Interested to Buy Any Domain ? << Click Here >> for more details...
which is more expensive hash or modulus partitioning? when do
you use modulus partitioning?
- 2 Answers
- 7729 Views
- TIAA CREF, I also Faced
- E-Mail Answers
Answers were Sorted based on User's Feedback
Answer / ankit gosain
Hi All,
Hash partitioning is more expensive than modulus but it'll
give better performance as well.
The basic difference is, you can apply Modulus partitioning
for the number fields only while you can apply Hash
partitioning for Varchar and any type of field.
If you have more queries, you can mail me on
ankitgosain@gmail.com
Cheers,
Ankit :)
| Is This Answer Correct ? | 4 Yes | 0 No |
Answer / nish
has partitioning when distinct key fields are less.
Modulus when the key field are very few and numeric.
Modulus = x%y so the field ought to be numeric
| Is This Answer Correct ? | 0 Yes | 0 No |
how many types of remove the duplicate records?
What are routines in datastage?
Distinguish between informatica & datastage. Which one would you choose and why?
Where do the datastage jobs get stored?
in datastage scenario this is sourec data i want this traget? tell me please? source target ------- ------- Mohan,Prasad,Naresh Mohan Prasad Naresh
sed,awk,head
when U have a remove dublicate option in sort stage, why we have a remove dublicate stage in PX, thought it is recamended to sort data before using a remove dublicate stage. I hae been thinking this from days....
A job is having only 2 stages I/p dataset and target table.Job is taking very long time to load 50 million records.How to improve performance of this job.
What is RCP?
what is A Datastage?
How to lode data in sequntional files perform faster?
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 .
Teradata 
