What is the difference between join lookup and merge in datastage

Ask Your Question. Explain lookup vs join vs merge in datastage? Join Lookup Merge Used when joining large tables. They are used while doing a range lookup within a small reference dataset. Used when multiple updates and reject links are required within a dataset. The performance of Join is increased while key-sorting data based on input links.

It does not require data on the input or the reference link to be sorted out. It can have any number of input links, but it has to be matched with the number of reject links.

The merge method has duplicate key values, which cannot be associated in duplicate records, so the merge method requires that the key value that is, the associated field is not duplicate. Official documentation:. Tech Community Register Log in. The difference between join, lookup and merge in datastage.

FaceBook Share. You can specify the correspondence between update links and reject links in the link order. The input data will be partitioned by key to ensure that records with the same key value are located in the same partition and processed by the same node, so only a few rows are fetched at a time, and the required memory is small. The mismatched records will be placed in the data output specified by reject. The search operation is based on the search key column of the reference table.

The lookup key column is defined in lookup. However, why would you have to choose? Yes merge and join are inherently different. At the time of joining we have to specify key column for that sorting required in both stages.

Hi, Comparing Join and Merge stages, first you have to check your requirement. If you want to hold the reject data from your reference tables then you go ahead with the Merge stage. This is the main difference between these stages.

I apologise — I am referring to the Funnel stage, not Merge, so please ignore my previous comment with regards to the merging the data. The comments referring to the correct Merge stage subsequent to mine are obviously correct :embarrassed emoticon:.

All I would say is test it. Take a test dataset and create 2 test jobs and run them single node, 2 nodes, 4 nodes, etc. Only then can you truly be certain of the performance difference as each DataStage environment, even in the same data centre, can be different. Forgot this — The only thing I would add — with the merge you need to de-duplicate your master dataset prior to merge processing.

This obviously adds overhead in how you perform this sort versus remove duplicates stage. Both Join and Merge require data to be sorted and partitioned on the key s required prior to the stage I know that Join offers the ability to sort and partition as part of the stage but I have found this inherently unreliable and always sort and partition regardless of operation in a separate sort stage as this adds no overhead in terms of operators. I do not require reject data.

Below are the job structures:. Idelly both should give almost same performance but it didnot…. No Account? Sign up.