Streaming & Queues

Event streaming, pub/sub, backpressure, and consumer groups at scale.

  • 6 Tracked terms
  • Last 30 days Feed window

What this topic collects on

An article joins this feed when it matches these terms. Each one is also a search of its own.

Latest in Streaming & Queues


medium.com > @ak757981 > kafka-part-four-idempotency-ad1e97c9fb31

Kafka Part Four: Idempotency

20+ hour, 49+ min ago   (819+ words) Part 4 — Learning Kafka by Rebuilding It How do we prevent duplicates -> Idempotency The safe configuration above assumes one more thing: retries. When a write fails …...


medium.com > @nikhilsinghthapa13nov > publish-subscribe-model-explained-like-youre-building-it-tomorrow-3111d0d34624

Publish–Subscribe Model, Explained Like You’re Building It Tomorrow

1+ day, 22+ hour ago   (228+ words) How the pattern behind Kafka, Redis, and Google Pub/Sub keeps modern systems decoupled, scalable, and resilient. You’ve …...


medium.com > @the_atomic_architect > uber-kafka-trillions-messages-66424e02530c

How Uber Handles Trillions of Kafka Messages Without Bringing Everything Down

1+ day, 22+ hour ago   (23+ words) How Uber built a Kafka architecture for trillions of daily messages using federated clusters, uReplicator, Chaperone, consumer proxies, dead-letter queues, and tiered storage....


medium.com > @shubham.patel191295 > kafka-delivery-semantics-explained-why-your-consumer-processes-records-twice-a584b89bc201

Kafka Delivery Semantics Explained: Why Your Consumer Processes Records Twice

2+ day, 14+ hour ago   (25+ words) At-most-once, at-least-once, and exactly-once — what each actually guarantees, why duplicates happen, and how to build …...


dzone.com > articles > stream-processing-mainframe-apache-flink

Stream Processing on the Mainframe With Apache Flink

4+ day, 1+ hour ago   (897+ words) Apache Flink on the IBM mainframe connects real-time processing with core systems, enabling hybrid cloud and AI without full migration. A few months ago, I wrote about integrating Apache Kafka with mainframe systems. The blog covered various real-world examples across…...


hackernoon.com > building-reliable-event-driven-systems-with-rabbitmq-lessons-from-production

Building Reliable Event-Driven Systems with RabbitMQ: Lessons From Production

5+ day, 14+ hour ago   (1289+ words) Building a distributed system is relatively straightforward when everything works as expected. The real engineering challenge begins when services fail, messages are delivered more than once, consumers restart halfway through processing, or traffic suddenly increases. Over the years, I have…...


dev.to > marketing_explyt_a7b53da9 > one-symptom-ten-problems-a-kotlin-spring-boot-kafka-debugging-case-k4j

One symptom, ten problems: a Kotlin + Spring Boot + Kafka debugging case

6+ day, 7+ hour ago   (559+ words) A single visible symptom led to many defects, half unrelated to it but all tied to one invariant... Tagged with ai, programming, webdev, javascript....


pctechmag.com > 2026 > 08 > rabbitmq-licensing-what-businesses-and-developers-need-to-know

RabbitMQ Licensing: What Businesses and Developers Need to Know

6+ day, 11+ hour ago   (798+ words) The RabbitMQ server is currently distributed under the Mozilla Public License 2.0, commonly known as MPL 2.0. This open source licence allows individuals and organisations to use RabbitMQ for personal, internal, and commercial purposes. However, certain obligations may apply when you modify…...


dev.to > dev_oza > message-queues-the-simple-explanation-302i

Message Queues: The Simple Explanation

1+ week, 1+ day ago   (177+ words) When building a backend application, it's tempting to do everything inside a single API request. For example, when a user uploads a video: The user has to wait for all of these operations to finish. Instead, we can move the…...


about.roblox.com > newsroom > 2026 > 09 > how-roblox-scaled-kafka-platform-over-18-trillion-messages-a-day

How Roblox Scaled Its Kafka Platform to Over 18 Trillion Messages a Day

1+ week, 3+ day ago   (280+ words) By Huizhi Lu, Julian Kudszus, Peter Yao, Jeffrey Zhong, and Sen Li Published Sep 4, 2026 A common misconception about large-scale systems is that capacity is mostly a hardware problem. In our experience, the most impactful improvements are made by treating scalability…...