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Posted to issues@aurora.apache.org by "Chris Lambert (JIRA)" <ji...@apache.org> on 2015/02/23 23:58:12 UTC

[jira] [Resolved] (AURORA-690) Add support for external update coordination

     [ https://issues.apache.org/jira/browse/AURORA-690?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Chris Lambert resolved AURORA-690.
----------------------------------
    Resolution: Fixed

All issues in this epic have been completed!

> Add support for external update coordination
> --------------------------------------------
>
>                 Key: AURORA-690
>                 URL: https://issues.apache.org/jira/browse/AURORA-690
>             Project: Aurora
>          Issue Type: Epic
>          Components: Client, Scheduler
>            Reporter: Maxim Khutornenko
>            Assignee: Maxim Khutornenko
>            Priority: Critical
>             Fix For: 0.8.0
>
>
> With the introduction of scheduler-driven job update orchestration (AURORA-610) it will be a bit harder for a user to interrupt a job update process went wrong (i.e. bad binary, incorrect settings, changed external conditions and etc.). Instead of aborting the update process via CTRL-C (client updater) users would have to run abort/pause command that risk to never reach scheduler in case of client network partitioning. 
> To compensate the above, it would be great for the scheduler to optionally support an inverted dependency model where the updater would willingly pause job update progress upon reaching certain checkpoints and wait for the client/external service to explicitly "ack" on it (i.e. resumeJobUpdate RPC). Such checkpoints could be:
> - predefined number of instances reached
> - percentage of completion
> - time-based heartbeat (HB) intervals
> Arguably, the time-based HB approach should be the most versatile addressing the majority case.
> Generalizing further, this feature would be useful for building external update coordination services where Aurora service job upgrades are controlled by application specific health tracking systems throttling individual job updates based on the internal health/traffic metrics.



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