/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements. See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License. You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package org.apache.kafka.streams.kstream;

import org.apache.kafka.common.utils.Bytes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StoreQueryParameters;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.processor.api.Record;
import org.apache.kafka.streams.state.ReadOnlyWindowStore;
import org.apache.kafka.streams.state.TimestampedWindowStore;
import org.apache.kafka.streams.state.WindowStore;

import java.time.Duration;

/**
 * {@code TimeWindowedKStream} is an abstraction of a <em>windowed</em> record stream of {@link Record key-value} pairs.
 * It is an intermediate representation of a {@link KStream}, that is aggregated into a windowed {@link KTable}
 * (a <em>windowed</em> {@link KTable} is a {@link KTable} with key type {@link Windowed Windowed<K>}).
 *
 * <p>A {@code TimeWindowedKStream} can represent one of four different windowed types:
 * <ul>
 *   <li>a {@link TimeWindows tumbling/hopping} window</li>
 *   <li>a {@link UnlimitedWindows landmark} window</li>
 *   <li>a {@link SlidingWindows sliding} window</li>
 * </ul>
 *
 * The result of the aggregation is written into a local {@link WindowStore} (which is basically an ever-updating
 * materialized view) that can be queried using the name provided in the {@link Materialized} instance.
 * Furthermore, updates to the store are sent downstream into a windowed {@link KTable} changelog stream, where
 * "windowed" implies that the {@link KTable} key is a combined key of the original record key and a window ID.
 * New events are added to {@link TimeWindows}/{@link SlidingWindows} until their grace period ends
 * (see {@link TimeWindows#ofSizeAndGrace(Duration, Duration)} or
 * {@link SlidingWindows#ofTimeDifferenceAndGrace(Duration, Duration)};
 * note that landmark windows don't have a grace period).
 *
 * <p>A {@code TimeWindowedKStream} is obtained from a {@link KStream} by {@link KStream#groupByKey() grouping} and
 * windowing
 * (cf. {@link KGroupedStream#windowedBy(Windows) KGroupedStream#windowedBy(Windows) [for tumbling/hopping/landmark]}
 * or {@link KGroupedStream#windowedBy(SlidingWindows)}).
 *
 * @param <K> the key type of this time-windowed stream
 * @param <V> the value type of this time-windowed stream
 *
 * @see SessionWindowedKStream
 */
public interface TimeWindowedKStream<K, V> {

    /**
     * Count the number of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view).
     * The default key serde from the config will be used for serializing the result.
     * If a different serde is required then you should use {@link #count(Materialized)}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key.
     * The rate of propagated updates depends on your input data rate, the number of distinct keys, the number of
     * parallel running Kafka Streams instances, and the {@link StreamsConfig configuration} parameters for
     * {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore}) will be backed by
     * an internal changelog topic that will be created in Kafka.
     * The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "internalStoreName" is an internal name
     * and "-changelog" is a fixed suffix.
     * Note that the internal store name may not be queryable through Interactive Queries.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys and {@link Long} values
     * that represent the latest (rolling) count (i.e., number of records) for each key within a window
     */
    KTable<Windowed<K>, Long> count();

    /**
     * Count the number of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view).
     * The default key serde from the config will be used for serializing the result.
     * If a different serde is required then you should use {@link #count(Named, Materialized)}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key.
     * The rate of propagated updates depends on your input data rate, the number of distinct keys, the number of
     * parallel running Kafka Streams instances, and the {@link StreamsConfig configuration} parameters for
     * {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore}) will be backed by
     * an internal changelog topic that will be created in Kafka.
     * The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "internalStoreName" is an internal name
     * and "-changelog" is a fixed suffix.
     * Note that the internal store name may not be queryable through Interactive Queries.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param named  a {@link Named} config used to name the processor in the topology. Cannot be {@code null}.
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys and {@link Long} values
     * that represent the latest (rolling) count (i.e., number of records) for each key within a window
     */
    KTable<Windowed<K>, Long> count(final Named named);

    /**
     * Count the number of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view)
     * that can be queried using the name provided with {@link Materialized}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates
     * to the same window and key if caching is enabled on the {@link Materialized} instance.
     * When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * To query the local {@link ReadOnlyWindowStore} it must be obtained via
     * {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
     * StoreQueryParameters<ReadOnlyKeyValueStore<K, ValueAndTimestamp<Long>>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedWindowStore());
     * ReadOnlyWindowStore<K, ValueAndTimestamp<Long>> localWindowStore = streams.store(storeQueryParams);
     *
     * K key = "some-word";
     * long fromTime = ...;
     * long toTime = ...;
     * WindowStoreIterator<ValueAndTimestamp<Long>> countForWordsForWindows = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
     * }</pre>
     * For non-local keys, a custom RPC mechanism must be implemented using {@link KafkaStreams#metadataForAllStreamsClients()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore} -- regardless of what
     * is specified in the parameter {@code materialized}) will be backed by an internal changelog topic that will be created in Kafka.
     * Therefore, the store name defined by the Materialized instance must be a valid Kafka topic name and cannot
     * contain characters other than ASCII alphanumerics, '.', '_' and '-'.
     * The changelog topic will be named "${applicationId}-${storeName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "storeName" is the name of the store defined
     * in {@code Materialized}, and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param materialized  an instance of {@link Materialized} used to materialize a state store. Cannot be {@code null}.
     *                      Note: the valueSerde will be automatically set to {@link org.apache.kafka.common.serialization.Serdes#Long() Serdes#Long()}
     *                      if there is no valueSerde provided
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys and {@link Long} values
     * that represent the latest (rolling) count (i.e., number of records) for each key within a window
     */
    KTable<Windowed<K>, Long> count(final Materialized<K, Long, WindowStore<Bytes, byte[]>> materialized);

    /**
     * Count the number of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view)
     * that can be queried using the name provided with {@link Materialized}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates
     * to the same window and key if caching is enabled on the {@link Materialized} instance.
     * When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}
     * <p>
     * To query the local {@link ReadOnlyWindowStore} it must be obtained via
     * {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
     * StoreQueryParameters<ReadOnlyKeyValueStore<K, ValueAndTimestamp<Long>>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedWindowStore());
     * ReadOnlyWindowStore<K, ValueAndTimestamp<Long>> localWindowStore = streams.store(storeQueryParams);
     *
     * K key = "some-word";
     * long fromTime = ...;
     * long toTime = ...;
     * WindowStoreIterator<ValueAndTimestamp<Long>> countForWordsForWindows = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
     * }</pre>
     * For non-local keys, a custom RPC mechanism must be implemented using {@link KafkaStreams#metadataForAllStreamsClients()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore} -- regardless of what
     * is specified in the parameter {@code materialized}) will be backed by an internal changelog topic that will be created in Kafka.
     * Therefore, the store name defined by the Materialized instance must be a valid Kafka topic name and cannot
     * contain characters other than ASCII alphanumerics, '.', '_' and '-'.
     * The changelog topic will be named "${applicationId}-${storeName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "storeName" is the name of the store defined
     * in {@code Materialized}, and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param named         a {@link Named} config used to name the processor in the topology. Cannot be {@code null}.
     * @param materialized  an instance of {@link Materialized} used to materialize a state store. Cannot be {@code null}.
     *                      Note: the valueSerde will be automatically set to {@link org.apache.kafka.common.serialization.Serdes#Long() Serdes#Long()}
     *                      if there is no valueSerde provided
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys and {@link Long} values
     * that represent the latest (rolling) count (i.e., number of records) for each key within a window
     */
    KTable<Windowed<K>, Long> count(final Named named,
                                    final Materialized<K, Long, WindowStore<Bytes, byte[]>> materialized);

    /**
     * Combine the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value
     * (cf. {@link #aggregate(Initializer, Aggregator)}).
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view).
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * The default key and value serde from the config will be used for serializing the result.
     * If a different serde is required then you should use {@link #reduce(Reducer, Materialized)} .
     * <p>
     * The value of the first record per window initialized the aggregation result.
     * The specified {@link Reducer} is applied for each additional input record per window and computes a new
     * aggregate using the current aggregate (first argument) and the record's value (second argument):
     * <pre>{@code
     * // At the example of a Reducer<Long>
     * new Reducer<Long>() {
     *   public Long apply(Long aggValue, Long currValue) {
     *     return aggValue + currValue;
     *   }
     * }
     * }</pre>
     * Thus, {@code reduce()} can be used to compute aggregate functions like sum, min, or max.
     * <p>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key.
     * The rate of propagated updates depends on your input data rate, the number of distinct keys, the number of
     * parallel running Kafka Streams instances, and the {@link StreamsConfig configuration} parameters for
     * {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore}) will be backed by
     * an internal changelog topic that will be created in Kafka.
     * The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "internalStoreName" is an internal name
     * and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param reducer  a {@link Reducer} that computes a new aggregate result. Cannot be {@code null}.
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    KTable<Windowed<K>, V> reduce(final Reducer<V> reducer);

    /**
     * Combine the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view).
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * The default key and value serde from the config will be used for serializing the result.
     * If a different serde is required then you should use {@link #reduce(Reducer, Named, Materialized)} .
     * <p>
     * The value of the first record per window initialized the aggregation result.
     * The specified {@link Reducer} is applied for each additional input record per window and computes a new
     * aggregate using the current aggregate (first argument) and the record's value (second argument):
     * <pre>{@code
     * // At the example of a Reducer<Long>
     * new Reducer<Long>() {
     *   public Long apply(Long aggValue, Long currValue) {
     *     return aggValue + currValue;
     *   }
     * }
     * }</pre>
     * Thus, {@code reduce()} can be used to compute aggregate functions like sum, min, or max.
     * <p>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key.
     * The rate of propagated updates depends on your input data rate, the number of distinct keys, the number of
     * parallel running Kafka Streams instances, and the {@link StreamsConfig configuration} parameters for
     * {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore}) will be backed by
     * an internal changelog topic that will be created in Kafka.
     * The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "internalStoreName" is an internal name
     * and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param reducer  a {@link Reducer} that computes a new aggregate result. Cannot be {@code null}.
     * @param named    a {@link Named} config used to name the processor in the topology. Cannot be {@code null}.
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    KTable<Windowed<K>, V> reduce(final Reducer<V> reducer, final Named named);

    /**
     * Combine the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view)
     * that can be queried using the store name as provided with {@link Materialized}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The value of the first record per window initialized the aggregation result.
     * The specified {@link Reducer} is applied for each additional input record per window and computes a new
     * aggregate using the current aggregate (first argument) and the record's value (second argument):
     * <pre>{@code
     * // At the example of a Reducer<Long>
     * new Reducer<Long>() {
     *   public Long apply(Long aggValue, Long currValue) {
     *     return aggValue + currValue;
     *   }
     * }
     * }</pre>
     * Thus, {@code reduce()} can be used to compute aggregate functions like sum, min, or max.
     * <p>
     * Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates
     * to the same window and key if caching is enabled on the {@link Materialized} instance.
     * When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * To query the local {@link ReadOnlyWindowStore} it must be obtained via
     * {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
     * StoreQueryParameters<ReadOnlyKeyValueStore<K, ValueAndTimestamp<V>>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedWindowStore());
     * ReadOnlyWindowStore<K, ValueAndTimestamp<V>> localWindowStore = streams.store(storeQueryParams);
     *
     * K key = "some-word";
     * long fromTime = ...;
     * long toTime = ...;
     * WindowStoreIterator<ValueAndTimestamp<V>> reduceStore = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
     * }</pre>
     * For non-local keys, a custom RPC mechanism must be implemented using {@link KafkaStreams#metadataForAllStreamsClients()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore} -- regardless of what
     * is specified in the parameter {@code materialized}) will be backed by an internal changelog topic that will be created in Kafka.
     * Therefore, the store name defined by the Materialized instance must be a valid Kafka topic name and cannot
     * contain characters other than ASCII alphanumerics, '.', '_' and '-'.
     * The changelog topic will be named "${applicationId}-${storeName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "storeName" is the name of the store defined
     * in {@code Materialized}, and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param reducer       a {@link Reducer} that computes a new aggregate result. Cannot be {@code null}.
     * @param materialized  a {@link Materialized} config used to materialize a state store. Cannot be {@code null}.
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    KTable<Windowed<K>, V> reduce(final Reducer<V> reducer,
                                  final Materialized<K, V, WindowStore<Bytes, byte[]>> materialized);

    /**
     * Combine the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value.
     * <p>
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view)
     * that can be queried using the store name as provided with {@link Materialized}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The value of the first record per window initialized the aggregation result.
     * The specified {@link Reducer} is applied for each additional input record per window and computes a new
     * aggregate using the current aggregate (first argument) and the record's value (second argument):
     * <pre>{@code
     * // At the example of a Reducer<Long>
     * new Reducer<Long>() {
     *   public Long apply(Long aggValue, Long currValue) {
     *     return aggValue + currValue;
     *   }
     * }
     * }</pre>
     * Thus, {@code reduce()} can be used to compute aggregate functions like sum, min, or max.
     * <p>
     * Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates
     * to the same window and key if caching is enabled on the {@link Materialized} instance.
     * When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * To query the local {@link ReadOnlyWindowStore} it must be obtained via
     * {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
     * StoreQueryParameters<ReadOnlyKeyValueStore<K, ValueAndTimestamp<V>>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedWindowStore());
     * ReadOnlyWindowStore<K, ValueAndTimestamp<V>> localWindowStore = streams.store(storeQueryParams);
     *
     * K key = "some-word";
     * long fromTime = ...;
     * long toTime = ...;
     * WindowStoreIterator<ValueAndTimestamp<V>> reduceStore = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
     * }</pre>
     * For non-local keys, a custom RPC mechanism must be implemented using {@link KafkaStreams#metadataForAllStreamsClients()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore} -- regardless of what
     * is specified in the parameter {@code materialized}) will be backed by an internal changelog topic that will be created in Kafka.
     * Therefore, the store name defined by the Materialized instance must be a valid Kafka topic name and cannot
     * contain characters other than ASCII alphanumerics, '.', '_' and '-'.
     * The changelog topic will be named "${applicationId}-${storeName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "storeName" is the name of the store defined
     * in {@link Materialized}, and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param reducer       a {@link Reducer} that computes a new aggregate result. Cannot be {@code null}.
     * @param named         a {@link Named} config used to name the processor in the topology. Cannot be {@code null}.
     * @param materialized  a {@link Materialized} config used to materialize a state store. Cannot be {@code null}.
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    KTable<Windowed<K>, V> reduce(final Reducer<V> reducer,
                                  final Named named,
                                  final Materialized<K, V, WindowStore<Bytes, byte[]>> materialized);

    /**
     * Aggregate the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer) combining via reduce(...)} as it, for example,
     * allows the result to have a different type than the input values.
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view).
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The specified {@link Initializer} is applied directly before the first input record (per key) in each window is
     * processed to provide an initial intermediate aggregation result that is used to process the first record for
     * the window (per key).
     * The specified {@link Aggregator} is applied for each input record and computes a new aggregate using the current
     * aggregate (or for the very first record using the intermediate aggregation result provided via the
     * {@link Initializer}) and the record's value.
     * Thus, {@code aggregate()} can be used to compute aggregate functions like count (cf. {@link #count()}).
     * <p>
     * The default key and value serde from the config will be used for serializing the result.
     * If a different serde is required then you should use {@link #aggregate(Initializer, Aggregator, Materialized)}.
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key.
     * The rate of propagated updates depends on your input data rate, the number of distinct keys, the number of
     * parallel running Kafka Streams instances, and the {@link StreamsConfig configuration} parameters for
     * {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore}) will be backed by
     * an internal changelog topic that will be created in Kafka.
     * The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "internalStoreName" is an internal name
     * and "-changelog" is a fixed suffix.
     * Note that the internal store name may not be queryable through Interactive Queries.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer  an {@link Initializer} that computes an initial intermediate aggregation result. Cannot be {@code null}.
     * @param aggregator   an {@link Aggregator} that computes a new aggregate result. Cannot be {@code null}.
     * @param <VOut>         the value type of the resulting {@link KTable}
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    <VOut> KTable<Windowed<K>, VOut> aggregate(final Initializer<VOut> initializer,
                                               final Aggregator<? super K, ? super V, VOut> aggregator);

    /**
     * Aggregate the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer) combining via reduce(...)} as it, for example,
     * allows the result to have a different type than the input values.
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view).
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The specified {@link Initializer} is applied directly before the first input record (per key) in each window is
     * processed to provide an initial intermediate aggregation result that is used to process the first record for
     * the window (per key).
     * The specified {@link Aggregator} is applied for each input record and computes a new aggregate using the current
     * aggregate (or for the very first record using the intermediate aggregation result provided via the
     * {@link Initializer}) and the record's value.
     * Thus, {@code aggregate()} can be used to compute aggregate functions like count (cf. {@link #count()}).
     * <p>
     * The default key and value serde from the config will be used for serializing the result.
     * If a different serde is required then you should use
     * {@link #aggregate(Initializer, Aggregator, Named, Materialized)}.
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key.
     * The rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore}) will be backed by
     * an internal changelog topic that will be created in Kafka.
     * The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "internalStoreName" is an internal name
     * and "-changelog" is a fixed suffix.
     * Note that the internal store name may not be queryable through Interactive Queries.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer  an {@link Initializer} that computes an initial intermediate aggregation result. Cannot be {@code null}.
     * @param aggregator   an {@link Aggregator} that computes a new aggregate result. Cannot be {@code null}.
     * @param named        a {@link Named} config used to name the processor in the topology. Cannot be {@code null}.
     * @param <VOut>         the value type of the resulting {@link KTable}
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    <VOut> KTable<Windowed<K>, VOut> aggregate(final Initializer<VOut> initializer,
                                               final Aggregator<? super K, ? super V, VOut> aggregator,
                                               final Named named);

    /**
     * Aggregate the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer) combining via reduce(...)} as it, for example,
     * allows the result to have a different type than the input values.
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view)
     * that can be queried using the store name as provided with {@link Materialized}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The specified {@link Initializer} is applied directly before the first input record (per key) in each window is
     * processed to provide an initial intermediate aggregation result that is used to process the first record for
     * the window (per key).
     * The specified {@link Aggregator} is applied for each input record and computes a new aggregate using the current
     * aggregate (or for the very first record using the intermediate aggregation result provided via the
     * {@link Initializer}) and the record's value.
     * Thus, {@code aggregate()} can be used to compute aggregate functions like count (cf. {@link #count()}).
     * <p>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same window and key if caching is enabled on the {@link Materialized} instance.
     * When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
     * <p>
     * To query the local {@link ReadOnlyWindowStore} it must be obtained via
     * {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
     * StoreQueryParameters<ReadOnlyKeyValueStore<K, ValueAndTimestamp<VR>>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedWindowStore());
     * ReadOnlyWindowStore<K, ValueAndTimestamp<VR>> localWindowStore = streams.store(storeQueryParams);
     *
     * K key = "some-word";
     * long fromTime = ...;
     * long toTime = ...;
     * WindowStoreIterator<ValueAndTimestamp<VR>> aggregateStore = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
     * }</pre>
     * For non-local keys, a custom RPC mechanism must be implemented using {@link KafkaStreams#metadataForAllStreamsClients()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore} -- regardless of what
     * is specified in the parameter {@code materialized}) will be backed by an internal changelog topic that will be created in Kafka.
     * Therefore, the store name defined by the {@link Materialized} instance must be a valid Kafka topic name and
     * cannot contain characters other than ASCII alphanumerics, '.', '_' and '-'.
     * The changelog topic will be named "${applicationId}-${storeName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "storeName" is the
     * name of the store defined in {@link Materialized}, and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer   an {@link Initializer} that computes an initial intermediate aggregation result. Cannot be {@code null}.
     * @param aggregator    an {@link Aggregator} that computes a new aggregate result. Cannot be {@code null}.
     * @param materialized  a {@link Materialized} config used to materialize a state store. Cannot be {@code null}.
     * @param <VOut>          the value type of the resulting {@link KTable}
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    <VOut> KTable<Windowed<K>, VOut> aggregate(final Initializer<VOut> initializer,
                                               final Aggregator<? super K, ? super V, VOut> aggregator,
                                               final Materialized<K, VOut, WindowStore<Bytes, byte[]>> materialized);

    /**
     * Aggregate the values of records in this stream by the grouped key and defined windows.
     * Records with {@code null} key or value are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer) combining via reduce(...)} as it, for example,
     * allows the result to have a different type than the input values.
     * The result is written into a local {@link WindowStore} (which is basically an ever-updating materialized view)
     * that can be queried using the store name as provided with {@link Materialized}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The specified {@link Initializer} is applied directly before the first input record (per key) in each window is
     * processed to provide an initial intermediate aggregation result that is used to process the first record for
     * the window (per key).
     * The specified {@link Aggregator} is applied for each input record and computes a new aggregate using the current
     * aggregate (or for the very first record using the intermediate aggregation result provided via the
     * {@link Initializer}) and the record's value.
     * Thus, {@code aggregate()} can be used to compute aggregate functions like count (cf. {@link #count()}).
     * <p>
     * Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates
     * to the same window and key if caching is enabled on the {@link Materialized} instance.
     * When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct
     * keys, the number of parallel running Kafka Streams instances, and the {@link StreamsConfig configuration}
     * parameters for {@link StreamsConfig#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}
     * <p>
     * To query the local {@link ReadOnlyWindowStore} it must be obtained via
     * {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
     * StoreQueryParameters<ReadOnlyKeyValueStore<K, ValueAndTimestamp<VR>>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedWindowStore());
     * ReadOnlyWindowStore<K, ValueAndTimestamp<VR>> localWindowStore = streams.store(storeQueryParams);
     *
     * K key = "some-word";
     * long fromTime = ...;
     * long toTime = ...;
     * WindowStoreIterator<ValueAndTimestamp<VR>> aggregateStore = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
     * }</pre>
     * For non-local keys, a custom RPC mechanism must be implemented using {@link KafkaStreams#metadataForAllStreamsClients()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store (which always will be of type {@link TimestampedWindowStore} -- regardless of what
     * is specified in the parameter {@code materialized}) will be backed by an internal changelog topic that will be created in Kafka.
     * Therefore, the store name defined by the {@link Materialized} instance must be a valid Kafka topic name and
     * cannot contain characters other than ASCII alphanumerics, '.', '_' and '-'.
     * The changelog topic will be named "${applicationId}-${storeName}-changelog", where "applicationId" is
     * user-specified in {@link StreamsConfig} via parameter
     * {@link StreamsConfig#APPLICATION_ID_CONFIG APPLICATION_ID_CONFIG}, "storeName" is the
     * name of the store defined in {@link Materialized}, and "-changelog" is a fixed suffix.
     * <p>
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer   an {@link Initializer} that computes an initial intermediate aggregation result. Cannot be {@code null}.
     * @param aggregator    an {@link Aggregator} that computes a new aggregate result. Cannot be {@code null}.
     * @param named         a {@link Named} config used to name the processor in the topology. Cannot be {@code null}.
     * @param materialized  a {@link Materialized} config used to materialize a state store. Cannot be {@code null}.
     * @param <VOut>          the value type of the resulting {@link KTable}
     * @return a windowed {@link KTable} that contains "update" records with unmodified keys, and values that represent
     * the latest (rolling) aggregate for each key within a window
     */
    <VOut> KTable<Windowed<K>, VOut> aggregate(final Initializer<VOut> initializer,
                                               final Aggregator<? super K, ? super V, VOut> aggregator,
                                               final Named named,
                                               final Materialized<K, VOut, WindowStore<Bytes, byte[]>> materialized);

    /**
     * Configure when the aggregated result will be emitted for {@code TimeWindowedKStream}.
     * <p>
     * For example, for {@link EmitStrategy#onWindowClose} strategy, the aggregated result for a
     * window will only be emitted when the window closes. For {@link EmitStrategy#onWindowUpdate()}
     * strategy, the aggregated result for a window will be emitted whenever there is an update to
     * the window. Note that whether the result will be available in downstream also depends on
     * cache policy.
     *
     * @param emitStrategy {@link EmitStrategy} to configure when the aggregated result for a window will be emitted.
     * @return a {@code TimeWindowedKStream} with {@link EmitStrategy} configured.
     */
    TimeWindowedKStream<K, V> emitStrategy(final EmitStrategy emitStrategy);
}
