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Splunk kool-aid drinker here; pardon my ignorant question, but why not just use Splunk? Actually I think my real question is, why are there such a proliferation of these monitoring/logging/visualization -AAS startups? Who are the target customers, in terms of spends?
https://cnc.fandom.com/wiki/Chronosphere_(Red_Alert_3)
That’s my personal reference to the word, but searching around a bit, it seems that it was registered as a trademark by a medical company already in 1991, 5 years before Red Alert.
https://trademark.trademarkia.com/chronosphere-74147725.html
Earlier quoted context omitted.
New Relic touts collecting trillions of data points per day.
According to https://eng.uber.com/m3/ > Released in 2015, M3 now houses over 6.6 billion time series. M3 aggregates 500 million metrics per second and persists 20 million resulting metrics-per-second to storage globally (with M3DB), using a quorum write to persist each metric to three replicas in a region. So, if that's accurate, they're collecting one trillion data points every two seconds .
With a 25:1 reduction/summarization before writing. If they're smart, they do that summarization on the way in, rather than at the back-end layer. That's a billion data points written per minute, or a trillion and a half written per day!
Earlier quoted context omitted.
New Relic touts collecting trillions of data points per day.
According to https://eng.uber.com/m3/ > Released in 2015, M3 now houses over 6.6 billion time series. M3 aggregates 500 million metrics per second and persists 20 million resulting metrics-per-second to storage globally (with M3DB), using a quorum write to persist each metric to three replicas in a region. So, if that's accurate, they're collecting one trillion data points every two seconds .
This was 3.6 trillion metric samples per hour or 2.5 trillion metric datapoints stored a day (after aggregating samples).
It should be trivial, and the fact that it's not there and what you find instead is terms like "Uber-scale" is slightly worrying.
I'm not trying to take anything away from the achievements made here by the guys at Uber, but anyone seriously considering using this in production would probably need a better contrastive comparison between alternatives.
Earlier quoted context omitted.
According to https://eng.uber.com/m3/ > Released in 2015, M3 now houses over 6.6 billion time series. M3 aggregates 500 million metrics per second and persists 20 million resulting metrics-per-second to storage globally (with M3DB), using a quorum write to persist each metric to three replicas in a region. So, if that's accurate, they're collecting one trillion data points every two seconds .
No, they're collecting one BILLION (with a b) data points every two seconds. Gotta go to 2000 seconds (a little over half an hour) for the TRILLION. With a 25:1 reduction/summarization before writing. If they're smart, they do that summarization on the way in, rather than at the back-end layer. That's a billion data points written per minute, or a trillion and a half written per day!
The technical details of their software are described in https://eng.uber.com/m3/ This looks like a competitor to Cortex ( https://www.cncf.io/blog/2018/12/18/cortex-a-multi-tenant-ho... ).
They took a different path on the ”never build your own database” question.
[1] https://github.com/VictoriaMetrics/VictoriaMetrics/
[2] https://github.com/VictoriaMetrics/VictoriaMetrics/wiki/Exte...
[3] https://medium.com/@valyala/measuring-vertical-scalability-f...
One thing that I often miss when reading about this stuff is benchmarks. So it's faster than Prometheus? Prove it. So it's faster than Postgres, or TimescaleDB? Prove it. It should be trivial, and the fact that it's not there and what you find instead is terms like "Uber-scale" is slightly worrying. I'm not trying to take anything away from the achievements made here by the guys at Uber, but anyone seriously consider…
Benchmarks tend to favor the authors and are frequently game-ified, look at GPU benchmarks like 3DMark that frequently had manufacturers release optimizations that were really only utilized in specific benchmarks.