each consumer group is a subscriber to one or more kafka topics. You can replace it with org.apache.kafka.clients.consumer.RoundRobinAssignor. Suppose that we are three seconds after the most recent commit and a rebalance is triggered. The default is 1 MB, which means that when KafkaConsumer.poll() returns ConsumerRecords, the record object will use at most max.partition.fetch.bytes per partition assigned to the consumer. But if we know that this is the last commit before we close the consumer, or before a rebalance, we want to make extra sure that the commit succeeds. ShutdownHook runs in a separate thread, so the only safe action we can take is to call wakeup to break out of the poll loop. With autocommit enabled, a call to poll will always commit the last offset returned by the previous poll. When you decide to exit the poll loop, you will need another thread to call consumer.wakeup(). Compatibility, Deprecation, and Migration Plan, This is a backward compatible change. Only one consumer can read from a single partition at a time, and thus, the number of partitions of a topic is the maximum possible degree of parallelization. A consumer group basically represents the name of an application. Committing the latest offset only allows you to commit as often as you finish processing batches. New tables are being created constantly to support features and demands of our fast-growing business. As long as all your consumers are up, running, and churning away, this will have no impact. If these are set to -1, the OS defaults will be used. Rebalances are upper-bounded in time by the slowest-reacting consumer. Just like everything else in the consumer, the automatic commits are driven by the poll loop. We assume that the records we consume will have String objects as both the key and the value of the record. Before using this convenient option, however, it is important to understand the consequences. When closing a consumer cleanly, the consumer will notify the group coordinator that it is leaving, and the group coordinator will trigger a rebalance immediately, reducing the gap in processing. All the consumer configuration is documented in Apache Kafka documentation. As we saw in the previous section, consumers in a consumer group share ownership of the partitions in the topics they subscribe to. The main body of a consumer will look as follows: This is indeed an infinite loop. Think about this common scenario: Your application is reading events from Kafka (perhaps a clickstream of users in a website), processes the data (perhaps remove records that indicate clicks from automated programs rather than users), and then stores the results in a database, NoSQL store, or Hadoop. This can be any string, and will be used by the brokers to identify messages sent from the client. It is also useful in the sense that this configuration gives Admin/Ops teams better control over the cluster, limiting the ways in which novice developers can shoot themselves in the foot (via large consumer groups). Fortunately, the consumer API allows you to call commitSync() and commitAsync() and pass a map of partitions and offsets that you wish to commit. Most developers exercise more control over the time at which offsets are committed—both to eliminate the possibility of missing messages and to reduce the number of messages duplicated during rebalancing. So, let’s discuss how to exit cleanly. If there was an error in seek() (e.g., the offset does not exist), the exception will be thrown by poll(). Here, the goal is to keep a running count of customers from each county, so we update a hashtable and print the result as JSON. In the previous example, if we add a new consumer group G2 with a single consumer, this consumer will get all the messages in topic T1 independent of what G1 is doing. The only new property here is group.id, which is the name of the consumer group this consumer belongs to. Here, we decide to commit current offsets every 1,000 records. Keep in mind that seek() only updates the position we are consuming from, so the next poll() will fetch the right messages. large consumer groups are not very practical with our current model due to two reasons: 1. Keep in mind that there is no point in adding more consumers than you have partitions in a topic—some of the consumers will just be idle. As discussed in the previous chapter, Kafka producers require serializers to convert objects into byte arrays that are then sent to Kafka. See Figure 4-6. Kafka consumer group is basically a number of Kafka Consumers who can read data in parallel from a Kafka topic. # bin/kafka-consumer-groups.sh --new-consumer --describe --group consumer-tutorial-group … Large consumer groups can be seen as an anti-pattern. It doesn’t know which events were actually processed, so it is critical to always process all the events returned by poll() before calling poll() again. A Kafka Consumer Group has the following properties: All the Consumers in a group have the same group.id. If we add more consumers to a single group with a single topic than we have partitions, some of the consumers will be idle and get no messages at all. This will close the network connections and sockets. This property controls the maximum number of bytes the server will return per partition. This example is a bit truncated, but you can view the full example at http://bit.ly/2u47e9A. Each consumer group is a subscriber to one or more Kafka topics. If you configure enable.auto.commit=true, then every five seconds the consumer will commit the largest offset your client received from poll(). We specify the generated class, Customer, as the type for the record value. We followed the theoretical discussion with a practical example of a consumer subscribing to a topic and continuously reading events. In order to know where to pick up the work, the consumer will read the latest committed offset of each partition and continue from there. If G1 has four consumers, then each will read messages from a single partition. See Figure 4-7. Perhaps you also need to close file handles, database connections, and such. So if consumers C1 and C2 are subscribed to two topics, T1 and T2, and each of the topics has three partitions, then C1 will be assigned partitions 0 and 1 from topics T1 and T2, while C2 will be assigned partition 2 from those topics. We’ll start by explaining some of the important concepts, and then we’ll go through some examples that show the different ways consumer APIs can be used to implement applications with varying requirements. It should be obvious that the serializer used to produce events to Kafka must match the deserializer that will be used when consuming events. In addition, when partitions are moved from one consumer to another, the consumer loses its current state; if it was caching any data, it will need to refresh its caches—slowing down the application until the consumer sets up its state again. In our current model of consumer groups, whenever a rebalance happens every consumer from that group experiences downtime - their poll() calls block until every other consumer in the group calls poll(). Normally, occasional failures to commit without retrying are not a huge problem because if the problem is temporary, the following commit will be successful. If you are running the consumer loop in the main thread, this can be done from ShutdownHook. This is one of the benefits of using Avro and the Schema Repository for serializing and deserializing—the AvroSerializer can make sure that all the data written to a specific topic is compatible with the schema of the topic, which means it can be deserialized with the matching deserializer and schema. It is common to use the callback to log commit errors or to count them in a metric, but if you want to use the callback for retries, you need to be aware of the problem with commit order: We send the commit and carry on, but if the commit fails, the failure and the offsets will be logged. Once we know which partitions we want, we call assign() with the list. Consumer groups allow a group of machines or processes to coordinate access to a list of topics, distributing the load among the consumers. The expression can match multiple topic names, and if someone creates a new topic with a name that matches, a rebalance will happen almost immediately and the consumers will start consuming from the new topic. Just like multiple producers can write to the same topic, we need to allow multiple consumers to read from the same topic, splitting the data between them. This means that as a developer you need to keep track of which serializers were used to write into each topic, and make sure each topic only contains data that the deserializers you use can interpret. When we add a new consumer to the group, it starts consuming messages from partitions previously consumed by another consumer. Obviously there is a need to scale consumption from topics. See Figure 4-5. A consumer group may contain multiple consumers. During those seconds, no messages will be processed from the partitions owned by the dead consumer. static String CREATE_TABLE_KE_CONSUMER_BSCREEN = " CREATE TABLE IF NOT EXISTS `ke_consumer_bscreen` (`cluster` varchar(64) DEFAULT NULL,`group` varchar(128) DEFAULT … We need a mechanism to enforce quotas on a per-client basis. Old clients will still fail by converting the new error to the non-retriable UnknownServerException. You will need to handle this by checking consumer.partitionsFor() periodically or simply by bouncing the application whenever partitions are added. If the consumer stops sending heartbeats for long enough, its session will time out and the group coordinator will consider it dead and trigger a rebalance. As you recall, the consumer must call poll() frequently enough to avoid session timeout and subsequent rebalance. Each consumer only sees his own assignment—the leader is the only client process that has the full list of consumers in the group and their assignments. heartbeat.interval.ms controls how frequently the KafkaConsumer poll() method will send a heartbeat to the group coordinator, whereas session.timeout.ms controls how long a consumer can go without sending a heartbeat. Clearly, managing offsets has a big impact on the client application. That is due to the fact that every consumer needs to call JoinGroup in a rebalance scenario in order to confirm it is still in the group. Suppose you have an application that needs to read messages from a Kafka topic, run some validations against them, and write the results to another data store. You can’t have multiple consumers that belong to the same group in one thread and you can’t have multiple threads safely use the same consumer. Powered by a free Atlassian Confluence Open Source Project License granted to Apache Software Foundation. Sometimes you know you have a single consumer that always needs to read data from all the partitions in a topic, or from a specific partition in a topic. If you know your consumer is about to lose ownership of a partition, you will want to commit offsets of the last event you’ve processed. It tightly couples producers and consumers and is fragile and error-prone. (4 replies) I have been playing around with ACLs and was hoping to limit access to a topic and consumer group by IP, but was unable to get it working. Let’s take topic T1 with four partitions. In this KIP, we will discuss a proposal to implement quotas in Kafka. This is where we’ll start reading next time we start. You’ll want to catch the exception to make sure your application doesn’t exit unexpectedly, but there is no need to do anything with it. The poll loop does a lot more than just get data. When the consumer first starts, after we subscribe to topics, we call poll() once to make sure we join a consumer group and get assigned partitions, and then we immediately seek() to the correct offset in the partitions we are assigned to. In those cases, we want each application to get all of the messages, rather than just a subset. Heartbeats are sent when the consumer polls (i.e., retrieves records) and when it commits records it has consumed. As long as the consumer is sending heartbeats at regular intervals, it is assumed to be alive, well, and processing messages from its partitions. Another imaginary method: this time we update a table storing the offsets in our database. Memory usage of stable groups is not very high, but the runaway consumer group scenario described in KAFKA-7610 can reach large consumer numbers, CPU spikes - there are a number of O(N) operations done on the consumers collection for a group, Rebalance times do not grow linearly with the consumer group size - unfortunately we do not have any concrete results, just anecdotes. Here is what a commit of specific offsets looks like: This is the map we will use to manually track offsets. We concluded by discussing the deserializers used by consumers to turn bytes stored in Kafka into Java objects that the applications can process. I chose to call commitAsync(), but commitSync() is also completely valid here. In order to consume messages in a consumer group, '-group' command is used. In general, if all consumers are subscribed to the same topics (a very common scenario), RoundRobin assignment will end up with all consumers having the same number of partitions (or at most 1 partition difference). This is useful for applications that need to consume from multiple topics and can handle the different types of data the topics will contain. Perhaps messages from partition 0 and 2 go to C1 and messages from partitions 1 and 3 go to consumer C2. kafka-consumer-groups --bootstrap-server localhost:9092 --delete --group … The root of the problem isn't necessarily the client's behavior (clients can behave any way they want), it is the fact that the broker has no way to shield itself from such a scenario.  where N faulty (or even malicious) clients could result in the broker thinking more than N consumers are joining during the rebalance. We will now look at how to create custom deserializers for your own objects and how to use Avro and its deserializers. Most of the parameters have reasonable defaults and do not require modification, but some have implications on the performance and availability of the consumers. However, sometimes you want to start reading at a different offset. SimpleConsumer is a thin wrapper around the Kafka APIs that allows you to consume from specific partitions and offsets. Automatic commits are convenient, but they don’t give developers enough control to avoid duplicate messages. Here we assume that updating records is fast, so we do an update on every record, but commits are slow, so we only commit at the end of the batch. For example, if you have 24 threads, a max queue size of 10, and a fetch.size of 1.2 megabytes, your consumer is going to take 288 megabytes of heap space (24 threads * 10 fetches * 1.2 … The WakeupException doesn’t need to be handled, but before exiting the thread, you must call consumer.close(). If the committed offset is larger than the offset of the last message the client actually processed, all messages between the last processed offset and the committed offset will be missed by the consumer group. In this chapter we discussed the Java KafkaConsumer client that is part of the org.apache.kafka.clients package. See Figure 4-2. (Just like poll(), close() also commits offsets automatically.) In the next section we will show a more involved example that also demonstrates the use of onPartitionsAssigned(): We start by implementing a ConsumerRebalanceListener. Note that we are committing the latest offsets we’ve processed, not the latest offsets in the batch we are still processing. In this case, the offset is three seconds old, so all the events that arrived in those three seconds will be processed twice. Subscribing to multiple topics using a regular expression is most commonly used in applications that replicate data between Kafka and another system. The Consumer Group name is global across a Kafka cluster, so you should be careful that any 'old' logic Consumers be shutdown before starting new code. This will limit the throughput of the application. record.value() is a Customer instance and we can use it accordingly. If C1 and C2 described previously used RoundRobin assignment, C1 would have partitions 0 and 2 from topic T1 and partition 1 from topic T2. However, if a consumer crashes or a new consumer joins the consumer group, this will trigger a rebalance. Before exiting the consumer, make sure you close it cleanly. This name is referred to as the Consumer Group. KIP-389: Introduce a configurable consumer group size limit, Consumer groups are an essential mechanism of Kafka. Kafka has four core APIs: The Producer API allows an application to publish a stream of records to one or more Kafka topics. And we are using commitSync() to make sure the offsets are committed before the rebalance proceeds. While it is possible to create consumers that do not belong to any consumer group, this is uncommon, so for most of the chapter we will assume the consumer is part of a group. Kafka has two built-in partition assignment policies, which we will discuss in more depth in the configuration section. So far, we have discussed consumer groups, which are where partitions are assigned automatically to consumers and are rebalanced automatically when consumers are added or removed from the group. So if there is a topic with four partitions, and a consumer group … Setting session.timeout.ms lower than the default will allow consumer groups to detect and recover from failure sooner, but may also cause unwanted rebalances as a result of consumers taking longer to complete the poll loop or garbage collection. Each partition in the topic is read by only one Consumer. A record gets delivered to only one consumer in a consumer group. Set it to false if you prefer to control when offsets are committed, which is necessary to minimize duplicates and avoid missing data. As a rule of thumb, if you care about latency, it’s probably a good idea to limit the number of partitions per broker to 100 x b x r, where b is the number of brokers in a Kafka cluster and r is the … This is the most important line in the chapter. Throughout this chapter we will discuss how to safely handle rebalances and how to avoid unnecessary ones. In your application, you can commit based on time or perhaps content of the records. kafka-console-consumer is a consumer command line that: read data from a Kafka topic and write it to standard output (console). The consumer code that uses this serializer will look similar to this example: Again, it is important to note that implementing a custom serializer and deserializer is not recommended. Subscribed to topic Hello-kafka offset = 3, key = null, value = Test consumer group 01. d. Further, the output of the Second Process. Because each topic has an uneven number of partitions and the assignment is done for each topic independently, the first consumer ends up with more partitions than the second. Once we create a consumer, the next step is to subscribe to one or more topics. In fact, one of the main design goals in Kafka was to make the data produced to Kafka topics available for many use cases throughout the organization. This way the consumer can use the schema that was registered by the producer to deserialize the message. schema.registry.url is a new parameter. With the new consumer API, the broker handles everything including metadata deletion: the group is deleted automatically when the last committed offset for the group expires. Another thread calling wakeup will cause poll to throw a WakeupException. If you are limited to a single consumer reading and processing the data, your application may fall farther and farther behind, unable to keep up with the rate of incoming messages. The subcribe() method takes a list of topics as a parameter, so it’s pretty simple to use: Here we simply create a list with a single element: the topic name customerCountries. KIP-11 has the following line about consumer groups: In order to consume from a topic using the new consumer … There are many different ways to implement exactly-once semantics by storing offsets and data in an external store, but all of them will need to use the ConsumerRebalanceListener and seek() to make sure offsets are stored in time and that the consumer starts reading messages from the correct location. You do this by passing a ConsumerRebalanceListener when calling the subscribe() method we discussed previously. The following sections cover those concepts. Kafka … You add consumers to an existing consumer group to scale the reading and processing of messages from the topics, so each additional consumer in a group will only get a subset of the messages. To make sure an application gets all the messages in a topic, ensure the application has its own consumer group. Now that you know how to produce and consume events with Kafka, the next chapter explains some of the internals of a Kafka implementation. The first time you call poll() with a new consumer, it is responsible for finding the GroupCoordinator, joining the consumer group, and receiving a partition assignment. Kafka APIs. This configuration is separate from session.timeout.ms, which controls the time it takes to detect a consumer crash and stop sending heartbeats. This is achieved by balancing the partitions between all members in the consumer group so that each partition is assigned to exactly one consumer in the group. We call the action of updating the current position in the partition a commit. If you set enable.auto.commit to true, then you might also want to control how frequently offsets will be committed using auto.commit.interval.ms. When the consumer group and topic combination has a previously stored offset, the Kafka Consumer origin receives messages starting with the next unprocessed message after the stored offset. It is used the exact same way as in KafkaProducer (you can refer to Chapter 3 for details on how this is defined). The reason it does not retry is that by the time commitAsync() receives a response from the server, there may have been a later commit that was already successful. Consumer groups are an essential mechanism of Kafka. Reading data from Kafka is a bit different than reading data from other messaging systems, and there are few unique concepts and ideas involved. This fine-grained configurability does not seem needed for the time being and may best be left for the future if the need arises, There are other ways of limiting how long a rebalance can take, discussed, In the form of time - have a max rebalance timeout (decoupled from `max.poll.interval.ms`), Lack strictness, a sufficiently buggy/malicious client could still overload the broker in a small time period, In the form of memory - have a maximum memory bound that can be taken up by a single group, Lacks intuitiveness, users shouldn't think about how much memory a consumer group is taking, Large consumer groups are currently considered an anti-pattern and a sensible default value would hint at that well, It is better to be considerate of possible deployments that already pass that threshold. STATUS The simplest and most reliable of the commit APIs is commitSync(). Once the consumer subscribes to topics, the poll loop handles all details of coordination, partition rebalances, heartbeats, and data fetching, leaving the developer with a clean API that simply returns available data from the assigned partitions. Closing the consumer will commit offsets if needed and will send the group coordinator a message that the consumer is leaving the group. The first consumer to participate in a group … {"serverDuration": 74, "requestCorrelationId": "374d40e647661ebc"}, KIP-345: Introduce static membership protocol to reduce consumer rebalances, KIP-394: Require member.id for initial join group request.
2020 kafka consumer group limit