Byron Ruth’s Post

There's a lot to like about the Kafka protocol, but numeric partitions were clearly a mistake. For example, let's say you made a transactions topic with 16 partitions: 1. Why 16? What happens if you need more? Less? Everyone knows how painful this is. 2. How do you consume all transactions for Alice? Subscribe to partition 4. What?! It would be way better if partitions were light-weight and self-identifiable. e.g. a partition for Alice, a partition for Bob. Stream processing would get a whole lot simpler, too. I know EventStore has had these semantics for a while, and I think Astradot (new competitor) is out to do this. Can't wait to to see this abstraction become more widespread.

🚀 Understanding the Power of Parallel Processing in Kafka! 🌐 Dive into the dynamic world of Kafka and unlock the secrets of seamless parallel message processing. From a solo consumer efficiently managing workloads to multiple consumer groups collaborating on a single topic, Kafka's adaptability shines through. 🔄 Explore the partitioning mechanism, coupled with flexible consumer configurations, for scalability and optimal resource utilization. 🌈 Discover the magic of message distribution among consumer groups, where Kafka's round-robin assignment algorithm ensures parallelism and load balancing. 🌐 Whether orchestrating a single consumer or multiple groups independently, Kafka stands as a pillar for resilient, modular, and efficient distributed systems. 📚 Read the full article now on Medium #Kafka #ParallelProcessing #DistributedSystems #TechInnovation

Have you heard of the "Poisoned Pill" problem in Kafka? It's a hiccup that can halt or slow down your entire Kafka message consumption In Kafka, a message that a consumer cannot process is called a "poison pill" The consumer stumbles on this message and, depending on the configuration might even halt, attempting to process this message leading to a deadlock-like situation where new messages are not consumed Why does this happen?  Kafka's consumer operates in an "at-least-once" or "exactly-once" semantics. In "at-least-once," the consumer tries to process the message repeatedly until successful. In "exactly-once," the scenario is even more stringent, waiting for the acknowledgement from downstream systems One way to mitigate this is to implement a "dead-letter queue" where all poison pills get diverted after a certain number of retries. Thus, the consumer moves on to other tasks So, the next time you find your Kafka consumer gets stuck, you might be dealing with a poisoned pill. Knowing the internals helps you prescribe the right antidote

Kafka can be a great option for offloading workloads at scale. This is an interesting glimpse into how SigNoz integrated Kafka into their backend architecture for improved scalability and robustness:

Don't underestimate the power of stupidly simple ideas – they have the potential to be groundbreaking. For example, Consider appending data sequentially in a log file. This approach can outperform even the most sophisticated messaging systems, scaling up to millions of events per second. It addresses complex distributed challenges such as race conditions, data replication, consistency, secondary indexes, distributed consensus, partial failure, and more. In a nutshell: Kafka. The rest is history. #InnovativeIdeas #PowerOfSimplicity

Kafka is all about fault tolerance and scaling ⚙. Topics allow you to consume messages using multiple consumers unlike queues who usually care about delivering a message once. To take advantage of setting up multiple consumers and unlock ways to scale your consumers horizontally you have to understand how groups work in Kafka. This post will introduce you to consumer groups and how manage them 💡. https://lnkd.in/dPN2ATFX

If you use Kafka Streams applications with exactly-once semantics enabled, Apurva Mehta and Sophie Blee-Goldman have the blog post for you!  They cover how exactly-once semantics work in Kafka and Kafka Streams, how to tune your transactions, and how to solve issues which are commonly encountered when using Kafka Streams with exactly-once semantics enabled. Check it out! https://lnkd.in/edc8ZTXt

Kafka – The Definitive Guide: Real-Time Data and Stream Processing at Scale

Exactly Once Semantics(EOS) - Kafka and database! Thrilled to share the first article in my series on EOS with Kafka and databases, even in the face of failures! Perfect for those navigating data processing challenges. Dive in, share your thoughts, and stay alert for upcoming insights! #apachekafka #kafkaconsumer #exactlyoncesemantics #idempotentconsumer

Very exciting reading! It reads like a detective novel. :) The book pays a lot of attention to the details and to all possible situations of "what could go wrong". At the same time, level of explanation and communication patterns applied in the book, helps you stay focused and engaged in all steps of the algorithms. It is excitingly deep dive into how distributed systems work, but what is more important is the book style that teaches you a new way to think about the problems. It will definitely take your algorithmic thinking to the next level. The book teaches you how to carefully consider business scenarios and split complex problems into small steps and be prepared to all edge cases. You can also apply these patterns directly to your problems. Even if you are not using Kafka, you might still need to split some long-running transactions in small steps and persist intermediate data in some temporary transaction log. For example, some long-running Saga transactions that have many steps and which can end, depending on the previous state, or can continue indefinitely. You can think of any other use case. But after reading this book, I'm sure you will be able to use these partterns in practice, even if you are not Kafka contributor. :) Thank you, Unmesh Joshi ! Looking forward to new stories! 👍