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Ursa: A leaderless, object storage–based alternative to Kafka

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Re: Ursa: A leaderless, object storage–based alternative to Kafka

#2
Ursa published a blog post saying their leaderless, stateless, object storage–based Kafka replacement can reduce costs by up to 95%. Has anyone here tried Ursa in production? How much cost reduction have you actually seen compared to Kafka or MSK in real workloads?

Re: Ursa: A leaderless, object storage–based alternative to Kafka

#5

Ursa published a blog post saying their leaderless, stateless, object storage–based Kafka replacement can reduce costs by up to 95%. Has anyone here tried Ursa in production? How much cost reduction have you actually seen compared to Kafka or MSK in real workloads?

As near as I can tell, the claims of huge cost savings derive from the difficulty dynamically scaling Kafka and improved multitenancy. So if different pieces of your company each have overprovisioned kafka clusters, they could all move to Ursa and save all the overprovisioning.

I have not tried it, and full disclosure, I really like Kafka: it's one of the pieces of software that has been rock solid for me. I built a project where it quietly ingested low gb/s of data with year-long uptimes.

Re: Ursa: A leaderless, object storage–based alternative to Kafka

#7
Just share a blog post published before, which compares the costs of running a 5 GB/s Kafka workload using Ursa, Warpstream, MSK, and Redpanda:

https://streamnative.io/blog/how-we-run-a-5-gb-s-kafka-workl...

And the test result was verified by Databricks: https://www.linkedin.com/posts/kramasamy_incredible-streamna...

The analysis in the blog is based on two key assumptions:

- Multi-zone deployment on AWS - Tiered storage is not enabled

If you’re looking to estimate costs with tiered storage, you can ignore the differences in storage costs mentioned in the post.

One important point not covered in the blog is that Ursa compacts data directly into a Lakehouse (This is also the major differentiator from WarpStream). This means you maintain only a single copy of data, shared between both streaming reads and table queries. This significantly reduces costs related to:

- Managing and maintaining connectors - Duplicated data across streaming and Lakehouse systems

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