pengzhon-db opened a new pull request, #41318:
URL: https://github.com/apache/spark/pull/41318
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### What changes were proposed in this pull request?
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Streaming awaitTermination() is a long running API. Currently, it keeps
running on server even if client disconnects. This change periodically checks
if client has disconnected. If so, we can stop the operation and release
resources.
We use gRPC Context.isCancelled() to determine if client has disconnected
and response cannot be returned to the client. [Here is the reference of of
isCancelled()](https://grpc.github.io/grpc/cpp/classgrpc_1_1_server_context.html#af2d0f087805b4b475d01b12d73508f09):
> Return whether this RPC failed before the server could provide its status
back to the client.
> This could be because of explicit API cancellation from the client-side or
server-side, because of deadline exceeded, network connection reset, HTTP/2
parameter configuration (e.g., max message size, max connection age), etc. It
does NOT include failure due to a non-OK status return from the server
application's request handler, including
[Status::CANCELLED](https://grpc.github.io/grpc/cpp/classgrpc_1_1_status.html#a9994ffe95a0495915d82481c2ec594ab).
> IsCancelled is always safe to call when using sync or callback API. When
using async API, it is only safe to call IsCancelled after the
AsyncNotifyWhenDone tag has been delivered. Thread-safe.
### Why are the changes needed?
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The change improves handling of awaitTermination(). It avoids resource waste
of server side.
### Does this PR introduce _any_ user-facing change?
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No
### How was this patch tested?
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Existing unit tests.
Manually tested on local with:
1. start spark connect
2. create a streaming query
3. call query.awaitTermination()
4. exit() the client to disconnect it
5. check that an error (RPC context is cancelled when executing
awaitTermination()) is logged on server which verifies that awaitTermination()
is exited on server side when client disconnects
```
>>> query = (
... spark
... .readStream
... .format("rate")
... .option("numPartitions", "1")
... .load()
... .writeStream
... .format("memory")
... .queryName("tableName_35")
... .start()
... )
>>>
>>> query.awaitTermination()
...
>>> exit()
```
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