If the reader wants to know more about it, please go to the following link: https://docs.confluent.io/current/connect/intro.html. This is done by setting configurations that establish a compaction entry point and a retention entry point. Sign in try { I am also facing this issue. Consumers get stuck rarely but they still get stuck. If the old leader comes back again, it will resume the partitions processing as a new follower, and it will not insert the lost messages in case of a unclean election. @nivox The second alternative looks attractive since it contains the problem inside the KakaConsumerActor. I'm facing something similar issue in my project: This happens randomly after couple of hours (or even days) of running. This is important to keep in mind when setting a new consumer on Kafka, as increasing too much the number of consumers will just end up with idle resources not been used at all. If you try to create a consumer and delegate to run on another thread, theres a check on the consumer that will thrown a error. } finally { Kafka connect is a integration framework, like others such as Apache Camel, that ships with Kafka but runs on a cluster of his own and allows us to quickly develop integrations from/to Kafka to other systems. After creating a Java project, we will code our own producer wrapper. As we talked previously, using auto-commit can be a option on some solutions, depending on the situation. This is important to ensure data availability, but it also means that messages can take a significant amount of time to be delivered for consuming. Incremented index on a splited polyline in QGIS, Combine these two integrals into a single integral. We can check our configs were successfully set by using the following command: One last thing we need to know before moving on to our next topic is that compaction also allows messages to be removed. For both solution I've tested that everything works in the normal case but I'm not sure how to manually cause the issue (stopping the Kafka broker will not work as the consumer detects that the broker is not there and won't follow the code path leading to the wakeup). We are facing the same issue, increasing the wakeup timeout to 6s and setting the the max to 1 seems to resolve the issue but doesn't feel really solid to me. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. why did this happen? I hope to have passed for the reader a solid explanation of Kafka core concepts, as well as directions for complementary studies on his different usages and applications. This is important to watch out as can lead to message ordering issues if not taken with care. kafka version 2.1.1. Assuming we have a terminal with MY_IP environment variable set, this can be done using the following command: PS: All commands assume the name of the Kafka containers follows docker compose naming standards. As with Kafka connect and Streams, is a tool that deserves his own article, so it wont be covered here. Lets see this on practice. Note that I had to introduce another callback just to clean the state of the Single/SubSourceLogic to make things consistent. We will see that, as soon it joins the ConsumerGroupCoordinator, it will be assigned to one of the partitions: And if we see our old consumer, we will see that will be now reading from the other partition only: This show us the power of Kafka ConsumerGroup Coordinator, that takes care of everything for us. We were using the default configurations. I have the same problem. } The older segments are removed from disk according to the retention policies configured. Any update on this? A ack=0 could be useful on a solution that works with lots of messages that are not critic in case of losses monitoring events, for example, are short-lived information that could be lost at certain degree unlike, for example, bank account transactions, where ack=all is a must, since message losses are unacceptable on this kind of application. Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, I don't see it in the configuration but in logs i can see, the same as in my description -> PARTITION from 0-9 and CONSUMER-ID from 0-4 each consumer has 2 partitions, it's String - topic name and strategy is org.apache.kafka.clients.consumer.RoundRobinAssignor, Not all kafka consumers are getting assigned to partitions, Code completion isnt magic; it just feels that way (Ep. rev2022.7.20.42632. When sent this way with compaction enabled, it will remove all messages from the stream. try { But sure, if that solves the problem please go ahead and create a PR. @nivox i like the second solution. The reason looks to be this: (com.typesafe.akka:akka-stream-kafka_2.12:0.15 with kafka brokers 0.10). Kafka guarantees that all messages for a same given message key will always be sent to the same partition as long as the number of partitions on a topic stay the same; Value (payload): The value field is a required field and, as obvious, is the message itself that must be sended; fetch.min.bytes: This defines the minimum amount of bytes a consumer wants to receive from a bulk of messages. } This is because we are using default settings, so it is doing auto-commit. Kafkas ecosystem also need a Zookeeper cluster in order to run. What happens if a broker is down and no IS replicas are available? When creating a topic, we define how much replicas we want to have for each partition on the topic. (that will soon follow if need be); just wanted to get this out there in case there is a known issue behind it or something obviously wrong with how we're using the library. only half of the consumers have been assigned to the partitions while (true) { First, as said before, we create a ProducerRecord, that consist of 3 sections: All the fields from theProducerRecord must be serialized to byte arrays before sent to Kafka, so thats exactly what is done by the Serializer at the first step of our sending we will see later on our lab that we always define a serializer for our keys and value , after that, the records are sent to the Partitioner, that determines the partition to send the message. Lastly, we configured the cleanup policy, making compaction enabled. We saw previously that our example used default auto-commit to commit offsets after reading. As I stated in the previous comment this strategy tries to reconcile the new KafkaConsumer assignments with the Source's by manually calling the RebalanceListener callbacks for all revoked/added TopicPartition assignment. So, on Kafka, we have producers ingesting data, controlled by producer offsets, while we have consumers consuming data from topics, also with their offsets. System.out.println("Value: " + record.value()); I haven't seen the issue since.

It is important to note that it is possible to increase the number of consumers on a group, avoiding this situation altogether: The same consumer consuming from more then one partition on Kafka. "org.apache.kafka.common.serialization.StringDeserializer"); At the end, we can see links for his images. I experience exactly the same problem quite randomly after wakeup: After: I've noticed some callbacks being created and you mention this Single/SubSourceLogic, is that the best/only technique we have at the driver level? Im using 0.22 and i'm having the same issue. kafkaProps.put("bootstrap.servers", builder.toString()); Kafka adding new consumers does not rebalance the load, Understanding Kafka Topics and Partitions, Kafka partitions and consumer groups for at-least-once message delivery, Continuous consumer group rebalancing with more consumers than partitions, How does Kafka Consumer Consume from Multiple assigned Partition, Multiple Spark Kafka consumers with same groupId, Kafka consumer offsetForTimes method returns only few partitions offsets position not all. Which takes precedence: /etc/hosts.allow or firewalld? Lets keep in mind that, as we saw before, brokers are elected as partition leaders for partitions on topics, so when sending the messages, they are sent directly to the partition leaders broker. Any ideas on how to test this in a controlled environment? kafkaProps.put("acks", "all"); consumer gets stuck forever. On this section, we will learn the internals that compose a Kafka producer, responsible for sending messages to Kafka topics. Is the way reactive-kafka uses a wakeup mechanism an issue or vanilla kafka consumer implementation? producer = new KafkaProducer(kafkaProps); Lets begin with basic topic operations. [solution] interesting case I changed group.id and partition.assignment.strategy, added auto.offset.reset=earliest and it looks like it works Are you subscribing to a collection of topic name or java Pattern? On the command above, we ask Kafka that we want to redistribute the replica set from the current brokers to the brokers 1004,1005 and 1006. .put("value.deserializer", Thats why we see the first consumer losing all partitions before been reassigned to one of the old ones. Now that we learned Kafka main concepts, lets begin our hands-on Kafka and learn what we talked in practice! If you are subscribing to a Pattern , change partition.assignment.strategy to RoundRobinAssignor or StickyAssignor. It is a big issue because Kafka brokers think partitions are assign to the consumer. @schrepfler wouldn't the strategy I proposed in the last message fix the problem? consumer.subscribe(Collections.singletonList(topic)); Making statements based on opinion; back them up with references or personal experience. Lets revisit one of our diagrams from the offsets explained section: As we can see on the diagram above, we have 2 consumers groups in a stream. This is a important concept on Kafka: each consumer is responsible for consuming one partition on Kafka and each consumer group consumes the data individually, that is, there is no relation between the consumption of one group and the others. I had this issue on 0.16 and was fortunate to catch the 0.22 on the release day. Now, lets code our consumer. "org.apache.kafka.common.serialization.StringSerializer"); This means that applications are responsible for asking for new chunks of messages to process, allowing clients to process data at their paces. to your account. When starting the first consumer, we will see the following outputs on terminal: That shows our listener was invoked. It is important to notice that, as a broker is down, it could be possible that some messages wont be committed, causing messages to be processed twice when the partition processing is resumed. This is due to Kafka consumer not been thread safe. } bin/kafka-consumer-groups.sh --bootstrap-server [your kafka server] --group [your group name] --describe. We can check the status of reassignment by running: When reassignment is finished, we will see the following: We can also check the status of our topics by running the describe command, as follows: After our reassignment, it will output something like this: Kafkas offset lag refers to a situation where we have consumers lagging behind the head of a stream. Without knowing too many Kafka internals, I think WakeupException_s_ are not such a rare events in reality, is it acceptable to reconcile all the time? One way to self-check would be to use the driver consumer-coordinator-metrics and get the assigned partitions count which should subscribed to go up within a certain period and if not perhaps let it escalate to the supervisor? Data science, optimization, analytics, sports. By stream applications, that means applications that have streams as input and output as well, consisting typically of operations such as aggregation, reduction, etc. throw e; If we want to specify the partitions a consumer will be assigned to, we can use the assign method. There is also a docker compose stack that could be found there to get a Kafka cluster up and running. Kafka producer internal structure is divided as we can see on the following diagram: As we can see, there is a lot going on when producing messages to Kafka. The Partitioner then send the message to bulk processes, running on different threads, that stack the messages until a threshold is reached a certain number of bytes or a certain time without new messages, whatever it comes first and finally, after the threshold is reached, the messages are sent to the Kafka broker. To be clear, the KafkaConsumer has been assigned partitions in this case, but because the SingleSourceLogic doesn't know about them, they are never consumed from.

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