Class TimeWindowedKStreamImpl<K,V,W extends Window>
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- org.apache.kafka.streams.kstream.internals.AbstractStream<K,V>
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- org.apache.kafka.streams.kstream.internals.TimeWindowedKStreamImpl<K,V,W>
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- All Implemented Interfaces:
TimeWindowedKStream<K,V>
public class TimeWindowedKStreamImpl<K,V,W extends Window> extends AbstractStream<K,V> implements TimeWindowedKStream<K,V>
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Field Summary
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Fields inherited from class org.apache.kafka.streams.kstream.internals.AbstractStream
builder, keySerde, name, sourceNodes, streamsGraphNode, valSerde
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description <VR> KTable<Windowed<K>,VR>
aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator)
Aggregate the values of records in this stream by the grouped key.<VR> KTable<Windowed<K>,VR>
aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator, Materialized<K,VR,WindowStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)
Aggregate the values of records in this stream by the grouped key.KTable<Windowed<K>,java.lang.Long>
count()
Count the number of records in this stream by the grouped key and the defined windows.KTable<Windowed<K>,java.lang.Long>
count(Materialized<K,java.lang.Long,WindowStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)
Count the number of records in this stream by the grouped key and the defined windows.KTable<Windowed<K>,V>
reduce(Reducer<V> reducer)
Combine the values of records in this stream by the grouped key.KTable<Windowed<K>,V>
reduce(Reducer<V> reducer, Materialized<K,V,WindowStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)
Combine the values of records in this stream by the grouped key.-
Methods inherited from class org.apache.kafka.streams.kstream.internals.AbstractStream
internalTopologyBuilder, keySerde, valueSerde
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Method Detail
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count
public KTable<Windowed<K>,java.lang.Long> count()
Description copied from interface:TimeWindowedKStream
Count the number of records in this stream by the grouped key and the defined windows. Records withnull
key or value are ignored.Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to the same window and key. The rate of propagated updates depends on your input data rate, the number of distinct keys, the number of parallel running Kafka Streams instances, and the
configuration
parameters forcache size
, andcommit intervall
.For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in
StreamsConfig
via parameterAPPLICATION_ID_CONFIG
, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. Note that the internal store name may not be queriable through Interactive Queries. You can retrieve all generated internal topic names viaTopology.describe()
.- Specified by:
count
in interfaceTimeWindowedKStream<K,V>
- Returns:
- a
KTable
that contains "update" records with unmodified keys andLong
values that represent the latest (rolling) count (i.e., number of records) for each key
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count
public KTable<Windowed<K>,java.lang.Long> count(Materialized<K,java.lang.Long,WindowStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)
Description copied from interface:TimeWindowedKStream
Count the number of records in this stream by the grouped key and the defined windows. Records withnull
key or value are ignored.Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates to the same window and key if caching is enabled on the
Materialized
instance. When caching is enabled the rate of propagated updates depends on your input data rate, the number of distinct keys, the number of parallel running Kafka Streams instances, and theconfiguration
parameters forcache size
, andcommit intervall
To query the local windowed
KeyValueStore
it must be obtained viaKafkaStreams#store(...)
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For non-local keys, a custom RPC mechanism must be implemented usingKafkaStreams streams = ... // counting words Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance ReadOnlyWindowStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>windowStore()); String key = "some-word"; long fromTime = ...; long toTime = ...; WindowStoreIterator<Long> countForWordsForWindows = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
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
StreamsConfig
via parameterAPPLICATION_ID_CONFIG
, "storeName" is the provide store name defined inMaterialized
, and "-changelog" is a fixed suffix. You can retrieve all generated internal topic names viaTopology.describe()
.- Specified by:
count
in interfaceTimeWindowedKStream<K,V>
- Parameters:
materialized
- an instance ofMaterialized
used to materialize a state store. Cannot benull
. Note: the valueSerde will be automatically set toSerdes#Long()
if there is no valueSerde provided- Returns:
- a
KTable
that contains "update" records with unmodified keys andLong
values that represent the latest (rolling) count (i.e., number of records) for each key
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aggregate
public <VR> KTable<Windowed<K>,VR> aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator)
Description copied from interface:TimeWindowedKStream
Aggregate the values of records in this stream by the grouped key. Records withnull
key or value are ignored. Aggregating is a generalization ofcombining via reduce(...)
as it, for example, allows the result to have a different type than the input values. The result is written into a localKeyValueStore
(which is basically an ever-updating materialized view) that can be queried using the providedqueryableStoreName
. Furthermore, updates to the store are sent downstream into aKTable
changelog stream.The specified
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 specifiedAggregator
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 theInitializer
) and the record's value. Thus,aggregate(Initializer, Aggregator)
can be used to compute aggregate functions like count (c.f.TimeWindowedKStream.count()
).The default value serde from config will be used for serializing the result. If a different serde is required then you should use
TimeWindowedKStream.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
configuration
parameters forcache size
, andcommit intervall
.For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in
StreamsConfig
via parameterAPPLICATION_ID_CONFIG
, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. Note that the internal store name may not be queriable through Interactive Queries. You can retrieve all generated internal topic names viaTopology.describe()
.- Specified by:
aggregate
in interfaceTimeWindowedKStream<K,V>
- Type Parameters:
VR
- the value type of the resultingKTable
- Parameters:
initializer
- anInitializer
that computes an initial intermediate aggregation resultaggregator
- anAggregator
that computes a new aggregate result- Returns:
- a
KTable
that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key
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aggregate
public <VR> KTable<Windowed<K>,VR> aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator, Materialized<K,VR,WindowStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)
Description copied from interface:TimeWindowedKStream
Aggregate the values of records in this stream by the grouped key. Records withnull
key or value are ignored. Aggregating is a generalization ofcombining via reduce(...)
as it, for example, allows the result to have a different type than the input values. The result is written into a localKeyValueStore
(which is basically an ever-updating materialized view) that can be queried using the store name as provided withMaterialized
.The specified
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 specifiedAggregator
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 theInitializer
) and the record's value. Thus,aggregate(Initializer, Aggregator, Materialized)
can be used to compute aggregate functions like count (c.f.TimeWindowedKStream.count()
).Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates to the same window and key if caching is enabled on the
Materialized
instance. When caching is enable 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 theconfiguration
parameters forcache size
, andcommit intervall
To query the local windowed
KeyValueStore
it must be obtained viaKafkaStreams#store(...)
:KafkaStreams streams = ... // counting words Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance ReadOnlyWindowStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>windowStore()); String key = "some-word"; long fromTime = ...; long toTime = ...; WindowStoreIterator<Long> aggregateStore = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
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
StreamsConfig
via parameterAPPLICATION_ID_CONFIG
, "storeName" is the provide store name defined inMaterialized
, and "-changelog" is a fixed suffix. You can retrieve all generated internal topic names viaTopology.describe()
.- Specified by:
aggregate
in interfaceTimeWindowedKStream<K,V>
- Type Parameters:
VR
- the value type of the resultingKTable
- Parameters:
initializer
- anInitializer
that computes an initial intermediate aggregation resultaggregator
- anAggregator
that computes a new aggregate resultmaterialized
- an instance ofMaterialized
used to materialize a state store. Cannot benull
.- Returns:
- a
KTable
that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key
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reduce
public KTable<Windowed<K>,V> reduce(Reducer<V> reducer)
Description copied from interface:TimeWindowedKStream
Combine the values of records in this stream by the grouped key. Records withnull
key or value are ignored. Combining implies that the type of the aggregate result is the same as the type of the input value. The result is written into a localKeyValueStore
(which is basically an ever-updating materialized view) that can be queried using the providedqueryableStoreName
. Furthermore, updates to the store are sent downstream into aKTable
changelog stream.The specified
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 theReducer
is not applied and the new aggregate will be the record's value as-is. Thus,reduce(Reducer, String)
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
configuration
parameters forcache size
, andcommit intervall
.For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in
StreamsConfig
via parameterAPPLICATION_ID_CONFIG
, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. You can retrieve all generated internal topic names viaTopology.describe()
.
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reduce
public KTable<Windowed<K>,V> reduce(Reducer<V> reducer, Materialized<K,V,WindowStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)
Description copied from interface:TimeWindowedKStream
Combine the values of records in this stream by the grouped key. Records withnull
key or value are ignored. Combining implies that the type of the aggregate result is the same as the type of the input value. The result is written into a localKeyValueStore
(which is basically an ever-updating materialized view) that can be queried using the store name as provided withMaterialized
. Furthermore, updates to the store are sent downstream into aKTable
changelog stream.The specified
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 theReducer
is not applied and the new aggregate will be the record's value as-is. Thus,reduce(Reducer, String)
can be used to compute aggregate functions like sum, min, or max.Not all updates might get sent downstream, as an internal cache will be used to deduplicate consecutive updates to the same window and key if caching is enabled on the
Materialized
instance. When caching is enable 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 theconfiguration
parameters forcache size
, andcommit intervall
To query the local windowed
KeyValueStore
it must be obtained viaKafkaStreams#store(...)
:KafkaStreams streams = ... // counting words Store queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance ReadOnlyWindowStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>windowStore()); String key = "some-word"; long fromTime = ...; long toTime = ...; WindowStoreIterator<Long> reduceStore = localWindowStore.fetch(key, timeFrom, timeTo); // key must be local (application state is shared over all running Kafka Streams instances)
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
StreamsConfig
via parameterAPPLICATION_ID_CONFIG
, "storeName" is the provide store name defined inMaterialized
, and "-changelog" is a fixed suffix. You can retrieve all generated internal topic names viaTopology.describe()
.
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