*** Please not this does not apply to Planning Analytics as a Service, now available on AWS ***
To improve performance on the shut down and startup of your Planning Analytics database, customers should execute the SaveDataAll process periodically. Running this process regularly will also improve the startup\shut down performance during a maintenance window and provide a more stable environment.
Where is my Planning Analytics data stored?
Planning Analytics is powered by our TM1 in-memory database, so the data you enter and change is stored in memory for faster retrieval. The changes are also simultaneously written in the transaction log on disk in real time. Dimensional, Rule, and Process data are persisted to disk in real-time.
Where does my data go when Planning Analytics is shut down?
When the TM1 database is stopped, the system stores to disk in-memory data from cubes having updates since the previous disk update. The transaction log file is also truncated and archived with a time-stamp.
Can I store the data in memory to disk without stopping the Planning Analytics database?
Yes, you can create a simple process with the SaveDataAll command. When executed, this process stores to disk in-memory data from cubes having updates since the previous ‘SaveDataAll’ or Database shutdown. The Process can also be scheduled on a regular schedule as a chore.
When and how often should I run SaveDataAll?
General recommendation is to run SaveDataAll weekly. If you are using Planning Analytics on Cloud, IBM also asks that SaveDataAll be performed just before the 3rd Saturday maintenance window to ensure a fast and high quality recycling of your Database. Sufficient time should be planned to execute the SaveDataAll process to allow for completion before the maintenance window at 12:00pm UTC. The amount of time required to complete the process varies considerably based on the size of the model and how much change has occurred since the last execution of SaveDataAll. While the IBM team has seen cases of SaveDataAll taking 4 to 5 hours, it is more common for the process to complete in under 2 hours.
It is also recommended to run the process after a data load is performed, especially if transaction logging was disabled during the data load to improve performance. This will ensure data integrity of the service as the transaction logs are relied upon for the backups.
Some customers choose to run the process daily or more often based on high user interaction. If so, schedule it to run when there is the least amount of users in the system.
Does SaveDataAll block users from reading or writing to Cubes?
No, while this was historically a ‘locking’ activity, SaveDataAll no longer will interrupt concurrent user read and write activity to cubes being serialized to disk.
Where can I learn more?
To learn more about running a SaveDataAll process please see the Planning Analytics documentation and a Best Practices Technote
How to use SaveDataAll and schedule it?
These instructions are in Planning Analytics Workspace (PAW) Workbench and require that you have the following security rights:
- PAW Administrator or Modeler in order to create or modify Chores and TI Processes
- Planning Analytics database Admin or DataAdmin groups to be able to create ore edit chores
1. Open PAW Workbench and select your desired PA database
2. Click on '⋮' to right of 'Processes' and select "Create Process'
3. Provide a name for the Process and click 'Create'
4. Select 'Script' tab, then scroll down to the 'Epilog' section
5. Type 'SaveDataAll;' and don't forget the semi colon at the end
6. Click 'Save'
7. Click on '⋮' to right of 'Chores' and select "Create Process'
8. Provide a name for the Chore and click 'Save'
9. Click 'Add process' in the newly created Chore
10. Search for your previously created Process and click ⨁
11. Confirm that your Process is in Selected process list and click 'Add'
12. Select the 'Schedule' tab
13. Click the 'Scheduled' checkbox to enable the chore
14. Select date and time (please note the UTC equivalent will be shown), then click Save
15. Repeat above steps for all other PA databases