Using Hive

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This section describes how you interact through the Designer Cloud Powered by Trifacta® platform with your Hive data warehouse.

Hive is an open-source, scalable data warehouse built on top of the Hadoop infrastructure to enable SQL-like access to a datastore where processing is converted to Hadoop map/reduce tasks. Hive users can interact directly with the databases and tables using HiveQL, a querying language similar to SQL. For more information, see https://en.wikipedia.org/wiki/Apache_Hive.

Uses of Hive

The Designer Cloud Powered by Trifacta platform can use Hive for the following tasks:

Create datasets by reading from Hive tables.
Write data to Hive.

Before You Begin Using Hive

Read Access: Your Hadoop administrator must configure read permissions to Hive databases.
- Your Hadoop administrator should provide a database table or tables for data upload to your Hive datastore.
Write Access: You can write jobs directly to Hive or ad-hoc publish jobs results to Hive at a later time. See Writing to Hive below.

Secure Access

Depending on the security features you've enabled, the technical methods by which Alteryx users access Hive may vary. For more information, see Configure Hadoop Authentication.

Reading Partitioned Data

The Designer Cloud Powered by Trifacta platform can read in partitioned tables. However, it cannot read individual partitions of partitioned tables.

Tip: If you are reading data from a partitioned table, one of your early recipe steps in the Transformer page should filter out the unneeded table data so that you are reading only the records of the individual partition.

Storing Data in Hive

Your Hadoop administrator should provide datasets or locations and access for storing datasets within Hive.

Users should know where shared data is located and where personal data can be saved without interfering with or confusing other users.

NOTE: The Designer Cloud Powered by Trifacta platform does not modify source data in Hive. Datasets sourced from Hive are read without modification from their source locations.

Reading from Hive

You can create a Alteryx dataset from a table or view stored in Hive. For more information, see Hive Browser.

For more information on how data types are imporetd from Hive, see Hive Data Type Conversions.

Notes on reading from Hive views using custom SQL

If you have enabled custom SQL and are reading data from a Hive view, nested functions are written to a temporary filename, unless they are explicitly aliased.

Tip: If your custom SQL uses nested functions, you should create an explicit alias from the results. Otherwise, the job is likely to fail.

Problematic Example:

SELECT
 UPPER(`t1`.`colum1`),
 TRIM(`t1`.`column2`),...

When these are ready from a Hive view, the temporary column names are: _c0, _c1, etc. During job execution, Spark ignores the column1 and column2 reference.

Improved Example:

SELECT
 UPPER(`t1`.`column1`) as col1,
  TRIM(`t1`.`column2`) as col2,...

In this improved example, the two Hive view columns are aliased to the explicit column names, which are correctly interpreted and used by the Spark running environment during job execution.

Writing to Hive

You can write data back to Hive using one of the following methods:

NOTE: You cannot publish to a Hive database that is empty. The database must contain at least one table.

Job results can be written directly to Hive as part of the normal job execution. Create a new publishing action to write to Hive. See Run Job Page.
As needed, you can export results to Hive for previously executed jobs. See Export Results Window.
For more information on how data is converted to Hive, see Hive Data Type Conversions.

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