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Producer is a client who send or publish the record. Producer applications write data to topics and consumer applications read from topics.
Messages sent by a producer to a topic partition will be appended in the order they are sent. That is, if a record M1 is sent by the same producer as a record M2, and M1 is sent first, then M1 will have a lower offset than M2 and appear earlier in the log.
Consumer is a subscriber who consume the messages which predominantly stores in a partition. Consumer is a separate process and can be separate application altogether which run in individual machine.
If all the consumer falls into the same consumer group, then by using load balancer the message will be distributed over the consumer instances, if consumer instances falls in different group, than each message will be broadcast to all consumer group.
The working principle of Kafka follows the below order.
Apart from other benefits, below are the key advantages of using Kafka messaging framework.
Considering all the above advantages, Kafka is one of the most popular frameworks utilize in Micro service architecture, Big Data architecture, Enterprise Integration architecture, publish-subscribe architecture.
Considering the advantages, to setup and configure the Kafka ecosystem is bit difficult and one needs a good knowledge to implement, apart from that I listed some more use case.
Zookeeper is a distributed open source configuration, synchronization service along with the naming registry for distributed application.
Zookeeper is a separate component, which is not a mandatory component to implement with Kafka, however when we need to implement cluster, we have to setup as a coordination server.
Zookeeper plays a significant role when it comes to cluster management like fault tolerant and identify when one cluster down its replicate the messages to other cluster.
groupId = org.apache.spark artifactId = spark-streaming-kafka-0-10_2.11 version = 2.2.0 groupId = org.apache.zookeeper artifactId = zookeeper version=3.4.5
This dependency comes with child dependency which will download and add to the application as a part of parent dependency.
Kafka is a messaging framework developed by apache foundation, which is to create the create the messaging system along with can provide fault tolerant cluster along with the low latency system, to ensure end to end delivery.
Below are the bullet points:
Kafka required other component such as the zookeeper to create a cluster and act as a coordination server
Kafka provide a reliable delivery for messages from sender to receiver apart from that it has other key features as well.
To utilize all this key feature, we need to configure the Kafka cluster properly along with the zookeeper configuration.
Now a days kafka is a key messaging framework, not because of its features even for reliable transmission of messages from sender to receiver, however, below are the key points which should consider.
Considering the above features Kafka is one of the best options to use in Bigdata Technologies to handle the large volume of messages for a smooth delivery.
There is plethora of use case, where Kafka fit into the real work application, however I listed below are the real work use case which is frequently using.
Above are the use case where predominately require a Kafka framework, apart from that there are other cases which depends upon the requirement and design.
Let’s talk about some modern source of data now a days which is a data—transactional data such as orders, inventory, and shopping carts — is being augmented with things such as clicking, likes, recommendations and searches on a web page. All this data is deeply important to analyze the consumers behaviors, and it can feed a set of predictive analytics engines that can be the differentiator for companies.
So, when we need to handle this kind of volume of data, we need Kafka to solve this problem.
Kafka process diagram comprises the below essential component which is require to setup the messaging infrastructure.
Communication between the clients and the servers is done with a simple, high-performance, language agnostic TCP protocol. This protocol is versioned and maintains backwards compatibility with older version
Topic is a logical feed name to which records are published. Topics in Kafka supports multi-subscriber model, so that topic can have zero, one, or many consumers that subscribe to the data written to it.
Every partition has an ordered and immutable sequence of records which is continuously appended to—a structured commit log. The Kafka cluster durably persists all published records—whether they have been consumed—using a configurable retention period.
Kafka topic is shared into the partitions, which contains messages in an unmodifiable sequence.
The offset is a unique identifier of a record within a partition. It denotes the position of the consumer in the partition. Consumers can read messages starting from a specific offset and can read from any offset point they choose.
Topic can also have multiple partition logs like the click-topic has in the image to the right. This allows for multiple consumers to read from a topic in parallel.
Apache Kafka is an open-source stream-processing software program developed by Linkedin and donated to the Apache Software Foundation.
The popularity of Apache Kafka is going high leading to extensive job opportunities and career prospects. To mention, Apache Kafka has a market share of about 9.1%. It is the best opportunity to move ahead in your career.
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