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Message Queues & Streaming

message-queuessystem-designqueuepubsubkafka

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Full lesson · 8 learning steps Open any exercise for focused practice

Step 1 of 8 · intro card

Learn Message Queues & Streaming

Communication

A queue decouples producers from consumers and absorbs traffic spikes, while pub/sub fans an event out to many subscribers. Kafka is an ordered, partitioned log and RabbitMQ is a traditional broker; delivery is usually at-least-once, so consumers must be idempotent.

A message queue sits between a producer and a consumer. The producer drops a message and moves on; the consumer pulls it later. This decouples the two sides and lets the queue absorb spikes when consumers are slower than producers.

Pub/sub is the fan-out pattern: one published event is delivered to every subscriber, so many services can react to the same fact without the publisher knowing who they are.

Kafka models a topic as an ordered, partitioned log and keeps messages for replay; RabbitMQ is a classic broker that routes and acks messages. Most brokers offer at-least-once delivery, which can duplicate a message, so consumers must be idempotent. Kafka preserves order only within a partition.

Queue: decouples producer and consumer, absorbs spikes.

Pub/sub: fans one event out to many subscribers.

At-least-once delivery means consumers must be idempotent.

async:
  queue: decouple-producer-consumer
  pubsub: fan-out-events
  broker: kafka-or-rabbitmq
  delivery: at-least-once
  ordering: per-partition

Read the implementation

Connect each part of the code to the concept before you type.

1. Queue

A queue sits between a producer and a consumer so they run independently and the queue absorbs traffic spikes.

queue: decouple-producer-consumer

2. Pub/Sub

Pub/sub publishes an event once and fans it out to every interested subscriber.

pubsub: fan-out-events

3. Broker

Kafka is an ordered, partitioned log; RabbitMQ is a traditional message broker — both move messages between services.

broker: kafka-or-rabbitmq

4. Delivery

At-least-once delivery may deliver a message more than once, so consumers must handle duplicates idempotently.

delivery: at-least-once

5. Ordering

Kafka guarantees order within a partition, not across the whole topic.

ordering: per-partition

A complete learning loop

Understand

Read the concept map and follow the code breakdown.

Reconstruct

Type the implementation, repair mistakes and work through every recall exercise.

Verify

Use the final check without hints; revisit weak concepts in short practice sessions.