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Status Submitted
Workspace Planning Analytics
Created by Guest
Created on Feb 4, 2025

Enable Individual Storage Volume Configuration During Planning Analytics Workspace Updates on OpenShift

See this idea on ideas.ibm.com

Currently, when updating Planning Analytics Workspace (PAW) on OpenShift, the PA_KUBE_STORAGE_SIZE variable in the paw.env file applies uniformly to all storage volumes (MongoDB, Redis, etc.). This limitation creates issues when certain volumes have been individually resized to meet specific needs.

In our specific case:
- MongoDB volume has been extended to over 15 GB (from the default 1 GB)
- Other volumes remain close to their default size (1 GB or less)

When updating PAW from version 2.0.96 to 2.0.100, the process fails because it attempts to resize all volumes to 1 GB (default value), which is not allowed by OpenShift for already extended volumes.
If we change the PA_KUBE_STORAGE_SIZE variable to avoid issue during update, many volumes will be oversized (application will then ask for 4 times more volume than needed).

Proposed Solution:
We propose three alternative options:

Option 1:
Add the ability to individually specify volume sizes in the paw.env file with new variables:
- PA_KUBE_STORAGE_SIZE_MONGODB
- PA_KUBE_STORAGE_SIZE_MYSQL
- PA_KUBE_STORAGE_SIZE_REDIS
- PA_KUBE_STORAGE_SIZE_DEFAULT (for other volumes)

Option 2:
Add an option to ignore existing volume configuration during updates, only modifying newly created volumes.

Option 3:
Enhance the update script to:
1. Detect and preserve existing volume configurations
2. Create any new volumes required by the version upgrade using default or specified sizes
3. Only extend volumes when the new version requires more space than currently allocated
4. Never attempt to reduce existing volume sizes
This volume management would ensure smooth upgrades while maintaining custom configurations and only growing volumes when necessary.
 

Expected Benefits:
1. Better flexibility in storage resource management
2. Reduction of errors during updates
3. Cost optimization by avoiding over-allocation of storage to volumes that don't need it
4. Simplification of the update process for customized environments

Current Impact:
The current workaround (manual modification of YAML files after generation) is time-consuming and error-prone. It requires manual intervention for each update, which is not optimal for a production environnent.

Needed By Yesterday (Let's go already!)