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  • Date Submitted: 08/23/2011 10:22 AM
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DataStage Parallel jobs Vs DataStage Server jobs
1) The basic difference between server and parallel jobs is the degree of parallelism
Server job stages do not have in built partitoning and parallelism mechanism for extracting and loading data between different stages.

• All you can do to enhance the speed and perormance in server jobs is to enable inter process row buffering through the administrator. This helps stages to exchange data as soon as it is available in the link.
• You could use IPC stage too which helps one passive stage read data from another as soon as data is available. In other words, stages do not have to wait for the entire set of records to be read first and then transferred to the next stage. Link partitioner and link collector stages can be used to achieve a certain degree of partitioning paralellism.
• All of the above features which have to be explored in server jobs are built in datastage Px.

2) The Px engine runs on a multiprocessor system and takes full advantage of the processing nodes defined in the configuration file. Both SMP and MMP architecture is supported by datastage Px.

3) Px takes advantage of both pipeline parallelism and partitoning paralellism. Pipeline parallelism means that as soon as data is available between stages( in pipes or links), it can be exchanged between them without waiting for the entire record set to be read. Partitioning parallelism means that entire record set is partitioned into small sets and processed on different nodes(logical processors). For example if there are 100 records, then if there are 4 logical nodes then each node would process 25 records each. This enhances the speed at which loading takes place to an amazing degree. Imagine situations where billions of records have to be loaded daily. This is where datastage PX comes as a boon for ETL process and surpasses all other ETL tools in the market.

4) In parallel we have Dataset which acts as the intermediate data storage in...


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