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9.6 Upgrade Guide

Upgrading from any older version to 2.6.0 is possible: if upgrading from 2.3 or below, you will need to do two rolling bounces, where during the first rolling bounce phase you set the config upgrade.from="older version" (possible values are "0.10.0" - "2.3") and during the second you remove it. This is required to safely upgrade to the new cooperative rebalancing protocol of the embedded consumer. Note that you will remain using the old eager rebalancing protocol if you skip or delay the second rolling bounce, but you can safely switch over to cooperative at any time once the entire group is on 2.4+ by removing the config value and bouncing. For more details please refer to KIP-429:

  • prepare your application instances for a rolling bounce and make sure that config upgrade.from is set to the version from which it is being upgrade.
  • bounce each instance of your application once 
  • prepare your newly deployed 2.6.0 application instances for a second round of rolling bounces; make sure to remove the value for config upgrade.mode
  • bounce each instance of your application once more to complete the upgrade 

As an alternative, an offline upgrade is also possible. Upgrading from any versions as old as 0.10.0.x to 2.6.0 in offline mode require the following steps: 

  • stop all old (e.g., 0.10.0.x) application instances 
  • update your code and swap old code and jar file with new code and new jar file 
  • restart all new (2.6.0) application instances 

Starting in Kafka Streams 2.6.x, a new processing mode "exactly_once_beta" (configurable via parameter processing.guarantee) is available. To use this new feature, your brokers must be on version 2.5.x or newer. A switch from "exactly_once" to "exactly_once_beta" (or the other way around) is only possible if the application is on version 2.6.x. If you want to upgrade your application from an older version and enable this feature, you first need to upgrade your application to version 2.6.x, staying on "exactly_once", and then do second round of rolling bounces to switch to "exactly_once_beta". For a downgrade, do the reverse: first switch the config from "exactly_once_beta" to "exactly_once" to disable the feature in your 2.6.x application. Afterward, you can downgrade your application to a pre-2.6.x version.

To run a Kafka Streams application version 2.2.1, 2.3.0, or higher a broker version 0.11.0 or higher is required and the on-disk message format must be 0.11 or higher. Brokers must be on version 0.10.1 or higher to run a Kafka Streams application version 0.10.1 to 2.2.0. Additionally, on-disk message format must be 0.10 or higher to run a Kafka Streams application version 1.0 to 2.2.0. For Kafka Streams 0.10.0, broker version 0.10.0 or higher is required.

Since 2.6.0 release, Kafka Streams depends on a RocksDB version that requires MacOS 10.14 or higher.

Another important thing to keep in mind: in deprecated KStreamBuilder class, when a KTable is created from a source topic via KStreamBuilder.table(), its materialized state store will reuse the source topic as its changelog topic for restoring, and will disable logging to avoid appending new updates to the source topic; in the StreamsBuilder class introduced in 1.0, this behavior was changed accidentally: we still reuse the source topic as the changelog topic for restoring, but will also create a separate changelog topic to append the update records from source topic to. In the 2.0 release, we have fixed this issue and now users can choose whether or not to reuse the source topic based on the StreamsConfig#TOPOLOGY_OPTIMIZATION: if you are upgrading from the old KStreamBuilder class and hence you need to change your code to use the new StreamsBuilder, you should set this config value to StreamsConfig#OPTIMIZE to continue reusing the source topic; if you are upgrading from 1.0 or 1.1 where you are already using StreamsBuilder and hence have already created a separate changelog topic, you should set this config value to StreamsConfig#NO_OPTIMIZATION when upgrading to 2.6.0 in order to use that changelog topic for restoring the state store. More details about the new config StreamsConfig#TOPOLOGY_OPTIMIZATION can be found in KIP-295.

Streams API changes in 2.6.0

We added a new processing mode that improves application scalability using exactly-once guarantees (via KIP-447). You can enable this new feature by setting the configuration parameter processing.guarantee to the new value "exactly_once_beta". Note that you need brokers with version 2.5 or newer to use this feature.

As of 2.6.0 Kafka Streams deprecates KStream.through() in favor of the new KStream.repartition() operator (as per KIP-221).KStream.repartition() is similar to KStream.through(), however Kafka Streams will manage the topic for you. If you need to write into and read back from a topic that you mange, you can fall back to use KStream.to() in combination with StreamsBuilder#stream(). Please refer to the developer guide for more details about KStream.repartition().

The usability of StateStores within the Processor API is improved: ProcessorSupplier and TransformerSupplier now extend ConnectedStoreProvider as per KIP-401, enabling a user to provide StateStores with alongside Processor/Transformer logic so that they are automatically added and connected to the processor.

We added a --force option in StreamsResetter to force remove left-over members on broker side when long session time out was configured as per KIP-571.

Streams API changes in 2.5.0

We add a new cogroup() operator (via KIP-150) that allows to aggregate multiple streams in a single operation. Cogrouped streams can also be windowed before they are aggregated. Please refer to the developer guide for more details.

We added a new KStream.toTable() API to translate an input event stream into a changelog stream as per KIP-523.

We added a new Serde type Void in KIP-527 to represent null keys or null values from input topic.

Deprecated UsePreviousTimeOnInvalidTimestamp and replaced it with UsePartitionTimeOnInvalidTimeStamp as per KIP-530.

Deprecated KafkaStreams.store(String, QueryableStoreType) and replaced it with KafkaStreams.store(StoreQueryParameters) to allow querying for a store with variety of parameters, including querying a specific task and stale stores, as per KIP-562 and KIP-535 respectively.

Streams API changes in 2.4.0

As of 2.4.0 Kafka Streams offers a KTable-KTable foreign-key join (as per KIP-213). This joiner allows for records to be joined between two KTables with different keys. Both INNER and LEFT foreign-key joins are supported.

In the 2.4 release, you now can name all operators in a Kafka Streams DSL topology via KIP-307. Giving your operators meaningful names makes it easier to understand the topology description (Topology#describe()#toString()) and understand the full context of what your Kafka Streams application is doing. 
There are new overloads on most KStream and KTable methods that accept a Named object. Typically you’ll provide a name for the DSL operation by using Named.as("my operator name"). Naming of repartition topics for aggregation operations will still use Grouped and join operations will use either Joined or the new StreamJoined object.

Before the 2.4.0 version of Kafka Streams, users of the DSL could not name the state stores involved in a stream-stream join. If users changed their topology and added a operator before the join, the internal names of the state stores would shift, requiring an application reset when redeploying. In the 2.4.0 release, Kafka Streams adds the StreamJoined class, which gives users the ability to name the join processor, repartition topic(s) (if a repartition is required), and the state stores involved in the join. Also, by naming the state stores, the changelog topics backing the state stores are named as well. It’s important to note that naming the stores will not make them queryable via Interactive Queries. 
Another feature delivered by StreamJoined is that you can now configure the type of state store used in the join. You can elect to use in-memory stores or custom state stores for a stream-stream join. Note that the provided stores will not be available for querying via Interactive Queries. With the addition of StreamJoined, stream-stream join operations using Joined have been deprecated. Please switch over to stream-stream join methods using the new overloaded methods. You can get more details from KIP-479.

With the introduction of incremental cooperative rebalancing, Streams no longer requires all tasks be revoked at the beginning of a rebalance. Instead, at the completion of the rebalance only those tasks which are to be migrated to another consumer for overall load balance will need to be closed and revoked. This changes the semantics of the StateListener a bit, as it will not necessarily transition to REBALANCING at the beginning of a rebalance anymore. Note that this means IQ will now be available at all times except during state restoration, including while a rebalance is in progress. If restoration is occurring when a rebalance begins, we will continue to actively restore the state stores and/or process standby tasks during a cooperative rebalance. Note that with this new rebalancing protocol, you may sometimes see a rebalance be followed by a second short rebalance that ensures all tasks are safely distributed. For details on please see KIP-429.

The 2.4.0 release contains newly added and reworked metrics. KIP-444 adds new client level (i.e., KafkaStreams instance level) metrics to the existing thread-level, task-level, and processor-/state-store-level metrics. For a full list of available client level metrics, see the KafkaStreams monitoring section in the operations guide. 
Furthermore, RocksDB metrics are exposed via KIP-471. For a full list of available RocksDB metrics, see the RocksDB monitoring section in the operations guide.

Kafka Streams test-utils got improved via KIP-470 to simplify the process of using TopologyTestDriver to test your application code. We deprecated ConsumerRecordFactoryTopologyTestDriver#pipeInput()OutputVerifier, as well as TopologyTestDriver#readOutput() and replace them with TestInputTopic and TestOutputTopic, respectively. We also introduced a new class TestRecord that simplifies assertion code. For full details see the Testing section in the developer guide.

In 2.4.0, we deprecated WindowStore#put(K key, V value) that should never be used. Instead the existing WindowStore#put(K key, V value, long windowStartTimestamp) should be used (KIP-474).

Furthermore, the PartitionGrouper interface and its corresponding configuration parameter partition.grouper were deprecated (KIP-528) and will be removed in the next major release (KAFKA-7785. Hence, this feature won’t be supported in the future any longer and you need to updated your code accordingly. If you use a custom PartitionGrouper and stop to use it, the created tasks might change. Hence, you will need to reset your application to upgrade it.

Streams API changes in 2.3.0

Version 2.3.0 adds the Suppress operator to the kafka-streams-scala Ktable API.

As of 2.3.0 Streams now offers an in-memory version of the window (KIP-428) and the session (KIP-445) store, in addition to the persistent ones based on RocksDB. The new public interfaces inMemoryWindowStore() and inMemorySessionStore() are added to Stores and provide the built-in in-memory window or session store.

As of 2.3.0 we’ve updated how to turn on optimizations. Now to enable optimizations, you need to do two things. First add this line to your properties properties.setProperty(StreamsConfig.TOPOLOGY_OPTIMIZATION, StreamsConfig.OPTIMIZE);, as you have done before. Second, when constructing your KafkaStreams instance, you’ll need to pass your configuration properties when building your topology by using the overloaded StreamsBuilder.build(Properties) method. For example KafkaStreams myStream = new KafkaStreams(streamsBuilder.build(properties), properties).

In 2.3.0 we have added default implementation to close() and configure() for SerializerDeserializer and Serde so that they can be implemented by lambda expression. For more details please read KIP-331.

To improve operator semantics, new store types are added that allow storing an additional timestamp per key-value pair or window. Some DSL operators (for example KTables) are using those new stores. Hence, you can now retrieve the last update timestamp via Interactive Queries if you specify TimestampedKeyValueStoreType or TimestampedWindowStoreType as your QueryableStoreType. While this change is mainly transparent, there are some corner cases that may require code changes: Caution: If you receive an untyped store and use a cast, you might need to update your code to cast to the correct type. Otherwise, you might get an exception similar to java.lang.ClassCastException: class org.apache.kafka.streams.state.ValueAndTimestamp cannot be cast to class YOUR-VALUE-TYPE upon getting a value from the store. Additionally, TopologyTestDriver#getStateStore() only returns non-built-in stores and throws an exception if a built-in store is accessed. For more details please read KIP-258.

To improve type safety, a new operator KStream#flatTransformValues is added. For more details please read KIP-313.

Kafka Streams used to set the configuration parameter max.poll.interval.ms to Integer.MAX_VALUE. This default value is removed and Kafka Streams uses the consumer default value now. For more details please read KIP-442.

Default configuration for repartition topic was changed: The segment size for index files (segment.index.bytes) is no longer 50MB, but uses the cluster default. Similarly, the configuration segment.ms in no longer 10 minutes, but uses the cluster default configuration. Lastly, the retention period (retention.ms) is changed from Long.MAX_VALUE to -1 (infinite). For more details please read KIP-443.

To avoid memory leaks, RocksDBConfigSetter has a new close() method that is called on shutdown. Users should implement this method to release any memory used by RocksDB config objects, by closing those objects. For more details please read KIP-453.

RocksDB dependency was updated to version 5.18.3. The new version allows to specify more RocksDB configurations, including WriteBufferManager which helps to limit RocksDB off-heap memory usage. For more details please read KAFKA-8215.

Notable changes in Kafka Streams 2.2.1

As of Kafka Streams 2.2.1 a message format 0.11 or higher is required; this implies that brokers must be on version 0.11.0 or higher.

Streams API changes in 2.2.0

We’ve simplified the KafkaStreams#state transition diagram during the starting up phase a bit in 2.2.0: in older versions the state will transit from CREATED to RUNNING, and then to REBALANCING to get the first stream task assignment, and then back to RUNNING; starting in 2.2.0 it will transit from CREATED directly to REBALANCING and then to RUNNING. If you have registered a StateListener that captures state transition events, you may need to adjust your listener implementation accordingly for this simplification (in practice, your listener logic should be very unlikely to be affected at all).

In WindowedSerdes, we’ve added a new static constructor to return a TimeWindowSerde with configurable window size. This is to help users to construct time window serdes to read directly from a time-windowed store’s changelog. More details can be found in KIP-393.

In 2.2.0 we have extended a few public interfaces including KafkaStreams to extend AutoCloseable so that they can be used in a try-with-resource statement. For a full list of public interfaces that get impacted please read KIP-376.

Streams API changes in 2.1.0

We updated TopologyDescription API to allow for better runtime checking. Users are encouraged to use #topicSet() and #topicPattern() accordingly on TopologyDescription.Source nodes, instead of using #topics(), which has since been deprecated. Similarly, use #topic() and #topicNameExtractor() to get descriptions of TopologyDescription.Sink nodes. For more details, see KIP-321.

We’ve added a new class Grouped and deprecated Serialized. The intent of adding Grouped is the ability to name repartition topics created when performing aggregation operations. Users can name the potential repartition topic using the Grouped#as() method which takes a String and is used as part of the repartition topic name. The resulting repartition topic name will still follow the pattern of ${application-id}->name<-repartition. The Grouped class is now favored over Serialized in KStream#groupByKey()KStream#groupBy(), and KTable#groupBy(). Note that Kafka Streams does not automatically create repartition topics for aggregation operations. Additionally, we’ve updated the Joined class with a new method Joined#withName enabling users to name any repartition topics required for performing Stream/Stream or Stream/Table join. For more details repartition topic naming, see KIP-372. As a result we’ve updated the Kafka Streams Scala API and removed the Serialized class in favor of adding Grouped. If you just rely on the implicit Serialized, you just need to recompile; if you pass in Serialized explicitly, sorry you’ll have to make code changes.

We’ve added a new config named max.task.idle.ms to allow users specify how to handle out-of-order data within a task that may be processing multiple topic-partitions (see Out-of-Order Handling section for more details). The default value is set to 0, to favor minimized latency over synchronization between multiple input streams from topic-partitions. If users would like to wait for longer time when some of the topic-partitions do not have data available to process and hence cannot determine its corresponding stream time, they can override this config to a larger value.

We’ve added the missing SessionBytesStoreSupplier#retentionPeriod() to be consistent with the WindowBytesStoreSupplierwhich allows users to get the specified retention period for session-windowed stores. We’ve also added the missing StoreBuilder#withCachingDisabled() to allow users to turn off caching for their customized stores.

We added a new serde for UUIDs (Serdes.UUIDSerde) that you can use via Serdes.UUID() (cf. KIP-206).

We updated a list of methods that take long arguments as either timestamp (fix point) or duration (time period) and replaced them with Instant and Duration parameters for improved semantics. Some old methods base on long are deprecated and users are encouraged to update their code. 
In particular, aggregation windows (hopping/tumbling/unlimited time windows and session windows) as well as join windows now take Duration arguments to specify window size, hop, and gap parameters. Also, window sizes and retention times are now specified as Duration type in Stores class. The Window class has new methods #startTime() and #endTime() that return window start/end timestamp as Instant. For interactive queries, there are new #fetch(...) overloads taking Instant arguments. Additionally, punctuations are now registerd via ProcessorContext#schedule(Duration interval, ...). For more details, see KIP-358.

We deprecated KafkaStreams#close(...) and replaced it with KafkaStreams#close(Duration) that accepts a single timeout argument Note: the new #close(Duration) method has improved (but slightly different) semantics. For more details, see KIP-358.

The newly exposed AdminClient metrics are now available when calling the KafkaStream#metrics() method. For more details on exposing AdminClients metrics see KIP-324

We deprecated the notion of segments in window stores as those are intended to be an implementation details. Thus, method Windows#segments() and variable Windows#segments were deprecated. If you implement custom windows, you should update your code accordingly. Similarly, WindowBytesStoreSupplier#segments() was deprecated and replaced with WindowBytesStoreSupplier#segmentInterval(). If you implement custom window store, you need to update your code accordingly. Finally, Stores#persistentWindowStore(...) were deprecated and replaced with a new overload that does not allow to specify the number of segments any longer. For more details, see KIP-319 (note: KIP-328 and KIP-358 ‘overlap’ with KIP-319).

We’ve added an overloaded StreamsBuilder#build method that accepts an instance of java.util.Properties with the intent of using the StreamsConfig#TOPOLOGY_OPTIMIZATION config added in Kafka Streams 2.0. Before 2.1, when building a topology with the DSL, Kafka Streams writes the physical plan as the user makes calls on the DSL. Now by providing a java.util.Properties instance when executing a StreamsBuilder#build call, Kafka Streams can optimize the physical plan of the topology, provided the StreamsConfig#TOPOLOGY_OPTIMIZATION config is set to StreamsConfig#OPTIMIZE. By setting StreamsConfig#OPTIMIZE in addition to the KTable optimization of reusing the source topic as the changelog topic, the topology may be optimized to merge redundant repartition topics into one repartition topic. The original no parameter version of StreamsBuilder#build is still available for those who wish to not optimize their topology. Note that enabling optimization of the topology may require you to do an application reset when redeploying the application. For more details, see KIP-312

We are introducing static membership towards Kafka Streams user. This feature reduces unnecessary rebalances during normal application upgrades or rolling bounces. For more details on how to use it, checkout static membership design. Note, Kafka Streams uses the same ConsumerConfig#GROUP_INSTANCE_ID_CONFIG, and you only need to make sure it is uniquely defined across different stream instances in one application.

Streams API changes in 2.0.0

In 2.0.0 we have added a few new APIs on the ReadOnlyWindowStore interface (for details please read Streams API changes below). If you have customized window store implementations that extends the ReadOnlyWindowStore interface you need to make code changes.

In addition, if you using Java 8 method references in your Kafka Streams code you might need to update your code to resolve method ambiguities. Hot-swapping the jar-file only might not work for this case. See below a complete list of 2.0.0 API and semantic changes that allow you to advance your application and/or simplify your code base.

We moved Consumed interface from org.apache.kafka.streams to org.apache.kafka.streams.kstream as it was mistakenly placed in the previous release. If your code has already used it there is a simple one-liner change needed in your import statement.

We have also removed some public APIs that are deprecated prior to 1.0.x in 2.0.0. See below for a detailed list of removed APIs.

We have removed the skippedDueToDeserializationError-rate and skippedDueToDeserializationError-total metrics. Deserialization errors, and all other causes of record skipping, are now accounted for in the pre-existing metrics skipped-records-rate and skipped-records-total. When a record is skipped, the event is now logged at WARN level. If these warnings become burdensome, we recommend explicitly filtering out unprocessable records instead of depending on record skipping semantics. For more details, see KIP-274. As of right now, the potential causes of skipped records are:

  • null keys in table sources
  • null keys in table-table inner/left/outer/right joins
  • null keys or values in stream-table joins
  • null keys or values in stream-stream joins
  • null keys or values in aggregations on grouped streams
  • null keys or values in reductions on grouped streams
  • null keys in aggregations on windowed streams
  • null keys in reductions on windowed streams
  • null keys in aggregations on session-windowed streams
  • Errors producing results, when the configured default.production.exception.handler decides to CONTINUE (the default is to FAIL and throw an exception).
  • Errors deserializing records, when the configured default.deserialization.exception.handler decides to CONTINUE (the default is to FAIL and throw an exception). This was the case previously captured in the skippedDueToDeserializationError metrics.
  • Fetched records having a negative timestamp.

We’ve also fixed the metrics name for time and session windowed store operations in 2.0. As a result, our current built-in stores will have their store types in the metric names as in-memory-statein-memory-lru-staterocksdb-staterocksdb-window-state, and rocksdb-session-state. For example, a RocksDB time windowed store’s put operation metrics would now bekafka.streams:type=stream-rocksdb-window-state-metrics,client-id=([-.\w]+),task-id=([-.\w]+),rocksdb-window-state-id=([-.\w]+). Users need to update their metrics collecting and reporting systems for their time and session windowed stores accordingly. For more details, please read the State Store Metrics section.

We have added support for methods in ReadOnlyWindowStore which allows for querying a single window’s key-value pair. For users who have customized window store implementations on the above interface, they’d need to update their code to implement the newly added method as well. For more details, see KIP-261.

We have added public WindowedSerdes to allow users to read from / write to a topic storing windowed table changelogs directly. In addition, in StreamsConfig we have also added default.windowed.key.serde.inner and default.windowed.value.serde.inner to let users specify inner serdes if the default serde classes are windowed serdes. For more details, see KIP-265.

We’ve added message header support in the Processor API in Kafka 2.0.0. In particular, we have added a new API ProcessorContext#headers() which returns a Headers object that keeps track of the headers of the source topic’s message that is being processed. Through this object, users can manipulate the headers map that is being propagated throughout the processor topology as well. For more details please feel free to read the Developer Guide section.

We have deprecated constructors of KafkaStreams that take a StreamsConfig as parameter. Please use the other corresponding constructors that accept java.util.Properties instead. For more details, see KIP-245.

Kafka 2.0.0 allows to manipulate timestamps of output records using the Processor API (KIP-251). To enable this new feature, ProcessorContext#forward(...) was modified. The two existing overloads #forward(Object key, Object value, String childName) and #forward(Object key, Object value, int childIndex) were deprecated and a new overload #forward(Object key, Object value, To to) was added. The new class To allows you to send records to all or specific downstream processors by name and to set the timestamp for the output record. Forwarding based on child index is not supported in the new API any longer.

We have added support to allow routing records dynamically to Kafka topics. More specifically, in both the lower-level Topology#addSink and higher-level KStream#to APIs, we have added variants that take a TopicNameExtractor instance instead of a specific String typed topic name, such that for each received record from the upstream processor, the library will dynamically determine which Kafka topic to write to based on the record’s key and value, as well as record context. Note that all the Kafka topics that may possibly be used are still considered as user topics and hence required to be pre-created. In addition to that, we have modified the StreamPartitioner interface to add the topic name parameter since the topic name now may not be known beforehand; users who have customized implementations of this interface would need to update their code while upgrading their application to use Kafka Streams 2.0.0.

KIP-284 changed the retention time for repartition topics by setting its default value to Long.MAX_VALUE. Instead of relying on data retention Kafka Streams uses the new purge data API to delete consumed data from those topics and to keep used storage small now.

We have modified the ProcessorStateManger#register(...) signature and removed the deprecated loggingEnabled boolean parameter as it is specified in the StoreBuilder. Users who used this function to register their state stores into the processor topology need to simply update their code and remove this parameter from the caller.

Kafka Streams DSL for Scala is a new Kafka Streams client library available for developers authoring Kafka Streams applications in Scala. It wraps core Kafka Streams DSL types to make it easier to call when interoperating with Scala code. For example, it includes higher order functions as parameters for transformations avoiding the need anonymous classes in Java 7 or experimental SAM type conversions in Scala 2.11, automatic conversion between Java and Scala collection types, a way to implicitly provide SerDes to reduce boilerplate from your application and make it more typesafe, and more! For more information see the Kafka Streams DSL for Scala documentation and KIP-270.

We have removed these deprecated APIs:

  • KafkaStreams#toString no longer returns the topology and runtime metadata; to get topology metadata users can call Topology#describe() and to get thread runtime metadata users can call KafkaStreams#localThreadsMetadata (they are deprecated since 1.0.0). For detailed guidance on how to update your code please read here
  • TopologyBuilder and KStreamBuilder are removed and replaced by Topology and StreamsBuidler respectively (they are deprecated since 1.0.0). For detailed guidance on how to update your code please read here
  • StateStoreSupplier are removed and replaced with StoreBuilder (they are deprecated since 1.0.0); and the corresponding Stores#create and KStream, KTable, KGroupedStream overloaded functions that use it have also been removed. For detailed guidance on how to update your code please read here
  • KStream, KTable, KGroupedStream overloaded functions that requires serde and other specifications explicitly are removed and replaced with simpler overloaded functions that use Consumed, Produced, Serialized, Materialized, Joined (they are deprecated since 1.0.0). For detailed guidance on how to update your code please read here
  • Processor#punctuateValueTransformer#punctuateValueTransformer#punctuate and ProcessorContext#schedule(long) are removed and replaced by ProcessorContext#schedule(long, PunctuationType, Punctuator) (they are deprecated in 1.0.0). 
  • The second boolean typed parameter “loggingEnabled” in ProcessorContext#register has been removed; users can now use StoreBuilder#withLoggingEnabled, withLoggingDisabled to specify the behavior when they create the state store. 
  • KTable#writeAs, print, foreach, to, through are removed, users can call KTable#tostream()#writeAs instead for the same purpose (they are deprecated since For detailed list of removed APIs please read here
  • StreamsConfig#ZOOKEEPER_CONNECT_CONFIG are removed as we do not need ZooKeeper dependency in Streams any more (it is deprecated since 

Streams API changes in 1.1.0

We have added support for methods in ReadOnlyWindowStore which allows for querying WindowStores without the necessity of providing keys. For users who have customized window store implementations on the above interface, they’d need to update their code to implement the newly added method as well. For more details, see KIP-205.

There is a new artifact kafka-streams-test-utils providing a TopologyTestDriverConsumerRecordFactory, and OutputVerifier class. You can include the new artifact as a regular dependency to your unit tests and use the test driver to test your business logic of your Kafka Streams application. For more details, see KIP-247.

The introduction of KIP-220 enables you to provide configuration parameters for the embedded admin client created by Kafka Streams, similar to the embedded producer and consumer clients. You can provide the configs via StreamsConfig by adding the configs with the prefix admin.as defined by StreamsConfig#adminClientPrefix(String) to distinguish them from configurations of other clients that share the same config names.

New method in KTable

  • transformValues methods have been added to KTable. Similar to those on KStream, these methods allow for richer, stateful, value transformation similar to the Processor API.

New method in GlobalKTable

  • A method has been provided such that it will return the store name associated with the GlobalKTable or null if the store name is non-queryable. 

New methods in KafkaStreams:

  • added overload for the constructor that allows overriding the Time object used for tracking system wall-clock time; this is useful for unit testing your application code. 

New methods in KafkaClientSupplier

  • added getAdminClient(config) that allows to override an AdminClient used for administrative requests such as internal topic creations, etc. 

New error handling for exceptions during production:

  • added interface ProductionExceptionHandler that allows implementors to decide whether or not Streams should FAIL or CONTINUE when certain exception occur while trying to produce.
  • provided an implementation, DefaultProductionExceptionHandler that always fails, preserving the existing behavior by default.
  • changing which implementation is used can be done by settings default.production.exception.handler to the fully qualified name of a class implementing this interface.

Changes in StreamsResetter

  • added options to specify input topics offsets to reset according to KIP-171

Streams API changes in 1.0.0

With 1.0 a major API refactoring was accomplished and the new API is cleaner and easier to use. This change includes the five main classes KafkaStreamsKStreamBuilderKStreamKTable, and TopologyBuilder (and some more others). All changes are fully backward compatible as old API is only deprecated but not removed. We recommend to move to the new API as soon as you can. We will summarize all API changes in the next paragraphs.

The two main classes to specify a topology via the DSL (KStreamBuilder) or the Processor API (TopologyBuilder) were deprecated and replaced by StreamsBuilder and Topology (both new classes are located in package org.apache.kafka.streams). Note, that StreamsBuilder does not extend Topology, i.e., the class hierarchy is different now. The new classes have basically the same methods as the old ones to build a topology via DSL or Processor API. However, some internal methods that were public in KStreamBuilder and TopologyBuilder but not part of the actual API are not present in the new classes any longer. Furthermore, some overloads were simplified compared to the original classes. See KIP-120 and KIP-182 for full details.

Changing how a topology is specified also affects KafkaStreams constructors, that now only accept a Topology. Using the DSL builder class StreamsBuilder one can get the constructed Topology via StreamsBuilder#build(). Additionally, a new class org.apache.kafka.streams.TopologyDescription (and some more dependent classes) were added. Those can be used to get a detailed description of the specified topology and can be obtained by calling Topology#describe(). An example using this new API is shown in the quickstart section.

New methods in KStream:

  • With the introduction of KIP-202 a new method merge() has been created in KStream as the StreamsBuilder class’s StreamsBuilder#merge() has been removed. The method signature was also changed, too: instead of providing multiple KStreams into the method at the once, only a single KStream is accepted.

New methods in KafkaStreams:

  • retrieve the current runtime information about the local threads via localThreadsMetadata()
  • observe the restoration of all state stores via setGlobalStateRestoreListener(), in which users can provide their customized implementation of the org.apache.kafka.streams.processor.StateRestoreListener interface

Deprecated / modified methods in KafkaStreams:

  • toString()toString(final String indent) were previously used to return static and runtime information. They have been deprecated in favor of using the new classes/methods localThreadsMetadata() / ThreadMetadata (returning runtime information) and TopologyDescription / Topology#describe() (returning static information).
  • With the introduction of KIP-182 you should no longer pass in Serde to KStream#print operations. If you can’t rely on using toStringto print your keys an values, you should instead you provide a custom KeyValueMapper via the Printed#withKeyValueMapper call.
  • setStateListener() now can only be set before the application start running, i.e. before KafkaStreams.start() is called.

Deprecated methods in KGroupedStream

  • Windowed aggregations have been deprecated from KGroupedStream and moved to WindowedKStream. You can now perform a windowed aggregation by, for example, using KGroupedStream#windowedBy(Windows)#reduce(Reducer).

Modified methods in Processor:

  • The Processor API was extended to allow users to schedule punctuate functions either based on data-driven stream time or wall-clock time. As a result, the original ProcessorContext#schedule is deprecated with a new overloaded function that accepts a user customizable Punctuator callback interface, which triggers its punctuate API method periodically based on the PunctuationType. The PunctuationType determines what notion of time is used for the punctuation scheduling: either stream time or wall-clock time (by default, stream time is configured to represent event time via TimestampExtractor). In addition, the punctuate function inside Processoris also deprecated.Before this, users could only schedule based on stream time (i.e. PunctuationType.STREAM_TIME) and hence the punctuate function was data-driven only because stream time is determined (and advanced forward) by the timestamps derived from the input data. If there is no data arriving at the processor, the stream time would not advance and hence punctuation will not be triggered. On the other hand, When wall-clock time (i.e. PunctuationType.WALL_CLOCK_TIME) is used, punctuate will be triggered purely based on wall-clock time. So for example if the Punctuator function is scheduled based on PunctuationType.WALL_CLOCK_TIME, if these 60 records were processed within 20 seconds, punctuate would be called 2 times (one time every 10 seconds); if these 60 records were processed within 5 seconds, then no punctuate would be called at all. Users can schedule multiple Punctuator callbacks with different PunctuationTypes within the same processor by simply calling ProcessorContext#schedule multiple times inside processor’s init() method.

If you are monitoring on task level or processor-node / state store level Streams metrics, please note that the metrics sensor name and hierarchy was changed: The task ids, store names and processor names are no longer in the sensor metrics names, but instead are added as tags of the sensors to achieve consistent metrics hierarchy. As a result you may need to make corresponding code changes on your metrics reporting and monitoring tools when upgrading to 1.0.0. Detailed metrics sensor can be found in the Streams Monitoring section.

The introduction of KIP-161 enables you to provide a default exception handler for deserialization errors when reading data from Kafka rather than throwing the exception all the way out of your streams application. You can provide the configs via the StreamsConfig as StreamsConfig#DEFAULT_DESERIALIZATION_EXCEPTION_HANDLER_CLASS_CONFIG. The specified handler must implement the org.apache.kafka.streams.errors.DeserializationExceptionHandler interface.

The introduction of KIP-173 enables you to provide topic configuration parameters for any topics created by Kafka Streams. This includes repartition and changelog topics. You can provide the configs via the StreamsConfig by adding the configs with the prefix as defined by StreamsConfig#topicPrefix(String). Any properties in the StreamsConfig with the prefix will be applied when creating internal topics. Any configs that aren’t topic configs will be ignored. If you already use StateStoreSupplier or Materialized to provide configs for changelogs, then they will take precedence over those supplied in the config.

Streams API changes in

Updates in StreamsConfig

  • new configuration parameter processing.guarantee is added 
  • configuration parameter key.serde was deprecated and replaced by default.key.serde
  • configuration parameter value.serde was deprecated and replaced by default.value.serde
  • configuration parameter timestamp.extractor was deprecated and replaced by default.timestamp.extractor
  • method keySerde() was deprecated and replaced by defaultKeySerde()
  • method valueSerde() was deprecated and replaced by defaultValueSerde()
  • new method defaultTimestampExtractor() was added 

New methods in TopologyBuilder

  • added overloads for addSource() that allow to define a TimestampExtractor per source node 
  • added overloads for addGlobalStore() that allow to define a TimestampExtractor per source node associated with the global store 

New methods in KStreamBuilder

  • added overloads for stream() that allow to define a TimestampExtractor per input stream 
  • added overloads for table() that allow to define a TimestampExtractor per input table 
  • added overloads for globalKTable() that allow to define a TimestampExtractor per global table 

Deprecated methods in KTable

  • void foreach(final ForeachAction<? super K, ? super V> action)
  • void print()
  • void print(final String streamName)
  • void print(final Serde<K> keySerde, final Serde<V> valSerde)
  • void print(final Serde<K> keySerde, final Serde<V> valSerde, final String streamName)
  • void writeAsText(final String filePath)
  • void writeAsText(final String filePath, final String streamName)
  • void writeAsText(final String filePath, final Serde<K> keySerde, final Serde<V> valSerde)
  • void writeAsText(final String filePath, final String streamName, final Serde<K> keySerde, final Serde<V> valSerde)

The above methods have been deprecated in favor of using the Interactive Queries API. If you want to query the current content of the state store backing the KTable, use the following approach:

  • Make a call to KafkaStreams.store(final String storeName, final QueryableStoreType<T> queryableStoreType)
  • Then make a call to ReadOnlyKeyValueStore.all() to iterate over the keys of a KTable

If you want to view the changelog stream of the KTable then you could call KTable.toStream().print(Printed.toSysOut).

Metrics using exactly-once semantics: 

If "exactly_once" processing is enabled via the processing.guarantee parameter, internally Streams switches from a producer-per-thread to a producer-per-task runtime model. Using "exactly_once_beta" does use a producer-per-thread, so client.id doesn’t change, compared with "at_least_once" for this case). In order to distinguish the different producers, the producer’s client.id additionally encodes the task-ID for this case. Because the producer’s client.id is used to report JMX metrics, it might be required to update tools that receive those metrics.

Producer’s client.id naming schema: 

  • at-least-once (default): [client.Id]-StreamThread-[sequence-number]
  • exactly-once: [client.Id]-StreamThread-[sequence-number]-[taskId]
  • exactly-once-beta: [client.Id]-StreamThread-[sequence-number]

[client.Id] is either set via Streams configuration parameter client.id or defaults to [application.id]-[processId]([processId] is a random UUID). 

Notable changes in

Parameter updates in StreamsConfig:

  • The default config values of embedded producer’s retries and consumer’s max.poll.interval.ms have been changed to improve the resiliency of a Kafka Streams application 

Streams API changes in

New methods in KafkaStreams:

  • set a listener to react on application state change via setStateListener(StateListener listener)
  • retrieve the current application state via state()
  • retrieve the global metrics registry via metrics()
  • apply a timeout when closing an application via close(long timeout, TimeUnit timeUnit)
  • specify a custom indent when retrieving Kafka Streams information via toString(String indent)

Parameter updates in StreamsConfig:

  • parameter zookeeper.connect was deprecated; a Kafka Streams application does no longer interact with ZooKeeper for topic management but uses the new broker admin protocol (cf. KIP-4, Section “Topic Admin Schema”
  • added many new parameters for metrics, security, and client configurations 

Changes in StreamsMetrics interface: 

  • removed methods: addLatencySensor()
  • added methods: addLatencyAndThroughputSensor()addThroughputSensor()recordThroughput()addSensor()removeSensor()

New methods in TopologyBuilder

  • added overloads for addSource() that allow to define a auto.offset.reset policy per source node 
  • added methods addGlobalStore() to add global StateStore

New methods in KStreamBuilder

  • added overloads for stream() and table() that allow to define a auto.offset.reset policy per input stream/table 
  • added method globalKTable() to create a GlobalKTable

New joins for KStream

  • added overloads for join() to join with KTable
  • added overloads for join() and leftJoin() to join with GlobalKTable
  • note, join semantics in 0.10.2 were improved and thus you might see different result compared to 0.10.0.x and 0.10.1.x (cf. Kafka Streams Join Semantics in the Apache Kafka wiki)

Aligned null-key handling for KTable joins: 

  • like all other KTable operations, KTable-KTable joins do not throw an exception on null key records anymore, but drop those records silently 

New window type Session Windows

  • added class SessionWindows to specify session windows 
  • added overloads for KGroupedStream methods count()reduce(), and aggregate() to allow session window aggregations 

Changes to TimestampExtractor

  • method extract() has a second parameter now 
  • new default timestamp extractor class FailOnInvalidTimestamp (it gives the same behavior as old (and removed) default extractor ConsumerRecordTimestampExtractor
  • new alternative timestamp extractor classes LogAndSkipOnInvalidTimestamp and UsePreviousTimeOnInvalidTimestamps

Relaxed type constraints of many DSL interfaces, classes, and methods (cf. KIP-100). 

Streams API changes in

Stream grouping and aggregation split into two methods: 

  • old: KStream #aggregateByKey(), #reduceByKey(), and #countByKey() 
  • new: KStream#groupByKey() plus KGroupedStream #aggregate(), #reduce(), and #count() 
  • Example: stream.countByKey() changes to stream.groupByKey().count() 

Auto Repartitioning: 

  • a call to through() after a key-changing operator and before an aggregation/join is no longer required 
  • Example: stream.selectKey(…).through(…).countByKey() changes to stream.selectKey().groupByKey().count() 


  • methods #sourceTopics(String applicationId) and #topicGroups(String applicationId) got simplified to #sourceTopics() and #topicGroups() 

DSL: new parameter to specify state store names: 

  • The new Interactive Queries feature requires to specify a store name for all source KTables and window aggregation result KTables (previous parameter “operator/window name” is now the storeName) 
  • KStreamBuilder#table(String topic) changes to #topic(String topic, String storeName) 
  • KTable#through(String topic) changes to #through(String topic, String storeName) 
  • KGroupedStream #aggregate(), #reduce(), and #count() require additional parameter “String storeName”
  • Example: stream.countByKey(TimeWindows.of(“windowName”, 1000)) changes to stream.groupByKey().count(TimeWindows.of(1000), “countStoreName”) 


  • Windows are not named anymore: TimeWindows.of(“name”, 1000) changes to TimeWindows.of(1000) (cf. DSL: new parameter to specify state store names) 
  • JoinWindows has no default size anymore: JoinWindows.of(“name”).within(1000) changes to JoinWindows.of(1000)