Hi,

Generally most metrics-based systems won't be able to deal with that level
of cardinality, thus typically we would recommend against putting data like
customer IDs into label values (unless there are only a handful of
customers). Prometheus is for monitoring overall system health, but would
have trouble tracking behavior down to this level of detail. For looking
into details of individual customers, you will likely need logging and/or
tracing systems, unless you really cut down on the number of series per
customer and either shard your Prometheus setup heavily or use
a horizontally scalable Prometheus implementation like Cortex.

Regards,
Julius

On Mon, Jan 25, 2021 at 10:53 AM [email protected] <
[email protected]> wrote:

> Hello,
> I'm curious how to best use Prometheus for *large scale use cases*:
>
> Let's say you've got a successful e-commerce platform, using Prometheus to
> track some ~300 metrics related to requests, failures and interactions at
> various points throughout the system.
> Now, your platform is very generic, and you can easily support 10,000
> different e-commerce sites & customers.
> At this scale, Kubernetes spreads your application across ~100 pods.
> (There's no "session aware" ingress router, so any pod can support any
> customer.)
>
> *How would you handle these metrics?*
> Adding the 'customer' label to every metric results in 300(metrics) *
> 10000(customers) * 100(pods) = 300M timeseries, scraped every 15 seconds by
> default.
>
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>


-- 
Julius Volz
PromLabs - promlabs.com

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