Bulk Data Movement Guide

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Reader Partitioning

You can define pass-through or key-range partitions at the source qualifier, or reader, partition point to improve the performance of bulk data movement sessions. With reader partitioning, a session can use one or more partitions to read and process data from the source.

The degree of concurrent processing depends on the data source and partitioning scheme that you use.

For offloaded DB2 for z/OS unload data sets, VSAM data sets, and sequential files, PowerExchange opens a single connection to the data source and uses multiple partitions to read and process the source data.

For other data sources, PowerExchange opens multiple connections to the data source or reads the source data into a single partition. If PowerExchange reads data into a single partition, you can redistribute the data at a repartition point to use partitioning in subsequent pipeline stages.

The session might process data out of sequence because partitions process data at varying rates.

The following table summarizes the reader partitioning schemes that you can use for different types of bulk data sources:

Reader Partitioning Scheme	Supported Data Sources	Description
Key range	All relational bulk data sources	Rows from the data source are partitioned based on key range values. This partitioning scheme is recommended for relational data sources.
Pass-through partitioning without SQL overrides	Offloaded DB2 unload data sets, VSAM data sets, and sequential data sets	PowerExchange reads the source data once and automatically distributes the rows among the partitions. PowerExchange ignores the Worker Threads connection attribute when you use pass-through partitioning without SQL overrides. This partitioning scheme is recommended for the specified z/OS data sources.
Pass-through partitioning without SQL overrides	All other nonrelational bulk data sources	Data is read into the first partition only. You can use round-robin partitioning at a subsequent partition point to redistribute the data.
Pass-through partitioning with SQL overrides	All	Data for each row is read into a partition based on the SQL override. The partitions run independently of each other and treat each query as an independent PowerExchange request.

Pipeline Partitions in Bulk Data Movement Sessions

Setting Up Pass-Through Partitioning Without SQL Overrides for Data Sources

Setting Up Pass-Through Partitioning with SQL Overrides for Nonrelational Data Sources

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Table of Contents

Bulk Data Movement Guide

Bulk Data Movement Guide

Reader Partitioning

Reader Partitioning