Deprecated API

Contents

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

Deprecated Interfaces

Interface and Description
org.apache.kafka.common.security.auth.PrincipalBuilder As of Kafka 1.0.0, use KafkaPrincipalBuilder instead. This will be removed in a future major release.
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.common.security.auth.DefaultPrincipalBuilder As of Kafka 1.0.0. This will be removed in a future major release.
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org.apache.kafka.common.errors.GroupCoordinatorNotAvailableException As of Kafka 0.11, this has been replaced by CoordinatorNotAvailableException
org.apache.kafka.common.errors.GroupLoadInProgressException As of Kafka 0.11, this has been replaced by CoordinatorLoadInProgressException
org.apache.kafka.common.errors.NotCoordinatorForGroupException As of Kafka 0.11, this has been replaced by NotCoordinatorException
org.apache.kafka.streams.errors.TopologyBuilderException use Topology instead of TopologyBuilder

Deprecated Fields

Field and Description
org.apache.kafka.common.config.SslConfigs.DEFAULT_PRINCIPAL_BUILDER_CLASS As of 1.0.0. This field will be removed in a future major release. In recent versions, the config is optional and there is no default.
org.apache.kafka.common.config.SaslConfigs.DEFAULT_SASL_ENABLED_MECHANISMS As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SaslConfigs.DEFAULT_SASL_KERBEROS_PRINCIPAL_TO_LOCAL_RULES As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.streams.StreamsConfig.KEY_SERDE_CLASS_CONFIG Use StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG instead.
org.apache.kafka.common.config.SslConfigs.PRINCIPAL_BUILDER_CLASS_CONFIG As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SslConfigs.PRINCIPAL_BUILDER_CLASS_DOC As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SaslConfigs.SASL_ENABLED_MECHANISMS As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SaslConfigs.SASL_ENABLED_MECHANISMS_DOC As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SaslConfigs.SASL_KERBEROS_PRINCIPAL_TO_LOCAL_RULES As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SaslConfigs.SASL_KERBEROS_PRINCIPAL_TO_LOCAL_RULES_DOC As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SslConfigs.SSL_CLIENT_AUTH_CONFIG As of 1.0.0. This field will be removed in a future major release.
org.apache.kafka.common.config.SslConfigs.SSL_CLIENT_AUTH_DOC As of 1.0.0. This field will be removed in a future major release.
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.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.clients.producer.RecordMetadata.checksum() As of Kafka 0.11.0. Because of the potential for message format conversion on the broker, the computed checksum may not match what was stored on the broker, or what will be returned to the consumer. It is therefore unsafe to depend on this checksum for end-to-end delivery guarantees. Additionally, message format v2 does not include a record-level checksum (for performance, the record checksum was replaced with a batch checksum). To maintain compatibility, a partial checksum computed from the record timestamp, serialized key size, and serialized value size is returned instead, but this should not be depended on for end-to-end reliability.
org.apache.kafka.clients.consumer.ConsumerRecord.checksum() As of Kafka 0.11.0. Because of the potential for message format conversion on the broker, the checksum returned by the broker may not match what was computed by the producer. It is therefore unsafe to depend on this checksum for end-to-end delivery guarantees. Additionally, message format v2 does not include a record-level checksum (for performance, the record checksum was replaced with a batch checksum). To maintain compatibility, a partial checksum computed from the record timestamp, serialized key size, and serialized value size is returned instead, but this should not be depended on for end-to-end reliability.
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.common.security.auth.KafkaPrincipal.fromString(String) As of 1.0.0. This method will be removed in a future major release.
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.connect.sink.SinkTask.onPartitionsAssigned(Collection<TopicPartition>) Use SinkTask.open(Collection) for partition initialization.
org.apache.kafka.connect.sink.SinkTask.onPartitionsRevoked(Collection<TopicPartition>) Use SinkTask.close(Collection) instead for partition cleanup.
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.clients.consumer.NoOffsetForPartitionException.partition() please use NoOffsetForPartitionException.partitions
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.common.Metric.value() As of 1.0.0, use Metric.metricValue() instead. This will be removed in a future major release.
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
org.apache.kafka.clients.producer.RecordMetadata(TopicPartition, long, long, long, long, int, int) As of 0.11.0. Use @RecordMetadata.RecordMetadata(TopicPartition, long, long, long, Long, int, int).