Big Data Management User Guide > Profiles > Hadoop Environment

Hadoop Environment

You can run profiles and scorecards in the Hadoop environment on the Hive engine or Blaze engine. You can choose Hive or Hadoop option to run the profiles in the Hadoop run-time environment. After you choose the Hive option and select a Hadoop connection, the Data Integration Service pushes the profile logic to the Hive engine on the Hadoop cluster to run profiles. After you choose the Hadoop option and select a Hadoop connection, the Data Integration Service pushes the profile logic to the Blaze engine on the Hadoop cluster to run profiles.

When you run a profile in the Hadoop environment, the Analyst tool submits the profile jobs to the Profiling Service Module. The Profiling Service Module then breaks down the profile jobs into a set of mappings. The Data Integration Service pushes the mappings to the Hadoop environment through the Hadoop connection. The Hive engine or Blaze engine processes the mappings and the Data Integration Service writes the profile results to the profiling warehouse.

In the Developer tool, you can run single object profiles and multiple object profiles, and enterprise discovery profiles on the Blaze engine. In the Analyst tool, you can run column profiles, enterprise discovery profiles, and scorecards on the Blaze engine.

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.