Handling huge volumes of data, the Sequential Files and from DataStage perspective Sequential File Stage can itself become one of the major bottlenecks as reading and writing from this stage is slow. Certainly do not use sequential files for intermediate storage between jobs. It causes performance overhead, as it needs to do data conversion before writing and reading from a file. Rather Dataset stages should be used for intermediate storage between different jobs.
Datasets are key to good performance in a set of linked jobs. They help in achieving end-to-end parallelism by writing data in partitioned form and maintaining the sort order. No repartitioning or import/export conversions are needed.
In order to have faster reading from the Sequential File stage the number of readers per node can be increased (default value is one). This means, for example, that a single file can be partitioned as it is read (even though the stage is constrained to running sequentially on the conductor mode).
This is an optional property and only applies to files containing fixed-length records. But this provides a way of partitioning data contained in a single file. Each node reads a single file, but the file can be divided according to the number of readers per node, and written to separate partitions. This method can result in better I/O performance on an SMP (Symmetric Multi Processing) system.
It can also be specified that single files can be read by multiple nodes. This is also an optional property and only applies to files containing fixed-length records. Set this option to "Yes to allow individual files to be read by several nodes. This can improve performance on cluster systems.
IBM DataStage knows the number of nodes available, and using the fixed length record size, and the actual size of the file to be read, allocates to the reader on each node a separate region within the file to process. The regions will be of roughly equal size.