I suggest adding a `Task.stats()` method.
The method should return a dict with the task usage statistics.
Dict keys:
- total_time: a time between task creation and a moment of the call (or task finishing timestamp if the task has finished). The value includes a time for `await func()` waiting.
- real_time: a cumulative time for `Task._step()` executions
- switch_count: a count of `Task._step()` calls.

It can help with tasks activity analyzing.

Yuri, what do you think about?

another idea would be neat to somehow plug into the stats so people can trace (ex: via datadog tracing) "slow" tasks, similarly to things that are outputted via PYTHONASYNCIODEBUG, however in this case be able to trace slow steps and also slow accumulated steps.  In this scenario you don't know which task will be slow and don't want to track every task.

FYI, here's how trio handles such things – I'm sure it's not perfect, but might be useful for inspiration: https://trio.readthedocs.io/en/latest/reference-hazmat.html#instrument-api

> Yuri, what do you think about?

I plan to use contextvars module to introduce a full-blown tracing API to asyncio to selectively log events like tacks creations, event loop switching, IO done by transports etc.  The plan is to prototype it in uvloop first.

I'll update this issue with a link to a discussion.

Well, event based solution is more powerful than just statistic collection.
Please keep me in the loop.

I like trio-style instrumentation API because it could be used for more generic purposes, not only for statistics.

This stats or instrumentation API will greatly help me to utilize external monitoring services such as Datadog in my production deployments.