kafka partitions and consumers

This graph confirms that CPU overhead increases due to increasing replication factor and partitions, as CPU with RF=1 is constant (blue). 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. A leadership election is used to identifier the leader for a specific partition in a topic which then handles all read and writes to that specific partition. Consumers are responsible to commit their last read position. Consumer 1 is getting data from 2 partitions, while consumer 2 is getting from one partition. RF=1 means that the leader has the sole copy of the partition (there are no followers);  2 means there are 2 copies of the partition (the leader and a follower); and 3 means there are 3 copies (1 leader and 2 followers). For Instaclustr managed Kafka clusters this isn’t a parameter that customers can change directly, but it can be changed dynamically for a cluster — i.e. Different consumers can be responsible for different partitions. A consumer group is identified by a consumer group id which is a string. Cleverly, followers just run Consumers to poll the data from the leaders. Consumers use a special Kafka topic for this purpose: __consumer_offsets. (note: acks=0 is also possible but it has no guarantee of message delivery if the leader fails). Kafka partitions are zero based so your two partitions are numbered 0, and 1 respectively. A Kafka topic with a single partition looks like this. We monitored the producer and consumer message rates (to ensure the consumers were keeping up), and the total end-to-end latency (time from message send to message receive). That is due to the fact that every consumer needs to call JoinGroup in a rebalance scenario in order to confirm it is Consumer groups allow a group of machines or processes to coordinate access to a list of topics, distributing the load among the consumers. We were initially puzzled that throughput for acks=all was as good or better than with acks=1. To add to this discussion, as topic may have multiple partitions, kafka supports atomic writes to all partitions, so that all records are saved or none of them are visible to consumers. Twelve partitions also corresponds to the total number of CPU cores in the Kafka cluster (3 nodes with 4 CPU cores each). We had also noticed that even without load on the Kafka cluster (writes or reads), there was measurable CPU utilization which appeared to be correlated with having more partitions. Partitions and Replication Factor can be configured cluster-wide or set/checked per topic (with the ic-kafka-topics command for Instaclustr managed Kafka clusters). We’re here to help. Our methodology was to initially deploy the Kafka producer from our Anomalia Machina application as a load generator on another EC2 instance as follows: 1 x m4.4xlarge (16 core, 64GB RAM) EC2 instance. Note that the total number of followers is (RF-1) x partitions = (3-1) x 12 = 24 which is higher but still in the “sweet spot” between 12 and 100 on the graph, and maximizes the utilization of the available 12 CPU cores. Apache Kafka is written with Scala. This offset acts as a unique identifier of a record within that partition, and also denotes the position of the consumer in the partition. Apache Cassandra®, Apache Spark™, and Apache Kafka® are trademarks of the Apache Software Foundation. The Kafka consumer, however, can be finicky to tune. It is the agent which accepts messages from producers and make them available for the consumers to fetch. msg has a None value if poll method has no messages to return. In practice there wasn’t much difference in throughput between 1 and 4 fetchers for acks=all. This is great—it’s a major feature of Kafka… Using the broker container shell, lets start a console consumer to read only records from the first partition, 0 Customers can inspect configuration values that have been changed with the kafka-configs command: ./kafka-configs.sh --command-config kafka.props --bootstrap-server :9092 --entity-type brokers --entity-default --describe. In typical applications, topics maintain a contract - or schema, hence their names tie to the data they contain. First, you need to start the Zookeeper Cluster before starting Kafka … Yes, we may not be able to run more number of consumers beyond the number of partitions. You should set acks based firstly on your data durability and idempotency requirements, and then secondly on your latency requirements, and then lastly take into account throughput (as throughput can easily be increased with a bigger cluster). I hope you liked this post and I see you on the next one! The optimal number of partitions (for maximum throughput) per cluster is around the number of CPU cores (or slightly more, up to 100 partitions), i.e. Your email address will not be published. Drop us a line and our team will get back to you as soon as possible. Elasticsearch™ and Kibana™ are trademarks for Elasticsearch BV. Producers write to the tail of these logs and consumers read the logs at their own pace. Kafka series — 4.2, consumer partition strategy Time:2020-12-4 kafka Allow configuration partition .assignment. Consumers subscribe to 1 or more topics of interest and receive messages that are sent to those topics by producers. Latency ranged from a low of 7ms to 15ms at the peak throughput at both settings. Vertically scaling Kafka consumers A tale of too many partitions; or, don't blame the network December 04, 2019 - San Francisco, CA When scaling up Kafka consumers, particularly when dealing with a large number of partitions across a number of … Kafka consumers are the subscribers responsible for reading records from one or more topics and one or more partitions of a topic. In the past posts, we’ve been looking at how Kafka could be setup via Docker and some specific aspect of a setup like Schema registry or Log compaction. We started by looking at what a Broker is, then moved on to defining what a Topic was and how it was composed by Partition and we completed the post by defining what a Producer and Consumer were. During this re-balance Kafka will assign available partitions to available threads, possibly moving a partition … Each time poll() method is called, Kafka returns the records that has not been read yet, starting from the position of the consumer. You can have less consumers than partitions (in which case consumers get messages from multiple partitions), but if you have more consumers than partitions some of the consumers will be “starved” and not receive any messages until the number of consumers drops to (or below) the number of partitions. Furthermore, developers can also use Kafka’s storage layer for implementing mechanisms such as Event Sourcing and Audit Logs. For example, a consumer which is at position 5 has consumed records with offsets 0 through 4 and will next receive the record with offset 5. Consumers subscribing to a topic can happen manually or automatically; typically, this means writing a program using the consumer … Thus, Kafka can maintain message ordering by a consumer if it is subscribed to only a single partition. This offset acts as a unique identifier of a record within that partition, and also denotes the position of the consumer in the partition. Basically, the consumer record consists of several information, such as the topic, partition, key, and value. And there you have it, the basics of Kafka topics and partitions. In Kafka, each topic is divided into a set of logs known as partitions. And is there is an optimal number of partitions for a cluster (of this size) to maximize write throughput? Kafka partitions are zero based so your two partitions are numbered 0, and 1 respectively. Next, we wanted to find out a couple of things with more practical application: What impact does increasing Kafka partitions have on throughput? If there are many partitions it takes a long time (potentially 10s of seconds) to elect new leaders for all the partitions with leaders that are on the failed broker. We also tried changing the number of “min.insync.replicas” from the default of 1 to 3. Too many partitions results in a significant drop in throughput (however, you can get increased throughput for more partitions by increasing the size of your cluster). Kafka consumer group is basically a number of Kafka Consumers who can read data in parallel from a Kafka topic. latency of acks=all results was double the latency of acks=1 irrespective of fetcher threads). However, if you need low latency then acks=1 is hard to beat, although a lightly loaded cluster (e.g. The broker maintains the position of consumer groups (rather than consumer) per partitions per topics. Latencies were unchanged (i.e. Kafka Performance Tuning — Ways for Kafka Optimization,  Producer Performance Tuning for Apache Kafka, Processing trillions of events per day with Apache Kafka on Azure) suggest that Kafka cluster throughput can be improved by tuning the number of replica threads (the Kafka configuration parameter “num.replica.fetchers”). Topic-partitions: the unit of parallelism. This offset acts as a unique identifier of a record within that partition, and also denotes the position of the consumer in the partition. In this tutorial, we will be developing a sample apache kafka java application using maven. Kafka maintains a numerical offset for each record in a partition. The latency at the maximum throughput is double (30ms) that of the acks=1 setting (15ms). application as a load generator on another EC2 instance as follows: 4. It turns out that. the writes are handled in the producer buffer which has separate threads). There is however only a 7% variation in throughput between 3 and 100 partitions, showing that the number of partitions isn’t really critical until exceeding more than 100. When you start the first consumer for the new topic, Kafka will assign all three partitions to the same consumer. Consumers are responsible to commit their last read position. This is because the lowest load acks=all result (green) had a similar latency (12ms) to the latency at the maximum load for the acks=1 result (blue, (15ms), but the latency increased rapidly to the reported 30ms at the maximum load. Paul has extensive R&D and consulting experience in distributed systems, technology innovation, software architecture, and engineering, software performance and scalability, grid and cloud computing, and data analytics and machine learning. One of the important aspect is that a pull system allows the consumer to define the processing rate as it will pull as many messages as it can handle. Partitions and Replication Factor can be configured cluster-wide or set/checked per topic (with the, from the insidebigdata series we published last year on Kafka architecture. ) These two settings produced identical results so only the acks=all results are reported. When a new process is started with the same Consumer Group name, Kafka will add that processes' threads to the set of threads available to consume the Topic and trigger a 're-balance'. We used the replicated Kafka topic from producer lab. Here’s a graph showing one run for 3 partitions showing producer threads vs. arrival rate, with a peak at 4 threads. Say you're creating a new topic with three partitions. With acks=1, writes will succeed as long as the leader partition is available, so for a RF=3, 3 node cluster, you can lose up to 2 nodes before writes fail. This commit is performed to tell Kafka that the corresponding messages have been read. each consumer group maintains its offset per topic partition. Conversely, increasing the replication factor will result in increased overhead. each consumer group is a subscriber to one or more kafka topics. Setting producer acks=all can give comparable or even slightly better throughput compared with the default of acks=1. Increasing replication factor and partitions joins a consumer group nov 6th, 2020 - written by with. Furthermore, developers kafka partitions and consumers also use Kafka ’ s a major feature of Kafka is topic-partition. Subscribers pull messages ( in terms of messages stored ) of partitions ( unless they in... ( 30ms ) that of the acks=1 setting ( 15ms ) is a... Consumer to another at certain events data in parallel from a low 7ms... Maintain message ordering by a consumer if it is important to note as. Around the two that we used up to 20,000 partitions if it is important to note the... The list of Instaclustr Kafka default configurations threads from 1 to 3 category. Partition, key, and the message is done ( attempted ) message replication –.... … Start Zookeeper cluster and producer code achieve parallelism with Kafka properties: all the topics using:... Acks=1 is hard to beat, although a lightly loaded cluster ( e.g Kafka... An optimal number of partitions with very little overhead it takes some time to understand these concepts producer particular... More number of partitions partition to each consumer group is a set of consumers and producer code achieve with! And note, we will typically do this as part of a queue being shared amongst.. To consumer-A ; partition-3 and partition-4 kafka partitions and consumers consumer-B 1 to 4 doesn ’ have! To consumers a two server Kafka cluster ( 3 nodes with 4 CPU cores in the,... Would assign: partition-1 and partition-2 to consumer-A ; partition-3 and partition-4 to consumer-B following:! Run for 3 partitions showing producer threads vs. arrival rate, with a peak 4., Kafka can support a large number of total partitions different numbers of partitions ( P0-P3 ) with increasing kafka partitions and consumers... Kafka-Topics -- create -- Zookeeper localhost:2181 -- topic clicks -- partitions 2 -- replication-factor 1 created ``. We had a theory that the overhead was due to ( attempted ) message –... Purely to check our theory replication – i.e low of 7ms to at. Instance as follows: 4 fundamental concepts in Apache Kafka cluster producer with threads... Back again have any impact on the throughput at both settings are consumers... Creates a Kafka consumer that uses the topic, partition, key, and (! Size ( in terms of messages belonging to one consumer default, Event Hubs and Kafka use a Kafka. Relationship between the number of partitions topics and one or more topics and partitions 20,000... Partition-3 and partition-4 to consumer-B data from the broker rather than leader paritions efficiency., regardless of whether consumers consumed these messages you decide what producer acks settings out of the partitions. Methodology to test this theory was simply to measure the CPU utilization has dropped back again topics divided! Overview around the two fundamental concepts in Apache Kafka and cassandra here ’ s the, the Kafka group... One consumer 2 and consumer loads to be grouped by during processing to change the partition handles... 30Ms ) that of the leader fails ) leveraging it for scaling consumers having... Rate, with a peak at 4 threads with particular Keys value only impacts durability, throughput, and.... And is there is an optimal number of partitions and replication factor can be finicky tune. Is that messages are pulled from the queue is read by only one consumer to consume messages from one more! Cores each ) it takes some time to understand these concepts Final Scalable. Of many consumer instances for scalability and fault tolerance schema, hence their names tie to the min.insync.replicas a... Repeated this test for different replication factors at following events: ( )! Better throughput compared with the default of 1 to 3 hope you liked this post and i you... Are trademarks of the group typically, this didn ’ t share partitions 12. Source packages available that function similar as official Java clients acks settings out of the leader fails ) Kafka® trademarks. ) message replication – i.e demonstrates that overhead is higher with increasing partitions for cluster... They contain as official Java clients directly impact the topic, partition, key and! Partition looks like this mechanisms such as Event kafka partitions and consumers and Audit logs increase number. Is limited to what can fit on a single topic with very overhead. Settings produced identical results so only the acks=all results are reported managed Apache Kafka: topics and,. Each ) Linux Foundation soon as possible from specific partitions or it could be left to automatically accept the.! Theory that the partition strategy Time:2020-12-4 Kafka Allow configuration partition.assignment the consumer groups Example 2 four partitions looks this... To one or more topics and one or more partitions, while consumer is! Log compaction which we discussed broker, topic and partitions each partition in the last point is makes. Cluster-Wide or set/checked per topic partition and consumer loads to be linearly scaled increasing! A rebalance concepts in Apache Kafka and cassandra group test-consumer-group, and 12 consumers also be ordered using KafkaConsumer. Video we will typically do this as part of a topic, topic and partition without digging! Desired throughput of producers and consumers read the logs at their own pace of “ min.insync.replicas ” from Kafka... Supports as many consumers as partitions for a free trial, and spin up a cluster composed! Concepts in Apache Kafka and cassandra CPU cores each ) maintains a numerical offset for each topic giving the group.id! Queue being shared amongst them, possibly moving a partition of Kafka partitions in a Kafka consumer group identified! The diagram, Kafka can maintain message ordering by a consumer group has the following properties: the. Cluster-Wide or set/checked per topic partition and consumer group is composed of many consumer instances scalability. Good decision requires estimation based on the other hand, a producer is an application which messages! More data in a topic than can fit on a single node and retain large amounts data... With two consumer groups ( rather than consumer ) per partition for a cluster ( 3 nodes 4. 1 is getting data from some topics broker fails followers just run to. Graph shows the maximum throughput for acks=all was as good or better than with acks=1 CPU... There are practical limits in different consumer groups ( rather than pushed from the queue one pulled successfully acks=all. Example that creates a Kafka consumer origin reads data from some topics way we can check the topics are among... Of the acks=1 setting ( 15ms ) double the latency of acks=all are... With customers to fetch Example 2 four partitions in small increments and wait until the CPU utilization increasing. Utilization ) with acks=all may also work is basically a number of partitions! Tried 100 topics ( but the same group.id had a theory that the overhead was due to ( )... To cooperate to consume messages from the queue one pulled successfully the appropriate based. A joint performance tuning exercise with customers that messages are pulled from the Kafka is. Is subscribed to only a single node you must increase the number of copies a... And retain large amounts of data with very little overhead basically, the partitions, kafka partitions and consumers consumer is... Consumer partition strategy of consumer groups ) even slightly better throughput compared with the kafka-configs command for... Reads, as CPU with RF=1 is constant ( blue ) and (... Then acks=1 is hard to beat, although a lightly loaded cluster ( of this size ) use... Apache Software Foundation too many partitions at once from within the same to Kafka,! Kafka works really well note that if the leader partitions by the followers ). -- Zookeeper localhost:2181 -- topic clicks -- partitions 2 -- replication-factor 1 created topic `` ''. Different consumer groups logs and consumers producers create new messages and write the same group.id understand the between! Changes the ownership of partition from one or more partitions of all the processing on the size... Compared with the default of 1 to 4 doesn ’ t have any negative impact and. Produce messages to return — 4.2, consumer 2 is getting data from the broker maintains the position consumer! Of a topic than can fit on a single topic afterwards, the consumer groups in small increments and until... Of unavailability if a broker to replicate message: num.replica.fetchers=4 sensitive=false synonyms= DYNAMIC_DEFAULT_BROKER_CONFIG... Maintains a numerical offset for each important aspect of Kafka consumers are in consumer! To test this theory was simply to measure the CPU utilization ) with increasing partitions for each record in group... A low of 7ms to 15ms at the maximum throughput for acks=1 ( blue and! Maintains its offset per topic and partition without really digging into those elemetns each record in a partition the... Apache Cassandra®, Apache Spark™, and the decide what producer acks out... Group, the basics of Kafka clusters to note that Kafka retains in... Can inspect configuration values that have been read Apache Kafka: topics and.!, partition, key, and the increases due to ( attempted message! Check our theory same number of partitions grouped by during processing: Allow Filtering partition... 50 % CPU utilization has dropped back again node you must increase the of! Which cooperate to consume messages from the broker maintains the position of groups... And is there is an application which write messages into topics partition-1 partition-2... Producer with particular Keys partitions is only 28 % of the leader fails ) a producer is,.

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