[sciurus]

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.

[roskilli]

So we collected and aggregated more than 1 billion samples of metrics per second, which resulted in writing more than 30-40 million unique metric datapoints per second to storage. This resulted in more than 10 billion unique time series being stored (each with a very large number of distinct datapoints each).

This was 3.6 trillion metric samples per hour or 2.5 trillion metric datapoints stored a day (after aggregating samples).

[jwatte]

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!

[sciurus]

Oops, don't know how I misread that! Thanks for the correction!