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  • Data Engineering Integration 10.2.1
  • All Products

Table of Contents

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  1. Preface
  2. Introduction to Informatica Big Data Management
  3. Mappings in the Hadoop Environment
  4. Mapping Sources in the Hadoop Environment
  5. Mapping Targets in the Hadoop Environment
  6. Mapping Transformations in the Hadoop Environment
  7. Processing Hierarchical Data on the Spark Engine
  8. Configuring Transformations to Process Hierarchical Data
  9. Processing Unstructured and Semi-structured Data with an Intelligent Structure Model
  10. Stateful Computing on the Spark Engine
  11. Monitoring Mappings in the Hadoop Environment
  12. Mappings in the Native Environment
  13. Profiles
  14. Native Environment Optimization
  15. Cluster Workflows
  16. Connections
  17. Data Type Reference
  18. Function Reference
  19. Parameter Reference

Big Data Management User Guide

Big Data Management User Guide

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Column Profiles for Sqoop Data Sources

Column Profiles for Sqoop Data Sources

You can run a column profile on data objects that use Sqoop. You can select the Hive or Hadoop run-time environment to run the column profiles.
On the Hive engine, to run a column profile on a relational data object that uses Sqoop, you must set the Sqoop argument
m
 to 1 in the JDBC connection. Use the following syntax:
-m 1
When you run a column profile on a logical data object or customized data object, you can configure the num-mappers argument to achieve parallelism and optimize performance. You must also configure the split-by argument to specify the column based on which Sqoop must split the work units.
Use the following syntax:
--split-by <column_name>
If the primary key does not have an even distribution of values between the minimum and maximum range, you can configure the split-by argument to specify another column that has a balanced distribution of data to split the work units.
If you do not define the split-by column, Sqoop splits work units based on the following criteria:
  • If the data object contains a single primary key, Sqoop uses the primary key as the split-by column.
  • If the data object contains a composite primary key, Sqoop defaults to the behavior of handling composite primary keys without the split-by argument. See the Sqoop documentation for more information.
  • If a data object contains two tables with an identical column, you must define the split-by column with a table-qualified name. For example, if the table name is CUSTOMER and the column name is FULL_NAME, define the split-by column as follows:
    --split-by CUSTOMER.FULL_NAME
  • If the data object does not contain a primary key, the value of the m argument and num-mappers argument default to 1.
When you use Cloudera Connector Powered by Teradata or Hortonworks Connector for Teradata and the Teradata table does not contain a primary key, the split-by argument is required.
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