We also share information about your use of our site with our social media, advertising, and analytics partners. If true the consumer’s offset will be periodically committed in the background. Now we don’t need to worry about heartbeats since consumers use a separate thread to perform these (see KAFKA-3888) and they are not part of polling anymore. Auto-commit always happens during poll() method and auto.commit.interval.ms only defines the minimum delay between commits. The tricky part here is that we make sure the message is processed no matter if with success or not. With these configurations, when a new consumer is added the following situation might happen. Her main focus at Confluent is on Kafka Streams, where she has worked on improving the operational experience and high availability in the face of faults and dynamic scaling. All members are told to rejoin the group, and the current resources are refreshed and redistributed “evenly.” Of course, every application is different, which means that every consumer group is too. The stop-the-world rebalancing protocol has been haunting users of the Kafka clients, including Kafka Streams and ksqlDB up the stack, since the very beginning. There are cases in which you would need to assign partitions “manually” but in those cases, pay attention to what could happen if you mix both solutions. Unfortunately, the time has come to return to the real world. This is what allows your ksqlDB application to scale and smoothly handle failures. If the same message must be consumed by multiple consumers those need to be in different consumer groups. This means no downtime for consumer B, as illustrated in Figure 3. This is the worst place we can be in. A partition is owned by a broker (in a clustered environment). By doing this, we can guarantee that the assignor plays along nicely with the cooperative protocol. That’s why auto-committing is dangerous and should be switched off. In fact, you don’t even need to watch it—but monitoring your app is always a good practice. Another situation that might happen is the following. Instead of sitting idle for the entire rebalance, consumer A’s downtime lasts only as long as it takes to revoke one partition. Good news! We must ensure that all the messages are processed before calling poll again. Whether you’re building a plain consumer app from the ground up, doing some complicated stream processing with Kafka Streams, or unlocking new and powerful use cases with ksqlDB, the consumer group protocol is at the heart of your application. Franz upgrades his Streams application, carefully following the specific upgrade path outlined in the release notes to ensure a safe rolling upgrade to the cooperative protocol. The gap between the shiny “hello world” examples of demos and the gritty reality of messy data and imperfect formats is sometimes all too, Software engineering memes are in vogue, and nothing is more fashionable than joking about how complicated distributed systems can be. The cooperative rebalancing protocol. It automatically advances every time the consumer receives messages in a call to poll(Duration). Even worse, this issue has a snowball effect when we have several consumers: a rebalance (scale up only) will cause almost all consumers to raise a CommitFailedException and leave the group. The leader assigns partitions to consumers however it wants, but it must remove any partitions that are transferring ownership from the assignment. While there are clear advantages to cooperative rebalancing, concrete numbers always have the last word. This can … The first consumer to join the group becomes the group leader. Change ). To learn more about the rebalance protocol and how it works have a look to the fol… But as long as there are resources to manage, there’s going to be a need for rebalancing. This rule sounds simple enough, but it can be difficult to satisfy in a distributed system: the phrase “at the same time” may cause alarms to go off in your head. The group coordinator assembles all the subscriptions and sends them back to the group leader, also as before. Fortunately, you don’t have to. During the first phase, the group coordinator waits for each member to join the group. What might be less obvious is that it’s up to only the partition assignor—you can turn on cooperative rebalancing by simply plugging in a cooperative assignor. Despite the ribbing, many people adopt them. It’s critical to note that with auto-commit enabled, a call to poll will always commit the last offset returned by the previous poll. If the JoinGroup request timed out and the client disconnected, the member would nevertheless be left in the group until the rebalance completed and the session timeout expired. After the rebalance, only the new consumer will receive messages even though the partitions have been evenly distributed. Any changes to these require the group to react in order to ensure that all topic partitions are being consumed from and that all members are actively consuming. To learn how we did it, it’s time to peel back the layers below ksqlDB and get your hands dirty with the Apache Kafka® rebalancing protocol. The group coordinator is ultimately responsible for tracking two things: the partitions of subscribed topics and the members in the group. Take the RoundRobinAssignor as an example, which does not play nicely with cooperative rebalancing. You just need to start your application and watch it run. The assignment produced by the round robin assignor changes every time the group membership or topic metadata changes. Remember what we wrote above. Likewise, they will add any new partitions in the assignment. Depending on the time a consumer takes to restart and re-join the group, it might trigger a new rebalance that will cause, again, almost all consumers to raise a CommitFailedException and … KAFKA-8609: Add consumer rebalance metrics #7347 Merged guozhangwang merged 13 commits into apache : trunk from guozhangwang : K8609-consumer-rebalance-metrics Sep 21, 2019