Gauge contains most recent values of a metric, sampled every 1 min or so,
and exported by a user application, e.g. some latency sampled at 1 minute
intervals by a client application. Lets presume this time series (scraped
by Prometheus or sent via remote write) is absolute containing all the
information we need for calculating derived statistics. In the most raw
form, you can fetch the data points, sort them and calculate percentile.
Incidentally, legacy backend has efficient mechanisms to calculate
percentiles by scanning and reducing data using map-reduce.
On Sunday, August 7, 2022 at 7:49:05 AM UTC-4 sup...@gmail.com wrote:
> So, let's take a step back and find out some more information, because
> this question is sounding a lot like an XY Problem.
>
> How are the current applications generating their metrics right now?
> How are you getting the data to create these histograms?
>
> On Sun, Aug 7, 2022 at 9:23 AM Johny <mailitt...@gmail.com> wrote:
>
>> We are migrating telemetry backend from legacy database to Prometheus and
>> require estimating percentiles on gauge metrics published by user
>> applications. Estimating percentiles on a gauge metric in Prometheus is not
>> feasible and for a number of reasons, client applications will be difficult
>> to modify to start publishing histograms.
>>
>> I am exploring feasibility of creating a histogram in a recording rule in
>> Prometheus based on the metrics published by users. The partial work put in
>> so far seems inefficient, also illegible. Is there a recommended approach
>> to solve this problem? As stated earlier, it will be extremely hard to
>> solve the problem on the client side and I am looking for a solution within
>> Prometheus.
>>
>> *Current metric is a gauge with with values representing request latency.*
>> http_duration_milliseconds_gauge{instance="instance1:port1"}[1h]
>> 1659752188 100
>> 1659752068 120
>> ..
>> 1659751708 150
>> 1659751588 160
>>
>> *Desired histogram after conversion -*
>> http_duration_milliseconds_hist_bucket{instance="instance1:port1",
>> le=100} 133
>> http_duration_milliseconds_hist_bucket{instance="instance1:port1",
>> le=120} 222
>> http_duration_milliseconds_hist_bucket{instance="instance1:port1",
>> le=140} 311
>> http_duration_milliseconds_hist_bucket{instance="instance1:port1",
>> le=160} 330
>> http_duration_milliseconds_hist_bucket{instance="instance1:port1",
>> le=180} 339
>> http_duration_milliseconds_hist_bucket{instance="instance1:port1",
>> le=200} 340
>>
>>
>>
>>
>>
>>
>>
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>> .
>>
>
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