Answer / subhash

Normal lookup will provide data for an in-memory look up
whereas Sparse lookup will access the database directly.
Normal might provide poor performance when the reference
data is huge as it has to load large data into memory.
In such scenarios a join stage may work better(It will put
the data onto an internal dataset file for each link,
and then do the join based on the data provided). If your
stream/source data is small then a sparse lookup is
suggested
as a row is retrieved from the database for each input row
instead of the full reference data being brought into
memory.
Sparse lookup sends individual SQL statement for every
incoming row (If stream/source data is huge you can imagine
the number
of times it has to hit DB and hence the down side on
performance). It can be used when you want to get the next
sequence
number from your database (Again expensive overhead on your
job as noted before). Also note that sparse lookup is only
available for DB2 and Oracle. Normal Lookup stage can have
multiple reference link but Sparse can only have one
reference
link. When normal is used it is a good practice to choose
less volume data as reference data.

I/P --- ID Value 1 AB 2 ABC 3 ADE 4 A O/p --- ID Value 1 A 1 B 2 A 2 B 2 C 3 A 3 D 3 E 4 A

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Enlist various types of routines in datastage.

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Drop duplicate records ... SOURCE LIKE .......... ID flag1 flag2 100 N Y 100 N N 100 Y N 101 Y Y 101 N Y 102 Y N 103 N N 104 Y Y 105 N N 106 N Y 102 N Y 105 Y Y in above file if any id having both the flags as "N" then that corresponding id records should be dropped, in above case o/p should be as ID flag1 flag2 101 Y Y 101 N Y 102 Y N 102 N Y 104 Y Y 106 N Y Steps to do : 1) Identified the id’s that got duplicated (both the flag values having vales “N”) 2) Look up with these id’s to existing id’s to drop .

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What are the areas of application?

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What is the roundrobin collector?

0 Answers  

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