Hi,
Am 04.11.22 um 11:50 schrieb Burkhard Linke:

An example would be keeping daily incrementals for the last two month, weekly merged incrementals for the next 4 month, and monthly incrementals for the next 6 months. This would cover a whole year, result in (1+6+4*4+2*7=) 83 backup datasets per job, assuming 4 weeks and 30 days per month. The virtual full job merging the full and oldest incremental set can be scheduled to run at certain times of the year with low activity.

That sounds feasible and like something that AI should be able to do at some point in the future. I'm just not (yet) sure how to get this working as a general approach that we could ship in core.

We would implement this scheme as an external script and disable the standard consolidate job. Does the python binding allow to specify which jobs should be merged in a virtual full job, similar to the code in the consolidate job?

Yes and no.
What the core does is that it runs a virtualfull explicitly stating the jobids. Basically like running 'run job=your-job level=VirtualFull jobid=1,2,3 accurate=yes'. This will produce a backup with the level of the first job mentioned (in your case an Incremental). If the job has AlwaysIncremental and AlwaysIncerementalJobRetention configured, it will also prune the jobs that were consolidated.

So you can implement that if (and that's the hard part) you can reliably figure out which jobids should be consolidated together. If you can come up with a good solution for that, maybe you could share it and maybe we can implement it in core then.

Best Regards,
Andreas
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Andreas Rogge                             andreas.ro...@bareos.com
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