Never tried it, but looks promising
Redpanda claim of better performance but benchmarks showed no clear winner [3].
It will be interesting to test them together on the performance benchmarks.
I've got the feeling it's not due to programming language implementation of Scala/Java (Kafka), C++ (Redpanda) and Rust (Walrus).
It's the very architecture of Kafka itself due to the notorious head of line problem (check the top most comments [4].
[1] Redpanda – A Kafka-compatible streaming platform for mission-critical workloads (120 comments):
https://news.ycombinator.com/item?id=25075739
[2] Redpanda website:
[3] Kafka vs. Redpanda performance – do the claims add up? (141 comments):
https://news.ycombinator.com/item?id=35949771
[4] What If We Could Rebuild Kafka from Scratch? (220 comments):
Except a consumer can discard an unprocessable record? I'm not certain I understand how HOL applies to Kafka, but keen to learn more :)
We called it `tuberculosis`, or `tube` for short; of course, that is what killed Kafka.
roncohen•4h ago
Wasn't immediately clear to me if the data-plane level replication also happens through Raft or something home-rolled? Getting consistency and reliability right with something home-rolled is challenging.
Notes:
- Would love to see it in an S3-backed mode, either entirely diskless like WarpStream or as tiered storage.
- Love the simplified API. If possible, adding a Kafka compatible API interface is probably worth it to connect to the broader ecosystem.
Best of luck!
seanhunter•3h ago
zbentley•3h ago
EdwardDiego•2h ago
nubskr•2h ago
Also about the kafka API, I tried to implement that earlier, I had a sort of `translation` layer for that earlier, but it gets pretty complicated to maintain that because kafka is offset based, while walrus is message based.
EdwardDiego•2h ago
zellyn•2m ago
tl;dr they write to s3 once every 250ms to save costs. IIRC, they contend that when you keep things organized by writing to different files for each topic, it's the Linux disk cache being clever that turns the tangle of disk block arrangement into a clean view per file. They wrote their own version of that, so they can cheaply checkpoint heavily interleaved chunks of data while their in-memory cache provides a clean per-topic view. I think maybe they clean up later async, but my memory fails me.
I don't know how BufStream works.
The thing that really stuck with me from that interview is the 10x cost reduction you can get if you're willing and able to tolerate higher latency and increased complexity and use S3. Apparently they implemented that inside Datadog ("Labrador" I think?), and then did it again with WarpStream.
I highly recommend the whole episode (and the whole podcast, really).