Re: export / import difficulties from version 1.3.3 to 1.4.20
Thomas Arendsen Hein <[email protected]>
| Newsgroups | gmane.comp.bug-tracking.roundup.user |
|---|---|
| Message-ID | <[email protected]> |
* Chad Seys <[email protected]> [20140613 23:09]: > I am trying to migrate a tracker from version 1.3.3 to 1.4.20 . > # roundup-admin import /tmp/web/ > Traceback (most recent call last): > File "/usr/lib/python2.7/dist-packages/roundup/admin.py", line 1481, in > run_command > ret = function(args[1:]) > File "/usr/lib/python2.7/dist-packages/roundup/admin.py", line 1257, in > do_import > nodeid = cl.import_list(file_props, r) > File "/usr/lib/python2.7/dist-packages/roundup/backends/rdbms_common.py", > line 2837, in import_list > prop = properties[propname] > KeyError: 'topic' The issue attribute "topic" has been renamed to "keyword" to avoid confusion with "subject". I suggest adjusting the .csv files from your export. These are the commands I used for this: sed -i "s/'topic'/'keyword'/g" issue-journals.csv keyword-journals.csv sed -i "1s/topic/keyword/" issue.csv sed -i -e "s/\(=-\?\)topic/\1keyword/g" -e "s/topic=/keyword=/g" -e "s/,topic,/,keyword,/g" query-journals.csv query.csv You would still need to use the migrate command to adjust the database to the new roundup version. In fact I did it the other way: First I changed schema.py: issue = IssueClass(..., topic=Multilink("keyword"), ...) (instead of keyword=Multilink...) Then I did roundup-admin migrate, followed by export, above sed commands, remove database. Then revert the change to schema.py and import the exported+adjusted CSV. Regards, Thomas -- [email protected] - http://intevation.de/~thomas/ - OpenPGP key: 0x5816791A Intevation GmbH, Neuer Graben 17, 49074 Osnabrueck - AG Osnabrueck, HR B 18998 Geschaeftsfuehrer: Frank Koormann, Bernhard Reiter, Dr. Jan-Oliver Wagner ------------------------------------------------------------------------------ HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions Find What Matters Most in Your Big Data with HPCC Systems Open Source. Fast. Scalable. Simple. Ideal for Dirty Data. Leverages Graph Analysis for Fast Processing & Easy Data Exploration http://p.sf.net/sfu/hpccsystems