Table of Contents

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  1. Preface
  2. Performance Tuning Overview
  3. Bottlenecks
  4. Optimizing the Target
  5. Optimizing the Source
  6. Optimizing Mappings
  7. Optimizing Transformations
  8. Optimizing Sessions
  9. Optimizing Grid Deployments
  10. Optimizing the PowerCenter Components
  11. Optimizing the System
  12. Using Pipeline Partitions
  13. Performance Counters

Performance Tuning Guide

Performance Tuning Guide

Optimizing Datatype Conversions

Optimizing Datatype Conversions

You can increase performance by eliminating unnecessary datatype conversions. For example, if a mapping moves data from an Integer column to a Decimal column, then back to an Integer column, the unnecessary datatype conversion slows performance. Where possible, eliminate unnecessary datatype conversions from mappings.
Use the following datatype conversions to improve system performance:
  • Use integer values in place of other datatypes when performing comparisons using Lookup and Filter transformations.
    For example, many databases store U.S. ZIP code information as a Char or Varchar datatype. If you convert the zip code data to an Integer datatype, the lookup database stores the zip code 94303-1234 as 943031234. This helps increase the speed of the lookup comparisons based on zip code.
  • Convert the source dates to strings through port-to-port conversions to increase session performance.
    You can either leave the ports in targets as strings or change the ports to Date/Time ports.