James Henrich
02/06/2023, 9:46 AMresource = Resource(attributes = {
"profile": profile_name,
"environment": env
})
reader = PeriodicExportingMetricReader(
OTLPMetricExporter(
endpoint = _monitoring_api_url,
preferred_temporality = {Counter: AggregationTemporality.DELTA}
),
export_interval_millis = 60000
)
provider = MeterProvider(resource = resource, metric_readers = [reader])
metrics.set_meter_provider(provider)
Srikanth Chekuri
02/06/2023, 10:59 AMit makes it impossible to create an query that can alarm if there are x amount of errors in time interval y.You can use rate to achieve this.
A*30
where the A
is the rate query.James Henrich
02/06/2023, 7:26 PMSrikanth Chekuri
02/07/2023, 2:17 AMsum by (service_name) (rate(your_metric_name_here{your_filters}[5m]))
James Henrich
02/07/2023, 4:59 AMSrikanth Chekuri
02/07/2023, 5:00 AMaka each service counter is tracked separately as a time series and the sum of differences of those will be calculatedThis is correct.
since they are identical they are pooled in same time series?How are they identical? There should be some identifier that differentiates each scaled instance and that should come from the instrumentation.
James Henrich
02/07/2023, 5:03 AMSrikanth Chekuri
02/07/2023, 5:04 AM