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06/25/2024 12:31 AM - edited 06/25/2024 12:48 AM
Hello Everyone,
We are trying to understand the best practice from operational maintenance and performance perspective for technical rule creation. We have a requirement of creating 100+ unique technical rules for a current active user base of around 14k. There can be scenarios wherein ~100+ users can be onboarded in a single file import. We have a lot of conditions to check in each of the rules with respect to various user attributes to assign an enterprise role. We wanted to understand from the experts here on which option to follow so that we can make it less complex, more sustainable with improved performance. Please note that we have to run the Schema User Import job thrice/day as of now.
Option 1 : Put all the checks with values in the technical rules
Option 2 : Put all the values for checks in datasets and refer multiple datasets in each technical rule
06/25/2024 12:40 AM
You can refer to this link:
https://forums.saviynt.com/t5/lifecycle-policies-rules/tkb-p/lifecyclepolicies
06/25/2024 12:46 AM
Thank you for this link but this does not talk about using Datasets in technical rules and its implications, if any.
06/25/2024 09:25 PM
@Soumyadeep_Das as a past experience, I have gone through option 1. Create a optimized Technical Rule with required values. Make sure to carefully use brackets between condition and also the operators.
Of course this would als require regressive testing, hence prepare the all possible corner cases.
06/25/2024 09:30 PM
When dealing with a large number of users and complex conditions, the choice between putting all checks directly in technical rules or using datasets to manage these checks can significantly impact performance, maintainability, and scalability. Let's evaluate both options:
Pros:
Cons:
Pros:
Cons:
Based on the provided requirements and considering the need for sustainability, maintainability, and performance, Option 2 (putting values for checks in datasets and referring multiple datasets in each technical rule) is generally the better approach. This method offers better modularity, ease of maintenance, and scalability, which are crucial for managing a large number of users and complex conditions.