TechFedd

Your gateway to technical excellence. Curated content from industry experts.

Quick Links

  • Browse Sources
  • Categories
  • Latest Articles

Company

  • About Us
  • Contact
  • Privacy Policy
  • Terms of Service

Newsletter

Subscribe to get weekly updates on the latest technical content.

© 2025 TechFedd. All rights reserved.

PrivacyTermsSitemap
TechFedd LogoTechFedd
ArticlesSources
Sign InSign Up
  1. Home
  2. /
  3. Articles
  4. /
  5. System Design

Messaging Patterns Explained: Pub-Sub, Queues, and Event Streams

ByteByteGo

ByteByteGo

Alex Xu • Published about 1 month ago • 1 min read

Read Original
Messaging Patterns Explained: Pub-Sub, Queues, and Event Streams

The article explores common messaging patterns in distributed systems, focusing on Pub/Sub (Publish-Subscribe) as a scalable solution for decoupled communication. It contrasts Pub/Sub with other patterns like Point-to-Point and Request-Reply, highlighting its advantages in handling high-volume, real-time data streams. Key considerations include message brokers, topic-based routing, and trade-offs between latency and reliability.


Core Technical Concepts/Technologies

  • Pub/Sub (Publish-Subscribe)
  • Point-to-Point Messaging
  • Request-Reply Pattern
  • Message Brokers (e.g., Kafka, RabbitMQ)
  • Topics/Queues
  • Event-Driven Architecture

Main Points

  • Pub/Sub Basics:

    • Publishers send messages to topics; subscribers receive messages based on subscribed topics.
    • Decouples producers and consumers, enabling scalability.
  • Comparison with Other Patterns:

    • Point-to-Point: Direct communication between sender/receiver (e.g., task queues).
    • Request-Reply: Synchronous; used for immediate responses (e.g., HTTP).
  • Implementation:

    • Brokers (e.g., Kafka) manage topic partitioning, replication, and delivery guarantees.
    • Example: Kafka uses topics with partitions for parallel processing.
  • Trade-offs:

    • Pros: Scalability, loose coupling, real-time processing.
    • Cons: Complexity in message ordering, potential latency.

Technical Specifications/Code Examples

  • Kafka Topic Creation:
    kafka-topics --create --topic orders --partitions 3 --replication-factor 2
    
  • RabbitMQ Exchange Binding:
    channel.exchange_declare(exchange='logs', exchange_type='fanout')
    

Key Takeaways

  1. Scalability: Pub/Sub handles high-volume data streams efficiently.
  2. Decoupling: Producers/consumers operate independently.
  3. Broker Choice: Kafka excels in throughput; RabbitMQ offers simpler setup.
  4. Latency vs. Reliability: At-least-once delivery may increase latency.

Limitations/Caveats

  • Message Ordering: Challenging in distributed brokers without partitioning.
  • Complexity: Requires tuning (e.g., partition counts, retention policies).
  • Further Exploration: Compare with streaming frameworks (e.g., Apache Pulsar).

What happens when the downstream service is overloaded? Or slow? Or down entirely?

This article was originally published on ByteByteGo

Visit Original Source