widget0758
Cadet
- Joined
- Jan 3, 2024
- Messages
- 1
I currently have a primary server (Core 13.0-U6.1) and a secondary/backup server (Scale 23.10.2) that pulls snapshots from the former. The source system has hourly, daily, weekly, and monthly snapshots depending on the particular dataset involved and its frequency of change.
At present I have a daily task set to replicate the daily snapshots (multiple datasets) using a naming schema. I notice that if I setup a separate task to replicate weekly snapshots using a naming schema for a specific dataset then all daily snapshots are removed by this task for the target dataset.
Both tasks have:
- Replication from scratch
- Full filesystem replication
- Snapshot retention - same as source
I'm guessing the tasks are working exactly as expected given the naming and settings.
My question is therefore, what is the correct way to replicate snapshots of varying frequencies when datasets may have one or more frequencies? Is it to have a single replication task that uses a regular expression of, say, ".*" to simply pull all snapshots related to a dataset? My theory being the selection of all prevents the deletion effect I'm seeing currently.
At present I have a daily task set to replicate the daily snapshots (multiple datasets) using a naming schema. I notice that if I setup a separate task to replicate weekly snapshots using a naming schema for a specific dataset then all daily snapshots are removed by this task for the target dataset.
Both tasks have:
- Replication from scratch
- Full filesystem replication
- Snapshot retention - same as source
I'm guessing the tasks are working exactly as expected given the naming and settings.
My question is therefore, what is the correct way to replicate snapshots of varying frequencies when datasets may have one or more frequencies? Is it to have a single replication task that uses a regular expression of, say, ".*" to simply pull all snapshots related to a dataset? My theory being the selection of all prevents the deletion effect I'm seeing currently.
