Server versus Parallel Job
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Server versus Parallel Job
What is the difference between a Server and a Parallel job?
Difference comes with Performance.We can greately increase the performance with Parallel Jobs Since reading and writing take place simultaneously.You can get the details from the Parallel Jobs (pdf) where it is explained clearely
Here is an example from the pdf ,Hope this will help you better in understanding.
In Pipeline Parallelism,
If you ran the example job on a system with at least three processors, the
stage reading would start on one processor and start filling a pipeline with
the data it had read. The transformer stage would start running on another
processor as soon as there was data in the pipeline, process it and start
filling another pipeline. The stage writing the transformed data to the
target database would similarly start writing as soon as there was data
available. Thus all three stages are operating simultaneously.
Partition Parallelism
Using partition parallelism the same job would effectively be run simultaneously
by several processors, each handling a separate subset of the total
data.
At the end of the job the data partitions can be collected back together
again and written to a single data source.
But Server jobs are compiled and run on the server. These are for use on non-parallel systems
Here is an example from the pdf ,Hope this will help you better in understanding.
In Pipeline Parallelism,
If you ran the example job on a system with at least three processors, the
stage reading would start on one processor and start filling a pipeline with
the data it had read. The transformer stage would start running on another
processor as soon as there was data in the pipeline, process it and start
filling another pipeline. The stage writing the transformed data to the
target database would similarly start writing as soon as there was data
available. Thus all three stages are operating simultaneously.
Partition Parallelism
Using partition parallelism the same job would effectively be run simultaneously
by several processors, each handling a separate subset of the total
data.
At the end of the job the data partitions can be collected back together
again and written to a single data source.
But Server jobs are compiled and run on the server. These are for use on non-parallel systems
Hi Suma,
Pipeline parallelism and Partition Parallelism can still be achieved in Server jobs running on a SMP system by making use of IPC stage(Pipeline parallelism) and making use of Link Partitioner and Link collector stages(Partition Parallelism).
The Basic Difference is in that the Server Engine is based on Universe and Parallel Engine is based on Orchestrate.
The Parallel jobs are better in performance because partitioning is built into each stage and it can use the configuration file which defines how system resources can be used effectively.
For more information read chapter Optimizing Performance in Server Jobs in servjdev.pdf and Designing Parallel Extender Jobs chapter in parjdev.pdf.
HTH
--Rich
Pipeline parallelism and Partition Parallelism can still be achieved in Server jobs running on a SMP system by making use of IPC stage(Pipeline parallelism) and making use of Link Partitioner and Link collector stages(Partition Parallelism).
The Basic Difference is in that the Server Engine is based on Universe and Parallel Engine is based on Orchestrate.
The Parallel jobs are better in performance because partitioning is built into each stage and it can use the configuration file which defines how system resources can be used effectively.
For more information read chapter Optimizing Performance in Server Jobs in servjdev.pdf and Designing Parallel Extender Jobs chapter in parjdev.pdf.
HTH
--Rich