Motivations

Hive is widely applied as solution to numerous distinct problem types in the domain of big data. Quite clearly it often used for the ad hoc querying of large datasets. However it is also used to implement ETL type processes. Unlike ad hoc queries, the HQL written for ETLs has some distinct attributes:

...

Code exhibiting such properties is a strong candidate for unit test coverage as it is very prone to bugs, errors, and accidental breakage, all of which can represent risk to an organisation.

Challenges

There are a number of challenges posed by both Hive and HQL that can make it difficult to construct suites of unit tests for Hive based ETL systems. These can be broadly described as follows:

• Defining boundaries between components: How can and how should a problem be decomposed into smaller, testable units. The ability to do this is limited by the set of language features provided by HQL.
• Harness provision: Providing a local execution environment that seamlessly supports Hive’s features in a local IDE setting (UDFs etc). Ideally the harness should have no environmental dependencies such as a local Hive or Hadoop installation. Developers should be able to simply check out a project and run the tests.
• Speed of execution: The goal is to have large numbers of isolated, small tests. Test isolation requires frequent setup and teardown and the costs incurred are multiplied the number of tests. The Hive CLI is fairly heavy process to repeatedly start and stop and so some Hive test frameworks attempt to optimise this aspect of test execution.

Modularisation

By modularising processes implemented using Hive they become easier to test effectively and more resilient to change. Although Hive provides a number of vectors for modularisation it is not always clear how a large process can be decomposed. Features for encapsulation of query logic into components is separated into two perpendicular concerns: column level logic, and set level logic. Column level logic refers to the expressions applied to individual columns or groups of columns in the query, commonly described as ‘functions’. Set level logic concerns HQL constructs that manipulate groupings of data such as: column projection with SELECT, GROUP BY aggregates, JOINs, ORDER BY sorting, etc. In either case we expect individual components to live in their own source file or deployable artifact and imported as needed by the composition. For HQL based components, the SOURCE command provides this functionality.

Encapsulation of column level logic

In the case of column level logic Hive provides both UDFs and macros that allow the user to extract and reuse the expressions applied to columns. Once defined, UDFs and Macros can be readily isolated for testing. UDFs can be simply tested with existing Java/Python unit test tools such as JUnit whereas Macros require a Hive command line interface to execute the macro declaration and then exercise it with some sample SELECT statements.

Encapsulation of set level logic

Unlike column level logic, it is much less obvious how best to encapsulate and compose collections of set based logic. Consider the following example of a single complex query comprising joins, groupings, and column projections:

...

Tools and frameworks

When constructing tests it is helpful to have a framework that simplifies the declaration and execution of tests. Typically these tools allow the specification of many of the following:

...

• HiveRunner: Test cases are declared using Java, HQL and JUnit and can execute locally in your IDE. This library focuses on ease of use and execution speed. No local Hive/Hadoop installation required. Provides full test isolation, fine grained assertions, and seamless UDF integration (they need only be on the project classpath). The metastore is backed by an in-memory database to increase test performance.
• beetest: Test cases are declared using HQL and 'expected' data files. Test suites are executed using a script on the command line. Apparently requires HDFS to be installed in the environment in which the tests are executed.
• hive_test: Test cases are declared using Java, HQL and JUnit and can execute locally in your IDE.
• HiveQLUnit: Test your Hive scripts inside your favourite IDE. 
• How to utilise the Hive project's internal test framework.

Useful practices

The following Hive specific practices can be used to make processes more amenable to unit testing and assist in the simplification of individual tests.

...