如何提取一个函数,我可以将函数作为参数传递给 Java 8 中的这些链式 lambda?

How to extract a function where I can pass functions as parameters for these chained lambdas in Java 8?

我在一段使用 Kafka Streams 的代码中有一个代码模式不断重复,我做了一个映射,然后按键分组,然后减少。它看起来像这样:

KTable<ProjectKey, EventConfigurationIdsWithDeletedState> eventConfigurationsByProjectTable = eventConfigurationStream
        .map((key, value) -> {
            Map<String, Boolean> eventConfigurationUpdates = new HashMap<>();
            eventConfigurationUpdates.put(key.getEventConfigurationId(), value != null);
            ProjectKey projectKey = ProjectKey.newBuilder().setId(key.getProjectId()).build();
            EventConfigurationIdsWithDeletedState eventConfigurationIdsWithDeletedState = EventConfigurationIdsWithDeletedState.newBuilder().setEventConfigurations(eventConfigurationUpdates).build();
            return KeyValue.pair(projectKey, eventConfigurationIdsWithDeletedState);
        })
        .groupByKey()
        .reduce((aggValue, newValue) -> {
            Map<String, Boolean> newEventConfigurations = newValue.getEventConfigurations();
            Map<String, Boolean> aggEventConfigurations = aggValue.getEventConfigurations();
            Map.Entry<String, Boolean> newEntry = newEventConfigurations.entrySet().iterator().next();
            if (newEntry.getValue())
                aggEventConfigurations.putAll(newEventConfigurations);
            else
                aggEventConfigurations.remove(newEntry.getKey());
            if (aggEventConfigurations.size() == 0)
                return null;
            return aggValue;
        });

(eventConfigurationStream 的类型为 KStream<EventConfigurationKey, EventConfiguration>

遵循此模式的另一个示例。请注意,这里也有一个过滤器,但情况并非总是如此:

KTable<ProjectKey, NotificationSettingsTransition> globalNotificationSettingsPerProjectTable = notificationSettingTable.toStream()
        .filter((key, value) -> {
            return key.getEventConfigurationId() == null;
        })
        .map((key, value) -> {
            ProjectKey projectKey = ProjectKey.newBuilder().setId(key.getProjectId()).build();
            Map<String, NotificationSetting> notificationSettingsMap = new HashMap<>();
            notificationSettingsMap.put(getAsCompoundKeyString(key), value);
            NotificationSettingsTransition notificationSettingTransition = NotificationSettingsTransition
                    .newBuilder()
                    .setNotificationSettingCompoundKeyLastUpdate(getAsCompoundKey(key))
                    .setNotificationSettingLastUpdate(value)
                    .setEventConfigurationIds(new ArrayList<>())
                    .setNotificationSettingsMap(notificationSettingsMap)
                    .build();

            return KeyValue.pair(projectKey, notificationSettingTransition);
        })
        .groupByKey()
        .reduce((aggValue, newValue) -> {
            Map<String, NotificationSetting> notificationSettingMap = aggValue.getNotificationSettingsMap();
            String compoundKeyAsString = getAsString(newValue.getNotificationSettingCompoundKeyLastUpdate());
            if (newValue.getNotificationSettingLastUpdate() != null)
                notificationSettingMap.put(compoundKeyAsString, newValue.getNotificationSettingLastUpdate());
            else
                notificationSettingMap.remove(compoundKeyAsString);
            aggValue.setNotificationSettingCompoundKeyLastUpdate(newValue.getNotificationSettingCompoundKeyLastUpdate());
            aggValue.setNotificationSettingLastUpdate(newValue.getNotificationSettingLastUpdate());
            aggValue.setNotificationSettingsMap(notificationSettingMap);
            return aggValue;
        });

(notificationSettingsTable 的类型为 KTable<NotificationSettingKey, NotificationSetting> notificationSettingTable,但也立即转换为 KStream。)

我如何将其提取到一个函数中,在该函数中我为地图代码和 reduce 代码传递了一个函数,但不必重复 .map().groupByKey().reduce() 的模式?虽然 return 类型不同并且取决于 map 函数中的代码,但应保持类型化。理想情况下在 Java 8,但更高版本是可能的。当地图代码中 KeyValuePair 的内部类型不会改变时,我想我很清楚如何做到这一点,但现在不确定如何做到这一点。

您可以参数化您的函数以接受两个泛型函数,其中类型将在函数被调用时被推断(或者如果不可能则明确设置)。

对于 map 的输入,您需要 BiFunction<K, V, T>,对于 reduce,您需要 BiFunction<U, U, U>,其中:

  • Kmap函数中key的类型。
  • Vmap函数中value的类型。
  • Tmap 函数的 return 类型。
  • U 是聚合器的类型,值和 reduce 函数的 return 类型。

查看KStream and KGroupedStream,您可以获得更详细的类型信息以进一步约束函数。

这将使您的自定义函数变成这样:

<K, V, T, U> U mapGroupReduce(final KStream<K, V> stream, final BiFunction<K, V, T> mapper, final BiFunction<U, U, U> reducer) {
    return stream.map(mapper).groupByKey().reduce(reducer);
}

然后你可以这样称呼它:

mapGroupReduce(yourStream,
    (key, value) -> new KeyValue(k, v)),
    (acc, value) -> acc);

在您的情况下,您需要使用:

而不是使用 BiFunctions
  • KeyValueMapper<K, V, KeyValue<T, U>> 映射器
  • Reducer<U> 为减速器。

然而,这真的比每次都写 stream.map(M).groupByKey().reduce(R) 好得多吗?更详细的版本更明确,并且考虑到映射器和缩减器的相对大小,你并没有真正节省那么多。