Kafka Streams Upgrade Guide¶

Note

To upgrade from Confluent Platform versions earlier than 6.0.0, see Legacy Streams Upgrade Guide.

Upgrade to Confluent Platform 7.4.7 from Confluent Platform 6.0.x¶

Compatibility¶

Kafka Streams applications built with Confluent Platform 7.4.7 are forward and backward compatible with certain Kafka clusters.

Forward-compatible to newer clusters up to Confluent Platform 7.4.7:

Existing Kafka Streams applications built with Confluent Platform 3.x and later work with upgraded Kafka clusters running Confluent Platform 7.4.7.

Backward-compatible to older clusters down to Confluent Platform 3.0.x:

New Kafka Streams applications built with Confluent Platform 7.4.7 work with older Kafka clusters running Confluent Platform 6.x down to 3.0.x.
Kafka clusters running Confluent Platform 3.0.x, Confluent Platform 3.1.x, or Confluent Platform 3.2.x are not compatible with new Confluent Platform 7.4.7 Kafka Streams applications.

Compatibility Matrix:

	Kafka Broker (columns)
Streams API (rows)	3.0.x / 0.10.0.x	3.1.x / 0.10.1.x and 3.2.x / 0.10.2.x	3.3.x / 0.11.0.x and 4.0.x / 1.0.x and 4.1.x / 1.1.x and 5.0.x / 2.0.x and 5.1.x / 2.1.x and 5.2.x / 2.2.x and 5.3.x / 2.3.x and 5.4.x / 2.4.x and 5.5.x / 2.5.x and 6.0.x / 2.6.x and 6.1.x / 2.7.x and 6.2.x / 2.8.x and 7.0.x / 3.0.x and 7.1.x / 3.1.x and 7.2.x / 3.2.x and 7.3.x / 3.3.x and 7.4.x / 3.4.x
3.0.x / 0.10.0.x	compatible	compatible	compatible
3.1.x / 0.10.1.x and 3.2.x / 0.10.2.x		compatible	compatible
3.3.x / 0.11.0.x		compatible with exactly-once turned off (requires broker version Confluent Platform 3.3.x or higher)	compatible
4.0.x / 1.0.x and 4.1.x / 1.1.x and 5.0.x / 2.0.x and 5.1.x / 2.1.x and 5.2.0 / 2.2.0 and 5.2.1 / 2.2.0		compatible with exactly-once turned off (requires broker version Confluent Platform 3.3.x or higher); requires message format 0.10 or higher; message headers are not supported (requires broker version Confluent Platform 3.3.x or higher with message format 0.11 or higher)	compatible; requires message format 0.10 or higher; if message headers are used, message format 0.11 or higher required
5.2.2 / 2.2.1 and 5.3.x / 2.3.x and 5.4.x / 2.4.x and 5.5.x / 2.5.x and 6.0.x / 2.6.x and 6.1.x / 2.7.x and 6.2.x / 2.8.x and 7.0.x / 3.0.x and 7.1.x / 3.1.x and 7.2.x / 3.2.x and 7.3.x / 3.3.x and 7.4.x / 3.4.x			compatible; requires message format 0.11 or higher; enabling exactly-once v2 requires Confluent Platform 5.4.x or higher

The Streams API is not compatible with Kafka clusters running older Kafka versions (0.7, 0.8, 0.9).

Upgrade your Kafka Streams applications to Confluent Platform 7.4.7¶

To use Confluent Platform 7.4.7, update the Kafka Streams your application’s dependency to use the version number 7.4.7-ccs. You may need to make minor code changes, detailed below, and recompile your application.

For example, in your pom.xml file:

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <!-- update version to 7.4.7-ccs -->
    <version>7.4.7-ccs</version>
</dependency>

As of the Confluent Platform 6.0.0 release (Kafka Streams 2.6.0), Kafka Streams depends on a RocksDB version that requires MacOS 10.14 or higher.

Streams API changes in Confluent Platform 7.4.0¶

KIP-770: Replace cache.max.bytes.buffering with cache.max.bytes¶

KIP-770 deprecates the existing cache.max.bytes.buffering config and introduces a new cache.max.bytes config to replace it. The semantics and default value of the cache size config is unchanged. This KIP also adds a new cache.size metric at the DEBUG level for users to monitor the actual size of the Kafka Streams cache.

KIP-837: Allow MultiCasting a Result Record¶

KIP-837 enables you to multicast result records to every partition of downstream sink topics and adds functionality for choosing to drop result records without sending.

KIP-865: Support “–bootstrap-server” in kafka-streams-application-reset¶

KIP-865 updates the Kafka Streams application reset tool’s server parameter name to conform to the other Kafka tooling by deprecating the --bootstrap-servers parameter and introducing a new --bootstrap-server parameter in its place.

Streams API changes in Confluent Platform 7.3.0¶

Source/sink node metrics for consumed/produced throughput in Kafka Streams¶

Starting with Confluent Platform 7.3.0, source and sink node metrics for consumed and produced throughput are available in Kafka Streams.

Previously, with the metrics available in the plain consumer you could derive the consumed throughput of your applications at the subtopology level, but the same was not true for the produced throughput.

KIP-846 fills this gap and gives you a way to compute the production rate of each subtopology by introducing two new metrics for the throughput at sink nodes. Even though it’s possible to derive the consumed throughput with existing client-level metrics, KIP-846 also adds two new metrics for the throughput at source nodes, to provide an equally fine-grained metrics scope as for the newly added metrics at the sink nodes, and to simplify the user experience.

Pause/resume KafkaStreams topologies¶

KIP-834 adds the ability to pause and resume topologies. You can use this feature to reduce resources used or modify data pipelines. Paused topologies skip processing, punctuation, and standby tasks. For distributed Kafka Streams applications, each instance must be paused and resumed separately.

Consolidate KStream transform() and process() methods¶

KIP-820 generalizes the Kafka Streams API to consolidate Transformers, which could forward results, and Processors, which could not. The change makes use of the new type-safe Processor API, which simplifies Kafka Streams, making it easier to use and learn.

New KafkaStreams.close() API¶

KIP-812 introduces another form of the KafkaStreams.close() API that forces the member to leave the consumer group. This new method efficiently closes the stream permanently by forcing the member to leave the consumer group.

Streams API changes in Confluent Platform 7.2.0¶

Rack awareness for Kafka Streams¶

Starting with Confluent Platform 7.2.0, Kafka Streams can distribute its standby replicas over distinct “racks” with KIP-708. To form a “rack”, Kafka Streams uses tags in the application configuration. For example, Kafka Streams clients might be tagged with the cluster or the cloud region they are running in. Users can specify the tags that should be used for the rack-aware distribution of the standby replicas by setting the rack.aware.assignment.tags configuration. During task assignment, Kafka Streams tries to distribute the standby replicas over different task dimensions. Rack-aware standby assignment improves fault tolerance in case of the failure of an entire “rack”. This can be used, for example, to ensure that replicas are distributed over different availability zones in a cloud hosting provider.

Add record metadata to state store context¶

KIP-791 adds the recordMetadata() method to the StateStoreContext, providing access to the topic, partition, and offset of the record currently being processed. Exposing the current context in this way enables state stores to track their current offset in each input partition, allowing them to implement the consistency mechanisms introduced in KIP-796.

Interactive Query v2 preview¶

Confluent Platform 7.2.0 introduces Interactive Queries v2 in Kafka Streams (IQv2). IQv2 is a preview feature, and the interfaces of IQv2 are marked as @Evolving, which means that they may break compatibility in minor releases without a deprecation period if preview users find significant flaws in the current API.

KIP-796 specifies an improved interface for Interactive Queries in Kafka Streams (IQv2). The new interface makes querying the state store simpler and faster and reduces the maintenance cost when modifying existing state stores and adding new state stores. KIP-796 describes the generic interface for querying state stores with Interactive Queries. Specific query types can be added to Interactive Query v2 by implementing the Query interface. KIP-976 also defines the KeyQuery class to enable users to evaluate a key/value lookup by using IQv2.
KIP-805 adds the RangeQuery class to IQv2. The RangeQuery class is an implementation of the Query interface that enables querying state stores over a range specified by upper or lower key bounds or by scanning all records of a state store when no bounds are provided.
KIP-806 adds two implementations of the Query interface.
- The WindowKeyQuery class enables scanning over windows with a given key within a specified time range.
- The WindowRangeQuery class enables scanning over windows within a given time range independently of the windows’ keys.

Streams API changes in Confluent Platform 7.1.0¶

Java 17 support¶

In Confluent Platform 7.1.0, Kafka Streams supports Java 17.

Improved left/outer stream-stream join semantics¶

The semantics of left/outer stream-stream join were improved by KIP-633.

Previously, a left-/outer stream-stream join might have emitted so-called spurious left/outer results, due to an eager-emit strategy. The implementation was changed to emit left/outer join result records only after the join window is closed. The old API to specify the join window, JoinWindows.of(), that enables the eager-emit strategy, was deprecated in favor of the JoinWindows.ofTimeDifferenceAndGrace() and JoinWindows.ofTimeDifferencWithNoGrace() methods. The new semantics are enabled only if you use the new join window builders.

Additionally, KIP-633 makes setting a grace period mandatory for windowed aggregations, i.e., TimeWindows (hopping/tumbling), SessionWindows, and SlidingWindows. The corresponding builder methods .of(...) were deprecated in favor of the new .ofTimeDifferenceAndGrace() and .ofTimeDifferencWithNoGrace() methods.

New metrics to track blocking times¶

KIP-761 adds new metrics that enable tracking blocking times on the underlying consumer and producer clients. For more information, see Kafka Streams metrics.

Interactive Query improvements¶

Interactive Queries were improved by KIP-763 and KIP-766. Range queries now accept null as a lower/upper key-range bound to indicate an open-ended lower/upper bound.

Custom partitioners for foreign-key table-table joins¶

Foreign-key table-table joins now support custom partitioners via KIP-775. Previously, if an input table was partitioned by a non-default partitioner, joining records might fail. With KIP-775, you now can pass a custom StreamPartitioner into the join using the newly added TableJoined object.

Cooperative rebalancing protocol¶

Upgrading from any earlier version to Confluent Platform 7.1.0 is supported.

If you’re upgrading from Confluent Platform 5.3.x (Kafka Streams 2.3) or earlier, you must do two rolling bounces.

During the first rolling bounce, set the configuration upgrade.from="<older_version>" (possible values are “0.10.0” through “2.3”).
During the second bounce, remove the upgrade.from config.

This is required to upgrade safely to the new cooperative rebalancing protocol of the embedded consumer. If you skip or delay the second rolling bounce, your deployment continues using the previous eager rebalancing protocol , but you can switch safely to cooperative rebalancing at any time, once the entire group is on Confluent Platform 5.4.x (Kafka Streams 2.4) or later by removing the configuration value and bouncing. For more information, see KIP-429.

Prepare your application instances for a rolling bounce, and ensure that the upgrade.from config is set to the version from which it is being upgraded.
Bounce each instance of your application once.
Prepare your newly deployed Confluent Platform 7.1.0 (Kafka Streams 3.1.0) application instances for a second round of rolling bounces. Be sure to remove the value for the upgrade.from config.
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 Confluent Platform 3.0.x (Kafka Streams 0.10.x) to Confluent Platform 7.1.0 (Kafka Streams 3.1.0) in offline mode requires the following steps:

Stop all old (for example, Confluent Platform 3.0.x) application instances.
Update your code and swap old code and JAR files with new code and new JAR files.
Restart all new Confluent Platform 7.1.0 (Kafka Streams 3.1.0) application instances.

Streams API changes in Confluent Platform 7.0.0¶

Improved task idling semantics¶

In Confluent Platform 7.0.0, Kafka Streams provides stronger in-order join and merge-processing semantics. The new default value for max.task.idle.ms pauses processing on tasks with multiple input partitions when one of the partitions has no data buffered locally but has a non-zero lag. This means Kafka Streams waits to fetch records that are already available on the broker. This results in improved join semantics, because Kafka Streams can interleave the two input partitions in timestamp order, instead of processing whichever partition happens to be buffered. You can disable this new behavior or there is an option to make Kafka Streams wait longer for new records to be produced to the input partitions, which you can use to get stronger time semantics when you know that some of your producers may be slow. For more information, see KIP-695.

New exceptions for Interactive Queries¶

Interactive Queries may throw new exceptions for different errors:

UnknownStateStoreException: If the specified store name doesn’t exist in the topology, an UnknownStateStoreException is thrown, instead of the InvalidStateStoreException thrown in previous versions.
StreamsNotStartedException: If Streams state is CREATED, a StreamsNotStartedException is thrown.
InvalidStateStorePartitionException: If the specified partition doesn’t exist, a InvalidStateStorePartitionException is thrown.

For more information, see KIP-216

`exactly_once` processing guarantee deprecated¶

The StreamsConfig processing.guarantee configuration value exactly_once (for EOS version 1) is deprecated in favor of the improved EOS version 2, which was previously configured by using exactly_once_beta. To avoid confusion about the term “beta” in the config name and highlight the production readiness of EOS version 2, “eos-beta” is renamed to “eos-v2”, and the configuration value exactly_once_beta is deprecated and replaced with a new configuration value, exactly_once_v2. If you use exactly-once semantics, plan to migrate to the “eos-v2” config and prepare for the removal of the deprecated configs in Kafka Streams 4.0 or after at least a year from the release of 3.0, whichever comes last. Note that “eos-v2” requires broker version 2.5 or higher, like “eos-beta”, so you should begin to upgrade your Kafka cluster if necessary. For more information, see KIP-732.

Default `RocksDBConfigSetter#close()` implementation removed¶

The default implementation of RocksDBConfigSetter#close() is removed.

Default grace period reduced¶

The default 24-hour grace period for windowed operations such as Window or Session aggregates, or stream-stream joins is reduced. This period determines how long any out-of-order records will still be processed after a window ends. Records arriving after the grace period has elapsed are considered late and are dropped. But in operators such as suppression, a large grace period has the drawback of incurring an equally large output latency. The previous API made it too easy to miss the grace period config completely, leading you to wonder why your application seems to produce no output – it actually is, but not for 24 hours.

To prevent accidentally or unknowingly falling back to the default 24-hour grace period, all of the existing static constructors for the Windows classes, like TimeWindows#of, are deprecated. These constructors are replaced by new static constructors that have two flavors: #ofSizeAndGrace and #ofSizeWithNoGrace (these are for the TimeWindows class; analogous APIs exist for the JoinWindows, SessionWindows, and SlidingWindows classes). With these new APIs, you must set the grace period explicitly or consciously choose to opt out by selecting the WithNoGrace flavor, which sets it to 0 for situations where you don’t care about the grace period, such as during testing or when experimenting with Kafka Streams for the first time. Note that using the new APIs for the JoinWindows class also enables a fix for spurious left/outer join results, as described in the next section. For more information, see KIP-633.

Public fields on `TaskId` deprecated¶

The public topicGroupId and partition fields on TaskId are deprecated and replaced with getters. Instead, use the new TaskId.subtopology() method, which replaces topicGroupId, and the TaskId.partition() method. Also, the TaskId#readFrom and TaskId#writeTo methods have been deprecated and will be removed, as they were never intended for public use.

`TaskMetadata` class deprecated¶

The org.apache.kafka.streams.processsor.TaskMetadata class is deprecated and replaced with the new org.apache.kafka.streams.TaskMetadata interface. This change better reflects the fact that TaskMetadata was not intended to be instantiated outside of the Kafka codebase. Note that TaskMetadata offers APIs that better represent the task id as an actual TaskId object, instead of a String. Migrate to the new org.apache.kafka.streams.TaskMetadata interface, which offers these improved methods, for example, by using the new ThreadMetadata#activeTasks and ThreadMetadata#standbyTasks.

`ThreadMetadata` class deprecated¶

The org.apache.kafka.streams.processor.ThreadMetadata class is deprecated and replaced with new org.apache.kafka.streams.ThreadMetadata interface. In the new ThreadMetadata interface, any reference to the deprecated TaskMetadata is replaced by the new interface.

`StreamsMetadata` class deprecated¶

The org.apache.kafka.streams.state.StreamsMetadata is deprecated and replaced with org.apache.kafka.streams.StreamsMetadata.

Methods under `org.apache.kafka.streams.KafkaStreams` deprecated¶

The following methods that returned the previously deprecated classes are deprecated:

For KafkaStreams#allMetadata, migrate to the new KafkaStreams#metadataForAllStreamsClients.
For KafkaStreams#allMetadataForStore(String), migrate to the new KafkaStreams#streamsMetadataForStore(String).
For KafkaStreams#localThreadsMetadata, migrate to the new KafkaStreams#metadataForLocalThreads.

For more information, see KIP-740 and KIP-744.

Deprecated APIs removed¶

The following deprecated APIs are removed.

--zookeeper flag of the application reset tool: deprecated in Kafka 1.0.0 (KIP-198).
--execute flag of the application reset tool: deprecated in Kafka 1.1.0 (KIP-171).
StreamsBuilder#addGlobalStore (one overload): deprecated in Kafka 1.1.0 (KIP-233).
ProcessorContext#forward (some overloads): deprecated in Kafka 2.0.0 (KIP-251).
WindowBytesStoreSupplier#segments: deprecated in Kafka 2.1.0 (KIP-319).
segments, until, maintainMs on TimeWindows, JoinWindows, and SessionWindows: deprecated in Kafka 2.1.0 (KIP-328).
Overloaded JoinWindows#of, before, after, SessionWindows#with, TimeWindows#of, advanceBy, UnlimitedWindows#startOn, and KafkaStreams#close with long typed parameters: deprecated in Kafka 2.1.0 (KIP-358).
Overloaded KStream#groupBy, groupByKey, and KTable#groupBy with Serialized parameter: deprecated in Kafka 2.1.0 (KIP-372).
Joined#named, name: deprecated in Kafka 2.3.0 (KIP-307).
TopologyTestDriver#pipeInput, readOutput, OutputVerifier, and ConsumerRecordFactory classes (KIP-470).
KafkaClientSupplier#getAdminClient: deprecated in Kafka 2.4.0 (KIP-476).
Overloaded KStream#join, leftJoin, outerJoin with KStream and Joined parameters: deprecated in Kafka 2.4.0 (KIP-479).
WindowStore#put(K key, V value): deprecated in Kafka 2.4.0 (KIP-474).
UsePreviousTimeOnInvalidTimestamp: deprecated in Kafka 2.5.0 as renamed to UsePartitionTimeOnInvalidTimestamp (KIP-530).
Overloaded KafkaStreams#metadataForKey: deprecated in Kafka 2.5.0 (KIP-535).
Overloaded KafkaStreams#store: deprecated in Kafka 2.5.0 (KIP-562).

Dependencies removed from Kafka Streams¶

The following dependencies are removed from Kafka Streams:

Connect-json: Kafka Streams no longer has a compile time dependency on the “connect:json” module (KAFKA-5146). Projects that relied on this transitive dependency must declare it explicitly.

Default `replication.factor` changed¶

The default value for the replication.factor configuration parameter is changed to -1, which means to use the broker default replication factor. The replication.factor value of -1 requires broker version 2.4 or newer.

`ListSerde` introduced¶

The new ListSerde serde type is introduced:

Added class ListSerde to (de)serialize List-based objects
Introduced ListSerializer and ListDeserializer to power the new functionality

Streams API changes in Confluent Platform 6.2.0¶

The type-safe split() method replaces the branch() method for branching or splitting a KStream based on the supplied predicates into one or more KStream instances.

The following example code shows how you used the branch method before Confluent Platform 6.2.0:

final KStream<String, Event>[] branches = events.branch(
    (id, event) -> event.getTransactionValue() >= FRAUD_LIMIT,
    (id, event) -> event.getTransactionValue() < FRAUD_LIMIT
);
branches[0].to(suspiciousTransactionsTopicName);
branches[1].to(validatedTransactionsTopicName);

The following example code shows how to rewrite the previous code to use the split method:

// Canonical rewrite from the branch() method.
final Map<String, KStream<String, Event>> branches = events.split(Named.as("branch-"))
    .branch((id, event) -> event.getTransactionValue() >= FRAUD_LIMIT)
    .branch((id, event) -> event.getTransactionValue() < FRAUD_LIMIT)
    .defaultBranch();

// Rewrite to exploit the new API.
events.split()
    .branch(
        (id, event) -> event.getTransactionValue() >= FRAUD_LIMIT,
        Branched.withConsumer(ks -> ks.to(suspiciousTransactionsTopicName)))
    .branch(
        (id, event) -> event.getTransactionValue() < FRAUD_LIMIT,
        Branched.withConsumer(ks -> ks.to(validatedTransactionsTopicName)));

Streams API changes in Confluent Platform 6.1.0¶

Sliding Windows¶

Confluent Platform 6.1 adds SlidingWindows as an option for windowedBy() windowed aggregations as described in KIP-450. Sliding windows are fixed-time and data-aligned windows that allow for flexible and efficient windowed aggregations. Refer to Developer Guide for more details.

TRACE level end-to-end latency metrics¶

The end-to-end latency metrics introduced in 6.0 have been expanded to include store-level metrics. The new store-level metrics are recorded at the TRACE level, a new metrics recording level. Enabling TRACE level metrics automatically turns on all higher levels, i.e., INFO and DEBUG. For more information, see KIP-613.

Upgrading older Kafka Streams applications to Confluent Platform 6.1.x¶

Streams API changes in Confluent Platform 6.0.0¶

New processing mode that improves application scalability using exactly-once guarantees (KIP-447)
Highly available state stores (KIP-441)
New KStream.repartition() operator replaces KStream.through() (KIP-221)
New end-to-end latency metrics added (KIP-613)
New --force option in StreamsResetter to force remove left-over members (KIP-571)

Improved exactly-once processing¶

Starting in Confluent Platform 6.0, a new processing mode is available, named exactly_once_v2, which is configurable by using the processing.guarantee parameter. To use this new feature, your brokers must be on version Confluent Platform 5.5.x / Kafka 2.5.x or newer.

This implementation is more efficient, because it reduces client and broker resource utilization, like client threads and used network connections, and it enables higher throughput and improved scalability. For more information on how this is done inside the brokers and Kafka Streams, see KIP-447.

Also, as part of the KIP-447 implementation, the transaction timeout has been reduced from 60 seconds to 10 seconds.

If you want to upgrade your EOS application from an older version and enable this feature in version 6.0+, upgrade your application to version 6.0.x, staying on exactly_once, and then do second round of rolling bounces to switch to exactly_once_v2.

If you’re upgrading an EOS application from an older version (before Kafka 2.6) to a version between 2.6 and 2.8, follow the same steps but with the config exactly_once_beta instead. No special steps are required to upgrade an application using exactly_once_beta from version 2.6+ to 3.0 or higher: you can just change the config from exactly_once_beta to exactly_once_v2 during the rolling upgrade.

For a downgrade, do the reverse: first switch the config from exactly_once_v2 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.

Highly available state stores¶

For more highly available stateful applications, we’ve modified the task assignment algorithm to delay the movement of stateful active tasks to instances that aren’t yet caught up with that task’s state. Instead, to migrate a task from one instance to another (eg when scaling out), Kafka Streams assigns a warmup replica to the target instance so that it can begin restoring the state while the active task stays available on an instance that already had the task. The instances warming up tasks communicate their progress to the group so that, once ready, Kafka Streams can move active tasks to their new owners in the background. Check out KIP-441 for full details, including several new configs for control over this new feature.

End-to-end latency metrics¶

New end-to-end latency metrics have been added. These task-level metrics are logged at the INFO level and report the minimum and maximum end-to-end latency of a record at the beginning/source node(s) and end/terminal node(s) of a task. For more information, see KIP-613.

Replace through() with repartition() operator¶

The operator KStream.through() is deprecated in favor of the new KStream.repartition() operator (via 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 manage, you can fall back to use KStream.to() in combination with StreamsBuilder#stream(). If you only want to write into a topic as “side output” and continue processing, you can also fan-out your dataflow via myStream.xxx(); myStream.to() to avoid the need to read back the data from the cluster.

Upgrade from earlier versions¶