Kafka is a distributed streaming platform that allows for real-time data processing and message handling. It functions similarly to a message queue, enabling producers to publish messages and consumers to subscribe to them. Its distributed nature ensures fault tolerance and high scalability, allowing it to handle large volumes of data efficiently.
At the core of Kafka are several key concepts. A **topic** is a category or feed name to which messages are published. Messages are stored in **partitions**, which are ordered sequences of records. Each message has a unique **offset**, which serves as its identifier within a partition. **Producers** are responsible for sending messages to topics, while **consumers** read messages from those topics.
A **broker** is a Kafka server that manages one or more topics. Topics can be divided into multiple partitions, which are spread across different brokers for redundancy and performance. A **consumer group** is a set of consumers that work together to consume messages from a topic, ensuring load balancing and fault tolerance. If one consumer fails, others in the same group can continue processing without interruption.
In a Kafka cluster, each partition has a **leader** broker that handles all read and write operations, while **followers** replicate the data for backup. This setup ensures data consistency and availability even if a broker goes down.
The architecture of Kafka allows for horizontal scaling, making it suitable for high-throughput applications. For example, when a producer sends a message to a topic, it is assigned to a specific partition based on the message's key or using a round-robin approach. Consumers then process these messages in order, with each consumer in a group handling a subset of the partitions.
In this lab, we will use **kafka-python**, a Python client library, to implement a simple producer and consumer. The producer will send messages to a Kafka topic, and the consumer will read them back. We'll also explore how consumer groups help in achieving fault tolerance and how offsets allow for tracking the last processed message.
In Experiment 1, we'll create a test topic and run both a producer and a consumer. When the producer sends a message, the consumer should receive it, confirming that the system is working correctly.
In Experiment 2, we'll demonstrate the fault-tolerance feature of consumer groups. By running two consumers in the same group, they will divide the workload across the topic’s partitions. If one consumer fails, the other will take over, ensuring no data is lost.
Finally, in Experiment 3, we'll focus on **offset management**. Consumers can commit their current offset to Kafka so that, in case of a crash or restart, they can resume from where they left off. This is crucial for maintaining data integrity in long-running applications.
Through these experiments, you'll gain hands-on experience with Kafka and understand its core features, including message publishing, consumption, partitioning, and fault tolerance.
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