Deprecated API

Contents

• Deprecated Interfaces
• Deprecated Classes
• Deprecated Exceptions
• Deprecated Fields
• Deprecated Methods
• Deprecated Constructors

Deprecated Interfaces

Interface and Description
org.apache.kafka.streams.processor.StateStoreSupplier use StoreSupplier
org.apache.kafka.streams.state.Stores.InMemoryKeyValueFactory
org.apache.kafka.streams.state.Stores.PersistentKeyValueFactory

Deprecated Classes

Class and Description
org.apache.kafka.streams.kstream.KStreamBuilder Use StreamsBuilder instead
org.apache.kafka.streams.processor.TopologyBuilder use Topology instead

Deprecated Exceptions

Exceptions and Description
org.apache.kafka.streams.errors.TopologyBuilderException use Topology instead of TopologyBuilder

Deprecated Fields

Field and Description
org.apache.kafka.streams.StreamsConfig.KEY_SERDE_CLASS_CONFIG Use StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG instead.
org.apache.kafka.streams.StreamsConfig.TIMESTAMP_EXTRACTOR_CLASS_CONFIG Use StreamsConfig.DEFAULT_TIMESTAMP_EXTRACTOR_CLASS_CONFIG instead.
org.apache.kafka.streams.StreamsConfig.VALUE_SERDE_CLASS_CONFIG Use StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG instead.
org.apache.kafka.streams.StreamsConfig.ZOOKEEPER_CONNECT_CONFIG Kakfa Streams does not use Zookeeper anymore and this parameter will be ignored.

Deprecated Methods

Method and Description
org.apache.kafka.streams.StreamsBuilder.addGlobalStore(StoreBuilder, String, String, Consumed, String, ProcessorSupplier) use StreamsBuilder.addGlobalStore(StoreBuilder, String, Consumed, ProcessorSupplier) instead
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<T>, Aggregator<? super K, ? super V, T>, Merger<? super K, T>, SessionWindows, Serde<T>) use windowedBy(sessionWindows) followed by aggregate(initializer, aggregator, sessionMerger, Materialized.with(null, aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<T>, Aggregator<? super K, ? super V, T>, Merger<? super K, T>, SessionWindows, Serde<T>, StateStoreSupplier<SessionStore>) use windowedBy(sessionWindows) followed by aggregate(initializer, aggregator, sessionMerger, Materialized.as(KeyValueByteStoreSupplier).withValueSerde(aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<T>, Aggregator<? super K, ? super V, T>, Merger<? super K, T>, SessionWindows, Serde<T>, String) use windowedBy(sessionWindows) followed by aggregate(initializer, aggregator, sessionMerger, Materialized.as(queryableStoreName).withValueSerde(aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedTable.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Aggregator<? super K, ? super V, VR>, Serde<VR>) use aggregate(initializer, adder, subtractor, Materialized.with(null, aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedTable.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Aggregator<? super K, ? super V, VR>, Serde<VR>, String) use aggregate(initializer, adder, subtractor, Materialized.as(queryableStoreName).withValueSerde(aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedTable.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Aggregator<? super K, ? super V, VR>, StateStoreSupplier<KeyValueStore>) use aggregate(initializer, adder, subtractor, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedTable.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Aggregator<? super K, ? super V, VR>, String) use aggregate(initializer, adder, subtractor, Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Serde<VR>) use aggregate(initializer, aggregator, Materialized.with(null, aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Serde<VR>, String) use aggregate(initializer, aggregator, Materialized.as(queryableStoreName).withValueSerde(aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, StateStoreSupplier<KeyValueStore>) use aggregate(initializer, aggregator, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Windows<W>, Serde<VR>) use windowedBy(windows) followed by TimeWindowedKStream.aggregate(Initializer, Aggregator, Materialized) aggregate(initializer, aggregator, Materialized.with(null, aggValueSerde))}
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Windows<W>, Serde<VR>, String) use windowedBy(windows) followed by aggregate(initializer, aggregator, Materialized.as(queryableStoreName).withValueSerde(aggValueSerde))
org.apache.kafka.streams.kstream.KGroupedStream.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, Windows<W>, StateStoreSupplier<WindowStore>) use windowedBy(windows) followed by aggregate(initializer, aggregator, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.count(SessionWindows) use windowedBy(sessionWindows) followed by count()
org.apache.kafka.streams.kstream.KGroupedStream.count(SessionWindows, StateStoreSupplier<SessionStore>) use windowedBy(sessionWindows) followed by count(Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.count(SessionWindows, String) use windowedBy(sessionWindows) followed by count(Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedTable.count(StateStoreSupplier<KeyValueStore>) use count(Materialized.as(KeyValueByteStoreSupplier)
org.apache.kafka.streams.kstream.KGroupedStream.count(StateStoreSupplier<KeyValueStore>) use count(Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedTable.count(String) use count(Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedStream.count(String) use count(Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedStream.count(Windows<W>) use windowedBy(windows) followed by count()
org.apache.kafka.streams.kstream.KGroupedStream.count(Windows<W>, StateStoreSupplier<WindowStore>) use windowedBy(windows) followed by count(Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.count(Windows<W>, String) use windowedBy(windows) followed by count(Materialized.as(queryableStoreName))
org.apache.kafka.streams.state.Stores.create(String) use Stores.persistentKeyValueStore(String), Stores.persistentWindowStore(String, long, int, long, boolean) Stores.persistentSessionStore(String, long), Stores.lruMap(String, int), or Stores.inMemoryKeyValueStore(String)
org.apache.kafka.streams.kstream.KTable.filter(Predicate<? super K, ? super V>, StateStoreSupplier<KeyValueStore>) use filter(predicate, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KTable.filter(Predicate<? super K, ? super V>, String) use filter(predicate, Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KTable.filterNot(Predicate<? super K, ? super V>, StateStoreSupplier<KeyValueStore>) use filterNot(predicate, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KTable.filterNot(Predicate<? super K, ? super V>, String) use filterNot(predicate, Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KTable.foreach(ForeachAction<? super K, ? super V>) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use foreach(action) on the result.
org.apache.kafka.streams.kstream.KTable.groupBy(KeyValueMapper<? super K, ? super V, KeyValue<KR, VR>>, Serde<KR>, Serde<VR>) use groupBy(selector, Serialized.with(keySerde, valueSerde)
org.apache.kafka.streams.kstream.KStream.groupBy(KeyValueMapper<? super K, ? super V, KR>, Serde<KR>, Serde<V>) use groupBy(selector, Serialized.with(keySerde, valSerde))
org.apache.kafka.streams.kstream.KStream.groupByKey(Serde<K>, Serde<V>) use groupByKey(Serialized.with(keySerde, valSerde))
org.apache.kafka.streams.kstream.KStream.join(KStream<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, JoinWindows, Serde<K>, Serde<V>, Serde<VO>) use join(otherStream, joiner, windows, Joined.with(keySerde, thisValueSerde, otherValueSerde))
org.apache.kafka.streams.kstream.KTable.join(KTable<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, Serde<VR>, String) use join(other, joiner, Materialized.as(queryableStoreName).withValueSerde(joinSerde)
org.apache.kafka.streams.kstream.KTable.join(KTable<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, StateStoreSupplier<KeyValueStore>) use join(other, joiner, Materialized.as(KeyValueByteStoreSupplier)
org.apache.kafka.streams.kstream.KStream.join(KTable<K, VT>, ValueJoiner<? super V, ? super VT, ? extends VR>, Serde<K>, Serde<V>) use join(table, joiner, Joined.with(keySerde, valSerde, null))
org.apache.kafka.streams.StreamsConfig.keySerde()
org.apache.kafka.streams.kstream.KStream.leftJoin(KStream<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, JoinWindows, Serde<K>, Serde<V>, Serde<VO>) use leftJoin(otherStream, joiner, windows, Joined.with(keySerde, thisValSerde, otherValueSerde))
org.apache.kafka.streams.kstream.KTable.leftJoin(KTable<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, Serde<VR>, String) use leftJoin(other, joiner, Materialized.as(queryableStoreName).withValueSerde(joinSerde)
org.apache.kafka.streams.kstream.KTable.leftJoin(KTable<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, StateStoreSupplier<KeyValueStore>) use leftJoin(other, joiner, Materialized.as(KeyValueByteStoreSupplier)
org.apache.kafka.streams.kstream.KStream.leftJoin(KTable<K, VT>, ValueJoiner<? super V, ? super VT, ? extends VR>, Serde<K>, Serde<V>) use leftJoin(table, joiner, Joined.with(keySerde, valSerde, null))
org.apache.kafka.streams.kstream.KTable.mapValues(ValueMapper<? super V, ? extends VR>, Serde<VR>, StateStoreSupplier<KeyValueStore>) use mapValues(mapper, Materialized.as(KeyValueByteStoreSupplier).withValueSerde(valueSerde))
org.apache.kafka.streams.kstream.KTable.mapValues(ValueMapper<? super V, ? extends VR>, Serde<VR>, String) use mapValues(mapper, Materialized.as(queryableStoreName).withValueSerde(valueSerde))
org.apache.kafka.streams.kstream.KStream.outerJoin(KStream<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, JoinWindows, Serde<K>, Serde<V>, Serde<VO>) use outerJoin(otherStream, joiner, windows, Joined.with(keySerde, thisValueSerde, otherValueSerde))
org.apache.kafka.streams.kstream.KTable.outerJoin(KTable<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, Serde<VR>, String) use outerJoin(other, joiner, Materialized.as(queryableStoreName).withValueSerde(joinSerde)
org.apache.kafka.streams.kstream.KTable.outerJoin(KTable<K, VO>, ValueJoiner<? super V, ? super VO, ? extends VR>, StateStoreSupplier<KeyValueStore>) use outerJoin(other, joiner, Materialized.as(KeyValueByteStoreSupplier)
org.apache.kafka.streams.kstream.KStream.print() use print(Printed)
org.apache.kafka.streams.kstream.KTable.print() Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toSysOut()) on the result.
org.apache.kafka.streams.kstream.KStream.print(KeyValueMapper<? super K, ? super V, String>) use print(Printed.toSysOut().withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.print(KeyValueMapper<? super K, ? super V, String>, Serde<K>, Serde<V>) use print(Printed.toSysOut().withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.print(KeyValueMapper<? super K, ? super V, String>, Serde<K>, Serde<V>, String) use print(Printed.toSysOut().withLabel(label).withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.print(KeyValueMapper<? super K, ? super V, String>, String) use print(Printed.toSysOut().withLabel(label).withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.print(Serde<K>, Serde<V>) use print(Printed.toSysOut().withKeyValueMapper(...)
org.apache.kafka.streams.kstream.KTable.print(Serde<K>, Serde<V>) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toSysOut().withKeyValueMapper(...) on the result.
org.apache.kafka.streams.kstream.KStream.print(Serde<K>, Serde<V>, String) use print(Printed.toSysOut().withLabel(label).withKeyValueMapper(...)
org.apache.kafka.streams.kstream.KTable.print(Serde<K>, Serde<V>, String) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toSysOut().withLabel(label).withKeyValueMapper(...) on the result.
org.apache.kafka.streams.kstream.KStream.print(String) use print(Printed.toSysOut()
org.apache.kafka.streams.kstream.KTable.print(String) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toSysOut().withLabel(lable)) on the result.
org.apache.kafka.streams.kstream.Transformer.punctuate(long) Please use Punctuator functional interface instead.
org.apache.kafka.streams.kstream.ValueTransformer.punctuate(long) Please use Punctuator functional interface instead.
org.apache.kafka.streams.processor.Processor.punctuate(long) Please use Punctuator functional interface instead.
org.apache.kafka.streams.kstream.KGroupedTable.reduce(Reducer<V>, Reducer<V>, StateStoreSupplier<KeyValueStore>) use reduce(adder, subtractor, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedTable.reduce(Reducer<V>, Reducer<V>, String) use reduce(adder, subtractor, Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, SessionWindows) use windowedBy(sessionWindows) followed by reduce(reducer)
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, SessionWindows, StateStoreSupplier<SessionStore>) use windowedBy(sessionWindows) followed by reduce(reducer, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, SessionWindows, String) use windowedBy(sessionWindows) followed by reduce(reducer, Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, StateStoreSupplier<KeyValueStore>) use reduce(reducer, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, String) use reduce(reducer, Materialized.as(queryableStoreName))
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, Windows<W>) use windowedBy(windows) followed by reduce(reducer)
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, Windows<W>, StateStoreSupplier<WindowStore>) use windowedBy(windows) followed by reduce(reducer, Materialized.as(KeyValueByteStoreSupplier))
org.apache.kafka.streams.kstream.KGroupedStream.reduce(Reducer<V>, Windows<W>, String) use windowedBy(windows) followed by reduce(reducer, Materialized.as(queryableStoreName))
org.apache.kafka.streams.processor.ProcessorContext.schedule(long) Please use ProcessorContext.schedule(long, PunctuationType, Punctuator) instead.
org.apache.kafka.streams.kstream.KStream.through(Serde<K>, Serde<V>, StreamPartitioner<? super K, ? super V>, String) use through(topic, Produced.with(keySerde, valSerde, partitioner))
org.apache.kafka.streams.kstream.KTable.through(Serde<K>, Serde<V>, StreamPartitioner<? super K, ? super V>, String) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde, partitioner)) and StreamsBuilder#table(topic) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(Serde<K>, Serde<V>, StreamPartitioner<? super K, ? super V>, String, StateStoreSupplier<KeyValueStore>) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde, partitioner)) and StreamsBuilder#table(topic, Materialized.as(KeyValueBytesStoreSupplier)) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(Serde<K>, Serde<V>, StreamPartitioner<? super K, ? super V>, String, String) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde, partitioner)) and StreamsBuilder#table(topic, Materialized.as(queryableStoreName)) to read back as a KTable
org.apache.kafka.streams.kstream.KStream.through(Serde<K>, Serde<V>, String) use through(topic, Produced.with(keySerde, valSerde))
org.apache.kafka.streams.kstream.KTable.through(Serde<K>, Serde<V>, String) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde)) and StreamsBuilder#table(topic) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(Serde<K>, Serde<V>, String, StateStoreSupplier<KeyValueStore>) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde)) and StreamsBuilder#table(topic, Materialized.as(KeyValueBytesStoreSupplier) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(Serde<K>, Serde<V>, String, String) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde)) and StreamsBuilder#table(topic, Materialized.as(queryableStoreName)) to read back as a KTable
org.apache.kafka.streams.kstream.KStream.through(StreamPartitioner<? super K, ? super V>, String) use through(topic, Produced.withStreamPartitioner(partitioner))
org.apache.kafka.streams.kstream.KTable.through(StreamPartitioner<? super K, ? super V>, String) use KTable.toStream() followed by to(topic, Produced.streamPartitioner(partitioner)) and StreamsBuilder#table(topic) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(StreamPartitioner<? super K, ? super V>, String, StateStoreSupplier<KeyValueStore>) use KTable.toStream() followed by to(topic, Produced.streamPartitioner(partitioner)) and StreamsBuilder#table(topic, Materialized.as(KeyValueBytesStoreSupplier) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(StreamPartitioner<? super K, ? super V>, String, String) use KTable.toStream() followed by to(topic, Produced.streamPartitioner(partitioner)) and StreamsBuilder#table(topic, Materialized.as(queryableStoreName)) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(String) use KTable.toStream() followed by to(topic) and and StreamsBuilder#table(topic) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(String, StateStoreSupplier<KeyValueStore>) use KTable.toStream() followed by to(topic) and and StreamsBuilder#table(topic, Materialized.as(KeyValueBytesStoreSupplier)) to read back as a KTable
org.apache.kafka.streams.kstream.KTable.through(String, String) use KTable.toStream() followed by to(topic) and StreamsBuilder#table(topic, Materialized.as(queryableStoreName)) to read back as a KTable
org.apache.kafka.streams.kstream.KStream.to(Serde<K>, Serde<V>, StreamPartitioner<? super K, ? super V>, String) use to(topic, Produced.with(keySerde, valSerde, partitioner)
org.apache.kafka.streams.kstream.KTable.to(Serde<K>, Serde<V>, StreamPartitioner<? super K, ? super V>, String) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde, partioner)
org.apache.kafka.streams.kstream.KStream.to(Serde<K>, Serde<V>, String) use to(topic, Produced.with(keySerde, valSerde))
org.apache.kafka.streams.kstream.KTable.to(Serde<K>, Serde<V>, String) use KTable.toStream() followed by to(topic, Produced.with(keySerde, valSerde)
org.apache.kafka.streams.kstream.KStream.to(StreamPartitioner<? super K, ? super V>, String) use to(topic, Produced.withStreamPartitioner(partitioner))
org.apache.kafka.streams.kstream.KTable.to(StreamPartitioner<? super K, ? super V>, String) use KTable.toStream() followed by to(topic, Produced.withStreamPartitioner(partitioner)
org.apache.kafka.streams.kstream.KTable.to(String) use KTable.toStream() followed by to(topic)
org.apache.kafka.streams.KafkaStreams.toString() Use KafkaStreams.localThreadsMetadata() to retrieve runtime information.
org.apache.kafka.streams.KafkaStreams.toString(String) Use KafkaStreams.localThreadsMetadata() to retrieve runtime information.
org.apache.kafka.streams.StreamsConfig.valueSerde()
org.apache.kafka.streams.kstream.KStream.writeAsText(String) use print(Printed.toFile(filePath)
org.apache.kafka.streams.kstream.KTable.writeAsText(String) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toFile(filePath) on the result.
org.apache.kafka.streams.kstream.KStream.writeAsText(String, KeyValueMapper<? super K, ? super V, String>) use print(Printed.toFile(filePath).withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.writeAsText(String, Serde<K>, Serde<V>) use print(Printed.toFile(filePath).withKeyValueMapper(...)
org.apache.kafka.streams.kstream.KTable.writeAsText(String, Serde<K>, Serde<V>) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toFile(filePath).withKeyValueMapper(...) on the result.
org.apache.kafka.streams.kstream.KStream.writeAsText(String, Serde<K>, Serde<V>, KeyValueMapper<? super K, ? super V, String>) use print(Printed.toFile(filePath).withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.writeAsText(String, String) use print(Printed.toFile(filePath).withLabel(label)
org.apache.kafka.streams.kstream.KTable.writeAsText(String, String) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toFile(filePath).withLabel(label) on the result.
org.apache.kafka.streams.kstream.KStream.writeAsText(String, String, KeyValueMapper<? super K, ? super V, String>) use print(Printed.toFile(filePath).withLabel(label).withKeyValueMapper(mapper)
org.apache.kafka.streams.kstream.KStream.writeAsText(String, String, Serde<K>, Serde<V>) use print(Printed.toFile(filePath).withLabel(label).withKeyValueMapper(...)
org.apache.kafka.streams.kstream.KTable.writeAsText(String, String, Serde<K>, Serde<V>) Use the Interactive Queries APIs (e.g., KafkaStreams.store(String, QueryableStoreType) followed by ReadOnlyKeyValueStore.all()) to iterate over the keys of a KTable. Alternatively convert to a KStream using KTable.toStream() and then use print(Printed.toFile(filePath).withLabel(label).withKeyValueMapper(...) on the result.
org.apache.kafka.streams.kstream.KStream.writeAsText(String, String, Serde<K>, Serde<V>, KeyValueMapper<? super K, ? super V, String>) use print(Printed.toFile(filePath).withLabel(label).withKeyValueMapper(mapper)

Deprecated Constructors

Constructor and Description
org.apache.kafka.streams.KafkaStreams(TopologyBuilder, Properties) use KafkaStreams.KafkaStreams(Topology, Properties) instead
org.apache.kafka.streams.KafkaStreams(TopologyBuilder, StreamsConfig) use KafkaStreams.KafkaStreams(Topology, StreamsConfig) instead
org.apache.kafka.streams.KafkaStreams(TopologyBuilder, StreamsConfig, KafkaClientSupplier) use KafkaStreams.KafkaStreams(Topology, StreamsConfig, KafkaClientSupplier) instead