[jira] [Work logged] (BEAM-7917) Python datastore v1new fails on retry

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/BEAM-7917?focusedWorklogId=337569&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-337569 ]

ASF GitHub Bot logged work on BEAM-7917:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 01/Nov/19 21:43
            Start Date: 01/Nov/19 21:43
    Worklog Time Spent: 10m 
      Work Description: udim commented on pull request #9294: [BEAM-7917] Fix datastore writes failing on retry
URL: https://github.com/apache/beam/pull/9294#discussion_r341762012
 
 

 ##########
 File path: sdks/python/apache_beam/io/gcp/datastore/v1new/datastoreio.py
 ##########
 @@ -340,12 +340,13 @@ def finish_bundle(self):
     def _init_batch(self):
       self._batch_bytes_size = 0
       self._batch = self._client.batch()
-      self._batch.begin()
+      self._batch_mutations = []
 
     def _flush_batch(self):
       # Flush the current batch of mutations to Cloud Datastore.
       latency_ms = helper.write_mutations(
 
 Review comment:
   I think your choice is a valid compromise with what to do with the results of `element_to_client_batch_item`. I see 2 options here: 
   1. Save the batch items.
   2. Discard batch items after calling `ByteSize()` on them.
   
   The first option seems more CPU efficient while the second seems more memory efficient. I don't know which option is faster, but from my limited experience saving CPU at the expense of RAM seems like a good tradeoff.
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 337569)
    Time Spent: 3.5h  (was: 3h 20m)

> Python datastore v1new fails on retry
> -------------------------------------
>
>                 Key: BEAM-7917
>                 URL: https://issues.apache.org/jira/browse/BEAM-7917
>             Project: Beam
>          Issue Type: Bug
>          Components: io-py-gcp, runner-dataflow
>    Affects Versions: 2.14.0
>         Environment: Python 3.7 on Dataflow
>            Reporter: Dmytro Sadovnychyi
>            Assignee: Dmytro Sadovnychyi
>            Priority: Major
>              Labels: pull-request-available
>          Time Spent: 3.5h
>  Remaining Estimate: 0h
>
> Traceback (most recent call last):
>   File "apache_beam/runners/common.py", line 782, in apache_beam.runners.common.DoFnRunner.process
>   File "apache_beam/runners/common.py", line 454, in apache_beam.runners.common.SimpleInvoker.invoke_process
>   File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/datastore/v1new/datastoreio.py", line 334, in process
>     self._flush_batch()
>   File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/datastore/v1new/datastoreio.py", line 349, in _flush_batch
>     throttle_delay=util.WRITE_BATCH_TARGET_LATENCY_MS // 1000)
>   File "/usr/local/lib/python3.7/site-packages/apache_beam/utils/retry.py", line 197, in wrapper
>     return fun(*args, **kwargs)
>   File "/usr/local/lib/python3.7/site-packages/apache_beam/io/gcp/datastore/v1new/helper.py", line 99, in write_mutations
>     batch.commit()
>   File "/usr/local/lib/python3.7/site-packages/google/cloud/datastore/batch.py", line 271, in commit
>     raise ValueError("Batch must be in progress to commit()")
> ValueError: Batch must be in progress to commit()



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