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https://issues.apache.org/jira/browse/BEAM-7547?focusedWorklogId=259176&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-259176
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ASF GitHub Bot logged work on BEAM-7547:
----------------------------------------
Author: ASF GitHub Bot
Created on: 13/Jun/19 00:19
Start Date: 13/Jun/19 00:19
Worklog Time Spent: 10m
Work Description: scwhittle commented on pull request #8842: [BEAM-7547]
Avoid WindmillStateCache cache hits for stale work.
URL: https://github.com/apache/beam/pull/8842
Add the work id to the cache and use the sequencing of work ids to
prevent reading a cached value for stale work items. This prevents
viewing cached state that is inconsistent with the work. Reads of
persistent state to the Dataflow backend will fail with invalid work
token for stale work.
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 259176)
Time Spent: 10m
Remaining Estimate: 0h
> StreamingDataflowWorker can observe inconsistent cache for stale work items
> ---------------------------------------------------------------------------
>
> Key: BEAM-7547
> URL: https://issues.apache.org/jira/browse/BEAM-7547
> Project: Beam
> Issue Type: Bug
> Components: runner-dataflow
> Reporter: Sam Whittle
> Assignee: Sam Whittle
> Priority: Minor
> Time Spent: 10m
> Remaining Estimate: 0h
>
> 1. Dataflow backend generates a work item with a cache token C.
> 2. StreamingDataflowWorker receives the work item and reads the state using
> C, it either hits the cache or performs a read.
> 3. Dataflow backend sends a retry of the work item (possibly because it
> thinks original work item never reached the StreamingDataflowWorker).
> 4. StreamingDataflowWorker commits the work item and gets ack from dataflow
> backend. It caches the state for the key using C.
> 5. StreamingDataflowWorker receives the retried work item with cache token C.
> It uses the cached state and causes possible user consistency failures
> because the cache view is of after the work item completed processing.
> Note that this will not cause corrupted Dataflow persistent state because the
> commit of the retried work item using the inconsistent cache will fail.
> However it may cause failures in user logic for example if they keep the set
> of all seen items in state and throw an exception on duplicates which should
> have been removed by an upstream stage.
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