org.apache.kafka.streams.kstream.KGroupedStream Maven / Gradle / Ivy
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* 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.KeyValue;
import org.apache.kafka.streams.StoreQueryParameters;
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.ReadOnlyKeyValueStore;
import org.apache.kafka.streams.state.TimestampedKeyValueStore;
/**
* {@code KGroupedStream} is an abstraction of a grouped record stream of {@link KeyValue} pairs.
* It is an intermediate representation of a {@link KStream} in order to apply an aggregation operation on the original
* {@link KStream} records.
*
* It is an intermediate representation after a grouping of a {@link KStream} before an aggregation is applied to the
* new partitions resulting in a {@link KTable}.
*
* A {@code KGroupedStream} must be obtained from a {@link KStream} via {@link KStream#groupByKey() groupByKey()} or
* {@link KStream#groupBy(KeyValueMapper) groupBy(...)}.
*
* @param Type of keys
* @param Type of values
* @see KStream
*/
public interface KGroupedStream {
/**
* Count the number of records in this stream by the grouped key.
* Records with {@code null} key or value 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.
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore}) 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.
*
* 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 count();
/**
* Count the number of records in this stream by the grouped key.
* Records with {@code null} key or value 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.
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore}) 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.
*
* 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
*
* @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 count(final Named named);
/**
* Count the number of records in this stream by the grouped key.
* Records with {@code null} key or value are ignored.
* The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
* provided by the given store name in {@code 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 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}.
*
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}.
*
{@code
* KafkaStreams streams = ... // counting words
* String queryableStoreName = "storeName"; // the store name should be the name of the store as defined by the Materialized instance
* StoreQueryParameters>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedKeyValueStore());
* ReadOnlyKeyValueStore> localStore = streams.store(storeQueryParams);
* K key = "some-word";
* ValueAndTimestamp countForWord = localStore.get(key); // key must be local (application state is shared over all running Kafka Streams instances)
* }
* 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.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore} -- 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
* 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 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 {@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 count(final Materialized> materialized);
/**
* Count the number of records in this stream by the grouped key.
* Records with {@code null} key or value are ignored.
* The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
* provided by the given store name in {@code 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 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}.
*
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}.
*
{@code
* KafkaStreams streams = ... // counting words
* String queryableStoreName = "storeName"; // the store name should be the name of the store as defined by the Materialized instance
* StoreQueryParameters>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedKeyValueStore());
* ReadOnlyKeyValueStore> localStore = streams.store(storeQueryParams);
* K key = "some-word";
* ValueAndTimestamp countForWord = localStore.get(key); // key must be local (application state is shared over all running Kafka Streams instances)
* }
* 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.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore} -- 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
* 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 a {@link Named} config used to name the processor in the topology
* @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 {@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 count(final Named named,
final Materialized> materialized);
/**
* Combine the values of records in this stream by the grouped key.
* 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
* (c.f. {@link #aggregate(Initializer, Aggregator)}).
*
* The specified {@link Reducer} is applied for each input record and computes a new aggregate using the current
* aggregate and the record's value.
* 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)} can be used to compute aggregate functions like sum, min, or max.
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore}) 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.
*
* 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 {@link KTable} that contains "update" records with unmodified keys, and values that represent the
* latest (rolling) aggregate for each key. If the reduce function returns {@code null}, it is then interpreted as
* deletion for the key, and future messages of the same key coming from upstream operators
* will be handled as newly initialized value.
*/
KTable reduce(final Reducer reducer);
/**
* Combine the value of records in this stream by the grouped key.
* 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
* (c.f. {@link #aggregate(Initializer, Aggregator, Materialized)}).
* The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
* provided by the given store name in {@code materialized}.
* Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
*
* The specified {@link Reducer} is applied for each input record and computes a new aggregate using the current
* aggregate (first argument) and the record's value (second argument):
*
{@code
* // At the example of a Reducer
* new Reducer() {
* public Long apply(Long aggValue, Long currValue) {
* return aggValue + currValue;
* }
* }
* }
*
* 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, Materialized)} can be used to compute aggregate functions like sum, min, or
* max.
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}.
*
{@code
* KafkaStreams streams = ... // compute sum
* String queryableStoreName = "storeName" // the store name should be the name of the store as defined by the Materialized instance
* StoreQueryParameters>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedKeyValueStore());
* ReadOnlyKeyValueStore> localStore = streams.store(storeQueryParams);
* K key = "some-key";
* ValueAndTimestamp reduceForKey = localStore.get(key); // key must be local (application state is shared over all running Kafka Streams instances)
* }
* 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.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore} -- regardless of what
* is specified in the parameter {@code materialized}) 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.
*
* 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 an instance of {@link Materialized} used to materialize a 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 reduce(final Reducer reducer,
final Materialized> materialized);
/**
* Combine the value of records in this stream by the grouped key.
* 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
* (c.f. {@link #aggregate(Initializer, Aggregator, Materialized)}).
* The result is written into a local {@link KeyValueStore} (which is basically an ever-updating materialized view)
* provided by the given store name in {@code materialized}.
* Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
*
* The specified {@link Reducer} is applied for each input record and computes a new aggregate using the current
* aggregate (first argument) and the record's value (second argument):
*
{@code
* // At the example of a Reducer
* new Reducer() {
* public Long apply(Long aggValue, Long currValue) {
* return aggValue + currValue;
* }
* }
* }
*
* 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, Materialized)} can be used to compute aggregate functions like sum, min, or
* max.
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}.
*
{@code
* KafkaStreams streams = ... // compute sum
* String queryableStoreName = "storeName" // the store name should be the name of the store as defined by the Materialized instance
* StoreQueryParameters>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedKeyValueStore());
* ReadOnlyKeyValueStore> localStore = streams.store(storeQueryParams);
* K key = "some-key";
* ValueAndTimestamp reduceForKey = localStore.get(key); // key must be local (application state is shared over all running Kafka Streams instances)
* }
* 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.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore} -- regardless of what
* is specified in the parameter {@code materialized}) 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.
*
* 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.
* @param materialized an instance of {@link Materialized} used to materialize a 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. If the reduce function returns {@code null}, it is then interpreted as
* deletion for the key, and future messages of the same key coming from upstream operators
* will be handled as newly initialized value.
*/
KTable reduce(final Reducer reducer,
final Named named,
final Materialized> materialized);
/**
* Aggregate the values of records in this stream by the grouped key.
* 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 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.
* 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(Initializer, Aggregator)} can be used to compute aggregate functions like
* count (c.f. {@link #count()}).
*
* The default value serde from 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 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}.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore}) 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.
*
* You can retrieve all generated internal topic names via {@link Topology#describe()}.
*
* @param initializer an {@link Initializer} that computes an initial intermediate aggregation result
* @param aggregator an {@link Aggregator} that computes a new aggregate result
* @param the value type of the resulting {@link KTable}
* @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
* latest (rolling) aggregate for each key. If the aggregate function returns {@code null}, it is then interpreted as
* deletion for the key, and future messages of the same key coming from upstream operators
* will be handled as newly initialized value.
*/
KTable aggregate(final Initializer initializer,
final Aggregator aggregator);
/**
* Aggregate the values of records in this stream by the grouped key.
* 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 KeyValueStore} (which is basically an ever-updating materialized view)
* that can be queried by the given store name in {@code materialized}.
* Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
*
* 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.
* 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(Initializer, Aggregator, Materialized)} can be used to compute aggregate functions like
* count (c.f. {@link #count()}).
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
*
{@code
* KafkaStreams streams = ... // some aggregation on value type double
* String queryableStoreName = "storeName" // the store name should be the name of the store as defined by the Materialized instance
* StoreQueryParameters>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedKeyValueStore());
* ReadOnlyKeyValueStore> localStore = streams.store(storeQueryParams);
* K key = "some-key";
* ValueAndTimestamp aggForKey = localStore.get(key); // key must be local (application state is shared over all running Kafka Streams instances)
* }
* 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.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore} -- 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
* 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 computes an initial intermediate aggregation result
* @param aggregator an {@link Aggregator} that computes a new aggregate result
* @param materialized an instance of {@link Materialized} used to materialize a state store. Cannot be {@code null}.
* @param the value type of the resulting {@link KTable}
* @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
* latest (rolling) aggregate for each key
*/
KTable aggregate(final Initializer initializer,
final Aggregator aggregator,
final Materialized> materialized);
/**
* Aggregate the values of records in this stream by the grouped key.
* 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 KeyValueStore} (which is basically an ever-updating materialized view)
* that can be queried by the given store name in {@code materialized}.
* Furthermore, updates to the store are sent downstream into a {@link KTable} changelog stream.
*
* 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.
* 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(Initializer, Aggregator, Materialized)} can be used to compute aggregate functions like
* count (c.f. {@link #count()}).
*
* 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#STATESTORE_CACHE_MAX_BYTES_CONFIG cache size}, and
* {@link StreamsConfig#COMMIT_INTERVAL_MS_CONFIG commit interval}.
*
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
*
{@code
* KafkaStreams streams = ... // some aggregation on value type double
* String queryableStoreName = "storeName" // the store name should be the name of the store as defined by the Materialized instance
* StoreQueryParameters>> storeQueryParams = StoreQueryParameters.fromNameAndType(queryableStoreName, QueryableStoreTypes.timestampedKeyValueStore());
* ReadOnlyKeyValueStore> localStore = streams.store(storeQueryParams);
* K key = "some-key";
* ValueAndTimestamp aggForKey = localStore.get(key); // key must be local (application state is shared over all running Kafka Streams instances)
* }
* 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.
*
*
* For failure and recovery the store (which always will be of type {@link TimestampedKeyValueStore} -- 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
* 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 computes an initial intermediate aggregation result
* @param aggregator an {@link Aggregator} that computes a new aggregate result
* @param named a {@link Named} config used to name the processor in the topology
* @param materialized an instance of {@link Materialized} used to materialize a state store. Cannot be {@code null}.
* @param the value type of the resulting {@link KTable}
* @return a {@link KTable} that contains "update" records with unmodified keys, and values that represent the
* latest (rolling) aggregate for each key. If the aggregate function returns {@code null}, it is then interpreted as
* deletion for the key, and future messages of the same key coming from upstream operators
* will be handled as newly initialized value.
*/
KTable aggregate(final Initializer initializer,
final Aggregator aggregator,
final Named named,
final Materialized> materialized);
/**
* Create a new {@link TimeWindowedKStream} instance that can be used to perform windowed aggregations.
* @param windows the specification of the aggregation {@link Windows}
* @param the window type
* @return an instance of {@link TimeWindowedKStream}
*/
TimeWindowedKStream windowedBy(final Windows windows);
/**
* Create a new {@link TimeWindowedKStream} instance that can be used to perform sliding windowed aggregations.
* @param windows the specification of the aggregation {@link SlidingWindows}
* @return an instance of {@link TimeWindowedKStream}
*/
TimeWindowedKStream windowedBy(final SlidingWindows windows);
/**
* Create a new {@link SessionWindowedKStream} instance that can be used to perform session windowed aggregations.
* @param windows the specification of the aggregation {@link SessionWindows}
* @return an instance of {@link TimeWindowedKStream}
*/
SessionWindowedKStream windowedBy(final SessionWindows windows);
/**
* Create a new {@link CogroupedKStream} from the this grouped KStream to allow cogrouping other
* {@code KGroupedStream} to it.
* {@link CogroupedKStream} is an abstraction of multiple grouped record streams of {@link KeyValue} pairs.
* It is an intermediate representation after a grouping of {@link KStream}s, before the
* aggregations are applied to the new partitions resulting in a {@link KTable}.
*
* The specified {@link Aggregator} is applied in the actual {@link CogroupedKStream#aggregate(Initializer)
* aggregation} step for each input record and computes a new aggregate using the current aggregate (or for the very
* first record per key using the initial intermediate aggregation result provided via the {@link Initializer} that
* is passed into {@link CogroupedKStream#aggregate(Initializer)}) and the record's value.
*
* @param aggregator an {@link Aggregator} that computes a new aggregate result
* @param the type of the output values
* @return a {@link CogroupedKStream}
*/
CogroupedKStream cogroup(final Aggregator aggregator);
}