AggregatedRow(window, cdn, distId...
如你所见，这是相当混乱和难以维持。
有谁知道更好的方法吗？任何帮助都将不胜感激。
我想知道是否有更好的方法来展开嵌套的映射，或者是否有一个库允许您对集合进行分组。
最佳答案
您应该为地图创建自定义密钥。最简单的方法是使用Arrays.asList：
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Function<WebRecord, List<Object>> keyExtractor = wr ->
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Arrays.<Object>asList(wr.getFiveMinuteWindow(), wr.getCdn(), wr.getIsp(),
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wr.getResultCode(), wr.getTxnTime());
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Map<List<Object>, Integer> aggregatedData = webRecords.stream().collect(
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Collectors.groupingBy(keyExtractor, Collectors.summingInt(WebRecord::getReqBytes)));
在这种情况下，键是按固定顺序列出的5个元素。不是很面向对象，但很简单。或者，您可以定义自己的表示自定义键的类型，并创建适当的hashCode/equals实现。
参考链接：
转载：https://blog.csdn.net/fly910905/article/details/104005444
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