Im using 6 datasets using Left Join stage. 3rd dataset is not joining properly. Join works fine if i use the Hash partitioning on 3rd dataset and mainstream data.
But then should i have to revert back hash partitioning to Automatic so that output can join properly to other remaining datasets. Please suggest.
Automatic Partitioning Not Working
Moderators: chulett, rschirm, roy
As a pre-requisite to JOIN stage, the dataset data should be pre- hash partitioned and sorted on the JOIN key columns before they are fed to the JOIN stage.
If the data in the dataset is hash partitioned and sorted on JOIN keys in the previous job, then you can retain the same partitioning by setting Partitioning to "SAME" instead of "AUTO".
Hope this helps.
If the data in the dataset is hash partitioned and sorted on JOIN keys in the previous job, then you can retain the same partitioning by setting Partitioning to "SAME" instead of "AUTO".
Hope this helps.
As a pre-requisite to JOIN stage, the dataset data should be pre- hash partitioned and sorted on the JOIN key columns before they are fed to the JOIN stage.
If the data in the dataset is hash partitioned and sorted on JOIN keys in the previous job, then you can retain the same partitioning by setting Partitioning to "SAME" instead of "AUTO".
Hope this helps.
If the data in the dataset is hash partitioned and sorted on JOIN keys in the previous job, then you can retain the same partitioning by setting Partitioning to "SAME" instead of "AUTO".
Hope this helps.
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A clarification, in view of the thread subject. The second of these environment variables governs whether tsort operators will be inserted, and has nothing at all to do with partitioning.
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Any contribution to this forum is my own opinion and does not necessarily reflect any position that IBM may hold.
Any contribution to this forum is my own opinion and does not necessarily reflect any position that IBM may hold.