On Mar 22, 2013, at 11:56 AM, Lance Parsons wrote:
I have been running a Galaxy server for our sequencing researchers for a while now and it's become increasingly successful. The biggest resource challenge for us has been, and continues to be disk space. As such, I'd like to implement some additional cleanup scripts. I thought I run a few questions by this list before I got too far into things.
In general, I'm wondering how to implement updates/additions to the cleanup system that will be in line with the direction that the Galaxy project is headed. The pgcleanup.py script is the newest piece of code in this area (and even adds cleanup of exported histories, which are absent from the older cleanup scripts). Also, the pgcleanup.py script uses a "cleanup_event" table that I don't believe is used by the older cleanup_datasets.py script. However, the new pgcleanup.py script only works for Postgres, and worse, only for version 9.1+. I run my system on RedHat (CentOS) and thus we use version 8.4 of Postgres. Are there plans to support other databases or older versions of Postgres?
Hi Lance,
pgcleanup.py makes extensive use of Writable CTEs, so there is not really a way to port it to older versions. For 8.4 or MySQL, you can still use the older cleanup_datasets.py.
After looking at it a bit more, I see what you mean. Are there plans to
implement and additional cleanup scripts for non-postgres 9.1 users?
Just curious so I don't reinvent the wheel, I'd be happy to help with
existing efforts.
I'd like to implement a script to delete (set the deleted flag) for certain datasets (e.g. raw data imported from our archive, for old, inactive users, etc.). I'm wondering if it would make sense to try and extend pgcleanup.py or cleanup_datasets.py. Or perhaps it would be best to just implement a separate script, though that seems like I'd have to re-implement a lot of boilerplate code for configuration reading, connections, logging, etc. Any tips on generally acceptable (supported) procedures for marking a dataset as deleted?
You could probably reuse a lot of the code from either of the cleanup scripts for this.
Right. It seems to make sense to me to focus on the cleanup_datasets.py
since that will work for everyone. I would like to essentially mimic
the user deleting a dataset. I'd then email them to let them know that
some old data had been marked for deletion and let the rest of the
scripts proceed as normal, cleaning that up if they don't undelete it.