2 Replies Latest reply on May 3, 2016 7:41 AM by Gustavo del Gerbo

    Spoon job- Control CI update on Null values


      Hi I am working on a use case where we are updating only few attributes of the CI given in the CSV input step and keep existing values for the Fields where values are NUll.


      Now  offcourse we can control the CI Update manually by putting "Update" as "N" in the CMDBOutput step, everytime i get the spoon file.

      But in scenarios of automated workflows where Spoon files are coming and jobs are scheduled that won't be possible as sometimes all fields with values may come, sometimes does not  if user is giving only one field in the CSV to update- I want to keep existing values in those attributes which are given as blank.

      Let me know if it thats possible - somewhere we can put exception on the Null values >> not to update the Attributes

      Please advise.




        • 1. Re: Spoon job- Control CI update on Null values
          Carey Walker

          Rather than worrying about this at the AI stage, why not let the recon.job take care of the null values? So as the AI loaded data moves into BMC.ASSET (via a recon. job) you can tell it to not merge NULL values coming in from AI if BMC.ASSET has a non-null value (i.e. you can preserve the existing non-null values). This is the Defer If Null setting on the Recon. merge activity.

          2 of 2 people found this helpful
          • 2. Re: Spoon job- Control CI update on Null values
            Gustavo del Gerbo

            Agree with Carey,


            You need a 3 step reconciliation here.

            Step 1 - Dataset where AI is putting data, in this dataset data may or may not be null, we don't really care. This data is then merged to Step 2 using the DEFER if NULL option, thus making sure we only update non-NULL data that came from AI.

            Step 2 - Dataset where we have the TRUE data coming from the source. Here we have good values, and a NULL in here means we really do not know what this data is about. NULLs are important in this dataset as well as all the other data. This dataset will be merged to Asset.

            Step 3 - ASSET Dataset.


            So basically you need two reconciliation jobs and 2 datasets to achieve this.