Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Connections
  4. Mappings in the Hadoop Environment
  5. Mapping Objects in the Hadoop Environment
  6. Monitoring Mappings in the Hadoop Environment
  7. Mappings in the Native Environment
  8. Profiles
  9. Native Environment Optimization
  10. Data Type Reference
  11. Function Reference
  12. Parameter 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 and Spark engines, Sqoop ignores the argument. Sqoop creates map tasks based on the number of intermediate files that the Blaze and Spark engine create.


Updated November 09, 2018