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Kafka is a publish-subscribe messaging application that is coded in “Scala”. It is an open-source message broker project which was started by the Apache software. The design pattern of Kafka is mainly based on the design of the transactional log.
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The different components that are available in Kafka are as follows:
Consumer- responsible for subscribing to various topics and pulls the data from different brokers
Offset is nothing but a unique id that is assigned to the partitions. The messages are contained in these partitions. The important aspect or use of offset is that it identifies every message with the id which is available within the partition.
A consumer group is nothing but an exclusive concept of Kafka.
Within each and every Kafka consumer group, we will have one or more consumers who actually consume subscribed topics.
Within the Kafka environment, the zookeeper is used to store offset-related information which is used to consume a specific topic and by a specific consumer group.
No, it is not possible to use Kafka without the zookeeper. The user will not able to connect directly to the Kafka server in the absence of a zookeeper. For some reason, if the zookeeper is down then the individual will not able to access any of the client requests.
The concept of leader and follower is maintained in Kafka environment so that the overall system ensures load balancing on the servers.
ISR stands for In sync replicas.
They are classified as a set of message replicas that are synched to be leaders.
A replica can be defined as a list of essential nodes that are responsible to log for a particular partition, and it doesn't matter whether they actually play the role of a leader or not.
The main reason why replications are needed because they can be consumed again in an uncertain event of machine error or program malfunction or the system is down due to frequent software upgrades. So to make sure to overcome these, replication makes sure that the messages published are not lost.
If the replica stays out of the ISR for a very long time, or the replica is not in sync with the ISR then it means that the follower server is not able to grasp data as fast as the leader is doing. So basically the follower is not able to come up with the leader activities.
As the Kafka environment is run on a zookeeper, one has to make sure to run the zookeeper server first and then ignite the Kafka server.
Within the available producer, the main function of the partitioning key is to validate and direct the destination partition of the message. Normally, a hashing-based partitioner is used to assess the partition Id if the key is provided.
Well, if the producer is sending more messages to the broker and if it cannot handle this in the flow of the messages then we will experience QueueFullException.
The producers don't have any limitation so it doesn't know when to stop the overflow of the messages. So to overcome this problem one should add multiple brokers so that the flow of the messages can be handled perfectly and we won't fall into this exception again.
Kafka procedure API aims to do the producer functionality through one API call to the client.
In specific, Kafka producer API actually combines the efforts of Kafka. producer.SyncProducer and the Kafka.producer.async.Async Producer
Both Kafka and Flume are used for real-time processing where Kafka seems to be more scalable and you can trust the message durability.
Kafka is nothing but a cluster that holds multiple brokers as it is called a distributed system.
The topics within the system will hold multiple partitions.
Every broker within the system will hold multiple partitions. Based on this the producers and consumers actually exchange the message at the same time and the overall execution happens seamlessly.
The following are the advantages of using Kafka technology:
Yes, Apache Kafka is a streaming platform. A streaming platform contains the vital three capabilities, are as follows:
With the help of Kafka technology we can do the below:
They are four main core API’s:
All the communications between the clients happen over through high-performance language via TCP protocol.
The producer API is responsible where it will allow the application to push a stream of records to one of the Kafka topics.
The Consumer API is responsible where it allows the application to receive one or more topics and at the same time process the stream of data that is produced.
The Streams API is responsible where it allows the application to act as a processor and within the process, it will be effectively transforming the input streams to output streams.
The Connector API is responsible where it allows the application to stay connected and keeping a track of all the changes that happen within the system. For this to happen, we will be using reusable producers and consumers who stay connected to the Kafka topics.
A topic is nothing but a category classification or it can be a feed name out of which the records are actually published. Topics are always classified, the multi-subscriber.
Within the Kafka cluster, it retains all the published records. It doesn’t check whether they have been consumed or not. Using a configuration setting for the retention period, the records can be discarded. The main reason to discard the records from the Kafka cluster is that it can free up some space.
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The main components where the data is processed seamlessly is:
Yes, Apache Kafka is an open-source stream processing platform.
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