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the tradeoff with regards to slower queries and io only comes into play if the TSDB is performing a narrow lookup on a handful of series. In that case it’ll be faster. But when you scale up to 100s of millions, columnar…
Sizing for this kind of deployment was a lot of fun! We went ahead with- 5x Ingestors, each with 64 vcpu 128 GB 5x Queriers, each with 64 vcpu 192 GB The current utilization sits comfortably at 10-15 vcpu and 20-30 GB…
We haven't yet tried pushing it to the scale of billions yet. The max that we've gone to is 150-180 million.
Based on the feedback, we could have done a much better job with these results (lessons for our next experiment). But yes, the models were tested against the same dataset which was aggregated over different…
We'll definitely include it in our next experiment (shaping up to be quite big!)
At the moment our focus is on observability, hence the narrow scope of our dataset. A pretty good benchmark for observability seems to be Datadog's BOOM- https://huggingface.co/datasets/Datadog/BOOM But for general…
we're grateful for the honest feedback (and the awesome resource!), makes it easier to identify areas for improvement. Also, your point about using multiple metrics (based on use-cases, audience, etc) makes a lot of…
That's actually one of the use-cases that we set out to explore with these models. We'll release a head-to-head comparison soon!
This looks like a great benchmark! We've been thinking of doing a better and more detailed follow-up and this seems like the perfect dataset to do that with. Thanks!
Author here, we're trying these out for the first time for our use-cases so these are great points for us to improve upon!
[flagged]
the tradeoff with regards to slower queries and io only comes into play if the TSDB is performing a narrow lookup on a handful of series. In that case it’ll be faster. But when you scale up to 100s of millions, columnar…
Sizing for this kind of deployment was a lot of fun! We went ahead with- 5x Ingestors, each with 64 vcpu 128 GB 5x Queriers, each with 64 vcpu 192 GB The current utilization sits comfortably at 10-15 vcpu and 20-30 GB…
We haven't yet tried pushing it to the scale of billions yet. The max that we've gone to is 150-180 million.
Based on the feedback, we could have done a much better job with these results (lessons for our next experiment). But yes, the models were tested against the same dataset which was aggregated over different…
We'll definitely include it in our next experiment (shaping up to be quite big!)
At the moment our focus is on observability, hence the narrow scope of our dataset. A pretty good benchmark for observability seems to be Datadog's BOOM- https://huggingface.co/datasets/Datadog/BOOM But for general…
we're grateful for the honest feedback (and the awesome resource!), makes it easier to identify areas for improvement. Also, your point about using multiple metrics (based on use-cases, audience, etc) makes a lot of…
That's actually one of the use-cases that we set out to explore with these models. We'll release a head-to-head comparison soon!
This looks like a great benchmark! We've been thinking of doing a better and more detailed follow-up and this seems like the perfect dataset to do that with. Thanks!
Author here, we're trying these out for the first time for our use-cases so these are great points for us to improve upon!