Question
General Motors
US
Last activity: 27 Jan 2020 11:29 EST
Can decision data contain close to 1M records? What is the max limit?
This requirement is for one time use only and hence looking for a quicker solution.
Create a customer segment containing 700k records of customerIds. Decision strategy would return no recommendations if the customer is present in the list of 700k customers.
Better way is to have a flag in the customer table, and provide decisions based on the flag, as the service is real time.
However, 'm finding a quicker way to implement this(if any)- Is it logical to have such a large list in Decision data?
Accepted Solution
Updated: 5 Feb 2020 8:22 EST
Pegasystems Inc.
IN
Hi,
Couple of things about decision data rule
- During strategy execution 1 M records will be loading in memory so you may run into OOM issues
- Decision data is designed for managing propositions which may go maximum up to 5k records
so you may want to consider using data flows here
Thanks,
Matta
General Motors
US
I am trying to create a dataset using contents of a file. In this case, the operation "Browse by keys" isnt available in the dropdown. What am I
missing?
Pegasystems Inc.
US
Hi Gayathri,
I would move this question to a new post and we can provide some insight into DataSet-Execute from there. It would also be helpful to get some context on your DataSet "LoyalCustList" and how it is sourced.
Thanks,
Paul.
General Motors
US
Hi Paul, Let me create a new post.
Background- the marketing team is planning to target 1M customers for the month of July and offer them private offers. Instead of creating a table(which will later go to the bin) and dumping the file content into it, we are figuring out a way to use the file directly by using Dataset.
Unique identifier - Vehicle identification number.
Using dataset looks like a simpler and a quicker option. However, I have questions -
1. Browse by keys option isnt available.
2. Would it affect the performance.
Would like a detailed article to read on datasets.
Does this info. help?
Thanks
Gayathri