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https://issues.apache.org/jira/browse/BEAM-13459?focusedWorklogId=702105&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-702105
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ASF GitHub Bot logged work on BEAM-13459:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 29/Dec/21 22:15
Start Date: 29/Dec/21 22:15
Worklog Time Spent: 10m
Work Description: steveniemitz commented on a change in pull request
#16229:
URL: https://github.com/apache/beam/pull/16229#discussion_r776509376
##########
File path: sdks/python/apache_beam/runners/dataflow/internal/apiclient.py
##########
@@ -519,11 +523,16 @@ def __reduce__(self):
class DataflowApplicationClient(object):
+ _HASH_CHUNK_SIZE = 1024 * 8
+ _GCS_CACHE_PREFIX = "artifact_cache"
"""A Dataflow API client used by application code to create and query
jobs."""
- def __init__(self, options):
+ def __init__(self, options, root_staging_location=None):
Review comment:
it's used above
https://github.com/apache/beam/pull/16229/files#diff-d7e8ff5775f3fb328137483bd443e04d96caa92f3f68a188391f18a8e06075a0R574.
The runner mutates the staging location passed in to be unique per job, but
we need the root that the user actually passed in to derive the path for the
cache, otherwise the cache would be per-job, which would defeat the whole
purpose.
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Issue Time Tracking
-------------------
Worklog Id: (was: 702105)
Time Spent: 4h 20m (was: 4h 10m)
> Dataflow python runner should cache uploaded artifacts across job runs
> ----------------------------------------------------------------------
>
> Key: BEAM-13459
> URL: https://issues.apache.org/jira/browse/BEAM-13459
> Project: Beam
> Issue Type: Improvement
> Components: runner-dataflow
> Reporter: Steve Niemitz
> Assignee: Steve Niemitz
> Priority: P1
> Time Spent: 4h 20m
> Remaining Estimate: 0h
>
> Similar to how the jvm dataflow runner caches artifacts uploaded to GCS
> across job runs (based on their sha256), the python runner should do the same.
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