org.apache.kafka.streams

Class Topology

java.lang.Object

org.apache.kafka.streams.Topology

public class Topology
extends Object

A logical representation of a ProcessorTopology. A topology is an acyclic graph of sources, processors, and sinks. A source is a node in the graph that consumes one or more Kafka topics and forwards them to its successor nodes. A processor is a node in the graph that receives input records from upstream nodes, processes the records, and optionally forwarding new records to one or all of its downstream nodes. Finally, a sink is a node in the graph that receives records from upstream nodes and writes them to a Kafka topic. A Topology allows you to construct an acyclic graph of these nodes, and then passed into a new KafkaStreams instance that will then begin consuming, processing, and producing records.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	Topology.AutoOffsetReset Sets the auto.offset.reset configuration when adding a source processor or when creating KStream or KTable via StreamsBuilder.

Constructor Summary

Constructors

Constructor and Description
Topology()
Topology(TopologyConfig topologyConfigs)

Method Summary

Modifier and Type	Method and Description
<K,V> Topology	addGlobalStore(StoreBuilder<?> storeBuilder, String sourceName, org.apache.kafka.common.serialization.Deserializer<K> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<V> valueDeserializer, String topic, String processorName, ProcessorSupplier<K,V> stateUpdateSupplier) Deprecated. Since 2.7.0. Use addGlobalStore(StoreBuilder, String, Deserializer, Deserializer, String, String, ProcessorSupplier) instead.
<KIn,VIn> Topology	addGlobalStore(StoreBuilder<?> storeBuilder, String sourceName, org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer, String topic, String processorName, ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier) Adds a global StateStore to the topology.
<K,V> Topology	addGlobalStore(StoreBuilder<?> storeBuilder, String sourceName, TimestampExtractor timestampExtractor, org.apache.kafka.common.serialization.Deserializer<K> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<V> valueDeserializer, String topic, String processorName, ProcessorSupplier<K,V> stateUpdateSupplier) Deprecated. Since 2.7.0. Use addGlobalStore(StoreBuilder, String, TimestampExtractor, Deserializer, Deserializer, String, String, ProcessorSupplier) instead.
<KIn,VIn> Topology	addGlobalStore(StoreBuilder<?> storeBuilder, String sourceName, TimestampExtractor timestampExtractor, org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer, String topic, String processorName, ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier) Adds a global StateStore to the topology.
<KIn,VIn,KOut,VOut> Topology	addProcessor(String name, ProcessorSupplier<KIn,VIn,KOut,VOut> supplier, String... parentNames) Add a new processor node that receives and processes records output by one or more parent source or processor node.
Topology	addProcessor(String name, ProcessorSupplier supplier, String... parentNames) Deprecated. Since 2.7.0 Use addProcessor(String, ProcessorSupplier, String...) instead.
<KIn,VIn> Topology	addReadOnlyStateStore(StoreBuilder<?> storeBuilder, String sourceName, org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer, String topic, String processorName, ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier) Adds a read-only StateStore to the topology.
<KIn,VIn> Topology	addReadOnlyStateStore(StoreBuilder<?> storeBuilder, String sourceName, TimestampExtractor timestampExtractor, org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer, String topic, String processorName, ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier) Adds a read-only StateStore to the topology.
<K,V> Topology	addSink(String name, String topic, org.apache.kafka.common.serialization.Serializer<K> keySerializer, org.apache.kafka.common.serialization.Serializer<V> valueSerializer, StreamPartitioner<? super K,? super V> partitioner, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic.
<K,V> Topology	addSink(String name, String topic, org.apache.kafka.common.serialization.Serializer<K> keySerializer, org.apache.kafka.common.serialization.Serializer<V> valueSerializer, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic.
<K,V> Topology	addSink(String name, String topic, StreamPartitioner<? super K,? super V> partitioner, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic, using the supplied partitioner.
Topology	addSink(String name, String topic, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic.
<K,V> Topology	addSink(String name, TopicNameExtractor<K,V> topicExtractor, org.apache.kafka.common.serialization.Serializer<K> keySerializer, org.apache.kafka.common.serialization.Serializer<V> valueSerializer, StreamPartitioner<? super K,? super V> partitioner, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor.
<K,V> Topology	addSink(String name, TopicNameExtractor<K,V> topicExtractor, org.apache.kafka.common.serialization.Serializer<K> keySerializer, org.apache.kafka.common.serialization.Serializer<V> valueSerializer, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor.
<K,V> Topology	addSink(String name, TopicNameExtractor<K,V> topicExtractor, StreamPartitioner<? super K,? super V> partitioner, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor, using the supplied partitioner.
<K,V> Topology	addSink(String name, TopicNameExtractor<K,V> topicExtractor, String... parentNames) Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor.
Topology	addSource(String name, org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes.
Topology	addSource(String name, org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer, String... topics) Add a new source that consumes the named topics and forwards the records to child processor and/or sink nodes.
Topology	addSource(String name, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes.
Topology	addSource(String name, String... topics) Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes.
Topology	addSource(TimestampExtractor timestampExtractor, String name, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes.
Topology	addSource(TimestampExtractor timestampExtractor, String name, String... topics) Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, String name, org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, String name, org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer, String... topics) Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, String name, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, String name, String... topics) Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, String name, TimestampExtractor timestampExtractor, org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, String name, TimestampExtractor timestampExtractor, org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer, org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer, String... topics) Add a new source that consumes the named topics and forwards the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, TimestampExtractor timestampExtractor, String name, Pattern topicPattern) Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes.
Topology	addSource(Topology.AutoOffsetReset offsetReset, TimestampExtractor timestampExtractor, String name, String... topics) Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes.
Topology	addStateStore(StoreBuilder<?> storeBuilder, String... processorNames) Adds a state store.
Topology	connectProcessorAndStateStores(String processorName, String... stateStoreNames) Connects the processor and the state stores.
TopologyDescription	describe() Returns a description of the specified Topology.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Topology

public Topology()

Topology

public Topology(TopologyConfig topologyConfigs)

Method Detail

addSource

public Topology addSource(String name,
                          String... topics)

Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration. The default TimestampExtractor as specified in the config is used.

Parameters:

name - the unique name of the source used to reference this node when adding processor children.

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(String name,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration. The default TimestampExtractor as specified in the config is used.

Parameters:

name - the unique name of the source used to reference this node when adding processor children.

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          String name,
                          String... topics)

Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration. The default TimestampExtractor as specified in the config is used.

Parameters:

offsetReset - the auto offset reset policy to use for this source if no committed offsets found; acceptable values earliest or latest

name - the unique name of the source used to reference this node when adding processor children.

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          String name,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration. The default TimestampExtractor as specified in the config is used.

Parameters:

offsetReset - the auto offset reset policy value for this source if no committed offsets found; acceptable values earliest or latest.

name - the unique name of the source used to reference this node when adding processor children.

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(TimestampExtractor timestampExtractor,
                          String name,
                          String... topics)

Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration.

Parameters:

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

name - the unique name of the source used to reference this node when adding processor children.

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(TimestampExtractor timestampExtractor,
                          String name,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration.

Parameters:

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

name - the unique name of the source used to reference this node when adding processor children.

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          TimestampExtractor timestampExtractor,
                          String name,
                          String... topics)

Add a new source that consumes the named topics and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration.

Parameters:

offsetReset - the auto offset reset policy to use for this source if no committed offsets found; acceptable values earliest or latest

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

name - the unique name of the source used to reference this node when adding processor children.

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          TimestampExtractor timestampExtractor,
                          String name,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forward the records to child processor and/or sink nodes. The source will use the default key deserializer and default value deserializer specified in the stream configuration.

Parameters:

offsetReset - the auto offset reset policy value for this source if no committed offsets found; acceptable values earliest or latest.

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

name - the unique name of the source used to reference this node when adding processor children.

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(String name,
                          org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer,
                          org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer,
                          String... topics)

Add a new source that consumes the named topics and forwards the records to child processor and/or sink nodes. The source will use the specified key and value deserializers. The default TimestampExtractor as specified in the config is used.

Parameters:

name - the unique name of the source used to reference this node when adding processor children

keyDeserializer - key deserializer used to read this source, if not specified the default key deserializer defined in the configs will be used

valueDeserializer - value deserializer used to read this source, if not specified the default value deserializer defined in the configs will be used

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(String name,
                          org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer,
                          org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes. The source will use the specified key and value deserializers. The provided de-/serializers will be used for all matched topics, so care should be taken to specify patterns for topics that share the same key-value data format. The default TimestampExtractor as specified in the config is used.

Parameters:

name - the unique name of the source used to reference this node when adding processor children

keyDeserializer - key deserializer used to read this source, if not specified the default key deserializer defined in the configs will be used

valueDeserializer - value deserializer used to read this source, if not specified the default value deserializer defined in the configs will be used

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by name

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          String name,
                          org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer,
                          org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer,
                          String... topics)

Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes. The source will use the specified key and value deserializers. The provided de-/serializers will be used for all the specified topics, so care should be taken when specifying topics that share the same key-value data format.

Parameters:

offsetReset - the auto offset reset policy to use for this stream if no committed offsets found; acceptable values are earliest or latest

name - the unique name of the source used to reference this node when adding processor children

keyDeserializer - key deserializer used to read this source, if not specified the default key deserializer defined in the configs will be used

valueDeserializer - value deserializer used to read this source, if not specified the default value deserializer defined in the configs will be used

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by name

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          String name,
                          org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer,
                          org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes. The source will use the specified key and value deserializers. The provided de-/serializers will be used for all matched topics, so care should be taken to specify patterns for topics that share the same key-value data format.

Parameters:

offsetReset - the auto offset reset policy to use for this stream if no committed offsets found; acceptable values are earliest or latest

name - the unique name of the source used to reference this node when adding processor children

keyDeserializer - key deserializer used to read this source, if not specified the default key deserializer defined in the configs will be used

valueDeserializer - value deserializer used to read this source, if not specified the default value deserializer defined in the configs will be used

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by name

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          String name,
                          TimestampExtractor timestampExtractor,
                          org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer,
                          org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer,
                          String... topics)

Add a new source that consumes the named topics and forwards the records to child processor and/or sink nodes. The source will use the specified key and value deserializers.

Parameters:

offsetReset - the auto offset reset policy to use for this stream if no committed offsets found; acceptable values are earliest or latest.

name - the unique name of the source used to reference this node when adding processor children.

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

keyDeserializer - key deserializer used to read this source, if not specified the default key deserializer defined in the configs will be used

valueDeserializer - value deserializer used to read this source, if not specified the default value deserializer defined in the configs will be used

topics - the name of one or more Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by another source

addSource

public Topology addSource(Topology.AutoOffsetReset offsetReset,
                          String name,
                          TimestampExtractor timestampExtractor,
                          org.apache.kafka.common.serialization.Deserializer<?> keyDeserializer,
                          org.apache.kafka.common.serialization.Deserializer<?> valueDeserializer,
                          Pattern topicPattern)

Add a new source that consumes from topics matching the given pattern and forwards the records to child processor and/or sink nodes. The source will use the specified key and value deserializers. The provided de-/serializers will be used for all matched topics, so care should be taken to specify patterns for topics that share the same key-value data format.

Parameters:

offsetReset - the auto offset reset policy to use for this stream if no committed offsets found; acceptable values are earliest or latest

name - the unique name of the source used to reference this node when adding processor children.

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

keyDeserializer - key deserializer used to read this source, if not specified the default key deserializer defined in the configs will be used

valueDeserializer - value deserializer used to read this source, if not specified the default value deserializer defined in the configs will be used

topicPattern - regular expression pattern to match Kafka topics that this source is to consume

Returns:

itself

Throws:

TopologyException - if processor is already added or if topics have already been registered by name

addSink

public Topology addSink(String name,
                        String topic,
                        String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic. The sink will use the default key serializer and default value serializer specified in the stream configuration.

Parameters:

name - the unique name of the sink

topic - the name of the Kafka topic to which this sink should write its records

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and write to its topic

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, StreamPartitioner, String...), addSink(String, String, Serializer, Serializer, String...), addSink(String, String, Serializer, Serializer, StreamPartitioner, String...)

addSink

public <K,V> Topology addSink(String name,
                              String topic,
                              StreamPartitioner<? super K,? super V> partitioner,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic, using the supplied partitioner. The sink will use the default key serializer and default value serializer specified in the stream configuration.

The sink will also use the specified StreamPartitioner to determine how records are distributed among the named Kafka topic's partitions. Such control is often useful with topologies that use state stores in its processors. In most other cases, however, a partitioner needs not be specified and Kafka will automatically distribute records among partitions using Kafka's default partitioning logic.

Parameters:

name - the unique name of the sink

topic - the name of the Kafka topic to which this sink should write its records

partitioner - the function that should be used to determine the partition for each record processed by the sink

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and write to its topic

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, String...), addSink(String, String, Serializer, Serializer, String...), addSink(String, String, Serializer, Serializer, StreamPartitioner, String...)

addSink

public <K,V> Topology addSink(String name,
                              String topic,
                              org.apache.kafka.common.serialization.Serializer<K> keySerializer,
                              org.apache.kafka.common.serialization.Serializer<V> valueSerializer,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic. The sink will use the specified key and value serializers.

Parameters:

name - the unique name of the sink

topic - the name of the Kafka topic to which this sink should write its records

keySerializer - the key serializer used when consuming records; may be null if the sink should use the default key serializer specified in the stream configuration

valueSerializer - the value serializer used when consuming records; may be null if the sink should use the default value serializer specified in the stream configuration

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and write to its topic

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, String...), addSink(String, String, StreamPartitioner, String...), addSink(String, String, Serializer, Serializer, StreamPartitioner, String...)

addSink

public <K,V> Topology addSink(String name,
                              String topic,
                              org.apache.kafka.common.serialization.Serializer<K> keySerializer,
                              org.apache.kafka.common.serialization.Serializer<V> valueSerializer,
                              StreamPartitioner<? super K,? super V> partitioner,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to the named Kafka topic. The sink will use the specified key and value serializers, and the supplied partitioner.

Parameters:

name - the unique name of the sink

topic - the name of the Kafka topic to which this sink should write its records

keySerializer - the key serializer used when consuming records; may be null if the sink should use the default key serializer specified in the stream configuration

valueSerializer - the value serializer used when consuming records; may be null if the sink should use the default value serializer specified in the stream configuration

partitioner - the function that should be used to determine the partition for each record processed by the sink

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and write to its topic

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, String...), addSink(String, String, StreamPartitioner, String...), addSink(String, String, Serializer, Serializer, String...)

addSink

public <K,V> Topology addSink(String name,
                              TopicNameExtractor<K,V> topicExtractor,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor. The topics that it may ever send to should be pre-created. The sink will use the default key serializer and default value serializer specified in the stream configuration.

Parameters:

name - the unique name of the sink

topicExtractor - the extractor to determine the name of the Kafka topic to which this sink should write for each record

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and dynamically write to topics

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, StreamPartitioner, String...), addSink(String, String, Serializer, Serializer, String...), addSink(String, String, Serializer, Serializer, StreamPartitioner, String...)

addSink

public <K,V> Topology addSink(String name,
                              TopicNameExtractor<K,V> topicExtractor,
                              StreamPartitioner<? super K,? super V> partitioner,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor, using the supplied partitioner. The topics that it may ever send to should be pre-created. The sink will use the default key serializer and default value serializer specified in the stream configuration.

The sink will also use the specified StreamPartitioner to determine how records are distributed among the named Kafka topic's partitions. Such control is often useful with topologies that use state stores in its processors. In most other cases, however, a partitioner needs not be specified and Kafka will automatically distribute records among partitions using Kafka's default partitioning logic.

Parameters:

name - the unique name of the sink

topicExtractor - the extractor to determine the name of the Kafka topic to which this sink should write for each record

partitioner - the function that should be used to determine the partition for each record processed by the sink

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and dynamically write to topics

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, String...), addSink(String, String, Serializer, Serializer, String...), addSink(String, String, Serializer, Serializer, StreamPartitioner, String...)

addSink

public <K,V> Topology addSink(String name,
                              TopicNameExtractor<K,V> topicExtractor,
                              org.apache.kafka.common.serialization.Serializer<K> keySerializer,
                              org.apache.kafka.common.serialization.Serializer<V> valueSerializer,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor. The topics that it may ever send to should be pre-created. The sink will use the specified key and value serializers.

Parameters:

name - the unique name of the sink

topicExtractor - the extractor to determine the name of the Kafka topic to which this sink should write for each record

keySerializer - the key serializer used when consuming records; may be null if the sink should use the default key serializer specified in the stream configuration

valueSerializer - the value serializer used when consuming records; may be null if the sink should use the default value serializer specified in the stream configuration

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and dynamically write to topics

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, String...), addSink(String, String, StreamPartitioner, String...), addSink(String, String, Serializer, Serializer, StreamPartitioner, String...)

addSink

public <K,V> Topology addSink(String name,
                              TopicNameExtractor<K,V> topicExtractor,
                              org.apache.kafka.common.serialization.Serializer<K> keySerializer,
                              org.apache.kafka.common.serialization.Serializer<V> valueSerializer,
                              StreamPartitioner<? super K,? super V> partitioner,
                              String... parentNames)

Add a new sink that forwards records from upstream parent processor and/or source nodes to Kafka topics based on topicExtractor. The topics that it may ever send to should be pre-created. The sink will use the specified key and value serializers, and the supplied partitioner.

Parameters:

name - the unique name of the sink

topicExtractor - the extractor to determine the name of the Kafka topic to which this sink should write for each record

keySerializer - the key serializer used when consuming records; may be null if the sink should use the default key serializer specified in the stream configuration

valueSerializer - the value serializer used when consuming records; may be null if the sink should use the default value serializer specified in the stream configuration

partitioner - the function that should be used to determine the partition for each record processed by the sink

parentNames - the name of one or more source or processor nodes whose output records this sink should consume and dynamically write to topics

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

See Also:

addSink(String, String, String...), addSink(String, String, StreamPartitioner, String...), addSink(String, String, Serializer, Serializer, String...)

addProcessor

@Deprecated
public Topology addProcessor(String name,
                                         ProcessorSupplier supplier,
                                         String... parentNames)

Deprecated. Since 2.7.0 Use addProcessor(String, ProcessorSupplier, String...) instead.

Add a new processor node that receives and processes records output by one or more parent source or processor node. Any new record output by this processor will be forwarded to its child processor or sink nodes. The supplier should always generate a new instance each time ProcessorSupplier.get() gets called. Creating a single Processor object and returning the same object reference in ProcessorSupplier.get() would be a violation of the supplier pattern and leads to runtime exceptions. If supplier provides stores via ConnectedStoreProvider.stores(), the provided StoreBuilders will be added to the topology and connected to this processor automatically.

Parameters:

name - the unique name of the processor node

supplier - the supplier used to obtain this node's Processor instance

parentNames - the name of one or more source or processor nodes whose output records this processor should receive and process

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

addProcessor

public <KIn,VIn,KOut,VOut> Topology addProcessor(String name,
                                                 ProcessorSupplier<KIn,VIn,KOut,VOut> supplier,
                                                 String... parentNames)

Add a new processor node that receives and processes records output by one or more parent source or processor node. Any new record output by this processor will be forwarded to its child processor or sink nodes. If supplier provides stores via ConnectedStoreProvider.stores(), the provided StoreBuilders will be added to the topology and connected to this processor automatically.

Parameters:

name - the unique name of the processor node

supplier - the supplier used to obtain this node's Processor instance

parentNames - the name of one or more source or processor nodes whose output records this processor should receive and process

Returns:

itself

Throws:

TopologyException - if parent processor is not added yet, or if this processor's name is equal to the parent's name

addStateStore

public Topology addStateStore(StoreBuilder<?> storeBuilder,
                              String... processorNames)

Adds a state store.

Parameters:

storeBuilder - the storeBuilder used to obtain this state store StateStore instance

processorNames - the names of the processors that should be able to access the provided store

Returns:

itself

Throws:

TopologyException - if state store supplier is already added

addReadOnlyStateStore

public <KIn,VIn> Topology addReadOnlyStateStore(StoreBuilder<?> storeBuilder,
                                                String sourceName,
                                                TimestampExtractor timestampExtractor,
                                                org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer,
                                                org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer,
                                                String topic,
                                                String processorName,
                                                ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier)

Adds a read-only StateStore to the topology.

A read-only StateStore does not create a dedicated changelog topic but uses it's input topic as changelog; thus, the used topic should be configured with log compaction.

The auto.offset.reset property will be set to earliest for this topic.

The provided ProcessorSupplier will be used to create a processor for all messages received from the given topic. This processor should contain logic to keep the StateStore up-to-date.

Parameters:

storeBuilder - user defined store builder

sourceName - name of the SourceNode that will be automatically added

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

keyDeserializer - the Deserializer to deserialize keys with

valueDeserializer - the Deserializer to deserialize values with

topic - the topic to source the data from

processorName - the name of the ProcessorSupplier

stateUpdateSupplier - the instance of ProcessorSupplier

Returns:

itself

Throws:

TopologyException - if the processor of state is already registered

addReadOnlyStateStore

public <KIn,VIn> Topology addReadOnlyStateStore(StoreBuilder<?> storeBuilder,
                                                String sourceName,
                                                org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer,
                                                org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer,
                                                String topic,
                                                String processorName,
                                                ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier)

Adds a read-only StateStore to the topology.

A read-only StateStore does not create a dedicated changelog topic but uses it's input topic as changelog; thus, the used topic should be configured with log compaction.

The auto.offset.reset property will be set to earliest for this topic.

The provided ProcessorSupplier will be used to create a processor for all messages received from the given topic. This processor should contain logic to keep the StateStore up-to-date. The default TimestampExtractor as specified in the config is used.

Parameters:

storeBuilder - user defined store builder

sourceName - name of the SourceNode that will be automatically added

keyDeserializer - the Deserializer to deserialize keys with

valueDeserializer - the Deserializer to deserialize values with

topic - the topic to source the data from

processorName - the name of the ProcessorSupplier

stateUpdateSupplier - the instance of ProcessorSupplier

Returns:

itself

Throws:

TopologyException - if the processor of state is already registered

addGlobalStore

@Deprecated
public <K,V> Topology addGlobalStore(StoreBuilder<?> storeBuilder,
                                                 String sourceName,
                                                 org.apache.kafka.common.serialization.Deserializer<K> keyDeserializer,
                                                 org.apache.kafka.common.serialization.Deserializer<V> valueDeserializer,
                                                 String topic,
                                                 String processorName,
                                                 ProcessorSupplier<K,V> stateUpdateSupplier)

Deprecated. Since 2.7.0. Use addGlobalStore(StoreBuilder, String, Deserializer, Deserializer, String, String, ProcessorSupplier) instead.

Adds a global StateStore to the topology. The StateStore sources its data from all partitions of the provided input topic. There will be exactly one instance of this StateStore per Kafka Streams instance.

A SourceNode with the provided sourceName will be added to consume the data arriving from the partitions of the input topic.

The provided ProcessorSupplier will be used to create an ProcessorNode that will receive all records forwarded from the SourceNode. The supplier should always generate a new instance each time ProcessorSupplier.get() gets called. Creating a single Processor object and returning the same object reference in ProcessorSupplier.get() would be a violation of the supplier pattern and leads to runtime exceptions. This ProcessorNode should be used to keep the StateStore up-to-date. The default TimestampExtractor as specified in the config is used.

Parameters:

storeBuilder - user defined state store builder

sourceName - name of the SourceNode that will be automatically added

keyDeserializer - the Deserializer to deserialize keys with

valueDeserializer - the Deserializer to deserialize values with

topic - the topic to source the data from

processorName - the name of the ProcessorSupplier

stateUpdateSupplier - the instance of ProcessorSupplier

Returns:

itself

Throws:

TopologyException - if the processor of state is already registered

addGlobalStore

@Deprecated
public <K,V> Topology addGlobalStore(StoreBuilder<?> storeBuilder,
                                                 String sourceName,
                                                 TimestampExtractor timestampExtractor,
                                                 org.apache.kafka.common.serialization.Deserializer<K> keyDeserializer,
                                                 org.apache.kafka.common.serialization.Deserializer<V> valueDeserializer,
                                                 String topic,
                                                 String processorName,
                                                 ProcessorSupplier<K,V> stateUpdateSupplier)

Deprecated. Since 2.7.0. Use addGlobalStore(StoreBuilder, String, TimestampExtractor, Deserializer, Deserializer, String, String, ProcessorSupplier) instead.

Adds a global StateStore to the topology. The StateStore sources its data from all partitions of the provided input topic. There will be exactly one instance of this StateStore per Kafka Streams instance.

A SourceNode with the provided sourceName will be added to consume the data arriving from the partitions of the input topic.

The provided ProcessorSupplier will be used to create an ProcessorNode that will receive all records forwarded from the SourceNode. The supplier should always generate a new instance each time ProcessorSupplier.get() gets called. Creating a single Processor object and returning the same object reference in ProcessorSupplier.get() would be a violation of the supplier pattern and leads to runtime exceptions. This ProcessorNode should be used to keep the StateStore up-to-date.

Parameters:

storeBuilder - user defined key value store builder

sourceName - name of the SourceNode that will be automatically added

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

keyDeserializer - the Deserializer to deserialize keys with

valueDeserializer - the Deserializer to deserialize values with

topic - the topic to source the data from

processorName - the name of the ProcessorSupplier

stateUpdateSupplier - the instance of ProcessorSupplier

Returns:

itself

Throws:

TopologyException - if the processor of state is already registered

addGlobalStore

public <KIn,VIn> Topology addGlobalStore(StoreBuilder<?> storeBuilder,
                                         String sourceName,
                                         org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer,
                                         org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer,
                                         String topic,
                                         String processorName,
                                         ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier)

Adds a global StateStore to the topology. The StateStore sources its data from all partitions of the provided input topic. There will be exactly one instance of this StateStore per Kafka Streams instance.

A SourceNode with the provided sourceName will be added to consume the data arriving from the partitions of the input topic.

The provided ProcessorSupplier will be used to create an ProcessorNode that will receive all records forwarded from the SourceNode. This ProcessorNode should be used to keep the StateStore up-to-date. The default TimestampExtractor as specified in the config is used.

Parameters:

storeBuilder - user defined state store builder

sourceName - name of the SourceNode that will be automatically added

keyDeserializer - the Deserializer to deserialize keys with

valueDeserializer - the Deserializer to deserialize values with

topic - the topic to source the data from

processorName - the name of the ProcessorSupplier

stateUpdateSupplier - the instance of ProcessorSupplier

Returns:

itself

Throws:

TopologyException - if the processor of state is already registered

addGlobalStore

public <KIn,VIn> Topology addGlobalStore(StoreBuilder<?> storeBuilder,
                                         String sourceName,
                                         TimestampExtractor timestampExtractor,
                                         org.apache.kafka.common.serialization.Deserializer<KIn> keyDeserializer,
                                         org.apache.kafka.common.serialization.Deserializer<VIn> valueDeserializer,
                                         String topic,
                                         String processorName,
                                         ProcessorSupplier<KIn,VIn,Void,Void> stateUpdateSupplier)

Adds a global StateStore to the topology. The StateStore sources its data from all partitions of the provided input topic. There will be exactly one instance of this StateStore per Kafka Streams instance.

A SourceNode with the provided sourceName will be added to consume the data arriving from the partitions of the input topic.

The provided ProcessorSupplier will be used to create an ProcessorNode that will receive all records forwarded from the SourceNode. This ProcessorNode should be used to keep the StateStore up-to-date.

Parameters:

storeBuilder - user defined key value store builder

sourceName - name of the SourceNode that will be automatically added

timestampExtractor - the stateless timestamp extractor used for this source, if not specified the default extractor defined in the configs will be used

keyDeserializer - the Deserializer to deserialize keys with

valueDeserializer - the Deserializer to deserialize values with

topic - the topic to source the data from

processorName - the name of the ProcessorSupplier

stateUpdateSupplier - the instance of ProcessorSupplier

Returns:

itself

Throws:

TopologyException - if the processor of state is already registered

connectProcessorAndStateStores

public Topology connectProcessorAndStateStores(String processorName,
                                               String... stateStoreNames)

Connects the processor and the state stores.

Parameters:

processorName - the name of the processor

stateStoreNames - the names of state stores that the processor uses

Returns:

itself

Throws:

TopologyException - if the processor or a state store is unknown

describe

public TopologyDescription describe()

Returns a description of the specified Topology.

Returns:

a description of the topology.