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From "ASF GitHub Bot (JIRA)" <>
Subject [jira] [Work logged] (BEAM-3883) Python SDK stages artifacts when talking to job server
Date Wed, 16 May 2018 23:47:00 GMT


ASF GitHub Bot logged work on BEAM-3883:

                Author: ASF GitHub Bot
            Created on: 16/May/18 23:46
            Start Date: 16/May/18 23:46
    Worklog Time Spent: 10m 
      Work Description: tvalentyn commented on a change in pull request #5251: [BEAM-3883]
Refactor and clean to make it reusable with artifact service

 File path: sdks/python/apache_beam/runners/dataflow/internal/
 @@ -746,17 +745,32 @@ def translate_scalar_counter_float(accumulator, metric_update_proto):
     metric_update_proto.floatingPoint = accumulator.value
-class _ParameterizedStager(Stager):
-  def __init__(self, stage_artifact):
-    super(_ParameterizedStager, self).__init__()
-    self.stage_artifact_method = stage_artifact
+class _LegacyDataflowStager(Stager):
+  # TODO(silviuc): Staged files should have a job specific prefix.
+  # To prevent several jobs in the same project stomping on each other due to a
+  # shared staging location.
+  def __init__(self, dataflow_application_client):
+    super(_LegacyDataflowStager, self).__init__()
+    self.stage_artifact_method = dataflow_application_client._gcs_file_copy
 Review comment:
   The composition would be more transparent if we store the reference to the dataflow client
in the constructor, and call `self.dataflow_application_client._gcs_file_copy(...)` in stage_artifact.

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Issue Time Tracking

    Worklog Id:     (was: 102712)
    Time Spent: 12h  (was: 11h 50m)

> Python SDK stages artifacts when talking to job server
> ------------------------------------------------------
>                 Key: BEAM-3883
>                 URL:
>             Project: Beam
>          Issue Type: Sub-task
>          Components: sdk-py-core
>            Reporter: Ben Sidhom
>            Assignee: Ankur Goenka
>            Priority: Major
>          Time Spent: 12h
>  Remaining Estimate: 0h
> The Python SDK does not currently stage its user-defined functions or dependencies when
talking to the job API. Artifacts that need to be staged include the user code itself, any
SDK components not included in the container image, and the list of Python packages that must
be installed at runtime.
> Artifacts that are currently expected can be found in the harness boot code: []

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