Deprecated List (kafka 1.1.0 API)

Deprecated Interfaces
Interface	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	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	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org.apache.kafka.common.errors.GroupCoordinatorNotAvailableException	As of Kafka 0.11, this has been replaced by `CoordinatorNotAvailableException`
org.apache.kafka.common.errors.GroupLoadInProgressException	As of Kafka 0.11, this has been replaced by `CoordinatorLoadInProgressException`
org.apache.kafka.common.errors.NotCoordinatorForGroupException	As of Kafka 0.11, this has been replaced by `NotCoordinatorException`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	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	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>, String)	use `windowedBy(sessionWindows)` followed by `aggregate(initializer, aggregator, sessionMerger, Materialized.as(queryableStoreName).withValueSerde(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<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>, 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.aggregate(Initializer<VR>, Aggregator<? super K, ? super V, VR>, StateStoreSupplier<KeyValueStore>)	use `aggregate(initializer, aggregator, 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.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.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.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.streams.kstream.KGroupedStream.count(String)	use `count(Materialized.as(queryableStoreName))`
org.apache.kafka.streams.kstream.KGroupedStream.count(SessionWindows)	use `windowedBy(sessionWindows)` followed by `count()`
org.apache.kafka.streams.kstream.KGroupedStream.count(SessionWindows, String)	use `windowedBy(sessionWindows)` followed by `count(Materialized.as(queryableStoreName))`
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(Windows<W>)	use `windowedBy(windows)` followed by `count()`
org.apache.kafka.streams.kstream.KGroupedStream.count(Windows<W>, String)	use `windowedBy(windows)` followed by `count(Materialized.as(queryableStoreName))`
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(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.KGroupedTable.count(StateStoreSupplier<KeyValueStore>)	use `count(Materialized.as(KeyValueByteStoreSupplier)`
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>, String)	use `filter(predicate, Materialized.as(queryableStoreName))`
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.filterNot(Predicate<? super K, ? super V>, String)	use `filterNot(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.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.KStream.groupBy(KeyValueMapper<? super K, ? super V, KR>, Serde<KR>, Serde<V>)	use `groupBy(selector, Serialized.with(keySerde, valSerde))`
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.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.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.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.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.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.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.KTable.mapValues(ValueMapper<? super V, ? extends VR>, Serde<VR>, String)	use `mapValues(mapper, Materialized.as(queryableStoreName).withValueSerde(valueSerde))`
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.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.KStream.print(String)	use `print(Printed.toSysOut()`
org.apache.kafka.streams.kstream.KStream.print(Serde<K>, Serde<V>)	use `print(Printed.toSysOut().withKeyValueMapper(...)`
org.apache.kafka.streams.kstream.KStream.print(Serde<K>, Serde<V>, String)	use `print(Printed.toSysOut().withLabel(label).withKeyValueMapper(...)`
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>, String)	use `print(Printed.toSysOut().withLabel(label).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.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.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.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.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.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.KGroupedStream.reduce(Reducer<V>, String)	use `reduce(reducer, 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, String)	use `windowedBy(sessionWindows)` followed by `reduce(reducer, Materialized.as(queryableStoreName))`
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>, Windows<W>)	use `windowedBy(windows)` followed by `reduce(reducer)`
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.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>, StateStoreSupplier<KeyValueStore>)	use `reduce(reducer, 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.KGroupedTable.reduce(Reducer<V>, Reducer<V>, StateStoreSupplier<KeyValueStore>)	use `reduce(adder, subtractor, Materialized.as(KeyValueByteStoreSupplier))`
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>, String)	use `through(topic, Produced.with(keySerde, valSerde))`
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.KStream.through(StreamPartitioner<? super K, ? super V>, String)	use `through(topic, Produced.withStreamPartitioner(partitioner))`
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, 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.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(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, 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.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>, 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, 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.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(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, 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(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.KStream.to(Serde<K>, Serde<V>, String)	use `to(topic, Produced.with(keySerde, valSerde))`
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.KStream.to(StreamPartitioner<? super K, ? super V>, String)	use `to(topic, Produced.withStreamPartitioner(partitioner))`
org.apache.kafka.streams.kstream.KTable.to(String)	use `KTable.toStream()` followed by `to(topic)`
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.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.KTable.to(StreamPartitioner<? super K, ? super V>, String)	use `KTable.toStream()` followed by `to(topic, Produced.withStreamPartitioner(partitioner)`
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.KStream.writeAsText(String, String)	use `print(Printed.toFile(filePath).withLabel(label)`
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.KStream.writeAsText(String, String, Serde<K>, Serde<V>, KeyValueMapper<? super K, ? super V, String>)	use `print(Printed.toFile(filePath).withLabel(label).withKeyValueMapper(mapper)`
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, Serde<K>, Serde<V>)	use `print(Printed.toFile(filePath).withKeyValueMapper(...)`
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, KeyValueMapper<? super K, ? super V, String>)	use `print(Printed.toFile(filePath).withKeyValueMapper(mapper)`
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.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.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.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.

Deprecated Constructors
Constructor	Description
org.apache.kafka.clients.admin.ConfigEntry(String, String, boolean, boolean, boolean)	since 1.1.0. This constructor will be removed in a future release.
org.apache.kafka.streams.KafkaStreams(TopologyBuilder, Properties)	use `KafkaStreams(Topology, Properties)` instead
org.apache.kafka.streams.KafkaStreams(TopologyBuilder, StreamsConfig)	use `KafkaStreams(Topology, StreamsConfig)` instead
org.apache.kafka.streams.KafkaStreams(TopologyBuilder, StreamsConfig, KafkaClientSupplier)	use `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(TopicPartition, long, long, long, Long, int, int)`.

Deprecated API

Contents