My scrapbook about anything which I learned or want to remember, Sometime about tech tips, thoughts and rambling. If you find anything useful don't forget to give thumbs-up :)

Breaking

Sunday, November 13, 2016

DataStage Partitioning #3



Best allocation of Partitions in DataStage for storage area

Srno
No of Ways
Volume of Data
Best way of Partition
Allocation of Configuration File (Node)
1
DB2 EEE  extraction in serial
Low
-
1
2
DB2 EEE extraction in parallel
High
Node number = current node (key)
64 (Depends on how many nodes are allocated)
3
Partition or Repartition in the Stages of DataStage
Any
Modulus (It should be single key that to integer)
Hash (Any number of keys with different data type)
8 (Depends on how many nodes are allocated for the job)
4
Writing into DB2
Any
DB2
-
5
Writing into Dataset
Any
Same
1,2,4,8,16,32,64 etc… (Based on the incoming records it writes into it.)
6
Writing into Sequential File
Low
-
1

 

Best allocation of Partitions in DataStage for each stage

S. No
Stage
Best way of Partition
Important points
1
Join
Left and Right link: Hash or Modulus
All the input links should be sorted based on the joining key and partitioned with higher key order.

  1.  
Lookup
Main link: Hash or same
Reference link: Entire
Both the links need not be in the sorted order

  1.  
Merge
Master and update link: Hash or Modulus
All the input links should be sorted based on the merging key and partitioned with higher key order. Pre-sort makes merge “lightweight” for memory.

  1.  
Remove Duplicate, Aggregator
Hash or Modulus
If the input link is in sorted order based on the key it will perform better.

  1.  
Sort
Hash or Modulus
Sorting happens after partitioning


Transformer, Funnel, Copy, Filter
Same
None
7
Change Capture
Left and Right link: Hash or Modulus
Both the input links should be in the sorted order based on the key and partitioned with higher key order.





Like the below page to get update  
https://www.facebook.com/datastage4you
https://twitter.com/datagenx
https://plus.google.com/+AtulSingh0/posts
https://datagenx.slack.com/messages/datascience/

No comments:

Post a Comment

Disclaimer

The postings on this site are my own and don't necessarily represent IBM's or other companies positions, strategies or opinions. All content provided on this blog is for informational purposes and knowledge sharing only.
The owner of this blog makes no representations as to the accuracy or completeness of any information on this site or found by following any link on this site. The owner will not be liable for any errors or omissions in this information nor for the availability of this information. The owner will not be liable for any losses, injuries, or damages from the display or use of his information.