damccorm opened a new issue, #20940:
URL: https://github.com/apache/beam/issues/20940
I am building an application in Apache Beam and Python that runs in Google
DataFlow. I am using the `ReadFromSpanner` method in
`apache_beam.io.gcp.experimental.spannerio`. This works for most of my Spanner
tables but the really large ones that are \>16m rows tend to fail due to the
following error:
Traceback (most recent call last):
File
"/usr/local/lib/python3.8/site-packages/dataflow_worker/batchworker.py", line
649, in do_work
work_executor.execute()
File "/usr/local/lib/python3.8/site-packages/dataflow_worker/executor.py",
line 179, in execute
op.start()
File "dataflow_worker/shuffle_operations.py", line 63, in
dataflow_worker.shuffle_operations.GroupedShuffleReadOperation.start
File "dataflow_worker/shuffle_operations.py", line 64, in
dataflow_worker.shuffle_operations.GroupedShuffleReadOperation.start
File "dataflow_worker/shuffle_operations.py", line 79, in
dataflow_worker.shuffle_operations.GroupedShuffleReadOperation.start
File "dataflow_worker/shuffle_operations.py", line 80, in
dataflow_worker.shuffle_operations.GroupedShuffleReadOperation.start
File "dataflow_worker/shuffle_operations.py", line 84, in
dataflow_worker.shuffle_operations.GroupedShuffleReadOperation.start
File "apache_beam/runners/worker/operations.py", line 359, in
apache_beam.runners.worker.operations.Operation.output
File "apache_beam/runners/worker/operations.py", line 221, in
apache_beam.runners.worker.operations.SingletonConsumerSet.receive
File "dataflow_worker/shuffle_operations.py", line 261, in
dataflow_worker.shuffle_operations.BatchGroupAlsoByWindowsOperation.process
File "dataflow_worker/shuffle_operations.py", line 268, in
dataflow_worker.shuffle_operations.BatchGroupAlsoByWindowsOperation.process
File "apache_beam/runners/worker/operations.py", line 359, in
apache_beam.runners.worker.operations.Operation.output
File "apache_beam/runners/worker/operations.py", line 221, in
apache_beam.runners.worker.operations.SingletonConsumerSet.receive
File "apache_beam/runners/worker/operations.py", line 718, in
apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/worker/operations.py", line 719, in
apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/common.py", line 1241, in
apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 1306, in
apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam/runners/common.py", line 1239, in
apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 587, in
apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam/runners/common.py", line 1401, in
apache_beam.runners.common._OutputProcessor.process_outputs
File "apache_beam/runners/worker/operations.py", line 221, in
apache_beam.runners.worker.operations.SingletonConsumerSet.receive
File "apache_beam/runners/worker/operations.py", line 718, in
apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/worker/operations.py", line 719, in
apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/common.py", line 1241, in
apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 1306, in
apache_beam.runners.common.DoFnRunner._reraise_augmented
File "apache_beam/runners/common.py", line 1239, in
apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 587, in
apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam/runners/common.py", line 1401, in
apache_beam.runners.common._OutputProcessor.process_outputs
File "apache_beam/runners/worker/operations.py", line 221, in
apache_beam.runners.worker.operations.SingletonConsumerSet.receive
File "apache_beam/runners/worker/operations.py", line 718, in
apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/worker/operations.py", line 719, in
apache_beam.runners.worker.operations.DoOperation.process
File "apache_beam/runners/common.py", line 1241, in
apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 1321, in
apache_beam.runners.common.DoFnRunner._reraise_augmented
File "/usr/local/lib/python3.8/site-packages/future/utils/__init__.py",
line 446, in raise_with_traceback
raise exc.with_traceback(traceback)
File "apache_beam/runners/common.py", line 1239, in
apache_beam.runners.common.DoFnRunner.process
File "apache_beam/runners/common.py", line 587, in
apache_beam.runners.common.SimpleInvoker.invoke_process
File "apache_beam/runners/common.py", line 1374, in
apache_beam.runners.common._OutputProcessor.process_outputs
File
"/usr/local/lib/python3.8/site-packages/apache_beam/io/gcp/experimental/spannerio.py",
line 550, in process
for row in read_action(element['partitions']):
File
"/usr/local/lib/python3.8/site-packages/google/cloud/spanner_v1/streamed.py",
line 143, in __iter__
self._consume_next()
File
"/usr/local/lib/python3.8/site-packages/google/cloud/spanner_v1/streamed.py",
line 116, in _consume_next
response = six.next(self._response_iterator)
File
"/usr/local/lib/python3.8/site-packages/google/cloud/spanner_v1/snapshot.py",
line 45, in _restart_on_unavailable
for item in iterator:
File
"/usr/local/lib/python3.8/site-packages/google/api_core/grpc_helpers.py", line
116, in next
six.raise_from(exceptions.from_grpc_error(exc), exc)
File "<string\>", line 3, in raise_from
google.api_core.exceptions.DeadlineExceeded: 504 Deadline Exceeded [while
running 'Read from Spanner/Read From Partitions']
From my understanding this error comes from the `ReadFromSpanner` operation
as it's workers have timed out.
To solve this I have tried the following:
* Changed the `num_workers` and `disk_size_gb` and added the
`--experiments=shuffle_mode=service` flag as suggested in [Google's Common
error
guidance](https://cloud.google.com/dataflow/docs/guides/common-errors#tsg-rpc-timeout)
* Changed the Machine Type from `n1-standard-1` to `n1-standard-2` from
[here](https://cloud.google.com/compute/docs/machine-types#n1_machine_types)
My latest code is attached below. I am including `Transformation` for simple
data wrangling in the rows.
"""Set pipeline arguments."""
options = PipelineOptions(
region=RUNNER_REGION,
project=RUNNER_PROJECT_ID,
runner=RUNNER,
temp_location=TEMP_LOCATION,
job_name=JOB_NAME,
service_account_email=SA_EMAIL,
setup_file=SETUP_FILE_PATH,
disk_size_gb=500,
num_workers=10,
machine_type="n1-standard-2",
save_main_session=True)
"""Build and run the pipeline."""
with beam.Pipeline(options=options) as p:
(p
| "Read from Spanner" \>\> ReadFromSpanner(SPANNER_PROJECT_ID,
SPANNER_INSTANCE_ID, SPANNER_DB, sql=QUERY)
| "Transform elements into dictionary" \>\>
beam.ParDo(Transformation)
| "Write new records to BQ" \>\> WriteToBigQuery(
BIGQUERY_TABLE,
schema=SCHEMA,
write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND,
create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED)
)
A *potential solution* is to edit the timeout control; I have seen this
being available in Java but not in Python. How can I edit timeout control in
Python or is there any other solution to this issue?
Imported from Jira
[BEAM-11956](https://issues.apache.org/jira/browse/BEAM-11956). Original Jira
may contain additional context.
Reported by: sebastian-montero.
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