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https://issues.apache.org/jira/browse/BEAM-7874?focusedWorklogId=290865&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-290865
]
ASF GitHub Bot logged work on BEAM-7874:
----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Aug/19 23:21
Start Date: 07/Aug/19 23:21
Worklog Time Spent: 10m
Work Description: Hannah-Jiang commented on pull request #9218:
[BEAM-7874], [BEAM-7873] Distributed FnApiRunner bugfixs
URL: https://github.com/apache/beam/pull/9218#discussion_r311801800
##########
File path: sdks/python/apache_beam/runners/portability/fn_api_runner.py
##########
@@ -1319,55 +1320,62 @@ def stop_worker(self):
@WorkerHandler.register_environment(common_urns.environments.DOCKER.urn,
beam_runner_api_pb2.DockerPayload)
class DockerSdkWorkerHandler(GrpcWorkerHandler):
+
+ _lock = threading.Lock()
Review comment:
Yes, this is a workaround for subprocess.
I tried to add a function to handle calls to subprocess, but, I was not able
to find a good way to handle it. With `DockerSdkWorkerHandler`, we can lock
`start_worker()`, but we cannot lock `start_worker()` with
`SubprocessSdkWorkerHandler` handler. We should lock `subprocess.Popen()` only
with `SubprocessSdkWorkerHandler`, if we lock the whole `start_worker()`
function, it waits a worker to finish job, so the pipeline gets stuck.
In fact, I doubt `DockerSdkWorkerHandler` works with FnApi at the moment.
FnApi doesn't stage any artifacts, so docker bootstrap fails and container
cannot be created. I will make a change to `DockerSdkWorkerHandler` soon and
create only one container and handle multithreading/processing when a job
running within container, so we can get rid of lock here for
`DockerSdkWorkerHandler`.
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Issue Time Tracking
-------------------
Worklog Id: (was: 290865)
Time Spent: 2h (was: 1h 50m)
> FnApi only supports up to 10 workers
> ------------------------------------
>
> Key: BEAM-7874
> URL: https://issues.apache.org/jira/browse/BEAM-7874
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-core
> Reporter: Hannah Jiang
> Assignee: Hannah Jiang
> Priority: Blocker
> Fix For: 2.15.0
>
> Time Spent: 2h
> Remaining Estimate: 0h
>
> Because max_workers of grpc servers are hardcoded to 10, it only supports up
> to 10 workers, and if we pass more direct_num_workers greater than 10,
> pipeline hangs, because not all workers get connected to the runner.
> [https://github.com/apache/beam/blob/master/sdks/python/apache_beam/runners/portability/fn_api_runner.py#L1141]
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