Complete architectural overview of Apache Kafka — origins, the M×N integration problem, the 4 pillars of Kafka's extreme speed (sequential I/O, page cache, zero-copy, batching), message anatomy, and the dumb broker / smart consumer paradigm.
Deep dive into Kafka partitioning strategies — key-based, round-robin, sticky, custom partitioners, hot key mitigation, partition count sizing, and ordering trade-offs.
Combined guide to Kafka producers and consumers — internal architecture, delivery semantics, serialization, offset management, error handling, and production patterns.
A topic is a named, durable stream of messages in Kafka — the logical category or feed where producers write and consumers read.
Comprehensive system design comparison of Apache Kafka vs RabbitMQ vs AWS SQS. Covers event streams vs message queues, Claim Check pattern, Web Crawler SQS vs Kafka trade-offs, and single-broker sizing heuristics.
A partition is an ordered, immutable sequence of records within a topic — the fundamental unit of parallelism, replication, and storage scaling in Kafka.
The replication factor defines how many copies of each partition exist across the cluster to guarantee fault tolerance and high availability.
Partitions are the unit of parallelism in Kafka. Scaling them is critical for throughput but can break ordering for keyed topics. This guide covers the mechanics, risks, and migration strategies.
