org.apache.kafka.streams.kstream

Interface SessionWindowedKStream<K,V>

Type Parameters:

K - Type of keys

V - Type of values

public interface SessionWindowedKStream<K,V>

SessionWindowedKStream is an abstraction of a windowed record stream of KeyValue pairs. It is an intermediate representation after a grouping and windowing of a KStream before an aggregation is applied to the new (partitioned) windows resulting in a windowed KTable (a windowed KTable is a KTable with key type Windowed).

SessionWindows are dynamic data driven windows. They have no fixed time boundaries, rather the size of the window is determined by the records.

The result is written into a local SessionStore (which is basically an ever-updating materialized view) that can be queried using the name provided in the Materialized instance. Furthermore, updates to the store are sent downstream into a windowed KTable changelog stream, where "windowed" implies that the KTable key is a combined key of the original record key and a window ID. New events are added to sessions until their grace period ends (see SessionWindows.grace(Duration)).

A SessionWindowedKStream must be obtained from a KGroupedStream via KGroupedStream.windowedBy(SessionWindows).

See Also:

KStream, KGroupedStream, SessionWindows

Method Summary

Modifier and Type	Method and Description
<VR> KTable<Windowed<K>,VR>	aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator, Merger<? super K,VR> sessionMerger) Aggregate the values of records in this stream by the grouped key and defined sessions.
<VR> KTable<Windowed<K>,VR>	aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator, Merger<? super K,VR> sessionMerger, Materialized<K,VR,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized) Aggregate the values of records in this stream by the grouped key and defined sessions.
<VR> KTable<Windowed<K>,VR>	aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator, Merger<? super K,VR> sessionMerger, Named named) Aggregate the values of records in this stream by the grouped key and defined sessions.
<VR> KTable<Windowed<K>,VR>	aggregate(Initializer<VR> initializer, Aggregator<? super K,? super V,VR> aggregator, Merger<? super K,VR> sessionMerger, Named named, Materialized<K,VR,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized) Aggregate the values of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,Long>	count() Count the number of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,Long>	count(Materialized<K,Long,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized) Count the number of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,Long>	count(Named named) Count the number of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,Long>	count(Named named, Materialized<K,Long,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized) Count the number of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,V>	reduce(Reducer<V> reducer) Combine the values of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,V>	reduce(Reducer<V> reducer, Materialized<K,V,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized) Combine the values of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,V>	reduce(Reducer<V> reducer, Named named) Combine the values of records in this stream by the grouped key and defined sessions.
KTable<Windowed<K>,V>	reduce(Reducer<V> reducer, Named named, Materialized<K,V,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized) Combine the values of records in this stream by the grouped key and defined sessions.

Method Detail

count

KTable<Windowed<K>,Long> count()

Count the number of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored.

The result is written into a local SessionStore (which is basically an ever-updating materialized view). The default key serde from the config will be used for serializing the result. If a different serde is required then you should use count(Materialized). Furthermore, updates to the store are sent downstream into a KTable changelog stream. Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to the same session 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 for cache size, and commit intervall.

For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in StreamsConfig via parameter APPLICATION_ID_CONFIG, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. Note that the internal store name may not be queriable through Interactive Queries.

You can retrieve all generated internal topic names via Topology.describe().

Returns:

a windowed KTable that contains "update" records with unmodified keys and Long values that represent the latest (rolling) count (i.e., number of records) for each key per session

count

KTable<Windowed<K>,Long> count(Named named)

Count the number of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored.

The result is written into a local SessionStore (which is basically an ever-updating materialized view). The default key serde from the config will be used for serializing the result. If a different serde is required then you should use count(Named, Materialized). Furthermore, updates to the store are sent downstream into a KTable changelog stream. Not all updates might get sent downstream, as an internal cache is used to deduplicate consecutive updates to the same session 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 for cache size, and commit intervall.

For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in StreamsConfig via parameter APPLICATION_ID_CONFIG, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. Note that the internal store name may not be queriable through Interactive Queries.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

named - a Named config used to name the processor in the topology. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys and Long values that represent the latest (rolling) count (i.e., number of records) for each key per session

count

KTable<Windowed<K>,Long> count(Materialized<K,Long,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)

Count the number of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored.

The result is written into a local SessionStore (which is basically an ever-updating materialized view) that can be queried using the name provided with Materialized. Furthermore, updates to the store are sent downstream into a KTable changelog stream.

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 the configuration parameters for cache size, and commit intervall.

To query the local SessionStore it must be obtained via KafkaStreams#store(...):

 KafkaStreams streams = ... // compute sum
 Sting queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
 ReadOnlySessionStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>ReadOnlySessionStore<String, Long>);
 String key = "some-key";
 KeyValueIterator<Windowed<String>, Long> sumForKeyForWindows = localWindowStore.fetch(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 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 parameter APPLICATION_ID_CONFIG, "storeName" is the provide store name defined in Materialized, and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

materialized - an instance of Materialized used to materialize a state store. Cannot be null. Note: the valueSerde will be automatically set to Serdes#Long() if there is no valueSerde provided

Returns:

a windowed KTable that contains "update" records with unmodified keys and Long values that represent the latest (rolling) count (i.e., number of records) for each key per session

count

KTable<Windowed<K>,Long> count(Named named,
                               Materialized<K,Long,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)

Count the number of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored.

The result is written into a local SessionStore (which is basically an ever-updating materialized view) that can be queried using the name provided with Materialized. Furthermore, updates to the store are sent downstream into a KTable changelog stream.

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 the configuration parameters for cache size, and commit intervall.

To query the local SessionStore it must be obtained via KafkaStreams#store(...):

 KafkaStreams streams = ... // compute sum
 Sting queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
 ReadOnlySessionStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>ReadOnlySessionStore<String, Long>);
 String key = "some-key";
 KeyValueIterator<Windowed<String>, Long> sumForKeyForWindows = localWindowStore.fetch(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 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 parameter APPLICATION_ID_CONFIG, "storeName" is the provide store name defined in Materialized, and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

named - a Named config used to name the processor in the topology. Cannot be null.

materialized - an instance of Materialized used to materialize a state store. Cannot be null. Note: the valueSerde will be automatically set to Serdes#Long() if there is no valueSerde provided

Returns:

a windowed KTable that contains "update" records with unmodified keys and Long values that represent the latest (rolling) count (i.e., number of records) for each key per session

aggregate

<VR> KTable<Windowed<K>,VR> aggregate(Initializer<VR> initializer,
                                      Aggregator<? super K,? super V,VR> aggregator,
                                      Merger<? super K,VR> sessionMerger)

Aggregate the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored. Aggregating is a generalization of 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 SessionStore (which is basically an ever-updating materialized view). Furthermore, updates to the store are sent downstream into a KTable changelog stream.

The specified Initializer is applied directly before the first input record per session is processed to provide an initial intermediate aggregation result that is used to process the first record per session. The specified 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 Initializer) and the record's value. The specified Merger is used to merge two existing sessions into one, i.e., when the windows overlap, they are merged into a single session and the old sessions are discarded. Thus, aggregate() can be used to compute aggregate functions like count (c.f. count()).

The default key and value serde from the config will be used for serializing the result. If a different serde is required then you should use aggregate(Initializer, Aggregator, Merger, Materialized).

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 for cache size, and commit intervall.

For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in StreamsConfig via parameter APPLICATION_ID_CONFIG, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. Note that the internal store name may not be queriable through Interactive Queries.

You can retrieve all generated internal topic names via Topology.describe().

Type Parameters:

VR - the value type of the resulting KTable

Parameters:

initializer - an Initializer that computes an initial intermediate aggregation result. Cannot be null.

aggregator - an Aggregator that computes a new aggregate result. Cannot be null.

sessionMerger - a Merger that combines two aggregation results. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

aggregate

<VR> KTable<Windowed<K>,VR> aggregate(Initializer<VR> initializer,
                                      Aggregator<? super K,? super V,VR> aggregator,
                                      Merger<? super K,VR> sessionMerger,
                                      Named named)

Aggregate the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored. Aggregating is a generalization of 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 SessionStore (which is basically an ever-updating materialized view). Furthermore, updates to the store are sent downstream into a KTable changelog stream.

The specified Initializer is applied directly before the first input record per session is processed to provide an initial intermediate aggregation result that is used to process the first record per session. The specified 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 Initializer) and the record's value. The specified Merger is used to merge two existing sessions into one, i.e., when the windows overlap, they are merged into a single session and the old sessions are discarded. Thus, aggregate() can be used to compute aggregate functions like count (c.f. count()).

The default key and value serde from the config will be used for serializing the result. If a different serde is required then you should use aggregate(Initializer, Aggregator, Merger, Named, Materialized).

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 for cache size, and commit intervall.

For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in StreamsConfig via parameter APPLICATION_ID_CONFIG, "internalStoreName" is an internal name and "-changelog" is a fixed suffix. Note that the internal store name may not be queriable through Interactive Queries.

You can retrieve all generated internal topic names via Topology.describe().

Type Parameters:

VR - the value type of the resulting KTable

Parameters:

initializer - an Initializer that computes an initial intermediate aggregation result. Cannot be null.

aggregator - an Aggregator that computes a new aggregate result. Cannot be null.

sessionMerger - a Merger that combines two aggregation results. Cannot be null.

named - a Named config used to name the processor in the topology. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

aggregate

<VR> KTable<Windowed<K>,VR> aggregate(Initializer<VR> initializer,
                                      Aggregator<? super K,? super V,VR> aggregator,
                                      Merger<? super K,VR> sessionMerger,
                                      Materialized<K,VR,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)

Aggregate the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored. Aggregating is a generalization of 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 SessionStore (which is basically an ever-updating materialized view) that can be queried using the store name as provided with Materialized. Furthermore, updates to the store are sent downstream into a KTable changelog stream.

The specified Initializer is applied directly before the first input record per session is processed to provide an initial intermediate aggregation result that is used to process the first record per session. The specified 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 Initializer) and the record's value. The specified Merger is used to merge two existing sessions into one, i.e., when the windows overlap, they are merged into a single session and the old sessions are discarded. Thus, aggregate() can be used to compute aggregate functions like count (c.f. count()).

Not all updates might get sent downstream, as an internal cache is 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 the configuration parameters for cache size, and commit intervall.

To query the local SessionStore it must be obtained via KafkaStreams#store(...):

 KafkaStreams streams = ... // some windowed aggregation on value type double
 Sting queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
 ReadOnlySessionStore<String, Long> sessionStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>sessionStore());
 String key = "some-key";
 KeyValueIterator<Windowed<String>, Long> aggForKeyForSession = localWindowStore.fetch(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 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 parameter APPLICATION_ID_CONFIG, "storeName" is the provide store name defined in Materialized, and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Type Parameters:

VR - the value type of the resulting KTable

Parameters:

initializer - an Initializer that computes an initial intermediate aggregation result. Cannot be null.

aggregator - an Aggregator that computes a new aggregate result. Cannot be null.

sessionMerger - a Merger that combines two aggregation results. Cannot be null.

materialized - a Materialized config used to materialize a state store. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

aggregate

<VR> KTable<Windowed<K>,VR> aggregate(Initializer<VR> initializer,
                                      Aggregator<? super K,? super V,VR> aggregator,
                                      Merger<? super K,VR> sessionMerger,
                                      Named named,
                                      Materialized<K,VR,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)

Aggregate the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with null key or value are ignored. Aggregating is a generalization of 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 SessionStore (which is basically an ever-updating materialized view) that can be queried using the store name as provided with Materialized. Furthermore, updates to the store are sent downstream into a KTable changelog stream.

The specified Initializer is applied directly before the first input record per session is processed to provide an initial intermediate aggregation result that is used to process the first record per session. The specified 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 Initializer) and the record's value. The specified Merger is used to merge two existing sessions into one, i.e., when the windows overlap, they are merged into a single session and the old sessions are discarded. Thus, aggregate() can be used to compute aggregate functions like count (c.f. 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 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 the configuration parameters for cache size, and commit intervall.

To query the local SessionStore it must be obtained via KafkaStreams#store(...):

 KafkaStreams streams = ... // some windowed aggregation on value type double
 Sting queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
 ReadOnlySessionStore<String, Long> sessionStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>sessionStore());
 String key = "some-key";
 KeyValueIterator<Windowed<String>, Long> aggForKeyForSession = localWindowStore.fetch(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 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 parameter APPLICATION_ID_CONFIG, "storeName" is the provide store name defined in Materialized, and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Type Parameters:

VR - the value type of the resulting KTable

Parameters:

initializer - an Initializer that computes an initial intermediate aggregation result. Cannot be null.

aggregator - an Aggregator that computes a new aggregate result. Cannot be null.

sessionMerger - a Merger that combines two aggregation results. Cannot be null.

named - a Named config used to name the processor in the topology. Cannot be null.

materialized - a Materialized config used to materialize a state store. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

reduce

KTable<Windowed<K>,V> reduce(Reducer<V> reducer)

Combine the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with 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. aggregate(Initializer, Aggregator, Merger)). The result is written into a local SessionStore (which is basically an ever-updating materialized view). Furthermore, updates to the store are sent downstream into a KTable changelog stream. The default key and value serde from the config will be used for serializing the result. If a different serde is required then you should use reduce(Reducer, Materialized) .

The value of the first record per session initialized the session result. The specified Reducer is applied for each additional input record per session and computes a new aggregate using the current aggregate (first argument) and the record's value (second argument):

 // At the example of a Reducer<Long>
 new Reducer<Long>() {
   public Long apply(Long aggValue, Long currValue) {
     return aggValue + currValue;
   }
 }
 

Thus, reduce() 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 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 for cache size, and commit intervall.

For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in StreamsConfig via parameter APPLICATION_ID_CONFIG, "internalStoreName" is an internal name and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

reducer - a Reducer that computes a new aggregate result. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

reduce

KTable<Windowed<K>,V> reduce(Reducer<V> reducer,
                             Named named)

Combine the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with 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. aggregate(Initializer, Aggregator, Merger)). The result is written into a local SessionStore (which is basically an ever-updating materialized view). Furthermore, updates to the store are sent downstream into a KTable changelog stream. The default key and value serde from the config will be used for serializing the result. If a different serde is required then you should use reduce(Reducer, Named, Materialized) .

The value of the first record per session initialized the session result. The specified Reducer is applied for each additional input record per session and computes a new aggregate using the current aggregate (first argument) and the record's value (second argument):

 // At the example of a Reducer<Long>
 new Reducer<Long>() {
   public Long apply(Long aggValue, Long currValue) {
     return aggValue + currValue;
   }
 }
 

Thus, reduce() 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 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 for cache size, and commit intervall.

For failure and recovery the store will be backed by an internal changelog topic that will be created in Kafka. The changelog topic will be named "${applicationId}-${internalStoreName}-changelog", where "applicationId" is user-specified in StreamsConfig via parameter APPLICATION_ID_CONFIG, "internalStoreName" is an internal name and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

reducer - a Reducer that computes a new aggregate result. Cannot be null.

named - a Named config used to name the processor in the topology. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

reduce

KTable<Windowed<K>,V> reduce(Reducer<V> reducer,
                             Materialized<K,V,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)

Combine the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with 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. aggregate(Initializer, Aggregator, Merger)). The result is written into a local SessionStore (which is basically an ever-updating materialized view) that can be queried using the store name as provided with Materialized. Furthermore, updates to the store are sent downstream into a KTable changelog stream.

The value of the first record per session initialized the session result. The specified Reducer is applied for each additional input record per session and computes a new aggregate using the current aggregate (first argument) and the record's value (second argument):

 // At the example of a Reducer<Long>
 new Reducer<Long>() {
   public Long apply(Long aggValue, Long currValue) {
     return aggValue + currValue;
   }
 }
 

Thus, reduce() 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 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 the configuration parameters for cache size, and commit intervall.

To query the local SessionStore it must be obtained via KafkaStreams#store(...):

 KafkaStreams streams = ... // compute sum
 Sting queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
 ReadOnlySessionStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>ReadOnlySessionStore<String, Long>);
 String key = "some-key";
 KeyValueIterator<Windowed<String>, Long> sumForKeyForWindows = localWindowStore.fetch(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 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 parameter APPLICATION_ID_CONFIG, "storeName" is the provide store name defined in Materialized, and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

reducer - a Reducer that computes a new aggregate result. Cannot be null.

materialized - a Materialized config used to materialize a state store. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session

reduce

KTable<Windowed<K>,V> reduce(Reducer<V> reducer,
                             Named named,
                             Materialized<K,V,SessionStore<org.apache.kafka.common.utils.Bytes,byte[]>> materialized)

Combine the values of records in this stream by the grouped key and defined sessions. Note that sessions are generated on a per-key basis and records with different keys create independent sessions. Records with 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. aggregate(Initializer, Aggregator, Merger)). The result is written into a local SessionStore (which is basically an ever-updating materialized view) that can be queried using the store name as provided with Materialized. Furthermore, updates to the store are sent downstream into a KTable changelog stream.

The value of the first record per session initialized the session result. The specified Reducer is applied for each additional input record per session and computes a new aggregate using the current aggregate (first argument) and the record's value (second argument):

 // At the example of a Reducer<Long>
 new Reducer<Long>() {
   public Long apply(Long aggValue, Long currValue) {
     return aggValue + currValue;
   }
 }
 

Thus, reduce() 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 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 the configuration parameters for cache size, and commit intervall.

To query the local SessionStore it must be obtained via KafkaStreams.store(StoreQueryParameters) KafkaStreams#store(...)}:

 KafkaStreams streams = ... // compute sum
 Sting queryableStoreName = ... // the queryableStoreName should be the name of the store as defined by the Materialized instance
 ReadOnlySessionStore<String,Long> localWindowStore = streams.store(queryableStoreName, QueryableStoreTypes.<String, Long>ReadOnlySessionStore<String, Long>);
 String key = "some-key";
 KeyValueIterator<Windowed<String>, Long> sumForKeyForWindows = localWindowStore.fetch(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 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 parameter APPLICATION_ID_CONFIG, "storeName" is the provide store name defined in Materialized, and "-changelog" is a fixed suffix.

You can retrieve all generated internal topic names via Topology.describe().

Parameters:

reducer - a Reducer that computes a new aggregate result. Cannot be null.

named - a Named config used to name the processor in the topology. Cannot be null.

materialized - a Materialized config used to materialize a state store. Cannot be null.

Returns:

a windowed KTable that contains "update" records with unmodified keys, and values that represent the latest (rolling) aggregate for each key per session