/*
 * 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.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.state.KeyValueStore;
import org.apache.kafka.streams.state.QueryableStoreType;

/**
 * {@code KGroupedTable} is an abstraction of a <i>re-grouped changelog stream</i> from a primary-keyed table,
 * usually on a different grouping key than the original primary key.
 * <p>
 * It is an intermediate representation after a re-grouping of a {@link KTable} before an aggregation is applied to the
 * new partitions resulting in a new {@link KTable}.
 * <p>
 * A {@code KGroupedTable} must be obtained from a {@link KTable} via {@link KTable#groupBy(KeyValueMapper)
 * groupBy(...)}.
 *
 * @param <K> Type of keys
 * @param <V> Type of values
 * @see KTable
 */
public interface KGroupedTable<K, V> {

    /**
     * Count number of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper) mapped} to
     * the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * that can be queried using the provided {@code queryableStoreName}.
     * 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 is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * To query the local {@link KeyValueStore} it must be obtained via
     * {@link KafkaStreams#store(String, QueryableStoreType) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * ReadOnlyKeyValueStore<String, Long> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>keyValueStore());
     * String key = "some-word";
     * Long countForWord = localStore.get(key); // 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#allMetadata()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     *
     * <p>
     * For failure and recovery the store 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
     * provide store name defined in {@code Materialized}, and "-changelog" is a fixed suffix.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param materialized the instance of {@link Materialized} used to materialize the state store. Cannot be {@code null}
     * @return a {@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
     */
    KTable<K, Long> count(final Materialized<K, Long, KeyValueStore<Bytes, byte[]>> materialized);

    /**
     * Count number of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper) mapped} to
     * the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * that can be queried using the provided {@code queryableStoreName}.
     * 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 is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * To query the local {@link KeyValueStore} it must be obtained via
     * {@link KafkaStreams#store(String, QueryableStoreType) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * ReadOnlyKeyValueStore<String, Long> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>keyValueStore());
     * String key = "some-word";
     * Long countForWord = localStore.get(key); // 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#allMetadata()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     *
     * <p>
     * For failure and recovery the store 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
     * provide store name defined in {@code Materialized}, and "-changelog" is a fixed suffix.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param named        the {@link Named} config used to name the processor in the topology
     * @param materialized the instance of {@link Materialized} used to materialize the state store. Cannot be {@code null}
     * @return a {@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
     */
    KTable<K, Long> count(final Named named, final Materialized<K, Long, KeyValueStore<Bytes, byte[]>> materialized);

    /**
     * Count number of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper) mapped} to
     * the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * 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 is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * For failure and recovery the store 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 queriable through Interactive Queries.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @return a {@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
     */
    KTable<K, Long> count();


    /**
     * Count number of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper) mapped} to
     * the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * 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 is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * For failure and recovery the store 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 queriable through Interactive Queries.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param named        the {@link Named} config used to name the processor in the topology
     * @return a {@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
     */
    KTable<K, Long> count(final Named named);

    /**
     * Combine the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value
     * (c.f. {@link #aggregate(Initializer, Aggregator, Aggregator, Materialized)}).
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * that can be queried using the provided {@code queryableStoreName}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Reducer adder} is applied for each update record and computes a new aggregate using the
     * current aggregate (first argument) and the record's value (second argument) by adding the new record to the
     * aggregate.
     * The specified {@link Reducer subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate (first argument) and the record's value (second
     * argument) by "removing" the "replaced" record from the aggregate.
     * If there is no current aggregate the {@link Reducer} is not applied and the new aggregate will be the record's
     * value as-is.
     * Thus, {@code reduce(Reducer, Reducer, String)} can be used to compute aggregate functions like sum.
     * For sum, the adder and subtractor would work as follows:
     * <pre>{@code
     * public class SumAdder implements Reducer<Integer> {
     *   public Integer apply(Integer currentAgg, Integer newValue) {
     *     return currentAgg + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Reducer<Integer> {
     *   public Integer apply(Integer currentAgg, Integer oldValue) {
     *     return currentAgg - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * To query the local {@link KeyValueStore} it must be obtained via
     * {@link KafkaStreams#store(String, QueryableStoreType) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * ReadOnlyKeyValueStore<String, Long> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>keyValueStore());
     * String key = "some-word";
     * Long countForWord = localStore.get(key); // 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#allMetadata()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store 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
     * provide store name defined in {@code Materialized}, and "-changelog" is a fixed suffix.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param adder         a {@link Reducer} that adds a new value to the aggregate result
     * @param subtractor    a {@link Reducer} that removed an old value from the aggregate result
     * @param materialized  the instance of {@link Materialized} used to materialize the state store. Cannot be {@code null}
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    KTable<K, V> reduce(final Reducer<V> adder,
                        final Reducer<V> subtractor,
                        final Materialized<K, V, KeyValueStore<Bytes, byte[]>> materialized);


    /**
     * Combine the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value
     * (c.f. {@link #aggregate(Initializer, Aggregator, Aggregator, Materialized)}).
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * that can be queried using the provided {@code queryableStoreName}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Reducer adder} is applied for each update record and computes a new aggregate using the
     * current aggregate (first argument) and the record's value (second argument) by adding the new record to the
     * aggregate.
     * The specified {@link Reducer subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate (first argument) and the record's value (second
     * argument) by "removing" the "replaced" record from the aggregate.
     * If there is no current aggregate the {@link Reducer} is not applied and the new aggregate will be the record's
     * value as-is.
     * Thus, {@code reduce(Reducer, Reducer, String)} can be used to compute aggregate functions like sum.
     * For sum, the adder and subtractor would work as follows:
     * <pre>{@code
     * public class SumAdder implements Reducer<Integer> {
     *   public Integer apply(Integer currentAgg, Integer newValue) {
     *     return currentAgg + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Reducer<Integer> {
     *   public Integer apply(Integer currentAgg, Integer oldValue) {
     *     return currentAgg - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * To query the local {@link KeyValueStore} it must be obtained via
     * {@link KafkaStreams#store(String, QueryableStoreType) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * ReadOnlyKeyValueStore<String, Long> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>keyValueStore());
     * String key = "some-word";
     * Long countForWord = localStore.get(key); // 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#allMetadata()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store 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
     * provide store name defined in {@code Materialized}, and "-changelog" is a fixed suffix.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param adder         a {@link Reducer} that adds a new value to the aggregate result
     * @param subtractor    a {@link Reducer} that removed an old value from the aggregate result
     * @param named         a {@link Named} config used to name the processor in the topology
     * @param materialized  the instance of {@link Materialized} used to materialize the state store. Cannot be {@code null}
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    KTable<K, V> reduce(final Reducer<V> adder,
                        final Reducer<V> subtractor,
                        final Named named,
                        final Materialized<K, V, KeyValueStore<Bytes, byte[]>> materialized);

    /**
     * Combine the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * Combining implies that the type of the aggregate result is the same as the type of the input value
     * (c.f. {@link #aggregate(Initializer, Aggregator, Aggregator)}).
     * The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Reducer adder} is applied for each update record and computes a new aggregate using the
     * current aggregate and the record's value by adding the new record to the aggregate.
     * The specified {@link Reducer subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate and the record's value by "removing" the "replaced"
     * record from the aggregate.
     * If there is no current aggregate the {@link Reducer} is not applied and the new aggregate will be the record's
     * value as-is.
     * Thus, {@code reduce(Reducer, Reducer)} can be used to compute aggregate functions like sum.
     * For sum, the adder and subtractor would work as follows:
     * <pre>{@code
     * public class SumAdder implements Reducer<Integer> {
     *   public Integer apply(Integer currentAgg, Integer newValue) {
     *     return currentAgg + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Reducer<Integer> {
     *   public Integer apply(Integer currentAgg, Integer oldValue) {
     *     return currentAgg - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * For failure and recovery the store 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 queriable through Interactive Queries.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param adder      a {@link Reducer} that adds a new value to the aggregate result
     * @param subtractor a {@link Reducer} that removed an old value from the aggregate result
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    KTable<K, V> reduce(final Reducer<V> adder,
                        final Reducer<V> subtractor);

    /**
     * Aggregate the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer, Reducer, Materialized) 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 KeyValueStore} (which is basically an ever-updating materialized view)
     * that can be queried using the provided {@code queryableStoreName}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The specified {@link Initializer} is applied once directly before the first input record is processed to
     * provide an initial intermediate aggregation result that is used to process the first record.
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Aggregator adder} is applied for each update 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 by adding the new record to the aggregate.
     * The specified {@link Aggregator subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate and the record's value by "removing" the "replaced"
     * record from the aggregate.
     * Thus, {@code aggregate(Initializer, Aggregator, Aggregator, Materialized)} can be used to compute aggregate functions
     * like sum.
     * For sum, the initializer, adder, and subtractor would work as follows:
     * <pre>{@code
     * // in this example, LongSerde.class must be set as value serde in Materialized#withValueSerde
     * public class SumInitializer implements Initializer<Long> {
     *   public Long apply() {
     *     return 0L;
     *   }
     * }
     *
     * public class SumAdder implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer newValue, Long aggregate) {
     *     return aggregate + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer oldValue, Long aggregate) {
     *     return aggregate - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * To query the local {@link KeyValueStore} it must be obtained via
     * {@link KafkaStreams#store(String, QueryableStoreType) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * ReadOnlyKeyValueStore<String, Long> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>keyValueStore());
     * String key = "some-word";
     * Long countForWord = localStore.get(key); // 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#allMetadata()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store 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
     * provide store name defined in {@code Materialized}, and "-changelog" is a fixed suffix.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer   an {@link Initializer} that provides an initial aggregate result value
     * @param adder         an {@link Aggregator} that adds a new record to the aggregate result
     * @param subtractor    an {@link Aggregator} that removed an old record from the aggregate result
     * @param materialized  the instance of {@link Materialized} used to materialize the state store. Cannot be {@code null}
     * @param <VR>          the value type of the aggregated {@link KTable}
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    <VR> KTable<K, VR> aggregate(final Initializer<VR> initializer,
                                 final Aggregator<? super K, ? super V, VR> adder,
                                 final Aggregator<? super K, ? super V, VR> subtractor,
                                 final Materialized<K, VR, KeyValueStore<Bytes, byte[]>> materialized);


    /**
     * Aggregate the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable}.
     * Records with {@code null} key are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer, Reducer, Materialized) 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 KeyValueStore} (which is basically an ever-updating materialized view)
     * that can be queried using the provided {@code queryableStoreName}.
     * Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
     * <p>
     * The specified {@link Initializer} is applied once directly before the first input record is processed to
     * provide an initial intermediate aggregation result that is used to process the first record.
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Aggregator adder} is applied for each update 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 by adding the new record to the aggregate.
     * The specified {@link Aggregator subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate and the record's value by "removing" the "replaced"
     * record from the aggregate.
     * Thus, {@code aggregate(Initializer, Aggregator, Aggregator, Materialized)} can be used to compute aggregate functions
     * like sum.
     * For sum, the initializer, adder, and subtractor would work as follows:
     * <pre>{@code
     * // in this example, LongSerde.class must be set as value serde in Materialized#withValueSerde
     * public class SumInitializer implements Initializer<Long> {
     *   public Long apply() {
     *     return 0L;
     *   }
     * }
     *
     * public class SumAdder implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer newValue, Long aggregate) {
     *     return aggregate + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer oldValue, Long aggregate) {
     *     return aggregate - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * <p>
     * To query the local {@link KeyValueStore} it must be obtained via
     * {@link KafkaStreams#store(String, QueryableStoreType) KafkaStreams#store(...)}:
     * <pre>{@code
     * KafkaStreams streams = ... // counting words
     * ReadOnlyKeyValueStore<String, Long> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>keyValueStore());
     * String key = "some-word";
     * Long countForWord = localStore.get(key); // 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#allMetadata()} to
     * query the value of the key on a parallel running instance of your Kafka Streams application.
     * <p>
     * For failure and recovery the store 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
     * provide store name defined in {@code Materialized}, and "-changelog" is a fixed suffix.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer   an {@link Initializer} that provides an initial aggregate result value
     * @param adder         an {@link Aggregator} that adds a new record to the aggregate result
     * @param subtractor    an {@link Aggregator} that removed an old record from the aggregate result
     * @param named         a {@link Named} config used to name the processor in the topology
     * @param materialized  the instance of {@link Materialized} used to materialize the state store. Cannot be {@code null}
     * @param <VR>          the value type of the aggregated {@link KTable}
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    <VR> KTable<K, VR> aggregate(final Initializer<VR> initializer,
                                 final Aggregator<? super K, ? super V, VR> adder,
                                 final Aggregator<? super K, ? super V, VR> subtractor,
                                 final Named named,
                                 final Materialized<K, VR, KeyValueStore<Bytes, byte[]>> materialized);

    /**
     * Aggregate the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable} using default serializers and deserializers.
     * Records with {@code null} key are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer, Reducer) combining via reduce(...)} as it,
     * for example, allows the result to have a different type than the input values.
     * If the result value type does not match the {@link StreamsConfig#DEFAULT_VALUE_SERDE_CLASS_CONFIG default value
     * serde} you should use {@link #aggregate(Initializer, Aggregator, Aggregator, Materialized)}.
     * The result is written into a local {@link KeyValueStore} (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 once directly before the first input record is processed to
     * provide an initial intermediate aggregation result that is used to process the first record.
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Aggregator adder} is applied for each update 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 by adding the new record to the aggregate.
     * The specified {@link Aggregator subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate and the record's value by "removing" the "replaced"
     * record from the aggregate.
     * Thus, {@code aggregate(Initializer, Aggregator, Aggregator, String)} can be used to compute aggregate functions
     * like sum.
     * For sum, the initializer, adder, and subtractor would work as follows:
     * <pre>{@code
     * // in this example, LongSerde.class must be set as default value serde in StreamsConfig
     * public class SumInitializer implements Initializer<Long> {
     *   public Long apply() {
     *     return 0L;
     *   }
     * }
     *
     * public class SumAdder implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer newValue, Long aggregate) {
     *     return aggregate + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer oldValue, Long aggregate) {
     *     return aggregate - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * For failure and recovery the store 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 queriable through Interactive Queries.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer a {@link Initializer} that provides an initial aggregate result value
     * @param adder       a {@link Aggregator} that adds a new record to the aggregate result
     * @param subtractor  a {@link Aggregator} that removed an old record from the aggregate result
     * @param <VR>        the value type of the aggregated {@link KTable}
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    <VR> KTable<K, VR> aggregate(final Initializer<VR> initializer,
                                 final Aggregator<? super K, ? super V, VR> adder,
                                 final Aggregator<? super K, ? super V, VR> subtractor);


    /**
     * Aggregate the value of records of the original {@link KTable} that got {@link KTable#groupBy(KeyValueMapper)
     * mapped} to the same key into a new instance of {@link KTable} using default serializers and deserializers.
     * Records with {@code null} key are ignored.
     * Aggregating is a generalization of {@link #reduce(Reducer, Reducer) combining via reduce(...)} as it,
     * for example, allows the result to have a different type than the input values.
     * If the result value type does not match the {@link StreamsConfig#DEFAULT_VALUE_SERDE_CLASS_CONFIG default value
     * serde} you should use {@link #aggregate(Initializer, Aggregator, Aggregator, Materialized)}.
     * The result is written into a local {@link KeyValueStore} (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 once directly before the first input record is processed to
     * provide an initial intermediate aggregation result that is used to process the first record.
     * Each update to the original {@link KTable} results in a two step update of the result {@link KTable}.
     * The specified {@link Aggregator adder} is applied for each update 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 by adding the new record to the aggregate.
     * The specified {@link Aggregator subtractor} is applied for each "replaced" record of the original {@link KTable}
     * and computes a new aggregate using the current aggregate and the record's value by "removing" the "replaced"
     * record from the aggregate.
     * Thus, {@code aggregate(Initializer, Aggregator, Aggregator, String)} can be used to compute aggregate functions
     * like sum.
     * For sum, the initializer, adder, and subtractor would work as follows:
     * <pre>{@code
     * // in this example, LongSerde.class must be set as default value serde in StreamsConfig
     * public class SumInitializer implements Initializer<Long> {
     *   public Long apply() {
     *     return 0L;
     *   }
     * }
     *
     * public class SumAdder implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer newValue, Long aggregate) {
     *     return aggregate + newValue;
     *   }
     * }
     *
     * public class SumSubtractor implements Aggregator<String, Integer, Long> {
     *   public Long apply(String key, Integer oldValue, Long aggregate) {
     *     return aggregate - oldValue;
     *   }
     * }
     * }</pre>
     * Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to
     * the same 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#CACHE_MAX_BYTES_BUFFERING_CONFIG cache size}, and
     * {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit intervall}.
     * For failure and recovery the store 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 queriable through Interactive Queries.
     *
     * You can retrieve all generated internal topic names via {@link Topology#describe()}.
     *
     * @param initializer a {@link Initializer} that provides an initial aggregate result value
     * @param adder       a {@link Aggregator} that adds a new record to the aggregate result
     * @param subtractor  a {@link Aggregator} that removed an old record from the aggregate result
     * @param named       a {@link Named} config used to name the processor in the topology
     * @param <VR>        the value type of the aggregated {@link KTable}
     * @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
     * latest (rolling) aggregate for each key
     */
    <VR> KTable<K, VR> aggregate(final Initializer<VR> initializer,
                                 final Aggregator<? super K, ? super V, VR> adder,
                                 final Aggregator<? super K, ? super V, VR> subtractor,
                                 final Named named);
}
