mufiye commented on issue #10341:
URL: https://github.com/apache/skywalking/issues/10341#issuecomment-1440051786

   > I just read 
https://opentelemetry.io/docs/reference/specification/metrics/data-model/#exponentialhistogram,
 it seems it is just the typical Prometheus Histogram setup in practice.
   > 
   > Back to you question
   > 
   > > Should I make this exponentialHistogram type be supported or use summary 
type for timer metric?
   > 
   > We should transfer this to our histogram, I think. You need to get the 
bucket transfer correctly from `exponentialHistogram` to `histogram`.
   
   I will try to do it later. And there are some other essential points that 
need to be discussed.
   
   1. There are some labels in airflow metric names such as <job_name>, 
<task_id>, <dag_id>, <operator_name>, <pool_name> which represent the 
components in airflow. Which level should I classify these components to? In 
airflow concepts, `dag` contains lots of `tasks` to be run, `pool` is where 
tasks run in, `operator_name` is one kind of `task` and `job` I think is a 
larger concept than a task because it also includes the scheduler job. I think 
we classify all these components as endpoints?
   2. As before, we transform the "delta counter metric" to "gauge metric", we 
can represent some monotonic delta metric as the current metric in one specific 
period of time. For example, `ti_successes` metric describes "Overall task 
instances successes", so we can think the gauge metric as "the successful task 
instances in this period". But for some nonmonotonic delta counter metric, such 
as "dag_processing.processes" which means number of currently running DAG 
parsing processes, it can be negative in one period in my test. How should we 
handle this kind of metric? Maybe just show them originally for showing the 
trend of the value.


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