How We Can Convert Server Job To Parallel Job?

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Convert Server Job To Parallel Job
Convert Server Job To Parallel Job

In today’s DataStage tutorial, we are going to discuss how to convert server job to parallel job. But before diving into the topic, let me explain what you mean by Server Job and Parallel Job.

Incorporate or Convert Server Job to Parallel Job

You can incorporate Server job functionality in your Parallel jobs by the use of Server Shared Container stages.

You can incorporate Server job functionality in your Parallel jobs by the use of Server Shared Container stages. This allows you to, for example, use Server job plug-in stages to access data source that is not directly supported by Parallel jobs. (Some plug-ins have parallel versions that you can use directly in a parallel job.)

You create a new shared container in the Designer, add Server job stages as required, and then add the Server Shared Container to your Parallel job and connect it to the Parallel stages. Server Shared Container stages used in Parallel jobs have extra pages in their Properties dialog box, which enable you to specify details about parallel processing and partitioning and collecting data.

You can only use Server Shared Containers in this way on SMP systems (not MPP or cluster systems).

The following limitations apply to the contents of such Server Shared Containers:

  • There must be zero or one container inputs, zero or more container outputs, and at least one of either.
  • There can be no disconnected flows – all stages must be linked to the input or an output of the container directly or via an active stage. When the container has an input and one or more outputs, each stage must connect to the input and at least one of the outputs.

All of the columns supplied by a Server Shared Container stage used by the stage that follows the container in the parallel job.

Difference between server jobs and parallel jobs in DataStage

  • In parallel we have Dataset which acts as the intermediate data stored in the linked list, it is the best storage option it stores the data in DataStage internal format.
  • Here, we can choose to display OSH, which gives information about the how the job works.
  • In Parallel Transformer is no reference link possibility, in server stage reference given to transformer. A parallel stage can use both basic and parallel oriented functions.
  • Datastage server executed by DataStage server environment but parallel executed under control of DataStage runtime environment
  • Datastage compiled into BASIC(interpreted pseudo code) and Parallel compiled to OSH(Orchestrate Scripting Language).
  • Debugging and Testing Stages are available only in the Parallel Extender.

When to Convert Server Jobs to Parallel Jobs

  • The choice of server or parallel depends upon a time to implement, functionality and cost.
  • When we have lots of functionality to implement for lower volume and hardware is less and ease of implementation we can go for Server jobs.
  • Parallel jobs are costly due to the high scale of hardware, difficult to implement, extreme processing capabilities for absurd volumes
  • When the data volume is less it is better to go for Server job as parallel jobs can have a longer startup time.
  • Look up with the sequential file is possible in parallel jobs and not possible in server jobs.

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