Brokers in Apache Kafka | Replication factor & ISR in Kafka

This is the fourth video of our "Kafka for Data Engineers" playlist. In this video, we have tried to understand the brokers, replication factor and ISR.
Understanding and imagining Apache Kafka at its core is very important to understand its concept deeply.
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Kafka brokers are the central components in an Apache Kafka cluster that handle the storage, retrieval, and distribution of messages. Here’s a detailed explanation of their roles and functions:
Key Functions of Kafka Brokers:
1. Message Storage:
- Kafka brokers store messages in topics.
- Each topic is divided into partitions, and each partition is an ordered, immutable sequence of messages.
- Brokers write messages to disk for durability.
2. Message Retrieval:
- Kafka consumers connect to brokers to fetch messages.
- Brokers serve the messages stored in their partitions to the consumers based on their offset.
3. Replication:
- Kafka supports data replication for fault tolerance.
- Each partition can have multiple replicas spread across different brokers.
- One broker acts as the leader for a partition, handling all reads and writes, while others are followers that replicate the data.
4. Load Balancing:
- Kafka brokers distribute the load of message storage and retrieval.
- Kafka clients (producers and consumers) can connect to any broker in the cluster, which helps in balancing the load.
5. Leader Election:
- Each partition has a leader broker that handles all reads and writes for that partition.
- If the leader broker fails, Kafka automatically elects a new leader from the replicas.
6. Metadata Management:
- Brokers store metadata about the Kafka cluster, including information about the topics, partitions, and replicas.
- Producers and consumers use this metadata to determine which broker to connect to for a given topic or partition.
How #kafka Brokers Work:
1. Cluster Formation:
- A Kafka cluster is formed by one or more brokers.
- Each broker is identified by a unique ID within the cluster.
2. Producers and Consumers:
- Producers send data to the brokers, specifying the topic to which the data belongs.
- Consumers read data from the brokers by subscribing to topics and partitions.
3. Coordination with Zookeeper:
- Kafka uses Zookeeper to manage the cluster.
- Zookeeper helps maintain the configuration information, leader election, and cluster state.
4. High Availability:
- By replicating data across multiple brokers, Kafka ensures high availability and durability of messages.
- Even if some brokers fail, the data can still be accessed from the replicas on other brokers.
5. benefits of Kafka Brokers:
1. Scalability:
- Kafka brokers enable horizontal scaling by allowing the addition of more brokers to the cluster.
- This helps in handling increased load and providing better performance.
2. Fault Tolerance:
- Data replication across brokers ensures that data is not lost even if a broker fails.
- Automatic leader election ensures continued availability.
3. High Throughput:
- Kafka brokers are designed to handle large volumes of data with low latency.
- Efficient disk I/O and network usage allow Kafka to process millions of messages per second.
In summary, Kafka brokers are essential for the efficient operation of an Apache Kafka cluster. They manage the storage and retrieval of messages, ensure data replication and fault tolerance, and facilitate load balancing and high throughput in the system.
In Kafka, the Replication Factor and In-Sync Replica (ISR) are key concepts crucial in ensuring data durability, fault tolerance, and high availability.
Replication Factor:
- The replication factor is a configuration setting for a Kafka topic that determines each partition's number of copies (replicas).
- For e.g. if a topic has a replication factor of 3, each partition in that topic will have three copies stored on different brokers.
- The primary purpose of the replication factor is to provide fault tolerance. By having multiple copies of data, Kafka can continue to serve data even if some brokers fail.
In-Sync Replica (ISR):
- ISR is a set of replicas that are fully caught up with the leader’s log.
- Each partition in Kafka has one leader and several follower replicas.
- The leader handles all read and write requests for that partition, while followers replicate the data from the leader.
By using the replication factor and maintaining an ISR, Apache Kafka ensures that the system is highly available, fault-tolerant, and capable of recovering quickly from failures.
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