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தமிழ் |LIST OF GUARANTEES PROVIDED BY APACHE KAFKA REPLICATION ORDERING FAULT PERSISTENCE SCALABLE

LEARN APACHE KAFKA IN 2 MINUTES TAMIL TUTORIAL SERIES

Click here - https://www.youtube.com/channel/UCd0U_xlQxdZynq09knDszXA?sub_confirmation=1 to get notifications. தமிழ் |LIST OF GUARANTEES PROVIDED BY APACHE KAFKA REPLICATION ORDERING FAULT PERSISTENCE SCALABLE Apache Kafka provides several guarantees to ensure reliable and scalable message processing within distributed systems. Here's a comprehensive list of the guarantees offered by Apache Kafka: 1. **Message Persistence**: - Kafka guarantees that once a message is written to a topic partition, it will remain in the partition until its retention period expires, regardless of consumer consumption. 2. **Fault Tolerance**: - Kafka replicates topic partitions across multiple brokers to ensure fault tolerance. If a broker fails, partitions are automatically reassigned to other brokers with replicas. 3. **Scalability**: - Kafka scales horizontally by adding more brokers to the cluster and distributing topic partitions across them. This allows Kafka to handle high message throughput and storage requirements. 4. **Ordered Message Delivery**: - Kafka guarantees that messages within a single partition are delivered in the order they were produced. However, this ordering is not guaranteed across different partitions. 5. **Exactly-Once Semantics**: - Kafka supports exactly-once message delivery semantics for both producers and consumers when configured appropriately. This ensures that each message is processed exactly once, even in the presence of failures or retries. 6. **At-Least-Once Semantics**: - By default, Kafka provides at-least-once message delivery semantics. This means that messages may be duplicated in certain failure scenarios but ensures no message loss. 7. **Durability**: - Kafka ensures that messages written to topics are durable and persisted to disk before being acknowledged to the producer as successfully written. 8. **High Throughput**: - Kafka is designed to handle high message throughput by efficiently batching and compressing messages, as well as leveraging sequential disk I/O for message storage. 9. **Low Latency**: - Kafka aims to provide low end-to-end message latency by optimizing internal processing, minimizing replication delays, and supporting efficient consumer polling mechanisms. 10. **Leader-Follower Replication**: - Kafka's replication model uses leader-follower replication, where one broker acts as the leader for a partition, handling all read and write requests, while follower replicas replicate data from the leader to provide fault tolerance. 11. **Consistency**: - Kafka ensures strong consistency guarantees within a partition by maintaining a strict order of messages and replicating them across followers before acknowledging the write to the producer. 12. **Partitioning**: - Kafka allows data to be partitioned across multiple brokers, enabling parallel processing and distribution of data within a topic. This enhances scalability and performance. These guarantees make Apache Kafka a robust and reliable messaging system for building distributed data pipelines and real-time streaming applications.

About the course

**Apache Kafka Messaging System in 4000 Characters:** **Introduction:** Apache Kafka is an open-source distributed streaming platform designed for building real-time data pipelines and streaming applications. Developed by the Apache Software Foundation, Kafka has become a cornerstone technology for organizations dealing with large-scale, real-time data processing. **Key Concepts:** 1. **Publish-Subscribe Model:** - Kafka follows a publish-subscribe model where producers publish messages to topics, and consumers subscribe to those topics to receive the messages. This decouples data producers and consumers, enabling scalable and flexible architectures. 2. **Topics and Partitions:** - Data is organized into topics, acting as logical channels for communication. Topics are divided into partitions, allowing parallel processing and scalability. Each partition is a linear, ordered sequence of messages. 3. **Brokers and Clusters:** - Kafka brokers form a cluster, ensuring fault tolerance and high availability. Brokers manage the storage and transmission of messages. Kafka clusters can scale horizontally by adding more brokers, enhancing both storage and processing capabilities. 4. **Producers and Consumers:** - Producers generate and send messages to Kafka topics, while consumers subscribe to topics and process the messages. This separation allows for the decoupling of data producers and consumers, supporting scalability and flexibility. 5. **Event Log:** - Kafka maintains an immutable, distributed log of records (messages). This log serves as a durable event store, allowing for the replay and reprocessing of events. Each message in the log has a unique offset. 6. **Scalability:** - Kafka's scalability is achieved through partitioning and distributed processing. Topics can be partitioned, and partitions can be distributed across multiple brokers, enabling horizontal scaling to handle large volumes of data. **Use Cases:** 1. **Real-time Data Streams:** - Kafka excels in handling and processing real-time data streams, making it suitable for use cases like monitoring, fraud detection, and analytics where timely insights are crucial. 2. **Log Aggregation:** - It serves as a powerful solution for aggregating and centralizing logs from various applications and services. Kafka's durability ensures that logs are reliably stored for analysis and troubleshooting. 3. **Messaging Backbone:** - Kafka acts as a robust and fault-tolerant messaging system, connecting different components of a distributed application. Its durability and reliability make it a reliable backbone for messaging. 4. **Event Sourcing:** - Kafka is often used in event sourcing architectures where changes to application state are captured as a sequence of events. This approach enables reconstruction of the application state at any point in time. 5. **Microservices Integration:** - Kafka facilitates communication between microservices in a distributed system. It provides a resilient and scalable mechanism for asynchronous communication, ensuring loose coupling between services. **Components:** 1. **ZooKeeper:** - Kafka relies on Apache ZooKeeper for distributed coordination, managing configuration, and electing leaders within the Kafka cluster. ZooKeeper ensures the stability and coordination of Kafka brokers. 2. **Producer API:** - Producers use Kafka's Producer API to publish messages to topics. The API supports asynchronous and synchronous message publishing, providing flexibility for different use cases. 3. **Consumer API:** - Consumers use Kafka's Consumer API to subscribe to topics and process messages. Consumer groups allow parallel processing and load balancing, ensuring efficient utilization of resources. 4. **Connect API:** - Kafka Connect enables the integration of Kafka with external systems. Connectors, available for various data sources and sinks, simplify the development of data pipelines between Kafka and other systems. 5. **Streams API:** - Kafka Streams API facilitates the development of stream processing applications directly within Kafka. It enables transformations and analytics on streaming data, supporting real-time processing scenarios. **Reliability and Durability:** 1. **Replication:** - Kafka ensures data durability through replication. Each partition has a leader and multiple followers, with data replicated across brokers. This replication mechanism ensures fault tolerance and data redundancy. 2. **Retention Policies:** - Kafka allows the configuration of retention policies for topics. This determines how long messages are retained in a topic. Retention policies support both real-time and historical data analysis. **Ecosystem and Integration:**