But as there are multiple instances of consumers, the order of processing is now no more guaranteed. Hi ! Does a kafka consumer machine need to run zookeeper? Understanding Kafka Topics and Partitions - Stack Overflow Using this mode will lead to an increase in end-to-end latency because the consumer will only return a message when the brokers have written the transaction markers that record the result of the transaction (committed or aborted). But let's suppose the first consumer takes more time to process the task than the poll interval. **Maybe this video can be helpful to understand some core concepts better. If the number of consumers is the same as the number of topic partitions, then partition and consumer mapping can be like below, If the number of consumers is higher than the number of topic partitions, then partition and consumer mapping can be as seen below, Not effective, check Consumer 5. Kafka-Streams - Tips on How to Decrease Re-Balancing Impact for - DZone In the future, we do plan to improve some of those limitations to make Kafka more scalable in terms of the number of partitions. This is mostly just a configuration issue. If it does not evenly divide, the first few consumers will have an extra partition. 4 machines -> messages from approx 5 topics per machine and so on. Roughly, this broker will be the leader for about 1000 partitions. This distribution is irrespective of the keys hash value (or the key being null), so messages with the same key can end up in different partitions. Kafka consumers are the applications that read data from Kafka servers. Note, however, that you should avoid using any properties that cause conflict with the properties or guarantees provided by your application. rev2023.6.2.43474. Introduction to Apache Kafka Partitions - Confluent - For example, the retention was for 3 hours, then the time passes, how is the offset being handled on both sides? When there are two consumers already with the given group-id and a third consumer wants to consume with the same group-id. In that case consumer.commitSync() and consumer.commitAsync() can help manage offset. Currently, in Kafka, each broker opens a file handle of both the index and the data file of every log segment. Please correct me if I am wrong, when a message send by a producer and when it comes in the topic, it is copies it to the partitions as per the configurations and then consumer consumes it. - For example, The retention was for 3 hours, then the time passes, how is the offset being handled on both sides? In the common case when a broker is shut down cleanly, the controller will proactively move the leaders off the shutting down broker one at a time. We can configure the strategy that will be used to assign the partitions among the consumer instances. Kafka Consumer Rebalance - Learn. Write. Repeat. Paper leaked during peer review - what are my options? fetch.max.bytes Kafka Partition Strategy - Redpanda Sets a maximum limit in bytes on the amount of data fetched from the broker at one time. apache kafka - If you have less consumers than partitions, what happens This strategy is useful when the workload becomes skewed by a single key, meaning that many messages are being produced for the same key. GNSS approaches: Why does LNAV minima even exist? The consumer can then observe messages in the same order that they were committed to the broker. This custom assigner can then be used while initializing the consumer. Messages are sent to partitions in a round-robin fashion. Find centralized, trusted content and collaborate around the technologies you use most. If this happens for long enough, it is possible that the topic retention configurations might mean messages are deleted by the broker before theyre read by the consumer. In this case, the process of electing the new leaders wont start until the controller fails over to a new broker. You CANNOT have multiple consumers (in a consumer group) to consume data from a single parition. A similar issue exists in the consumer as well. Kafka makes it easy to stream and organize data between the applications that produce and consume events. This is a common question asked by many Kafka users. If you have less consumers than partitions, what happens? Before Confluent, Jun Rao was a senior staff engineer at LinkedIn where he led the development of Kafka. Messages in the partition have a sequential id number that uniquely But which one is the better choice for your organization? Custom assignor: It is also possible to write custom logic for an assignor. It involves reading and writing some metadata for each affected partition in ZooKeeper. With the auto.offset.reset property set as latest, which is the default, the consumer will start processing only new messages. You might want to do this if the amount of data being produced is low. max.poll.interval.ms I understand that messages from one topic will always go to a single machine. Be careful. So, the more partitions, the higher that one needs to configure the open file handle limit in the underlying operating system. However, you dont want to set the timeout so low that the broker fails to receive an heartbeat in time and triggers an unnecessary rebalance. If you add new consumer instances to the group, they will take over some partitons from old members. Kafka offers a versatile command line interface, including the ability to create a producer that sends data via the console. Does the policy change for AI-generated content affect users who (want to) Data Modeling with Kafka? The record key is not used as part of the partitioning strategy, so records with the same key are not guaranteed to be sent to the same partition. The goal of this post is to explain a few important determining factors and provide a few simple formulas for when you are self-managing your Kafka clusters. Consumer groups are very useful for scaling your consumers according to demand. However, if you have more consumers than partitions, some of the consumers will remain idle because there wont be any partitions left for them to feed on. Thanks for contributing an answer to Stack Overflow! 6 - What happens when a message is deleted from the queue? There are two types of rebalances. "I don't like it when it is rainy." You have the option to use a customized partitioner to have a better control, but it's totally optional. Misunderstanding producer retries and retriable exceptions From the broker side: 3. When looking to optimize your consumers, you will certainly want to control what happens to messages in the event of failure. Kafka Topics Configuration. The controller failover happens automatically but requires the new controller to read some metadata for every partition from ZooKeeper during initialization. This state can be periodically checkpointed. But convenience, as always, has a price. So expensive operations such as compression can utilize more hardware resources. Is that right? Yes, consumers join (or create if they're alone) a consumer group to share load. Before assigning partitions to a consumer, Kafka would first check if there are any existing consumers with the given group-id. However, if there're more than one consumer group, the same partition can be consumed by one (and only one) consumer in each consumer group. The commitSync API commits the offsets of all messages returned from polling. least one partition. Could entrained air be used to increase rocket efficiency, like a bypass fan? The consumer group coordinator can then use the id when identifying a new consumer instance following a restart. Kafka multiple producer writing to same topic? I am confused about whether to have a single consumer group for all 22 topics or have 22 consumer groups? What is more, if we define too small number, then the partitions may not get located on all possible brokers leading to nonuniform cluster utilization. After enough data has been accumulated or enough time has passed, the accumulated messages are removed from the buffer and sent to the broker. However, this approach had drawbacks in terms of batching efficiency and potential latency issues. The property partition.assignment.strategy can be used to configure the assignment strategy while setting up a consumer. Lets say your target throughput is t. Then you need to have at least max(t/p, t/c) partitions. In that i have a sample where you create topic with 3 partitions and then a consumer with ConsumerRebalanceListener telling you which consumer is handling which partition. Although its possible to increase the number of partitions over time, one has to be careful if messages are produced with keys. Suppose a new consumer application connects with a broker and presents a new consumer group id for the first time. Then Kafka assigns each partition to a consumer and consume Optimizing Kafka consumers - Strimzi In versions of Apache Kafka prior to 2.4, the partitioning strategy for messages without keys involved cycling through the partitions of the topic and sending a record to each one. Luckily, Kafka offers the schema registry to give us an easy way to identify and use the format specified by the producer. there are no existing consumers that are part of the group), the consumer group will be created automatically. How do messages get consumed in Kafka when there are more partitions than consumers? I'm the author of the accepted answer, but I think yours is really nice too, most notably on point number 3 where the diagrams make things 200% clearer ! When Kafka cluster sends data to a consumer group, all records of a partition will be sent to a single consumer in the group. Does it care about partitions? The more partitions that a consumer consumes, the more memory it needs. The __consumer_offsets topic does not yet contain any offset information for this new application. On the consumer side, Kafka always gives a single partitions data to one consumer thread. Jun Rao is the PMC chair of Apache Kafka and a committer of Apache Cassandra. Consumers within a group do not read data from the same partition, but can receive data exclusively from zero or more partitions. All network I/O happens in the thread of the application making the call. This helps in saving some of the overhead processing when topic partitions move from one consumer to another during a rebalance (discussed in a later section). one-to-one co-relation less than 1: some consumers might receive from more than 1 partition Build vs. Buy is being taken seriously again. It turns out that, in practice, there are a number of situations where Kafka's partition-level parallelism gets in the way of optimal design. What does "Rebalancing" mean in Apache Kafka context? It should give an error. How strong is a strong tie splice to weight placed in it from above? (atomicity, consistency, isolation, and durability) reliability If the application cannot process all the records returned from poll in time, you can avoid a rebalance by using this property to increase the interval in milliseconds between polls for new messages from a consumer. Before LinkedIn, Jun Rao was a researcher at IBM's Almaden research data center, where he conducted research on database and distributed systems. Of course, every time a new consumer joins the group, the Kafka "controller" let the leader consumer to know about that and it starts the . thanks for the question @MortenBork, version >= 2.4 onwards, setting, Sry :) It's a bit hard explaining the whole kafka process in 500 chars boxes, I suggest reading. . It doesn't need to be specified exclusively. The streaming data platform for developers. A basic consumer configuration must have a host:port bootstrap server address for connecting to a Kafka broker. Apache Kafka groups related messages into topics, allowing consumers and producers to categorize messages. By allowing your consumer to commit offsets, you are introducing a risk of data loss and duplication. Yes, even though, it's not Zookeeper the component responsible for this. Turning off the auto-commit functionality helps with data loss because you can write your code to only commit offsets when messages have actually been processed. So, for some partitions, their observed unavailability can be 5 seconds plus the time taken to detect the failure. As the name suggests, a consumer group is just a bunch of consumers. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. For a list of trademarks of The Linux Foundation, please see our Trademark Usage page. I guess the consumer has to somehow keep track of what messages it has already processed in case of duplicates? Why wouldn't a plane start its take-off run from the very beginning of the runway to keep the option to utilize the full runway if necessary? This means that the position of a consumer in each partition is just a single integer, the offset of the next message to consume. Issue with Kafka Consumer prioritizing specific partitions Adding more consumers to a group helps scale consumption. apache kafka - Maintaining order of events with multiple consumers in a Consumer being an application can die anytime. If a key exists, Kafka hashes the key, and the result is used to map the message to a specific partition. We have less consumers than the partitions and as such we have multiple Kafka partitions assigned to each consumer pod. org.apache.kafka.clients.consumer.CooperativeStickyAssignor: Follows the same StickyAssignor logic, but allows for cooperative rebalancing. The number of partitions defines the maximum number of consumers from a single consumer group. Another cause of rebalancing might actually be due to an insufficient poll interval configuration, which is then interpreted as a consumer failure. When a new consumer is added, it starts consuming messages from partitions previously assigned to a different consumer. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. You then assume responsibility for how your consumer application handles commits correctly. A rough formula for picking the number of partitions is based on throughput. Consumer should be aware of the number of partitions, as was discussed in question 3. Well look a bit more at targeting latency by increasing batch sizes in the next section. That means that each record in a series of consecutive records will be sent to a different partition until all the partitions are covered, and then the producer starts over again. The advantage here is that it aims to maximize the number of consumers used. This is dependent on linger.ms and batch.size. Specifically, consumer lag for a given consumer group indicates the delay between the last message added to a topic partition and the message last picked up by the consumer of that partition. The aim is to maximize the number of consumers used. This consumer polls the partition and receives the same, duplicate, batch of messages. If there are multiple threads, I will be able to achieve parallelism(utilize all the cores) without spinning another machine. Partitions allow a topics log to scale beyond a size that will fit on 2. Learn how to select the optimal partition strategy for your use case, and understand the pros and cons of different Kafka partitioning strategies. Again, you can use the earliest option in this situation so that the consumer returns to the start of a partition to avoid data loss if offsets were not committed. Kafka consumers will subscribe to specific topics or topic partitions and retrieve messages from those topics in real-time. The sample code is in github, http://www.javaworld.com/article/3066873/big-data/big-data-messaging-with-kafka-part-2.html. Kafka will take care of it. If a consumer group or standalone consumer is inactive and commits no offsets during the offsets retention period (offsets.retention.minutes) configured for a broker, previously committed offsets are deleted from __consumer_offsets. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Kafka: Single consumer group, no partitions and multiple topics, Building a safer community: Announcing our new Code of Conduct, Balancing a PhD program with a startup career (Ep. And you use two properties to do it: session.timeout.ms and heartbeat.interval.ms. Is that right? If it is a single thread, the thread may get overloaded. By clicking Post Your Answer, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct. If any consumer starts after the retention period, messages will be consumed as per auto.offset.reset configuration which could be latest/earliest. 6. Strimzi Uneven Distribution of messages in Kafka Partitions, Apache Kafka message consumption when partitions outnumber consumers, Why kafka 0.8.2 say that each partition is consumed by exactly one consumer in a consumer group. The partitions of a topic are distributed over the brokers in the This mapping, however, is consistent only as long as the number of partitions in the topic remains the same: If new partitions are added, new messages with the same key might get written to a different partition than old messages with the same key. Now you could play around with it by starting 1 or more consumers and see what happens. I am just interested in load distribution. A topic must have at Copyright Confluent, Inc. 2014-2023. Does the grammatical context of 1 Chronicles 29:10 allow for it to be declaring that God is our Father? When this broker fails uncleanly, all those 1000 partitions become unavailable at exactly the same time. Rebalancing is the time taken to assign a partition to active consumers in a group. Producers are applications that write data to partitions in Kafka topics. Sets a maximum limit in bytes on how much data is returned for each partition, which must always be larger than the number of bytes set in the broker or topic configuration for max.message.bytes. A common approach is to capitalize on the benefits of using both APIs, so the lower latency commitAsync API is used by default, but the commitSync API takes over before shutting the consumer down or rebalancing to safeguard the final commit. This configuration scales with the number of worker nodes. No two consumers of the same group-id would be assigned to the same partition, Offset is handled internally by Kafka. 1. They're not, but you can see from 3 that it's totally useless to have more consumers than existing partitions, so it's your maximum parallelism level for consuming. By increasing the values of these two properties, and allowing more data in each request, latency might be improved as there are fewer fetch requests. It will not be a part of any group. Does it care about partitions? The moving of a single leader takes only a few milliseconds. Indian Constitution - What is the Genesis of this statement? " Extends the AbstractPartitionAssignor class and overrides the assign method with custom logic. The per-partition throughput that one can achieve on the producer depends on configurations such as the batching size, compression codec, type of acknowledgement, replication factor, etc.