Thanks a lot, much appreciated. These are great insights and suggestions. 
I'll see how I can best use this to rework this monitoring strategy.

On Monday, January 25, 2021 at 1:47:42 PM UTC+1 [email protected] wrote:

> 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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