Translate to your Language

Wednesday, December 12, 2012

creating a unique counter in DataStage job

by Unknown  |  in Datastage at  12:30 AM


This entry describes various ways of creating a unique counter in DataStage jobs.
A parallel job has a surrogate key stage that creates unique IDs, however it is limited in that it does not support conditional code and it may be more efficient to add a counter to an existing transformer rather than add a new stage.
In a server job there are a set of key increment routines installed in the routine SDK samples that offer a more complex counter that remembers values between job executions.
The following section outlines a transformer only technique.
Steps
In a DataStage job the easiest way to create a counter is within the Transformer stage with a Stage Variable.
svMyCounter = svMyCounter + 1
This simple counter adds 1 each time a row is processed.
The counter can be given a seed value by passing a value in as a job parameter and setting the initial value of svMyCounter to that job parameter.
In a parallel job this simple counter will create duplicate values on each node as the transformer is split into parallel instances. It can be turned into a unique counter by using special parallel macros.
1.     Create a stage variable for the counter, eg. SVCounter.
2.     At the Stage Properties form set the Initial Value of the Stage Variable to "@PARTITIONNUM - @NUMPARTITIONS + 1".
3.     Set the derivation of the stage variable to "svCounter + @NUMPARTITIONS". You can embed this in an IF statement if it is a conditional counter.
Each instance will start at a different number, eg. -1, -2, -3, -4. When the counter is incremented each instance is increment by the number of partitions, eg. 4. This gives us a sequence in instance 1 of 1, 5, 9, 13... Instance 2 is 2, 6, 10, 14... etc.
Remember this method only works if your data is evenly balanced i.e. equal number of rows going through each partition. Alternative syntax is:
@INROWNUM * @NUMPARTITIONS + @PARTITIONNUM 

0 comments:

© Copyright © 2015Big Data - DW & BI. by