# Kafka Consumer for Confluent Platform An Apache Kafka® Consumer is a client application that subscribes to (reads and processes) events. This section provides an overview of the Kafka consumer and an introduction to the configuration settings for tuning. The Kafka consumer works by issuing “fetch” requests to the brokers leading the partitions it wants to consume. The consumer offset is specified in the log with each request. The consumer receives back a chunk of log that contains all of the messages in that topic beginning from the offset position. The consumer has significant control over this position and can rewind it to re-consume data if desired. ## Consumer groups A [consumer group](../_glossary.md#term-consumer-group) is a set of consumers that cooperate to consume data from some topics. You set the group for a consumer by setting its `group.id` in the properties file for the consumer. To use the `subscribe` or `commit` methods provided by the [KafkaConsumer API](https://docs.confluent.io/platform/current/clients/javadocs/javadoc/index.html?org/apache/kafka/clients/consumer/package-summary.html), you must assign the consumer to a consumer group by setting the `group.id` property. If you don’t, an exception occurs when these methods are called. When assigned to a group, the partitions of all the topics are divided among the consumers in the group. As new group members arrive and old members leave, the partitions are re-assigned so that each member receives a proportional share of the partitions. This is known as rebalancing the group. One of the brokers is designated as the group’s **coordinator** and is responsible for managing the members of the group as well as their partition assignments. The coordinator of each group is chosen from the leaders of the internal offsets topic, `__consumer_offsets`, which is used to store committed offsets. Basically, the group’s ID is hashed to one of the partitions for this topic, and the leader of that partition is selected as the coordinator. In this way, management of consumer groups is divided roughly equally across all the brokers in the cluster, which allows the number of groups to scale by increasing the number of brokers. When the consumer starts up, it finds the coordinator for its group and sends a request to join the group. The coordinator then begins a group rebalance so that the new member is assigned its fair share of the group’s partitions. Every rebalance results in a new **generation** of the group. Each member in the group must send heartbeats to the coordinator in order to remain a member of the group. If no heartbeat is received before expiration of the configured **session timeout**, then the coordinator will kick the member out of the group and reassign its partitions to another member. When a consumer commits offsets, the group coordinator validates that the consumer still owns the partitions it is committing for. This prevents consumers that have been fenced or whose partitions were reassigned from overwriting offsets for partitions they no longer own. The coordinator uses epoch validation for consumer groups using the consumer group protocol (`group.protocol=consumer`). The coordinator tracks an assignment epoch per partition rather than a single epoch per member, and uses those per-partition epochs to validate offset commits. For a short video that describes consumer groups, group leaders, and group coordinators, watch: ## Groups and rebalance protocols Recall that rebalancing is the process of assigning (or reassigning) Kafka topic partitions to the members of a consumer group. Kafka supports two rebalance protocols that determine how rebalancing happens within a consumer group. These protocols are: * *classic*, which was the only protocol before Kafka version 4.0 * *consumer*, a newer protocol which has General Availability (GA) in the Kafka 4.0 version (Confluent Platform 8.0) #### WARNING The classic rebalance protocol will be replaced by the consumer protocol in upcoming releases of Kafka clients. Kafka clients 4.3 and later (Confluent Platform 8.3 and later) : The consumer protocol is recommended. The classic protocol issues a warning that deprecation will occur in Kafka clients 5.0. Kafka clients 5.0 and later : The consumer protocol is the default. The classic protocol remains available but is deprecated. Kafka clients 6.0 and later : There is no client-level support for the classic protocol. For more information, see [KIP-1274: Deprecate and remove support for Classic rebalance protocol in KafkaConsumer](https://cwiki.apache.org/confluence/display/KAFKA/KIP-1274%3A+Deprecate+and+remove+support+for+Classic+rebalance+protocol+in+KafkaConsumer). ### Overview of the rebalance protocols Confluent Platform supports both the classic and the consumer rebalance protocol, so your consumer clients can use either protocol. The consumer rebalance protocol is preferred. The consumer rebalance protocol improves consumer group scalability by removing the group-wide synchronization barrier, making rebalancing truly incremental. Removing this barrier also enhances stability by preserving existing partition assignments as much as possible during rebalances. The consumer rebalance protocol also reduces rebalance times and simplifies consumers. By offloading or simplifying consumer-side responsibilities, the entire rebalance is streamlined and less prone to delays caused by complex client-side operations. Regardless of the rebalance protocol in use, the coordinator begins the group formation process by validating and finalizing the partition assignments. The coordinator computes assignments based on the topic subscriptions and assignment preferences communicated by the members (in the consumer protocol) or by the group leader (in the classic protocol). The following table shows the differences between the classic and consumer rebalance protocols: | Group behavior | Classic protocol | Consumer protocol | |----------------------|-------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------| | **use case** | Good for architectures that need to continue support for the classic
protocol. | Ideal for dynamic groups, such as deployments that scale up and down
frequently. | | **group leader** | One member performs assignment on behalf of the group. | Not used. No leader election occurs. | | **coordinator role** | Assignments computed by the group leader (a consumer) and applied by
the broker coordinator. | Broker coordinator computes assignments but does not receive one global
plan. | | **rebalance type** | Eager or cooperative: assignor dependent. In some circumstances, all
consumers can pause and revoke all partitions. | Incremental reassignment with minimal disruption. | | **join group flow** | Triggered on join or leave; reassigns all partitions. | Incremental assignment; a consumer joins a group by sending a heartbeat
request; fewer disruptions. | | **assignment** | Handled by one group leader among the consumers. | Broker coordinator receives consumer subscriptions, validates them,
computes, and then orchestrates assignment. | ### Enable the consumer rebalance protocol on brokers In Confluent Platform, you are responsible for enabling the consumer rebalance protocol on your own brokers. Two broker-side settings control availability of the protocol: * The `group.coordinator.rebalance.protocols` broker configuration must include `consumer` in its list of enabled rebalance protocols on Confluent Platform versions earlier than 8.0. This configuration is deprecated and will be removed with the Confluent Platform version aligned to Kafka 5.0. * The `group.version` feature flag must be set to a version that supports the protocol. On new Confluent Platform 8.0 or later clusters, `group.version=1` is enabled by default. On clusters upgraded from an earlier release, upgrade the feature explicitly: ```bash ${CONFLUENT_HOME}/bin/kafka-features --bootstrap-server localhost:9092 upgrade --feature group.version=1 ``` To disable the feature, downgrade it: ```bash ${CONFLUENT_HOME}/bin/kafka-features --bootstrap-server localhost:9092 downgrade --feature group.version=0 ``` For more information about broker-side settings that tune consumer groups using the consumer rebalance protocol, such as `group.consumer.session.timeout.ms`, `group.consumer.heartbeat.interval.ms`, and `group.consumer.assignors`, see [Kafka Broker Configurations](/platform/current/installation/configuration/broker-configs.html). ### How a group’s rebalance protocol is determined A Kafka consumer group is implicitly created when the first consumer with a specific `group.id` starts and attempts to subscribe to a topic. No explicit “create group” command or administrative action is required. The first consumer to join a group determines the rebalance protocol for that group. If this initial consumer has the configuration `group.protocol=consumer` set, the group uses the consumer rebalance protocol. Otherwise, the group defaults to the classic rebalance protocol. For example, you can change a group’s rebalance protocol by shutting down all of its consumers and bringing them back up. The following is a step-by-step explanation of how a consumer group’s protocol can change from classic to the consumer rebalance protocol: 1. Initially, in this scenario, the consumer group is operating with the `classic` protocol. The broker’s group coordinator stores metadata about this group, including the protocol it’s currently using (`classic`). 2. Shut down all the consumer instances belonging to this group. At this point, the group becomes empty from the broker’s perspective. No active members sending heartbeat requests. Even though the group is empty, the broker retains the metadata associated with that group, including the last known protocol it was using (`classic` in this scenario). The group isn’t entirely “forgotten.” 3. The first consumer instance you bring back up is configured with `group.protocol=consumer`. This consumer sends a request to the coordinator to join a group. This request explicitly specifies that it wants to use the `consumer` protocol. 4. Because the group is currently empty, the broker accepts this new member and updates the stored metadata for that consumer group. The broker now records that this group should use the consumer rebalance protocol. As other consumer instances (also configured with `group.protocol=consumer`) are brought back online and join using the same `group.id`, the broker sees that the group is now configured to use the `consumer` protocol and allows them to join using that protocol. A consumer using the classic protocol that attempts to join isn’t rejected unless it uses an incompatible protocol, for example, `Connect`. Typically, the join fails with an `InconsistentGroupProtocolException` exception. ### Upgrade or switch consumer protocols You can upgrade or switch consumer groups between the `classic` and `consumer` protocols. If you want to migrate or upgrade your clients to the consumer rebalance protocol, your choice is between a rolling upgrade or an empty group restart. When choosing between the two approaches, take these aspects of your consumers into consideration: * Client version compatibility. Check and ensure that your consumer libraries support the Kafka 4.0 version and the `group.protocol` configuration. For the consumer rebalance protocol to be effective, all consumers in the group should use it. * Broker support. The broker’s `group.version` feature flag must be set to a version that supports the desired protocol, and `group.coordinator.rebalance.protocols` must include `consumer`. For more information, see [Enable the consumer rebalance protocol on brokers](#enable-consumer-rebalance-protocol). Rolling upgrades are the preferred method for production environments because they minimize downtime. If your environment can tolerate some downtime, you can do an empty group restart. You can also switch back to the classic rebalance protocol from the consumer rebalance protocol. In this case, you would do a rolling upgrade, but switch from `group.protocol=consumer` back to the default, either by removing the setting or by setting it explicitly to `group.protocol=classic`. If you choose this path, make sure that you have a good understanding of the potential feature loss and use careful coordination among your team. #### NOTE If a consumer attempts to join a group operating with an incompatible protocol, the consumer receives an `InconsistentGroupProtocolException`. #### Before you migrate to the consumer rebalance protocol Check your consumers and adjust them as necessary before migrating or upgrading to the consumer rebalance protocol. A consumer uses the consumer rebalance protocol under the following conditions: * The consumer uses a client version that supports the `consumer` protocol. * The consumer configuration explicitly sets the `group.protocol=consumer` property. Ensure that classic legacy configurations are removed from your consumer properties. Several configurations and two APIs from the classic rebalance protocol are no longer applicable in the consumer rebalance protocol: - The `heartbeat.interval.ms` property on the consumer side is replaced by the server-side `group.consumer.heartbeat.interval.ms` in the consumer rebalance protocol. - The `session.timeout.ms` property on the consumer side is replaced by the server-side `group.consumer.session.timeout.ms` in the consumer rebalance protocol. - The `partition.assignment.strategy` property on classic consumers is replaced in the consumer rebalance protocol by the server-side `group.consumer.assignors` on the broker and the `group.remote.assignor` on the consumer. - The `enforceRebalance(String)` and `enforceRebalance()` APIs are no longer supported with consumers using the consumer rebalance protocol. If your consumer uses any legacy properties or methods, they are either ignored or result in errors when you use them with a consumer where `group.protocol=consumer`. The consumer rebalance protocol also includes `subscribe(SubscriptionPattern)` and `subscribe(SubscriptionPattern,ConsumerRebalanceListener)` methods. These methods allow consumers to subscribe to a regular expression. With these methods, the regular expression uses the [RE2J](https://github.com/google/re2j) format and is evaluated on the server side. #### Do a rolling deployment When you upgrade or switch a consumer group to use the `group.protocol=consumer` configuration with a rolling deployment, a group can temporarily exist in a transitional state where some consumers are using the consumer rebalance protocol and others the classic protocol. While Kafka attempts to interoperate between these protocols during this phase, this is not a recommended long-term operating model and can lead to issues and limitations such as the following: - The broker must maintain compatibility with both protocols, which can introduce overhead and prevent the group from fully leveraging the optimizations of the consumer protocol. - Managing a group with mixed protocols can increase the complexity of the rebalance process and potentially lead to less predictable behavior, especially in edge cases or during rapid membership changes. - The differences in how the protocols handle assignment can lead to temporary imbalances in partition distribution among consumers using different protocols. - Monitoring and troubleshooting the rebalance process and group health is more difficult when different consumers are operating under different protocol rules. Follow this procedure to do a rolling upgrade: 1. Ensure that all consumers in the group are using a client version that supports the consumer rebalance protocol. 2. Remove any classic configurations from the consumer properties. 3. Deploy new versions of your consumer applications configured with `group.protocol=consumer`, one at a time. 4. Restart each consumer instance after the configuration change. 5. As the first `consumer` protocol consumer joins an existing `classic` group with a compatible assignor, the group’s metadata on the broker is updated. You can use the `kafka-consumer-groups` command-line tool to check the migration progress. For information about how to use this tool, see [Kafka consumer group tool](#kafka-consumer-describe-group) later in this page. 6. Continue rolling out the new configuration to all consumers in the group. 7. After you upgrade all consumers, the group fully uses the consumer protocol. #### Do an empty group restart This method is simpler than a rolling restart but involves more downtime. 1. Shut down all consumer instances in the group, making the group empty. 2. Ensure that all consumers in the group are using a client version that supports the consumer rebalance protocol. 3. Remove any classic configurations from the consumer properties. 4. Bring all consumer instances back online with the `group.protocol=consumer` configuration. 5. The first joining consumer with the new protocol sets the group’s protocol for subsequent members. ## Offset management After the consumer receives its assignment from the coordinator, it must determine the initial position for each assigned partition. When the group is first created, before any messages have been consumed, the position is set according to a configurable offset reset policy (`auto.offset.reset`). Typically, consumption starts either at the earliest offset or the latest offset. As a consumer in the group reads messages from the partitions assigned by the coordinator, it must commit the offsets corresponding to the messages it has read. If the consumer crashes or is shut down, its partitions will be re-assigned to another member, which will begin consumption from the last committed offset of each partition. If the consumer crashes before any offset has been committed, then the consumer which takes over its partitions will use the reset policy. The offset commit policy is crucial to providing the message delivery guarantees needed by your application. By default, the consumer is configured to use an automatic commit policy, which triggers a commit on a periodic interval. The consumer also supports a commit API which can be used for manual offset management. Correct offset management is crucial because it affects [delivery semantics](/platform/current/streams/concepts.html#streams-concepts-processing-guarantees). By default, the consumer is configured to auto-commit offsets. The `auto.commit.offset.interval` property sets the upper time bound of the commit interval. Using auto-commit offsets can give you “at-least-once” delivery, but you must consume all data returned from a `ConsumerRecords poll(Duration timeout)` call before any subsequent `poll` calls, or before closing the consumer. To explain further; when auto-commit is enabled, every time the `poll` method is called and data is fetched, the consumer is ready to automatically commit the offsets of messages that have been returned by the poll. If the processing of these messages is not completed before the next auto-commit interval, there’s a risk of losing the message’s progress if the consumer crashes or is otherwise restarted. In this case, when the consumer restarts, it will begin consuming from the last committed offset. When this happens, the last committed position can be as old as the auto-commit interval. Any messages that have arrived since the last commit are read again. If you want to reduce the window for duplicates, you can reduce the auto-commit interval, but some users may want even finer control over offsets. The consumer therefore supports a commit API which gives you full control over offsets. Note that when you use the commit API directly, you should first disable auto-commit in the configuration by setting the `enable.auto.commit` property to `false`. Each call to the commit API results in an offset commit request being sent to the broker. Using the synchronous API, the consumer is blocked until that request returns successfully. This may reduce overall throughput since the consumer might otherwise be able to process records while that commit is pending. One way to deal with this is to increase the amount of data that is returned when polling. The consumer has a configuration setting `fetch.min.bytes` which controls how much data is returned in each fetch. The broker will hold on to the fetch until enough data is available (or `fetch.max.wait.ms` expires). The tradeoff, however, is that this also increases the amount of duplicates that have to be dealt with in a worst-case failure. A second option is to use asynchronous commits. Instead of waiting for the request to complete, the consumer can send the request and return immediately by using asynchronous commits. So if it helps performance, why not always use asynchronous commits? The main reason is that the consumer does not retry the request if the commit fails. This is something that committing synchronously gives you for free; it will will retry indefinitely until the commit succeeds or an unrecoverable error is encountered. The problem with asynchronous commits is dealing with commit ordering. By the time the consumer finds out that a commit has failed, you may already have processed the next batch of messages and even sent the next commit. In this case, a retry of the old commit could cause duplicate consumption. Instead of complicating the consumer internals to try and handle this problem in a sane way, the API gives you a callback which is invoked when the commit either succeeds or fails. If you like, you can use this callback to retry the commit, but you will have to deal with the same reordering problem. Offset commit failures are merely annoying if the following commits succeed since they won’t actually result in duplicate reads. However, if the last commit fails before a rebalance occurs or before the consumer is shut down, then offsets will be reset to the last commit and you will likely see duplicates. A common pattern is therefore to combine async commits in the poll loop with sync commits on rebalances or shut down. Committing on close is straightforward, but you need a way to hook into rebalances. Each rebalance has two phases: partition revocation and partition assignment. The revocation method is always called before a rebalance and is the last chance to commit offsets before the partitions are re-assigned. The assignment method is always called after the rebalance and can be used to set the initial position of the assigned partitions. In this case, the revocation hook is used to commit the current offsets synchronously. In general, asynchronous commits should be considered less safe than synchronous commits. Consecutive commit failures before a crash will result in increased duplicate processing. You can mitigate this danger by adding logic to handle commit failures in the callback or by mixing occasional synchronous commits, but you shouldn’t add too much complexity unless testing shows it is necessary. If you need more reliability, synchronous commits are there for you, and you can still scale up by increasing the number of topic partitions and the number of consumers in the group. But if you just want to maximize throughput and you’re willing to accept some increase in the number of duplicates, then asynchronous commits may be a good option. A somewhat obvious point, but one that’s worth making is that asynchronous commits only make sense for “at least once” message delivery. To get “at most once,” you need to know if the commit succeeded before consuming the message. This implies a synchronous commit unless you have the ability to “unread” a message after you find that the commit failed. In the examples, we show several detailed examples of the commit API and discuss the tradeoffs in terms of performance and reliability. When writing to an external system, the consumer’s position must be coordinated with what is stored as output. That is why the consumer stores its offset in the same place as its output. For example, a connector populates data in HDFS along with the offsets of the data it reads so that it is guaranteed that either data and offsets are both updated, or neither is. A similar pattern is followed for many other data systems that require these stronger semantics, and for which the messages do not have a primary key to allow for deduplication. This is how Kafka supports [exactly-once processing](/platform/current/streams/concepts.html#streams-concepts-processing-guarantees) in Kafka Streams, and the transactional producer or consumer can be used generally to provide exactly-once delivery when transferring and processing data between Kafka topics. Otherwise, Kafka guarantees at-least-once delivery by default, and you can implement at-most-once delivery by disabling retries on the producer and committing offsets in the consumer prior to processing a batch of messages. ## Kafka consumer configuration The full list of configuration settings are available in [Kafka Consumer Configurations](/platform/current/installation/configuration/consumer-configs.html). Several of the key configuration settings and how they affect the consumer’s behavior are highlighted below. ### Core configuration Following are some important consumer properties: - `bootstrap.servers`: You are required to set this property so that the consumer can find the Kafka cluster. - `client.id`: Optional, but you should set this property to easily correlate requests on the broker with the client instance which made it. Typically, all consumers within the same group will share the same client ID in order to enforce [client quotas](/platform/current/kafka/post-deployment.html#quotas). ### Group configuration The following properties apply to consumer groups. - `group.id`: Optional but you should always configure a group ID unless you are using the simple assignment API and you don’t need to store offsets in Kafka. - `session.timeout.ms`: Control the session timeout by overriding this value. The default is 10 seconds in the C/C++ and Java clients, but you can increase the time to avoid excessive rebalancing, for example due to poor network connectivity or long GC pauses. The main drawback to using a larger session timeout is that it will take longer for the coordinator to detect when a consumer instance has crashed, which means it will also take longer for another consumer in the group to take over its partitions. For normal shutdowns, however, the consumer sends an explicit request to the coordinator to leave the group which triggers an immediate rebalance. - `heartbeat.interval.ms`: This controls how often the consumer will send heartbeats to the coordinator. It is also the way that the consumer detects when a rebalance is needed, so a lower heartbeat interval will generally mean faster rebalancing. The default setting is three seconds. For larger groups, it may be wise to increase this setting. - `max.poll.interval.ms`: This property specifies the maximum time allowed time between calls to the consumers poll method (`Consume` method in .NET) before the consumer process is assumed to have failed. The default is 300 seconds and can be safely increased if your application requires more time to process messages. If you are using the Java consumer, you can also adjust `max.poll.records` to tune the number of records that are handled on every loop iteration. ### Offset management configuration There are two main settings for offset management; whether auto-commit is enabled and the offset reset policy. - `enable.auto.commit`: This setting enables auto-commit (the default), which means the consumer automatically commit offsets periodically at the interval set by `auto.commit.interval.ms`. The default interval is 5 seconds. - `auto.offset.reset`: Defines the behavior of the consumer when there is no committed position (which occurs when the group is first initialized) or when an offset is out of range. There are several ways to set the offset. You can choose either to reset the position to the `earliest` offset or the `latest` offset (the default). You can also reset to a configured `duration` from the current timestamp. ### Partition assignment configuration `partition.assignment.strategy` sets the partition assignment strategy for a consumer, meaning how partition ownership is distributed between consumer instances when group management is used. All consumers in the same consumer group must have the same partition strategy. The `partition.assignment.strategy` parameter is not supported when the consumer group protocol, `group.protocol=consumer`, is enabled. This configuration applies only when using the classic consumer group protocol, `group.protocol=classic`, which is the default. #### WARNING The classic rebalance protocol will be replaced by the consumer protocol in upcoming releases of Kafka clients. For the deprecation timeline and migration guidance, see [Groups and rebalance protocols](#kafka-consumer-rebalance-protocols). [partition.assignment.strategy](/platform/current/installation/configuration/consumer-configs.html#partition-assignment-strategy) accepts a comma-separated list of fully qualified class names that implement the [PartitionAssignor](/platform/current/clients/javadocs/javadoc/org/apache/kafka/clients/consumer/ConsumerPartitionAssignor.html) interface. The list enables you to update the strategy for a group, while temporarily keeping the old one for consumers that have not transitioned to the new strategy yet. When you configure Kafka consumers, the choice of assignment strategy is important and depends on the specific requirements for partition balancing, consumer group stability, and rebalance behavior. In most cases, the default (range assignment) works well, but for specific use cases, changing the assignment strategy can significantly impact performance and reliability. Available options are: * Range Assignment (Default) - How it Works: The `org.apache.kafka.clients.consumer.RangeAssignor` works by evenly distributing partitions of each topic across the consumers in a consumer group. It sorts both the partitions and consumers. Partitions are assigned to consumers in chunks (ranges), aiming for an even distribution. - Advantages: It works well when partition count is higher than consumer count, providing a simple and efficient means of partition distribution. - Disadvantages: Can result in uneven load distribution if the number of partitions is not a multiple of the number of consumers. * Round Robin Assignment - How it Works: The `org.apache.kafka.clients.consumer.RoundRobinAssignor` distributes partitions across all consumers one by one in a round-robin fashion. It ensures a more even distribution of partitions across consumers, regardless of the number of partitions. - Advantages: Leads to a more balanced partition allocation across consumers, useful when handling a varying number of partitions or when partitions have significantly different sizes. - Disadvantages: May lead to more rebalances compared to Range Assignment in some scenarios. * Sticky Assignor - How it Works: The `org.apache.kafka.clients.consumer.StickyAssignor` aims to maintain a stable partition assignment while still balancing the partitions across consumers. It tries to keep the previously assigned partitions to a consumer as unchanged as possible if a rebalance occurs. - Advantages: It minimizes the number of partition reassignments across rebalances, reducing the potential for missed messages or duplicated processing. - Disadvantages: While it offers stability, it might not always result in the most balanced partition distribution if the cluster or consumer group changes frequently. * Cooperative Sticky Assignor - How it Works: An evolution of the StickyAssignor, the `org.apache.kafka.clients.consumer.CooperativeStickyAssignor` enables more incremental rebalancing, which can reduce the latency and resources required during the rebalance process. - Advantages: It supports more granular changes to the consumer group memberships or to the partitions themselves, making rebalances less impactful. Note that this assignor reduces rebalance *impact*, not the frequency of rebalances. For consumer groups where all members subscribe to the same set of topics, the `org.apache.kafka.clients.consumer.ConstrainedCooperativeStickyAssignor` provides the same benefits and is optimized for this common scenario. - Disadvantages: Not all consumers or versions of Kafka support this assignor; requires careful management to ensure compatibility across the consumer group. ### Upgrade to CooperativeStickyAssignor To migrate an existing consumer group from an eager rebalance assignor (for example, `RangeAssignor` or `StickyAssignor`) to the cooperative protocol introduced by [KIP-429: Kafka Consumer Incremental Rebalance Protocol](https://cwiki.apache.org/confluence/display/KAFKA/KIP-429%3A+Kafka+Consumer+Incremental+Rebalance+Protocol), use a two-step rolling bounce to minimize disruption during the transition. After completing step 1 (configuring dual assignors), the consumer group operates in a mixed-mode period where members that still use an eager assignor continue to perform full-stop rebalances. After completing step 2 (switching to the cooperative assignor), rebalances become incremental and avoid full-stop rebalances. #### NOTE `CooperativeStickyAssignor` requires Kafka client version 2.4.0 or later. Ensure all consumers in your group meet this version requirement before beginning the upgrade. 1. Prepare (dual list, keep current first). Set [partition.assignment.strategy](/platform/current/installation/configuration/consumer-configs.html#partition-assignment-strategy) to include both your current assignor and `CooperativeStickyAssignor`, with the current assignor listed first. Perform a rolling restart so all members pick up the new list. The coordinator will continue to select the first commonly supported assignor (the existing one), so there is no behavior change yet. Example properties: ```properties partition.assignment.strategy=org.apache.kafka.clients.consumer.StickyAssignor,org.apache.kafka.clients.consumer.CooperativeStickyAssignor ``` Fully qualified class names shown here refer to the Java consumer. For other client libraries, configure the equivalent assignor using the client’s configuration API. Example Java configuration: ```java props.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG, Arrays.asList(StickyAssignor.class.getName(), CooperativeStickyAssignor.class.getName())); ``` 2. Switch (make cooperative first or only). After all members have been upgraded to client versions that support cooperative rebalancing, update the configuration to make `CooperativeStickyAssignor` the first (or only) value and roll the group again. The coordinator then selects cooperative rebalancing and the group transitions incrementally with minimal disruption. Example properties: ```properties partition.assignment.strategy=org.apache.kafka.clients.consumer.CooperativeStickyAssignor ``` Fully qualified class names shown here refer to the Java consumer. For other client libraries, configure the equivalent assignor using the client’s configuration API. Example Java configuration: ```java props.put(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG, Collections.singletonList(CooperativeStickyAssignor.class.getName())); ``` Verify the switch: after the rollout, check consumer application logs for messages indicating the selected partition assignor (for example, “Using partition assignor class …CooperativeStickyAssignor”). #### Notes - All members in the group must advertise a compatible, ordered list of assignors. The coordinator selects the first assignor that is present in every member’s list. - Do not switch to listing only `CooperativeStickyAssignor` until all members run client versions that support it (2.4.0 or later); otherwise the group fails to find a common assignor. - Static membership (`group.instance.id`) further reduces movement during restarts and pairs well with cooperative rebalancing. In Kubernetes environments, use StatefulSets to provide stable pod identities that can serve as static member IDs. - If using manual offset commits (`enable.auto.commit=false`), handle `RebalanceInProgressException` by calling `poll()` in the next loop iteration to complete the rebalancing process. - For background and the detailed upgrade rationale, see [KIP-429: Kafka Consumer Incremental Rebalance Protocol](https://cwiki.apache.org/confluence/display/KAFKA/KIP-429%3A+Kafka+Consumer+Incremental+Rebalance+Protocol) (see the “Compatibility and Upgrade Path” section). ## Message handling While the Java consumer does all IO and processing in the foreground thread, librdkafka-based clients (C/C++, Python, Go and C#) use a background thread. The main consequence of this is that polling is totally safe when used from multiple threads. You can use this to parallelize message handling in multiple threads. From a high level, poll is taking messages off of a queue which is filled in the background. Another consequence of using a background thread is that all heartbeats and rebalancing are executed in the background. The benefit of this is that you don’t need to worry about message handling causing the consumer to “miss” a rebalance. The drawback, however, is that the background thread will continue heart beating even if your message processor dies. If this happens, then the consumer will continue to hold on to its partitions and the read lag will continue to build until the process is shut down. Although the clients have taken different approaches internally, they are not as far apart as they seem. To provide the same abstraction in the Java client, you could place a queue in between the poll loop and the message processors. The poll loop would fill the queue and the processors would pull messages off of it. ## Kafka consumer group tool Kafka includes the `kafka-consumer-groups` command-line utility to view and manage consumer groups, which is also provided with Confluent Platform. Find the tool in the `bin` folder under your installation directory. You can also use the Confluent CLI to complete some of these tasks. For more information, see the [Confluent CLI reference](https://docs.confluent.io/confluent-cli/current/command-reference/kafka/consumer/group/index.html). ### List consumer groups You can get a list of the active groups in the cluster using the `kafka-consumer-groups` tool. On a large cluster, this may take a while since it collects the list by inspecting each broker in the cluster. ```bash bin/kafka-consumer-groups --bootstrap-server host:9092 --list ``` Your output will be a list of list of all consumer groups for the cluster, including consumers for internal use. The output might resemble: ```bash _confluent-controlcenter-7-6-0-1 ConfluentTelemetryReporterSampler--4418883999569981189 test-1234 _confluent-controlcenter-7-6-0-lastProduceTimeConsumer _confluent-controlcenter-7-6-0-1-command ``` To use the Confluent CLI for this task, see [confluent kafka consumer group list](https://docs.confluent.io/confluent-cli/current/command-reference/kafka/consumer/group/confluent_kafka_consumer_group_list.html). ### Describe groups The `kafka-consumer-groups` tool can also be used to collect information on a current group. For example, to see the current assignments for the `test-1234` group, you could use the following command: ```bash bin/kafka-consumer-groups --bootstrap-server host:9092 --describe --group test-1234 ``` The output from this command will list the clients, topics, partitions and more for that group. The output might resemble: ```bash GROUP TOPIC PARTITION CURRENT-OFFSET LOG-END-OFFSET LAG CONSUMER-ID HOST CLIENT-ID test-1234 test-metrics 5 258420 258519 - test-client-1234 /127.0.0.1 test-client test-1234 test-metrics 10 257002 257097 - test-client-1234 /127.0.0.1 test-client test-1234 test-metrics 4 259580 259660 - test-client-1234 /127.0.0.1 test-client test-1234 test-metrics 7 254004 254131 - test-client-1234 /127.0.0.1 test-client ``` If you happen to invoke this while a rebalance is in progress, the command will report an error. Retry again and you should see the assignments for all the members in the current generation. To use the Confluent CLI for this task, see [confluent kafka consumer group describe](https://docs.confluent.io/confluent-cli/current/command-reference/kafka/consumer/group/confluent_kafka_consumer_group_describe.html). ### Reset offsets You can also use this tool to reset the [consumer offset](../_glossary.md#term-consumer-offset) in scenarios where a consumer is stalled or significantly lagging. Make sure you deactivate the consumer before you reset its offset. You have many options for changing the offset. For example, you can reset offsets by shifting forward or backward with `shift-by` or reset them to the beginning with `--to-earliest`. For all the options, see the [kafka-consumer-group tool usage details](/kafka/operations-tools/kafka-tools.html#kafka-consumer-groups-sh). To reset the offsets back by 20 positions, use the following command: ```bash bin/kafka-consumer-groups.sh --bootstrap-server host:9092 --group test-1234 --reset-offsets --shift-by -20 --topic test-metrics -execute --group test-1234 ``` The output will contain the group, topic and partitions, and the new offset for each: ```bash GROUP TOPIC PARTITION NEW-OFFSET test-1234 test-metrics 5 258400 test-1234 test-metrics 10 256082 test-1234 test-metrics 4 259560 test-1234 test-metrics 7 254984 ``` ## Consumer examples Confluent provides a number of resources to help you get started with Kafka consumers. - For a tutorial on how to build a Kafka consumer that can read records from Confluent Cloud or Confluent Platform, see [How to build your first Apache KafkaConsumer application](https://developer.confluent.io/tutorials/creating-first-apache-kafka-consumer-application/confluent.html) - For consumer examples in several different languages, see [Get Started](https://developer.confluent.io/get-started/java/), and use the language selector to choose Java, Python, Go, .NET, JavaScript Client, C/C++, REST, Spring Boot, and more. Click **Build Consumer** in the navigation menu to see example consumer code for the language you chose. - Use **Confluent for VS Code** to generate a consumer project for you. You can choose from these languages: - Java - Go - Python For more information, see [Confluent for VS Code with Confluent Cloud](/cloud/current/client-apps/vs-code-extension.html) and [Confluent for VS Code with Confluent Platform](/platform/current/clients/vs-code-extension.html). ## Related content - [Consumer Design](/kafka/design/consumer-design.html) - For a step-by-step tutorial with thorough explanations that break down a sample Kafka Consumer application, check out [How to build your first Apache KafkaConsumer application](https://developer.confluent.io/tutorials/creating-first-apache-kafka-consumer-application/confluent.html). - For Hello World examples of Kafka clients in various programming languages including Java, see [Code Examples for Apache Kafka](https://developer.confluent.io/get-started/). - To see examples of consumers written in various languages, see [Kafka Client Examples for Confluent Platform](examples-index.md#client-examples). - Blog post: [Apache Kafka Data Access Semantics: Consumers and Membership](https://www.confluent.io/blog/apache-kafka-data-access-semantics-consumers-and-membership/) - Free course: [Apache Kafka 101](https://developer.confluent.io/learn-kafka/apache-kafka/consumers) - Interactive diagram: [Kafka Internals](https://developer.confluent.io/#kafka-internals) - Confluent Support: [How to reset an offset for a specific consumer group](https://support.confluent.io/hc/en-us/articles/360040784511)