Tutorial: Learn complex grouping with SQL #

Welcome to the Tutorial: Learn complex grouping with SQL tutorial, where you explore useful ways to craft your SQL statements using the TPC-H dataset. Using Complex grouping operations, you perform multi-column analysis within each SQL statement.

Requirements #

To complete the tutorial using Starburst Galaxy, you need to create a free trial or activate your user account.

The sample cluster and tpch catalog are pre-configured and ready for querying.

  • If you do not see the tpch catalog automatically available, create a TPC-H dataset catalog named tpch.
  • If you do not see the sample cluster automatically available, create a cluster named sample and add the tpch catalog.

In the query editor, navigate to the Cluster explorer.

  1. Select the sample cluster dropdown in the left hand navigation to view catalogs.
  2. Select the tpch catalog dropdown.

      Query editor sample cluster explorer

Select the cluster sample and the catalog tpch in the top right in order to run the queries without specifying the cluster and the catalog before each table.

  Query editor use sample catalog button

The namespace for a table is typically specified as <catalog_name>.<schema_name>.<table_name>. For the pre-defined TPC-H dataset, this configuration is as follows:

tpch.tiny.<table_name>

If you choose to name your catalog differently, either adjust the queries accordingly or select the appropriately catalog in the top right corner.

If you choose to name your cluster differently, select the appropriately named cluster in the top right corner.

Complex grouping operations #

Starburst Galaxy supports complex grouping operations using the ROLLUP, CUBE, and GROUPING SETS syntax. This syntax allows users to perform analysis that requires aggregation on multiple sets of columns in a single query.

ROLLUP #

The ROLLUP operator generates all possible subtotals for a given set of columns.

Query the customers table and view the data available for analysis.

SELECT
  *
FROM
  tpch.tiny.customer;

Group the customers by market segment to get the total count in each segment.

SELECT
  mktsegment,
  count() AS customers
FROM
  tpch.tiny.customer
GROUP BY
  mktsegment
ORDER BY
  mktsegment;

Instead of using the GROUP BY clause to organize the market segments, use the ROLLUP operator. Also, reduce the market segments to focus only on three distinct categories: AUTOMOBILE, BUILDING, and MACHINERY.

SELECT
  mktsegment,
  count() AS customers
FROM
  tpch.tiny.customer
WHERE
  mktsegment IN ('AUTOMOBILE', 'BUILDING', 'MACHINERY')
GROUP BY
  ROLLUP(mktsegment)
ORDER BY
  mktsegment;

Add another column to the ROLLUP operator and include the nationkey grouping to generate multiple grouping sets.

SELECT
  mktsegment,
  nationkey,
  count() AS orders
FROM
  tpch.tiny.customer
WHERE
  mktsegment IN ('AUTOMOBILE', 'BUILDING', 'MACHINERY')
  AND nationkey BETWEEN 1 AND 2
GROUP BY
  ROLLUP(mktsegment, nationkey)
ORDER BY
  mktsegment,
  nationkey;

CUBE #

The CUBE operator generates all the possible grouping sets for a given set of columns.

Switch to the CUBE operator to obtain the order count breakdown of each nation key for the market segments not explicitly specified.

SELECT
  mktsegment,
  nationkey,
  count() AS orders
FROM
  tpch.tiny.customer
WHERE
  mktsegment IN ('AUTOMOBILE', 'BUILDING', 'MACHINERY')
  AND nationkey BETWEEN 1 AND 2
GROUP BY
  CUBE(mktsegment, nationkey)
ORDER BY
  mktsegment,
  nationkey;

GROUPING SETS #

The result of the CUBE query can also be achieved using GROUPING SETS. Grouping sets allow users to specify multiple lists of columns to group on, so the manually specifying all the combination of columns can equate to the same output as the CUBE operator.

SELECT
  mktsegment,
  nationkey,
  count() AS orders
FROM
  tpch.tiny.customer
WHERE
  mktsegment IN ('AUTOMOBILE', 'BUILDING', 'MACHINERY')
  AND nationkey BETWEEN 1 AND 2
GROUP BY
  GROUPING SETS (
     (mktsegment),
     (nationkey),
     (mktsegment, nationkey),
     ()
  )
ORDER BY
  mktsegment,
  nationkey;

Next steps #

Check out our other tutorials, or dive right into the SQL documentation and experiment with your own data.