100% Free Forever
AI-Powered Learning
Industry Expert Content
Certificates & Badges
Learn At Your Own Pace
Big Data & Distributed Computing
30 minintermediate

Producers and Consumers in Python

The producer and consumer are the two sides of every Kafka pipeline. A producer publishes messages to Kafka topics; a consumer reads messages from those topics. The `kafka-python` library provides the standard Python implementation of both, wrapping the Kafka protocol in a Python-friendly API that handles connection management, serialisation, retries, and offset tracking transparently. Understanding the key configuration options for both — particularly the producer's durability settings and the consumer's offset commit strategy — determines whether the pipeline achieves at-least-once, at-most-once, or effectively-exactly-once message delivery.

The most important conceptual distinction in Kafka pipeline design is between the producer's responsibility (getting a message into Kafka durably) and the consumer's responsibility (processing that message exactly once and committing the offset at the right moment). These are independent contracts — a durable producer combined with a consumer that commits before processing gives at-most-once delivery, while the same producer combined with a consumer that processes before committing gives at-least-once delivery. Neither combination alone gives exactly-once; that requires additional coordination at the destination.

Analogy🏏Cricket
🏏 Think of it like cricket: Imagine the DRS review system deployed across three independent video-review centres in Mumbai, Chennai, and London, each holding a copy of the ball-tracking data. A CAP partition is a network outage that cuts communication between them. A CP system says: if the centres cannot synchronise, no review decision is issued — no player walks until the system is restored. Consistency is guaranteed; availability is sacrificed. An AP system says: each centre issues its own decision based on its local data, even if that means Mumbai says out and London says not out — reviews continue but different centres may give different verdicts. Partition tolerance is non-negotiable because the network always has the possibility of failing; the choice is whether umpires wait for consensus or proceed with local data.
Lesson 20 of 35
0% complete