Showing posts with label Sequence. Show all posts
Showing posts with label Sequence. Show all posts

Thursday, 14 September 2017

Evaluation Sequence in Transformer Stage - A Quick DataStage Recipe



Recipe:

What is evaluation sequence in Transformer Stage Or Order of Stage & Loop Variable and Derivations

Ingredients:

1. Transformer Stage
     a. Stage Variables
     b. Loop Variables
     c. Derivations


How To:

Evaluate each stage variable initial value
For each input row to process:
Evaluate each stage variable derivation value, unless the derivation is empty
For each output link:
Evaluate each column derivation value
Write the output record
Next output link
Next input row


** The stage variables and the columns within a link are evaluated in the order in which they are displayed in the Transformer editor. Similarly, the output links are also evaluated in the order in which they are displayed




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Monday, 11 September 2017

Datastage Calling script in Remote Server


Step 1: Setting up UNIX server to automatically login without prompt-ing a password.

1. SSH must be installed in both servers. (primary and remote)
2. User ID for both servers

You can find the Step by Step detail on this Link - Configuring_SSH_on_Linux


Step 2: Creating Datastage job to run script in a remote server

1. Create a new sequencer job
2. Add an Execute Command stage
3. In the Command text value in the ExecCommand tab,

type -

 ssh UserB@ServerB ksh /home/b/test.ksh

This command will execute a script in the remote server.




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Thursday, 19 May 2016

5 Tips For Better DataStage Design #13



1. The query used in the database should be in such a way that required number of rows are fetched. Do not extract the columns which are not required.

2. For parallel jobs, sequential File should not be read using same partitioning.


http://www.datagenx.net/2016/05/5-tips-for-better-datastage-design-13.html


3. For huge amount of data, use of sequential file stage is not a good practice. This stage also should not be used for intermediate storage between jobs. It degrades the performance of the job.

4. The number of lookups in a job design should be minimum. Join stage is a good alternative to lookup stage.

5. For parallel jobs, proper portioning method is to be used for better job performance and accurate flow of data.





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Tuesday, 23 February 2016

DataStage Scenario #15 - Get First & Last Date of Last Month



Design a job which can generate the First and Last date of Last Month and pass this into an SQL which executes inside a parallel job?









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Wednesday, 23 December 2015

5 Tips For Better DataStage Design #6



#1. If you are using a copy or a filter stage either immediately after or immediately before a transformer stage, you are reducing the efficiency by using more stages because a transformer does the job of both copy stage as well as a filter stage

#2. Work done by "COPY Stage"
a) Columns order can be altered.
b) And columns can be dropped.
c) We can change the column names.



#3. When you need to run the same sequence of jobs again and again, better create a sequencer with all the jobs that you need to run. Running this sequencer will run all the jobs. You can provide the sequence as per your requirement.

#4. Sort the data as much as possible in DB and reduced the use of DS-Sort for better performance of jobs. Avoid the work done by DataStage which is possible in DB. But it doesn't mean you have to put all the complexity in SQL only, for that we are using datastage.

#5. Ensure that all the character fields are trimmed before any processing. Normally extra spaces in the data may lead to some errors like lookup mismatch which are hard to detect.





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