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

Error Tracing

Error Tracing

To improve performance, reduce the number of log events generated by the Data Integration Service when it runs the mapping. Improve mapping performance by updating the mapping optimizer level through the mapping configuration or mapping deployment properties. Use the cost-based optimization method to optimize mappings.
Consider the following solutions for error tracing bottlenecks:
Set the tracing level in the mapping properties to Terse
If a mapping contains a large number of transformation errors, and you do not need to correct them, set the tracing level in the mapping properties to Terse. At this tracing level, the Data Integration Service does not write error messages or row-level information for reject data.
If you need to debug the mapping and you set the tracing level to Verbose, you may experience significant performance degradation when you run the mapping. Do not use Verbose tracing when you tune performance. The mapping tracing level overrides any transformation-specific tracing levels within the mapping. This is not recommended as a long-term response to high levels of transformation errors.
Change the optimizer level for the mapping.
If a mapping takes an excessive amount of time to run, you might want to change the optimizer level for the mapping. The optimizer level determines which optimization methods the Data Integration Service applies to the mapping at run-time.
You set the optimizer level for a mapping in the mapping configuration or mapping deployment properties. The Data Integration Service applies different optimizer levels to the mapping depending on how you run the mapping.
Use the cost-based optimization method.
The cost-based optimization method causes the Data Integration Service to evaluate a mapping, generate semantically equivalent mappings, and run the mapping with the best performance. This method is most effective for mappings that contain multiple Joiner transformations. It reduces run time for mappings that perform adjacent, unsorted, inner-join operations.
Semantically equivalent mappings are mappings that perform identical functions and produce the same results. To generate semantically equivalent mappings, the Data Integration Service divides the original mapping into fragments. The Data Integration Service then determines which mapping fragments it can optimize.

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