Multiple concurrent gRPC subscriptions

I am struggling to work out how to subscribe to multiple streams of information. In my case that I am testing with at the moment, I want to subscribe to multiple bits of BGP information across multiple devices and react to changes.

Devices:
``path_elements = ["DatasetInfo", "Devices"]``

Then for each device it's BGP VRFS:
``path_elements = ["Devices", , "versioned-data", "routing", "bgp", "status", "vrf"]``

Then for each device VRF, it's BGP Peers:
``path_elements = ["Devices", , "versioned-data", "routing", "bgp", "status", "vrf", , "bgpPeerInfoStatusEntry"]``

My script then reacts to event changes in the BGP peer states, and also adds or removed devices VRFs and peers as the updates come in. It almost works beautifully, but...

Because the ``grpc_client.subscribe(query)`` never returns, I initially ran it in a thread. This works for a few devices, VRFs and Peers, but is not very scalable. Once I got to 129 threads, I could not run any more. I tried using asyncio, but it appears while grpcio is supposed to support asyncio the cloudvision module does not. The examples as far as I can see only have very simplistic subscribe models. For example, the get_if_rate.py only gets a single metric from a single interface.

```
with GRPCClient(apiserverAddr, token=token, certs=cert, key=key,
ca=ca) as client:
for batch in client.subscribe(query):
for notif in batch["notifications"]:
pretty_print(notif["updates"])
```

I can't see a good way to deal with subscriptions in a scalable way using the cloudvision module. can someone please clarify how this should be done. It would be good if this can also be reflected in the Docs or examples. If it is already in there, and I am blind or stupid, can someone please point me in the right direction.

Inizia con l’esempio get_if_rate.py e il punto di ingresso GRPCClient.subscribe(query) descritto nell’issue. Analizza come gestire più stream di dispositivi, VRF e peer senza un thread per ogni sottoscrizione, inclusa la limitazione di asyncio segnalata. Il lavoro sarà considerato completato quando l’approccio scalabile supportato sarà chiarito nella documentazione o in un nuovo esempio.

Scritto dal modello di indicizzazione a partire dal testo della issue.