org.apache.kafka.streams.kstream.CogroupedKStream Maven / Gradle / Ivy
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* 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
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*
* 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,
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* See the License for the specific language governing permissions and
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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;
/**
* {@code 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}.
*
* A {@code CogroupedKStream} must be obtained from a {@link KGroupedStream} via
* {@link KGroupedStream#cogroup(Aggregator) cogroup(...)}.
*
* @param Type of keys
* @param Type of values after agg
*/
public interface CogroupedKStream {
/**
* Add an already {@link KGroupedStream grouped KStream} to this {@code CogroupedKStream}.
*
* The added {@link KGroupedStream grouped KStream} must have the same number of partitions as all existing
* streams of this {@code CogroupedKStream}.
* If this is not the case, you would need to call {@link KStream#repartition(Repartitioned)} before
* {@link KStream#groupByKey() grouping} the {@link KStream} and specify the "correct" number of
* partitions via {@link Repartitioned} parameter.
*
* The specified {@link Aggregator} is applied in the actual {@link #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 #aggregate(Initializer)}) and the record's value.
*
* @param groupedStream a group stream
* @param aggregator an {@link Aggregator} that computes a new aggregate result
* @param Type of input values
* @return a {@code CogroupedKStream}
*/
CogroupedKStream cogroup(final KGroupedStream groupedStream,
final Aggregator aggregator);
/**
* Aggregate the values of records in these streams 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)
* 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.
*
* To compute the aggregation the corresponding {@link Aggregator} as specified in
* {@link #cogroup(KGroupedStream, Aggregator) cogroup(...)} is used per input stream.
* The specified {@link Initializer} is applied once per key, directly before the first input record per key is
* processed to provide an initial intermediate aggregation result that is used to process the first record.
*
* 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}.
*
* 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
* ReadOnlyKeyValueStore> localStore = streams.store(queryableStoreName, QueryableStoreTypes.> timestampedKeyValueStore());
* 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#allMetadata()} to query
* the value of the key on a parallel running instance of your Kafka Streams application.
*
* 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 a generated value, 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. 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 aggregate(final Initializer initializer);
/**
* Aggregate the values of records in these streams 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)
* 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.
*
* To compute the aggregation the corresponding {@link Aggregator} as specified in
* {@link #cogroup(KGroupedStream, Aggregator) cogroup(...)} is used per input stream.
* The specified {@link Initializer} is applied once per key, directly before the first input record per key is
* processed to provide an initial intermediate aggregation result that is used to process the first record.
* The specified {@link Named} is applied once to the processor combining the grouped streams.
*
* 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}.
*
* 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
* ReadOnlyKeyValueStore> localStore = streams.store(queryableStoreName, QueryableStoreTypes.> timestampedKeyValueStore());
* 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#allMetadata()} to query
* the value of the key on a parallel running instance of your Kafka Streams application.
*
* 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 computes an initial intermediate aggregation
* result. Cannot be {@code null}.
* @param named name the processor. 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 aggregate(final Initializer initializer,
final Named named);
/**
* Aggregate the values of records in these streams 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)
* 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.
*
* To compute the aggregation the corresponding {@link Aggregator} as specified in
* {@link #cogroup(KGroupedStream, Aggregator) cogroup(...)} is used per input stream.
* The specified {@link Initializer} is applied once per key, directly before the first input record per key is
* processed to provide an initial intermediate aggregation result that is used to process the first record.
*
* 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}.
*
* 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
* ReadOnlyKeyValueStore> localStore = streams.store(queryableStoreName, QueryableStoreTypes.> timestampedKeyValueStore());
* 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#allMetadata()} to query
* the value of the key on a parallel running instance of your Kafka Streams application.
*
* 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 computes an initial intermediate aggregation
* 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 aggregate(final Initializer initializer,
final Materialized> materialized);
/**
* Aggregate the values of records in these streams 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)
* 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.
*
* To compute the aggregation the corresponding {@link Aggregator} as specified in
* {@link #cogroup(KGroupedStream, Aggregator) cogroup(...)} is used per input stream.
* The specified {@link Initializer} is applied once per key, directly before the first input record per key is
* processed to provide an initial intermediate aggregation result that is used to process the first record.
* The specified {@link Named} is used to name the processor combining the grouped streams.
*
* 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}.
*
* To query the local {@link org.apache.kafka.streams.state.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
* ReadOnlyKeyValueStore> localStore = streams.store(queryableStoreName, QueryableStoreTypes.> timestampedKeyValueStore());
* 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#allMetadata()} to query
* the value of the key on a parallel running instance of your Kafka Streams application.
*
* 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 computes an initial intermediate aggregation
* result. Cannot be {@code null}.
* @param materialized an instance of {@link Materialized} used to materialize a state store.
* Cannot be {@code null}.
* @param named name the processors. 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 aggregate(final Initializer initializer,
final Named named,
final Materialized> materialized);
/**
* Create a new {@link TimeWindowedCogroupedKStream} 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 TimeWindowedCogroupedKStream}
*/
TimeWindowedCogroupedKStream windowedBy(final Windows windows);
/**
* Create a new {@link TimeWindowedCogroupedKStream} instance that can be used to perform sliding
* windowed aggregations.
*
* @param windows the specification of the aggregation {@link SlidingWindows}
* @return an instance of {@link TimeWindowedCogroupedKStream}
*/
TimeWindowedCogroupedKStream windowedBy(final SlidingWindows windows);
/**
* Create a new {@link SessionWindowedCogroupedKStream} instance that can be used to perform session
* windowed aggregations.
*
* @param windows the specification of the aggregation {@link SessionWindows}
* @return an instance of {@link SessionWindowedCogroupedKStream}
*/
SessionWindowedCogroupedKStream windowedBy(final SessionWindows windows);
}