One of the top questions we received was: “How well does it perform at high throughput?”
We ran a load test and would like to share some results with you.
Summary of the test:
- Tested on 20m records
- Kafka produced 55,000 records/sec
- Processing rate of GlassFlow (deduplication): 9,000+ records/sec
- Measured on a MacBook Pro (M3 Max)
- End-to-end latency: <0.12 ms per request
Here is the blog post with full test results and tried with different parameters (rps, # of publishers, etc.): https://www.glassflow.dev/blog/load-test-glass-flow-for-clic...
It was important to us to set up the testing in a way that everybody could reproduce. Here are the docs: https://docs.glassflow.dev/load-test/setup
We would love to get feedback, especially from folks consuming high-throughput in ClickHouse.
Thanks for reading!
Ashish and Armend (founders)
Everything was running on the same machine?
My laptop can run 70B LLMs at usable speeds.
I know. Doesn’t scale. No redundancy. No auto redeploy on failures. This is what I mean.
Do we really have to sacrifice this much efficiency for those things or are we doing it wrong? Does the ability to redeploy on failures, cluster, and scale really require order of magnitude performance penalties across the whole stack?
20m records and 9k/sec isn’t very impressive. I would imagine most prospective customers have larger workloads, as you could throw this behind Postgres and call it a day. FWIW I was interested but your metrics made me second guess and wonder what was wrong.
