Hi!
Thank you for the hint. We will try with the mitigation from the issue.
We did already tried everything from
https://cloud.google.com/dataflow/docs/guides/common-errors#worker-lost-contact
, but lets hope upgrading the dependency will help. Will keep reply to
this thread once I get confirmation.
BTW great job on the investigation of bug that you mentioned.
Impressive. Seems like a nasty one.
Best,
Wiśniowski Piotr
On 24.04.2024 00:31, Valentyn Tymofieiev via user wrote:
You might be running into https://github.com/apache/beam/issues/30867.
Among the error messages you mentioned, the following is closer to
rootcause: ``Error message from worker: generic::internal: Error
encountered with the status channel: There are 10 consecutive failures
obtaining SDK worker status info from sdk-0-0. The last success
response was received 3h20m2.648304212s ago at
2024-04-23T11:48:35.493682768+00:00. SDK worker appears to be
permanently unresponsive. Aborting the SDK. For more information, see:
https://cloud.google.com/dataflow/docs/guides/common-errors#worker-lost-contact```
<https://cloud.google.com/dataflow/docs/guides/common-errors#worker-lost-contact```>
If mitigations in https://github.com/apache/beam/issues/30867 don't
resolve your issue, please see
https://cloud.google.com/dataflow/docs/guides/common-errors#worker-lost-contact
for insturctions on how to find what causes the workers to be stuck.
Thanks!
On Tue, Apr 23, 2024 at 12:17 PM Wiśniowski Piotr
<contact.wisniowskipi...@gmail.com> wrote:
Hi,
We are investigating an issue with our Python SDK streaming
pipelines, and have few questions, but first context.
Our stack:
- Python SDK 2.54.0 but we tried also 2.55.1
- DataFlow Streaming engine with sdk in container image (we tried
also Prime)
- Currently our pipelines do have low enough traffic, so that
single node handles it most of the time, but occasionally we do
scale up.
- Deployment by Terraform `google_dataflow_flex_template_job`
resource, which normally does job update when re-applying Terraform.
- We do use a lot `ReadModifyWriteStateSpec`, other states and
watermark timers, but we do keep a the size of state under control.
- We do use custom coders as Pydantic avro.
The issue:
- Occasionally watermark progression stops. The issue is not
deterministic, and happens like 1-2 per day for few pipelines.
- No user code errors reported- but we do get errors like this:
```INTERNAL: The work item requesting state read is no longer
valid on the backend. The work has already completed or will be
retried. This is expected during autoscaling events.
[type.googleapis.com/util.MessageSetPayload='[dist_proc.dax.internal.TrailProto]
<http://type.googleapis.com/util.MessageSetPayload='%5Bdist_proc.dax.internal.TrailProto%5D>
{ trail_point { source_file_loc { filepath:
"dist_proc/windmill/client/streaming_rpc_client.cc" line: 767 } }
}']```
```ABORTED: SDK harness sdk-0-0 disconnected. This usually means
that the process running the pipeline code has crashed. Inspect
the Worker Logs and the Diagnostics tab to determine the cause of
the crash.
[type.googleapis.com/util.MessageSetPayload='[dist_proc.dax.internal.TrailProto]
<http://type.googleapis.com/util.MessageSetPayload='%5Bdist_proc.dax.internal.TrailProto%5D>
{ trail_point { source_file_loc { filepath:
"dist_proc/dax/workflow/worker/fnapi_control_service.cc" line: 217
} } } [dist_proc.dax.MessageCode] { origin_id: 5391582787251181999
[dist_proc.dax.workflow.workflow_io_message_ext]: SDK_DISCONNECT
}']```
```Work item for sharding key 8dd4578b4f280f5d tokens
(1316764909133315359, 17766288489530478880) encountered error
during processing, will be retried (possibly on another worker):
generic::internal: Error encountered with the status channel: SDK
harness sdk-0-0 disconnected. with MessageCode:
(93f1db2f7a4a325c): SDK disconnect.```
```Python (worker sdk-0-0_sibling_1) exited 1 times: signal:
segmentation fault (core dumped) restarting SDK process```
- We did manage to correlate this with either vertical autoscaling
event (when using Prime) or other worker replacements done by
Dataflow under the hood, but this is not deterministic.
- For few hours watermark progress does stop, but other workers do
process messages.
- and after few hours:
```Error message from worker: generic::internal: Error encountered
with the status channel: There are 10 consecutive failures
obtaining SDK worker status info from sdk-0-0. The last success
response was received 3h20m2.648304212s ago at
2024-04-23T11:48:35.493682768+00:00. SDK worker appears to be
permanently unresponsive. Aborting the SDK. For more information,
see:
https://cloud.google.com/dataflow/docs/guides/common-errors#worker-lost-contact```
- And the pipeline starts to catch up and watermark progresses again.
- Job update by Terraform apply also fixes the issue.
- We do not see any extensive use of worker memory nor disk. CPU
utilization is also most of the time close to idle. I do not think
we do use C/C++ code with python. Nor use parallelism/threads
outside beam parallelization.
Questions:
1. What could be potential causes of such behavior? How to get
more insights to this problem?
2. I have seen `In Python pipelines, when shutting down inactive
bundle processors, shutdown logic can overaggressively hold the
lock, blocking acceptance of new work` in Beam release docs as
known issue. What is the status of this? Can this potentially be
related?
Really appreciate any help, clues or hints how to debug this issue.
Best regards
Wiśniowski Piotr
