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GROUP BY clause

  • Άρθρο

Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime

The GROUP BY clause is used to group the rows based on a set of specified grouping expressions and compute aggregations on the group of rows based on one or more specified aggregate functions. Databricks SQL also supports advanced aggregations to do multiple aggregations for the same input record set via GROUPING SETS, CUBE, ROLLUP clauses. The grouping expressions and advanced aggregations can be mixed in the GROUP BY clause and nested in a GROUPING SETS clause.

See more details in the Mixed/Nested Grouping Analytics section.

When a FILTER clause is attached to an aggregate function, only the matching rows are passed to that function.

Syntax

GROUP BY ALL

GROUP BY group_expression [, ...] [ WITH ROLLUP | WITH CUBE ]

GROUP BY { group_expression | { ROLLUP | CUBE | GROUPING SETS } ( grouping_set [, ...] ) } [, ...]

grouping_set
   { expression |
     ( [ expression [, ...] ] ) }

While aggregate functions are defined as

aggregate_name ( [ DISTINCT ] expression [, ...] ) [ FILTER ( WHERE boolean_expression ) ]

Parameters

  • ALL

    Applies to: check marked yes Databricks SQL check marked yes Databricks Runtime 12.2 LTS and above

    A shorthand notation to add all SELECT-list expressions not containing aggregate functions as group_expressions. If no such expression exist GROUP BY ALL is equivalent to omitting the GROUP BY clause which results in a global aggregation.

    GROUP BY ALL is not guaranteed to produce a set of group expressions which can be resolved. Azure Databricks raises UNRESOLVED_ALL_IN_GROUP_BY or MISSING_AGGREGATION if the produced clause is not well-formed.

  • group_expression

    Specifies the criteria for grouping rows together. The grouping of rows is performed based on result values of the grouping expressions. A grouping expression may be a column name like GROUP BY a, column position like GROUP BY 0, or an expression like GROUP BY a + b. If group_expression contains an aggregate function Azure Databricks raises a GROUP_BY_AGGREGATE error.

  • grouping_set

    A grouping set is specified by zero or more comma-separated expressions in parentheses. When the grouping set has only one element, parentheses can be omitted. For example, GROUPING SETS ((a), (b)) is the same as GROUPING SETS (a, b).

  • GROUPING SETS

    Groups the rows for each grouping set specified after GROUPING SETS. For example:

    GROUP BY GROUPING SETS ((warehouse), (product)) is semantically equivalent to a union of results of GROUP BY warehouse and GROUP BY product.

    This clause is a shorthand for a UNION ALL where each leg of the UNION ALL operator performs aggregation of each grouping set specified in the GROUPING SETS clause.

    Similarly, GROUP BY GROUPING SETS ((warehouse, product), (product), ()) is semantically equivalent to the union of results of GROUP BY warehouse, product, GROUP BY product and a global aggregate.

Note

For Hive compatibility Databricks SQL allows GROUP BY ... GROUPING SETS (...). The GROUP BY expressions are usually ignored, but if they contain extra expressions in addition to the GROUPING SETS expressions, the extra expressions will be included in the grouping expressions and the value is always null. For example, in SELECT a, b, c FROM ... GROUP BY a, b, c GROUPING SETS (a, b), the output of column c is always null.

  • ROLLUP

    Specifies multiple levels of aggregations in a single statement. This clause is used to compute aggregations based on multiple grouping sets. ROLLUP is a shorthand for GROUPING SETS. For example:

    GROUP BY warehouse, product WITH ROLLUP

    or

    GROUP BY ROLLUP(warehouse, product)

    is equivalent to

    GROUP BY GROUPING SETS((warehouse, product),
                       (warehouse),
                       ())

    While

    GROUP BY ROLLUP(warehouse, product, (warehouse, location))

    is equivalent to

    GROUP BY GROUPING SETS((warehouse, product, location),
                       (warehouse, product),
                       (warehouse),
                       ())

    The N elements of a ROLLUP specification result in N+1 GROUPING SETS.

  • CUBE

    The CUBE clause is used to perform aggregations based on a combination of grouping columns specified in the GROUP BY clause. CUBE is a shorthand for GROUPING SETS. For example:

    GROUP BY warehouse, product WITH CUBE

    or

    GROUP BY CUBE(warehouse, product)

    is equivalent to:

    GROUP BY GROUPING SETS((warehouse, product),
                       (warehouse),
                       (product),
                       ())

    While

    GROUP BY CUBE(warehouse, product, location)

    is equivalent to the following:

    GROUP BY GROUPING SETS((warehouse, product, location),
                       (warehouse, product),
                       (warehouse, location),
                       (product, location),
                       (warehouse),
                       (product),
                       (location),
                       ())

    The N elements of a CUBE specification results in 2^N GROUPING SETS.

  • aggregate_name

    An aggregate function name (MIN, MAX, COUNT, SUM, AVG, etc.).

  • DISTINCT

    Removes duplicates in input rows before they are passed to aggregate functions.

  • FILTER

    Filters the input rows for which the boolean_expression in the WHERE clause evaluates to true are passed to the aggregate function; other rows are discarded.

Mixed/Nested Grouping Analytics

A GROUP BY clause can include multiple group_expressions and multiple CUBE, ROLLUP, and GROUPING SETSs.

GROUPING SETS can also have nested CUBE, ROLLUP, or GROUPING SETS clauses. For example:

GROUPING SETS(ROLLUP(warehouse, location),
              CUBE(warehouse, location)),
GROUPING SETS(warehouse,
              GROUPING SETS(location,
                            GROUPING SETS(ROLLUP(warehouse, location),
                                          CUBE(warehouse, location))))

CUBE and ROLLUP is just syntax sugar for GROUPING SETS.

Please refer to the sections above for how to translate CUBE and ROLLUP to GROUPING SETS.

group_expression can be treated as a single-group GROUPING SETS in this context.

For multiple GROUPING SETS in the GROUP BY clause, Databricks SQL generates a single GROUPING SETS by doing a cross-product of the original GROUPING SETS.

For nested GROUPING SETS in the GROUPING SETS clause, Databricks SQL takes its grouping sets and strips them. For example, the following queries:

GROUP BY warehouse,
         GROUPING SETS((product), ()),
         GROUPING SETS((location, size),
         (location),
         (size),
         ());

GROUP BY warehouse,
         ROLLUP(product),
         CUBE(location, size);

are equivalent to the following:

GROUP BY GROUPING SETS( (warehouse, product, location, size),
                        (warehouse, product, location),
                        (warehouse, product, size),
                        (warehouse, product),
                        (warehouse, location, size),
                        (warehouse, location),
                        (warehouse, size),
                        (warehouse))

While

GROUP BY GROUPING SETS(GROUPING SETS(warehouse),
                       GROUPING SETS((warehouse, product)))`

is equivalent to

GROUP BY GROUPING SETS((warehouse),
                       (warehouse, product))`.

Examples

CREATE TEMP VIEW dealer (id, city, car_model, quantity) AS
VALUES (100, 'Fremont', 'Honda Civic', 10),
       (100, 'Fremont', 'Honda Accord', 15),
       (100, 'Fremont', 'Honda CRV', 7),
       (200, 'Dublin', 'Honda Civic', 20),
       (200, 'Dublin', 'Honda Accord', 10),
       (200, 'Dublin', 'Honda CRV', 3),
       (300, 'San Jose', 'Honda Civic', 5),
       (300, 'San Jose', 'Honda Accord', 8);

-- Sum of quantity per dealership. Group by `id`.
> SELECT id, sum(quantity) FROM dealer GROUP BY id ORDER BY id;
  id sum(quantity)
 --- -------------
 100            32
 200            33
 300            13

-- Use column position in GROUP by clause.
> SELECT id, sum(quantity) FROM dealer GROUP BY 1 ORDER BY 1;
  id sum(quantity)
 --- -------------
 100            32
 200            33
 300            13

-- Multiple aggregations.
-- 1. Sum of quantity per dealership.
-- 2. Max quantity per dealership.
> SELECT id, sum(quantity) AS sum, max(quantity) AS max
    FROM dealer GROUP BY id ORDER BY id;
  id sum max
 --- --- ---
 100  32  15
 200  33  20
 300  13   8

-- Count the number of distinct dealers in cities per car_model.
> SELECT car_model, count(DISTINCT city) AS count FROM dealer GROUP BY car_model;
    car_model count
 ------------ -----
  Honda Civic     3
    Honda CRV     2
 Honda Accord     3

-- Count the number of distinct dealers in cities per car_model, using GROUP BY ALL
> SELECT car_model, count(DISTINCT city) AS count FROM dealer GROUP BY ALL;
    car_model count
 ------------ -----
  Honda Civic     3
    Honda CRV     2
 Honda Accord     3

-- Sum of only 'Honda Civic' and 'Honda CRV' quantities per dealership.
> SELECT id,
         sum(quantity) FILTER (WHERE car_model IN ('Honda Civic', 'Honda CRV')) AS `sum(quantity)`
    FROM dealer
    GROUP BY id ORDER BY id;
  id sum(quantity)
 --- -------------
 100            17
 200            23
 300             5

-- Aggregations using multiple sets of grouping columns in a single statement.
-- Following performs aggregations based on four sets of grouping columns.
-- 1. city, car_model
-- 2. city
-- 3. car_model
-- 4. Empty grouping set. Returns quantities for all city and car models.
> SELECT city, car_model, sum(quantity) AS sum
    FROM dealer
    GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ())
    ORDER BY city;
      city    car_model sum
 --------- ------------ ---
      null         null  78
      null  HondaAccord  33
      null     HondaCRV  10
      null   HondaCivic  35
    Dublin         null  33
    Dublin  HondaAccord  10
    Dublin     HondaCRV   3
    Dublin   HondaCivic  20
   Fremont         null  32
   Fremont  HondaAccord  15
   Fremont     HondaCRV   7
   Fremont   HondaCivic  10
  San Jose         null  13
  San Jose  HondaAccord   8
  San Jose   HondaCivic   5

-- Group by processing with `ROLLUP` clause.
-- Equivalent GROUP BY GROUPING SETS ((city, car_model), (city), ())
> SELECT city, car_model, sum(quantity) AS sum
    FROM dealer
    GROUP BY city, car_model WITH ROLLUP
    ORDER BY city, car_model;
      city    car_model sum
 --------- ------------ ---
      null         null  78
    Dublin         null  33
    Dublin  HondaAccord  10
    Dublin     HondaCRV   3
    Dublin   HondaCivic  20
   Fremont         null  32
   Fremont  HondaAccord  15
   Fremont     HondaCRV   7
   Fremont   HondaCivic  10
  San Jose         null  13
  San Jose  HondaAccord   8
  San Jose   HondaCivic   5

-- Group by processing with `CUBE` clause.
-- Equivalent GROUP BY GROUPING SETS ((city, car_model), (city), (car_model), ())
> SELECT city, car_model, sum(quantity) AS sum
    FROM dealer
    GROUP BY city, car_model WITH CUBE
    ORDER BY city, car_model;
      city    car_model sum
 --------- ------------ ---
      null         null  78
      null  HondaAccord  33
      null     HondaCRV  10
      null   HondaCivic  35
    Dublin         null  33
    Dublin  HondaAccord  10
    Dublin     HondaCRV   3
    Dublin   HondaCivic  20
   Fremont         null  32
   Fremont  HondaAccord  15
   Fremont     HondaCRV   7
   Fremont   HondaCivic  10
  San Jose         null  13
  San Jose  HondaAccord   8
  San Jose   HondaCivic   5

--Prepare data for ignore nulls example
> CREATE TEMP VIEW person (id, name, age) AS
   VALUES (100, 'Mary', NULL),
          (200, 'John', 30),
          (300, 'Mike', 80),
          (400, 'Dan' , 50);

--Select the first row in column age
> SELECT FIRST(age) FROM person;
  first(age, false)
 --------------------
  NULL

--Get the first row in column `age` ignore nulls,last row in column `id` and sum of column `id`.
> SELECT FIRST(age IGNORE NULLS), LAST(id), SUM(id) FROM person;
  first(age, true)    last(id, false)    sum(id)
 ------------------- ------------------ ----------
  30                  400                1000