Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings
  4. Sources
  5. Targets
  6. Transformations
  7. Data Preview
  8. Cluster Workflows
  9. Profiles
  10. Monitoring
  11. Hierarchical Data Processing
  12. Hierarchical Data Processing Configuration
  13. Hierarchical Data Processing with Schema Changes
  14. Intelligent Structure Models
  15. Stateful Computing
  16. Appendix A: Connections
  17. Appendix B: Data Type Reference
  18. Appendix C: Function Reference

m or num-mappers

m or num-mappers

The m or num-mappers argument defines the number of map tasks that Sqoop must use to import and export data in parallel.
Use the following syntax:
-m <number of map tasks>
--num-mappers <number of map tasks>
If you configure the m argument or num-mappers argument, you must also configure the split-by argument to specify the column based on which Sqoop must split the work units.
Use the m argument or num-mappers argument to increase the degree of parallelism. You might have to test different values for optimal performance.
When you configure the m argument or num-mappers argument and run Sqoop mappings on the Spark or Blaze engines, Sqoop dynamically creates partitions based on the file size.
If you configure the num-mappers argument to export data on the Blaze or Spark engine, Sqoop ignores the argument. Sqoop creates map tasks based on the number of intermediate files that the Blaze or Spark engine creates.


Updated July 10, 2020