Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, Zookeeper)

Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, and Zookeeper).

Event Streaming is a significant part of businesses, demanding real time data access. Companies today are still are highly reliable on Apache Kafka, since it is a prominent real time data streaming software. It leverages open source architecture for storing, reading and determining Streaming Data.

Moreover, after a rise in the demand for Scalable High Throughput Infrastructures, its leading architectural ideas developed Kafka Clusters, a software capable of storing, analyzing and reprocessing Streaming Data.

By working on multiple servers in a Distributed Architecture, Apache Kafka has enabled its user to utilize the processing power and storage capacities of a wide variety of systems. This way, it will manage incoming data efficiently, thereby becoming the most reliable tool for Real Time Data Analysis and Data Streaming a company can use.

What is Apache Kafka

Being a Distributed Open Source System for Publishing and Subscribing, Apache Kafka enables you to share a large number of messages from one end to another. It uses Broker for duplicating and persisting messages in a fault tolerant way and even separates them into subjects.

This software is also used for developing Kafka Real Time Streaming Data Pipelines and Streaming Applications, which allow you to convert and share data from the given source to the destination.

This software enables you to create applications that are capable of producing and consuming streams of Data records with the help of a Message Broker. This way, you can route messages from Publishers to Subscribers.

Next, lets talk about Apache Kafka Pros and next move onto Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, and Zookeeper).

Features of Kafka

  • Kafka has extensively low end to end latency of up to 10 milliseconds for a large number of data.
  • Kafka can decouple messages and store them effectively so that they can publish, subscribe and process data records in real time.
  • Its seamless messaging features make the process of dealing with a large amount of data effortless and even deliver precise business communications and scalability.
  • It sustains its performance when applied to variations in applications and processing demands.
  • The distributed design of this software helps you in handling the volume and speed of incoming messages efficiently.
  • It is fault tolerant and reliable as it duplicates and distributes data to other servers or Brokers.
  • It is capable of integrating with a wide variety of Data Processing Frameworks and Services.

Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, and Zookeeper)

Crucial Components Of Kafka Architecture

Kafka architecture is created around emphasizing the performance and scalability of brokers. This way, producers only have to manage the controlling responsibility received by partition via messages. The crucial components of Kafka’s Architecture are as follows:

Clusters

Kafka that includes more than one broker is known as Kafka Cluster. This cluster is expandable and can be used without downtime. It helps in managing the persistence and replication of messages of data. Therefore, even if the primary cluster goes down, other Kafka clusters can be used to deliver similar services without any delay.

Broker

Kafka Server is referred to as Broker. It is responsible for storing the Topic’s Messages. Kafka Clusters constitute more than one Broker with which they tend to balance load effortlessly. However, because of their stateless nature, users need to take the help of Zookeeper to preserve the Kafka Cluster state.

Producers

In Kafka Cluster Architecture, a producer is used for sending or publishing data or messages to the Topic. Within an application, different Kafka Producers submit data to Kafka Cluster to store innumerable data. However, you should be aware that the Producers is capable of delivering messages as instantly as the Broker can handle them.

When the producer adds the data to the Topic, it gets published to the Topic’s Leader. Then, these records are attached to the Leader’s Commit Log. Here, the record offset increases. Each of these data is collected on the cluster. However, these records are only released to the Consumer when it is committed.

Therefore, it becomes essential for producers to acquire metadata related to the cluster from the Broker before sharing the records.

Consumers

Next in the Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, and Zookeeper) is a Kafka Consumer. It refers to the one who reads and consumes the Kafka Cluster messages. Consumers are provided with the option of reading messages starting at a specific point and ending at the point they desire. This allows customers to join the Kafka Clusters at any moment.

Kafka usually includes two types of customers, namely Low Level Consumers and High Level Consumers. The role of Low Level Consumers is to specify the Topics and Partitions and the offset that enables them to read. It can either be fixed or variable. High level Consumers, on the other hand, comprise one or more consumers.

Zookeeper

Another part of Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, and Zookeeper) is a Zookeeper. He stores the details and information related to the Kafka Cluster of the Consumers. It is a Master Management Node that is responsible for managing and maintaining the Brokers, Topics, and Partitions of the Cluster. It also traces the Kafka Brokers and even evaluates Brokers that have crashed or have been added to the Cluster recently.

It then notifies the Producer or Consumer of Kafka queues. This way, they facilitate the coordination of work with active Brokers.

Topics

A Kafka Topics is a Collection of Messages, belonging to a specific category or with a particular feed name. It arranges all the records of Kafka, which allows Consumer apps to read data. Topics are also separated into customizable sections called partitions.

Partitions

Within the Kafka Cluster, topics are divided into partitions, which are replicated across brokers. Consumers can read from a topic parallelly from every partition. Moreover, by utilizing keys, users can guarantee the order of processing for messages in Kafka, sharing similar keys. It is highly reliable for applications that need complete control over the records. You can also create limitless partitions in Kafka architecture.

Replicas

Replicas act as a backup for partition in Kafka. It ensures that no data is lost because of failure or planned shutdown. In short, copies of the partition are considered Replicas.

Leader and Follower

The partition in Kafka comprises only one server whose role is to lead a particular partition. It is also responsible for performing all the read and write tasks for the partition.

Advantages of Kafka Architecture

You can use Kafka for multiple beneficial reasons. Some of them are as follows:

Scalability And Performance – Kafka provides high performance sequential writes and even shards topics into partitions to make reads and writes highly scalable. As a result, Kafka enables producers and consumers to read and write simultaneously. Moreover, topics divided across several partitions can use storage all across the servers, which enables the application to utilize the combined power of multiple disks.

Reliable – Kafka Architecture obtains failover by using replication. The partition of topics is replicated on several brokers or nodes. This causes an ISR to take over the leadership and continue serving it seamlessly without any kind of interruption.

Disaster Recovery – Apart from providing failover, Kafka tends to deliver a full featured disaster recovery solution. It replicates the entire Kafka cluster and enables Kafka deployment to manage seamless operations throughout micro sales disasters.

Disadvantages of Kafka Architecture

Apart from the above mentioned advantages, Kafka Architectures is susceptible to disadvantages as well. They are as follows:

  • Tweaking messages in Kafka creates performance problems. It is because Kafka is suitable for cases where the messages do not change.
  • Specific message paradigms like point to point queues and requests/replies are not supported by Kafka.
  • Kafka does not provide any support to wildcard topic selection. The topic name should exactly be the same.
  • Since large messages are compressed or decompressed, it affects the throughput and performance of Kafka.

Kafka Architecture (Cluster, Topics, Producers, Partitions, Consumers, and Zookeeper) Conclusion

Kafka provides its users with one of the most sturdy and versatile architectures to make the streaming workloads scalable, reliable and it offers extensive performance. It is considered to be the more efficient and effortless choice for users who demand real time data access. It assist immensely to grow the business much further.

Avatar for Hitesh Jethva
Hitesh Jethva

I am a fan of open source technology and have more than 10 years of experience working with Linux and Open Source technologies. I am one of the Linux technical writers for Cloud Infrastructure Services.

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