This guide walks you through the core Kafka commands for starting and stopping the service, creating, listing, describing, and deleting topics, as well as producing and consuming messages, while explaining key parameters such as Zookeeper, partitions, and replication factors.
This article explains Kafka's role as a messaging system, covering core concepts such as topics, partitions, producers, consumers, messages, cluster architecture, replicas, consumer groups, controller coordination with Zookeeper, and performance optimizations like sequential writes and zero‑copy networking.
This article explains Kafka's core concepts—including messages as byte arrays, optional keys for partition control, topic and partition organization, producer and consumer roles, offsets, consumer groups, and broker clusters—providing a concise technical overview for developers learning Kafka.
This article presents a detailed case study of RocketMQ's fast‑failure mechanism, explains the root causes of a massive message‑write surge, analyzes broker thread models, and proposes topic‑level rate‑limiting solutions with concrete implementation and code examples.
This article provides a comprehensive overview of Kafka’s core components—including brokers, topics, partitions, producers, and consumers—explaining their roles, relationships, replication, leader/follower dynamics, and architectural diagrams, while also highlighting configuration details and operational considerations for building reliable distributed streaming systems.
This article provides a comprehensive overview of Kafka's core components—including brokers, topics, partitions, producers, and consumers—explaining their roles, how they interact within a Kafka cluster, and illustrating the architecture with diagrams while also noting recent version changes and related disclaimer information.
This article provides a comprehensive overview of Kafka's core components—including brokers, topics, partitions, producers, and consumers—explaining their roles, relationships, replication strategies, and how they work together within a Kafka cluster to ensure reliable, scalable message streaming.
This article provides a visual walkthrough of Kafka's fundamental concepts—including its basic architecture, producer‑consumer model, topics, partitions, and cluster design—offering clear explanations and diagrams to help readers grasp how Kafka enables reliable, scalable message streaming in modern systems.
This article provides a clear, visual walkthrough of Kafka’s fundamental architecture, explaining how producers and consumers interact, the role of topics and partitions, consumer groups, and ZooKeeper’s coordination, helping readers grasp message flow, storage, ordering, and fault‑tolerance in a distributed streaming system.
This article explains Kafka's topic architecture, detailing how topics are split into partitions for scalability and parallelism, the role of logs, key-based and round-robin partitioning, replication with leaders, followers, ISR, and how these mechanisms enable fault‑tolerance and high‑performance consumer failover.
This guide explains how RabbitMQ exchanges—Direct, Topic, Headers, and Fanout—route messages, compares their performance, and provides practical examples and configuration tips for efficient backend messaging.
This guide explains how to use the kafka‑topics.sh script to increase a Kafka topic's partition count, warns about the impact on keyed messages and ordering, and details why Kafka does not support decreasing partitions, offering alternative strategies for replication changes.
This article explains Kafka’s core architecture—including brokers, topics, partitions, offsets, producer and consumer mechanics, replication, availability, consistency, persistence, performance optimizations, and Zookeeper integration—providing a comprehensive guide for building reliable distributed messaging systems.
