Hi ,
We have one job ,it is designed such away that to fed in coming data volume is less.But all of sudden due to new requirements,data volume is coming very high twice in week and job is running very long .
design
dataset-> sparse look up-> transformer-> table
here the sparse look is with the table and the query is to pull the two fields that is
select A,B from C where A=Orchestrate.D
here A and D are varchar fields
and want to pass the D also to the next stage transformer.Since they want both fields ,not able to use join and both are VARCHAR fields.
In normal scenario data will be less than 1000 records and completes fine with in 10 min,but when we get 150k to 300k it is taking 7 to 15 hours to complete.Can you please help me out changing the job design
Datastage Sparse look up on varchar fields.
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ray.wurlod
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Sparse lookups are notoriously slow (compared to other methods) because of the overheads involved (separate single-row queries against the database server, possibly exacerbated by network latency issues).
Have you considered using a Join stage?
Another possiblity is loading all the keys into a temporary table in the reference data source and joining that to the reference table.
Have you considered using a Join stage?
Another possiblity is loading all the keys into a temporary table in the reference data source and joining that to the reference table.
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ray.wurlod
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Possibly yes, as mentioned in my post.
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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.