Prometheus provides avg_over_time for a range vector. That seems to be
better suited for this usecase.
On Tue, Jan 12, 2021 at 6:53 PM Chesnay Schepler <ches...@apache.org> wrote:
> The cumulative time probably isn't that useful to detect changes in the
> behavior of the application.
>
> On 1/12/2021 12:30 PM, Chesnay Schepler wrote:
>
> I mean the difference itself, not cumulative.
>
> On 1/12/2021 12:08 PM, Manish G wrote:
>
> Can you elaborate the second approach more?
> Currently I am exposing the difference itself. OR do you mean the
> cumulative difference?ie I maintain a member variable, say timeSoFar, and
> update it with time consumed by each method call and then expose it.
> Something like this:
>
> timeSoFar += timeConsumedByCurrentInvocation
> this.simpleGaug.setValue( timeSoFar );
>
> On Tue, Jan 12, 2021 at 4:24 PM Chesnay Schepler <ches...@apache.org>
> wrote:
>
>> That approach will generally not work for jobs that run for a long time,
>> because it will be nigh impossible for anomalies to affect the average. You
>> want to look into exponential moving averages.
>> Alternatively, just expose the diff as an absolute value and calculate
>> the average in prometheus.
>>
>> On 1/12/2021 11:50 AM, Manish G wrote:
>>
>> OK, got it.
>> So I would need to accumulate the time value over the calls as well as
>> number of times it is called...and then calculate average(accumulated time/
>> number of times called) and then set calculated value into gauge as above.
>>
>> On Tue, Jan 12, 2021 at 4:12 PM Chesnay Schepler <ches...@apache.org>
>> wrote:
>>
>>> A gauge just returns a value, and Flink exposes it as is. As such you
>>> need to calculate the average over time yourself, taking 2 time
>>> measurements (before and after the processing of each).
>>>
>>> On 1/12/2021 11:31 AM, Manish G wrote:
>>>
>>> startTime is set at start of function:
>>>
>>> long startTime = System.currentTimeMillis();
>>>
>>>
>>> On Tue, Jan 12, 2021 at 3:59 PM Manish G <manish.c.ghildi...@gmail.com>
>>> wrote:
>>>
>>>> My code is:
>>>>
>>>> public class SimpleGauge<T> implements Gauge<T> {
>>>>
>>>> private T mValue;
>>>>
>>>> @Override
>>>> public T getValue() {
>>>> return mValue;
>>>> }
>>>>
>>>> public void setValue(T value){
>>>> mValue = value;
>>>> }
>>>> }
>>>>
>>>> And in flatmap function:
>>>>
>>>> float endTime = (System.currentTimeMillis() - startTime) / 1000F;
>>>> this.simplegauge.setValue(endTime);
>>>>
>>>>
>>>> So does it mean when flink calls my getValue function to accumulate the
>>>> value, and not to take it as snapshot?
>>>>
>>>>
>>>> On Tue, Jan 12, 2021 at 3:53 PM Chesnay Schepler <ches...@apache.org>
>>>> wrote:
>>>>
>>>>> Sure, that might work. Be aware though that time measurements are,
>>>>> compared to the logic within a function, usually rather expensive and
>>>>> may impact performance.
>>>>>
>>>>> On 1/12/2021 10:57 AM, Manish G wrote:
>>>>> > Hi All,
>>>>> >
>>>>> > I have implemented a flatmap function and I want to collect metrics
>>>>> > for average time for this function which I plan to monitor via
>>>>> prometheus.
>>>>> >
>>>>> > What would be good approach for it? I have added a gauge to the
>>>>> > method(extending Gauge interface from flink API). Would it work for
>>>>> my
>>>>> > needs?
>>>>> >
>>>>> >
>>>>>
>>>>>
>>>
>>
>
>
