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:

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:

SELECT ... FROM (                  -- Query 1
  SELECT ... FROM (                --  Query 2
    SELECT ... FROM (              --   Query 3
      SELECT ... FROM a WHERE ...  --    Query 4
    ) A LEFT JOIN (                --   Query 3
      SELECT ... FROM b            --    Query 5
    ) B ON (...)                   --   Query 3 
  ) ab FULL OUTER JOIN (           --  Query 2
    SELECT ... FROM c WHERE ...    --   Query 6
  ) C ON (...)                     --  Query 2
) abc LEFT JOIN (                  -- Query 1
  SELECT ... FROM d WHERE ...      --  Query 7
) D ON (...)                       -- Query 1
GROUP BY ...;                      -- Query 1

This query has a very broad set of responsibilities which cannot be easily verified in isolation. On closer inspection it appears that it is in fact formed of at least 7 distinct queries. To effectively unit test the process that this query represents an approach must be applied that separates and encapsulates each of the subqueries so that they can be tested independently. Possible approaches to this include: VIEWs, sequential execution of components with intermediate (possibly TEMPORARY) tables, and even variable substitution of query fragments.

This section requires some definitive guidance.

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:

The precise details are of course framework specific, but generally speaking tools manage the full lifecycle of tests by composing the artifacts provided by the developer into a sequence such as:

  1. Configure Hive execution environment.
  2. Setup test input data.
  3. Execute HQL script under test.
  4. Extract data written by the executed script.
  5. Make assertions on the data extracted.

At this time there are are a number of concrete approaches to choose from:

Useful practices