This section describes how to enable the  to read sources in Hive and write results back to Hive.

  • A Hive source is a single table in a selected Hive database.
  • Apache Hive is a data warehouse system for managing queries against large datasets distributed across a Hadoop cluster. Queries are managed using HiveQL, a SQL-like querying language. See
  • The platform can publish results to Hive as part of any normal job or on an ad-hoc basis for supported output formats.
  • Hive is also used by the  to publish metadata results. This capability shares the same configuration described below.


  1. HDFS has been specified as your base storage layer. See Set Base Storage Layer .

  2. Hive Server 2 and your Hive databases must already be installed in your Hadoop cluster.

    NOTE: For JDBC interactions, the  supports HiveServer2 only.

  3. You have verified that Hive is working correctly.
  4. You have acquired and deployed the hive-site.xml configuration file into your . See Configure for Hadoop.

Known Limitations

  1. Changes to the underlying Hive schema are not picked up by the  and will break the source and datasets that use it.
  2. High availability is not supported for Hive. 
  3. During import, the JDBC data types from Hive are converted to . When data is written back to Hive, the original Hive data types may not be preserved. For more information, see Type Conversions
  4. Publish to partitioned tables in Hive is supported.
    1. The schema of the results and the partitioned table must be the same.
    2. If they do not match, you may see an SchemaMismatched Exception error in the UI. You can try a drop and republish action on the data. However, the newly generated table does not have partitions.
    3. For errors publishing to partitioned columns, additional information may be available in the logs. 

NOTE: Running user-defined functions for an external service, such as Hive, is not supported from within a recipe step. As a workaround, you may be able to execute recipes containing such external UDFs on the Photon running environment. Performance issues should be expected on larger datasets.

Configure for Hive

Hive user

The user with which Hive connects to read from HDFS should be a member of the user group  or whatever group is used to access HDFS from the .

Verify that the Unix or LDAP group has read access to the Hive warehouse directory.

Hive user for Spark:

NOTE: If you are executing jobs in the Spark running environment, additional permissions may be required. If the Hive source is a reference or references to files stored elsewhere in HDFS, the Hive user or its group must have read and execute permissions on the source HDFS directories or files.


Enable Data Service

In platform configuration, the    must be enabled.

Please verify the following:

"data-service.enabled": true,

Locate the Hive JDBC Jar

In platform configuration, you must verify that the following parameter is pointing to the proper location for the Hive JDBC JAR file. The example below identfies the location for Cloudera 5.10:

NOTE: This parameter varies for each supported distribution and version.

"data-service.hiveJdbcJar": "hadoop-deps/cdh-5.10/build/libs/cdh-5.10-hive-jdbc.jar",

Enable Hive Support for Spark Job Service

If you are using the Spark running environment for execution and profiling jobs, you must enable Hive support within the Spark Job Service configuration block.

NOTE: The Spark running environment is the default running environment. When this change is made, the platform requires that a valid hive-site.xml cluster configuration file be installed on the .



  1. Locate the following setting and verify that it is set to true:

    "spark-job-service.enableHiveSupport" : true,

  2. Modify the following parameter to point to the location where Hive dependencies are installed. This example points to the location for Cloudera 5.10:

    NOTE: This parameter value is distribution-specific. Please update based on your specific distribution.


  3. Save your changes.

Enable Hive Database Access for Spark Job Service

The Spark Job Services requires read access to the Hive databases. Please verify that the Spark user can access the required Hive databases and tables.

For more information, please contact your Hive administrator.

Configure managed table format

The publishes to Hive using managed tables. When writing to Hive, the platform pushes to an externally staged table. Then, from this staging table, the platform selects and inserts into a managed table.

By default, the platform published to managed tables in Parquet format. As needed, you can apply the following values into platform configuration to change the format to which the platform writes when publishing a managed table:

  • PARQUET (default)
  • AVRO

To change the format, please modify the following parameter.


  1. Locate the following parameter and modify it using one of the above values:

    "data-service.hiveManagedTableFormat": "PARQUET",

  2. Save your changes and restart the platform.

Create Hive Connection

For more information, see Create Hive Connections.

Optional Configuration

Depending on your Hadoop environment, you may need to perform additional configuration to enable connectivity with your Hadoop cluster.

Additional Configuration for Secure Environments

For secure impersonation


NOTE: You should have already configured the to use secure impersonation. For more information on basic configuration, see Configure for Hive.

You must add the Hive principal value to your Hive connection:

  1. If you created your connection via the application: Add the following principal value to the Connect String Options textbox.
  2. If you created your connection via the CLI: Add it in connectStrOpts entry for your params file:
  "connectStrOpts": ";principal=<principal_value>",


For Kerberos with secure impersonation

NOTE: If you are enabling Hive in a Kerberized environment, you must also enable secure impersonation. When connecting to Hive, Kerberos without secure impersonation is not supported.

NOTE: You should have already enabled basic Kerberos integration. For more information, see Set up for a Kerberos-enabled Hadoop cluster.

NOTE: You should have already configured the to use secure impersonation and added the Hive principal to the connectStrOpts value in your params file. For more information on basic configuration, see Configure for secure impersonation.

Additional Configuration for Sentry

The  can be configured to use Sentry to authorize access to Hive. See Configure for Hive with Sentry.

Validate Configuration

NOTE: The platform cannot publish to a default database in Hive that is empty. Please create at least one table in your default database.

Build example Hive dataset


  1. Download and unzip the following dataset: Dataset-HiveExampleData.
  2. Store the dataset in the following example directory: 


  3. Use the following command to create your table:

    create table test (name string, id bigint, id2 bigint, randomName string, description string, dob string, title string, corp string, fixedOne bigint, fixedTwo int) row format delimited fields terminated by ',' STORED AS TEXTFILE;

  4. Add the example dataset to the above test table:

    load data local inpath '/tmp/hiveTest_5mb' into table test;

Check basic connectivity


  1. After restart, login to the . See Login.
  2. If the platform is able to connect to Hive, you should see a Hive tab in the Import Data page. Create a new dataset and verify that the data from the Hive data has been ingested in the Transformer page.
  3. If not, please check /opt/trifacta/logs/data-service.log for errors.
  4. For more information, see Verify Operations.