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  • Common Content for Data Integration 10.5.7
  • All Products

Table of Contents

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  1. Preface
  2. Performance Tuning Overview
  3. Target Optimization
  4. Source Optimization
  5. Transformation Optimization
  6. Mapping Optimization
  7. Partitioned Mapping Optimization
  8. Run-time Optimization
  9. SQL Data Service Optimization
  10. Web Service Optimization
  11. Connections Optimization
  12. Data Transformation Optimization

Performance Tuning Guide

Performance Tuning Guide

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Datatype Conversion Optimization

Datatype Conversion Optimization

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.
Consider the following solutions for datatype conversion bottlenecks:
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 mapping performance.
You can either leave the ports in targets as strings or change the ports to Date/Time ports.
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