kafka KGroupedTable 源码
kafka KGroupedTable 代码
文件路径：/streams/src/main/java/org/apache/kafka/streams/kstream/KGroupedTable.java
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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
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*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
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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.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 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 interval}.
* <p>
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
* <pre>{@code
* KafkaStreams streams = ... // counting words
* ReadOnlyKeyValueStore<K, ValueAndTimestamp<Long>> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<K, ValueAndTimestamp<Long>> timestampedKeyValueStore());
* K key = "some-word";
* ValueAndTimestamp<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#metadataForAllStreamsClients()} to
* query the value of the key on a parallel running instance of your Kafka Streams application.
*
* <p>
* For failure and recovery the store 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 interval}.
* <p>
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
* <pre>{@code
* KafkaStreams streams = ... // counting words
* ReadOnlyKeyValueStore<K, ValueAndTimestamp<Long>> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<K, ValueAndTimestamp<Long>> timestampedKeyValueStore());
* K key = "some-word";
* ValueAndTimestamp<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#metadataForAllStreamsClients()} to
* query the value of the key on a parallel running instance of your Kafka Streams application.
*
* <p>
* For failure and recovery the store 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 interval}.
* <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 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<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 interval}.
* <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 queryable 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 interval}.
* <p>
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
* <pre>{@code
* KafkaStreams streams = ... // counting words
* ReadOnlyKeyValueStore<K, ValueAndTimestamp<V>> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<K, ValueAndTimestamp<V>> timestampedKeyValueStore());
* K key = "some-word";
* ValueAndTimestamp<V> reduceForWord = 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#metadataForAllStreamsClients()} to
* query the value of the key on a parallel running instance of your Kafka Streams application.
* <p>
* For failure and recovery the store 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 interval}.
* <p>
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
* <pre>{@code
* KafkaStreams streams = ... // counting words
* ReadOnlyKeyValueStore<K, ValueAndTimestamp<V>> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<K, ValueAndTimestamp<V>> timestampedKeyValueStore());
* K key = "some-word";
* ValueAndTimestamp<V> reduceForWord = 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#metadataForAllStreamsClients()} to
* query the value of the key on a parallel running instance of your Kafka Streams application.
* <p>
* For failure and recovery the store 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 interval}.
* <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 queryable 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 interval}.
* <p>
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
* <pre>{@code
* KafkaStreams streams = ... // counting words
* ReadOnlyKeyValueStore<K, ValueAndTimestamp<VR>> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<K, ValueAndTimestamp<VR>> timestampedKeyValueStore());
* K key = "some-word";
* ValueAndTimestamp<VR> aggregateForWord = 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#metadataForAllStreamsClients()} to
* query the value of the key on a parallel running instance of your Kafka Streams application.
* <p>
* For failure and recovery the store 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 interval}.
* <p>
* To query the local {@link ReadOnlyKeyValueStore} it must be obtained via
* {@link KafkaStreams#store(StoreQueryParameters) KafkaStreams#store(...)}:
* <pre>{@code
* KafkaStreams streams = ... // counting words
* ReadOnlyKeyValueStore<K, ValueAndTimestamp<VR>> localStore = streams.store(queryableStoreName, QueryableStoreTypes.<K, ValueAndTimestamp<VR>> timestampedKeyValueStore());
* K key = "some-word";
* ValueAndTimestamp<VR> aggregateForWord = 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#metadataForAllStreamsClients()} to
* query the value of the key on a parallel running instance of your Kafka Streams application.
* <p>
* For failure and recovery the store 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 interval}.
* 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 queryable 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 interval}.
* 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 queryable 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);
}
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