namratachaudhary opened a new pull request, #64207:
URL: https://github.com/apache/airflow/pull/64207
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Timer and timing metrics in the OTel logger were recorded using a `Gauge`
instrument, which does not preserve multiple observations within an export
interval. In practice, if the scheduler loop runs many times between exports,
only a single value is retained, and the distribution of durations is lost.
This PR switches `timing()` and `timer()` to use a `Histogram` instrument,
which captures duration distributions via count, sum, and bucketed
observations. This enables accurate downstream analysis such as percentiles
(p50, p95, p99).
The [OTel supplementary
guidelines](https://opentelemetry.io/docs/specs/otel/metrics/supplementary-guidelines/)
recommend Histogram for measurements where "the statistics about this thing
are likely to be meaningful" and durations are a canonical example.
**Affected metrics:** all timer/timing metrics emitted via the OTel logger
(e.g. `scheduler.scheduler_loop_duration`, `dagrun.dependency-check`,
`task.duration`, etc.)
**Breaking change for OTel users:** the instrument type for timer/timing
metrics changes from `Gauge` to `Histogram`. Dashboards or alerts treating
these as Gauges may need to be updated.
### Changes
- `_OtelTimer.stop()` and `SafeOtelLogger.timing()` now call
`record_histogram_value()` instead of `set_gauge_value()`
- New `InternalHistogram` class wrapping `meter.create_histogram(unit="ms")`
- New `MetricsMap.record_histogram_value()` method
- Tests updated: `test_timing_existing_metric` now verifies both
observations are recorded (`record.call_count == 2`), not just the last value
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