Apache Kafka Topics: Architecture and Partitions
An introductory look into Apache Kafka and the architecture that makes it up. We'll cover topics, partitions, and what they mean for devs and data engineers.
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What Is a Kafka Topic?
A Kafka topic is essentially a named stream of records. Kafka stores topics in logs. However, a topic log in Apache Kafka is broken up into several partitions. And, further, Kafka spreads those log’s partitions across multiple servers or disks. In other words, we can say a topic in Kafka is a category, stream name, or a feed.
In addition, we can say topics in Apache Kafka are a pub-sub style of messaging. Moreover, there can be zero to many subscribers called Kafka consumer groups in a Kafka topic. Basically, these topics in Kafka are broken up into partitions for speed, scalability, as well as size.
How to Create a Kafka Topic
At first, run
kafka-topics.sh and specify the topic name, replication factor, and other attributes, to create a topic in Kafka:
/bin/kafka-topics.sh --create \ --zookeeper <hostname>:<port> \ --topic <topic-name> \ --partitions <number-of-partitions> \ --replication-factor <number-of-replicating-servers>
Now, with one partition and one replica, the below example creates a topic named “test1”:
bin/kafka-topics.sh --create \ --zookeeper localhost:2181 \ --replication-factor 1 \ --partitions 1 \ --topic text
Further, run the list topic command, to view the topic:
> bin/kafka-topics.sh --list --zookeeper localhost:2181 test1
Make sure, when the applications attempt to produce, consume, or fetch metadata for a nonexistent topic, the
auto.create.topics.enable property, when set to true, automatically creates topics.
Kafka Topic Partitions
Further, Kafka breaks topic logs up into several partitions, usually by record key if the key is present and round-robin. A record is stored on a partition while the key is missing (default behavior). By default, the key which helps to determine what partition a Kafka Producer sends the record to is the Record Key.
Basically, to scale a topic across many servers for producer writes, Kafka uses partitions. Also, in order to facilitate parallel consumers, Kafka uses partitions. Moreover, while it comes to failover, Kafka can replicate partitions to multiple Kafka Brokers.
Kafka Topic Log Partition’s Ordering and Cardinality
Well, we can say, only in a single partition, Kafka does maintain a record order, as a partition is also an ordered, immutable record sequence. And, by using the partition as a structured commit log, Kafka continually appends to partitions. In partitions, all records are assigned one sequential id number which we further call an offset. That offset further identifies each record location within the partition.
In addition, in order to scale beyond a size that will fit on a single server, Topic partitions permit Kafka logs. While topics can span many partitions hosted on many servers, topic partitions must fit on servers which host it. Moreover, topic partitions in Apache Kafka are a unit of parallelism. This means that at any one time, a partition can only be worked on by one Kafka consumer in a consumer group. Basically, a consumer in Kafka can only run within their own process or their own thread. Although, Kafka spreads partitions across the remaining consumer in the same consumer group, if a consumer stops.
Kafka Topic Partition Replication
For the purpose of fault tolerance, Kafka can perform replication of partitions across a configurable number of Kafka servers. Basically, there is a leader server and a given number of follower servers in each partition. Also, for a partition, leaders are those who handle all read and write requests.
However, if the leader dies, the followers replicate leaders and take over. Additionally, for parallel consumer handling within a group, Kafka also uses partitions.
Replication: Kafka Partition Leaders, Followers, and ISRs.
By using ZooKeeper, Kafka chooses one broker’s partition replicas as the leader. Also, we can say, for the partition, the broker which has the partition leader handles all reads and writes of records. Moreover, to the leader partition to followers (node/partition pair), Kafka replicates writes.
A follower which is in sync is what we call an ISR (in-sync replica). Although, Kafka chooses a new ISR as the new leader if a partition leader fails.
Kafka Architecture: Kafka Replication – Replicating to Partition 0
When all ISRs for partitions write to their log(s), the record is considered “committed.” However, we can only read the committed records from the consumer.
Published at DZone with permission of anjita agrawal. See the original article here.
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