Snowpark Migration Accelerator: Group By¶

Description¶

The GROUP BY clause groups rows based on specified expressions and calculates aggregate functions for each group. Databricks SQL provides advanced grouping options through GROUPING SETS, CUBE, and ROLLUP clauses, which allow multiple aggregations on the same dataset. You can combine regular grouping expressions with these advanced options in the GROUP BY clause, and nest them within GROUPING SETS. (Databricks SQL Language Reference GROUP BY (https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select-groupby.html))

Groups rows that share the same values in specified columns and calculates aggregate functions (such as SUM, COUNT, or AVG) for each group. The GROUP BY clause can include:

The name of a column
A number that refers to a position in the SELECT list
Any valid expression

Extensions:

GROUP BY CUBE, GROUP BY GROUPING SETS, and GROUP BY ROLLUP

Snowflake SQL Language Reference GROUP BY

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 [, ...] ] ) }

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SELECT ...
  FROM ...
  [ ... ]
  GROUP BY groupItem [ , groupItem [ , ... ] ]
  [ ... ]
  
SELECT ...
  FROM ...
  [ ... ]
  GROUP BY ALL
  [ ... ]
groupItem ::= { <column_alias> | <position> | <expr> }

SELECT ...
FROM ...
[ ... ]
GROUP BY CUBE ( groupCube [ , groupCube [ , ... ] ] )
[ ... ]

groupCube ::= { <column_alias> | <position> | <expr> }

SELECT ...
FROM ...
[ ... ]
GROUP BY GROUPING SETS ( groupSet [ , groupSet [ , ... ] ] )
[ ... ]

groupSet ::= { <column_alias> | <position> | <expr> }

SELECT ...
FROM ...
[ ... ]
GROUP BY ROLLUP ( groupRollup [ , groupRollup [ , ... ] ] )
[ ... ]

groupRollup ::= { <column_alias> | <position> | <expr> }

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Sample Source Patterns¶

Setup data¶

Databricks¶

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);

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Snowflake¶

CREATE TEMP TABLE dealer (id INT, city STRING, car_model STRING, quantity INT);
INSERT INTO dealer 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);

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Pattern code¶

Databricks¶

-- 1. Sum of quantity per dealership. Group by `id`.
SELECT id, sum(quantity) FROM dealer GROUP BY id ORDER BY id;

-- 2. Use column position in GROUP by clause.
SELECT id, sum(quantity) FROM dealer GROUP BY 1 ORDER BY 1;

-- 3. Multiple aggregations.
-- 3.1. Sum of quantity per dealership.
-- 3.2. Max quantity per dealership.
SELECT id, sum(quantity) AS sum, max(quantity) AS max
    FROM dealer GROUP BY id ORDER BY id;

-- 4. 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;

-- 5. 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;

-- 6. 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;

-- 7. Aggregations using multiple sets of grouping columns in a single statement.
-- Following performs aggregations based on four sets of grouping columns.
-- 7.1. city, car_model
-- 7.2. city
-- 7.3. car_model
-- 7.4. 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;

-- 8.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;

-- 9. 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;

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id	sum(quantity)
100	32
200	33
300	13

id	sum(quantity)
100	32
200	33
300	13

id	sum	max
100	32	15
200	33	20
300	13	8

car_model	count
Honda Civic	3
Honda CRV	2
Honda Accord	3

car_model	count
Honda Civic	3
Honda CRV	2
Honda Accord	3

id	sum(quantity)
100	17
200	23
300	5

city	car_model	sum
NULL	Honda Civic	35
NULL	Honda Accord	33
NULL	NULL	78
NULL	Honda CRV	10
Dublin	Honda Civic	20
Dublin	NULL	33
Dublin	Honda CRV	3
Dublin	Honda Accord	10
Fremont	Honda Accord	15
Fremont	Honda Civic	10
Fremont	NULL	32
Fremont	Honda CRV	7
San Jose	Honda Accord	8
San Jose	NULL	13
San Jose	Honda Civic	5

city	car_model	sum
NULL	NULL	78
Dublin	NULL	33
Dublin	Honda Accord	10
Dublin	Honda CRV	3
Dublin	Honda Civic	20
Fremont	NULL	32
Fremont	Honda Accord	15
Fremont	Honda CRV	7
Fremont	Honda Civic	10
San Jose	NULL	13
San Jose	Honda Accord	8
San Jose	Honda Civic	5

city	car_model	sum
NULL	NULL	78
NULL	Honda Accord	33
NULL	Honda CRV	10
NULL	Honda Civic	35
Dublin	NULL	33
Dublin	Honda Accord	10
Dublin	Honda CRV	3
Dublin	Honda Civic	20
Fremont	NULL	32
Fremont	Honda Accord	15
Fremont	Honda CRV	7
Fremont	Honda Civic	10
San Jose	NULL	13
San Jose	Honda Accord	8
San Jose	Honda Civic	5

Snowflake¶

-- 1. Sum of quantity per dealership. Group by `id`.
SELECT id, sum(quantity) FROM dealer GROUP BY id ORDER BY id;

-- 2. Use column position in GROUP by clause.
SELECT id, sum(quantity) FROM dealer GROUP BY 1 ORDER BY 1;

-- 3. Multiple aggregations.
-- 3.1. Sum of quantity per dealership.
-- 3.2. Max quantity per dealership.
SELECT id, sum(quantity) AS sum, max(quantity) AS max
    FROM dealer GROUP BY id ORDER BY id;

-- 4. 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;

-- 5. 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;

-- 6. Sum of only 'Honda Civic' and 'Honda CRV' quantities per dealership.
SELECT 
    id,
    SUM(CASE WHEN car_model='Honda Civic' OR car_model='Honda CRV' THEN quantity ELSE NULL END) AS `sum(quantity)`
    FROM dealer
    GROUP BY id ORDER BY id;

-- 7. Aggregations using multiple sets of grouping columns in a single statement.
-- Following performs aggregations based on four sets of grouping columns.
-- 7.1. city, car_model
-- 7.2. city
-- 7.3. car_model
-- 7.4. 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 NULLS FIRST;
 

-- 8. 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 ROLLUP (city, car_model)
    ORDER BY city NULLS FIRST, car_model NULLS FIRST;

-- 9. 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 CUBE (city, car_model)
    ORDER BY city NULLS FIRST, car_model NULLS FIRST;

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id	sum(quantity)
100	32
200	33
300	13

id	sum(quantity)
100	32
200	33
300	13

id	sum	max
100	32	15
200	33	20
300	13	8

car_model	count
Honda Civic	3
Honda CRV	2
Honda Accord	3

car_model	count
Honda Civic	3
Honda CRV	2
Honda Accord	3

id	sum(quantity)
100	17
200	23
300	5

city	car_model	sum
NULL	Honda Civic	35
NULL	Honda Accord	33
NULL	NULL	78
NULL	Honda CRV	10
Dublin	Honda Civic	20
Dublin	NULL	33
Dublin	Honda CRV	3
Dublin	Honda Accord	10
Fremont	Honda Accord	15
Fremont	Honda Civic	10
Fremont	NULL	32
Fremont	Honda CRV	7
San Jose	Honda Accord	8
San Jose	NULL	13
San Jose	Honda Civic	5

city	car_model	sum
NULL	NULL	78
Dublin	NULL	33
Dublin	Honda Accord	10
Dublin	Honda CRV	3
Dublin	Honda Civic	20
Fremont	NULL	32
Fremont	Honda Accord	15
Fremont	Honda CRV	7
Fremont	Honda Civic	10
San Jose	NULL	13
San Jose	Honda Accord	8
San Jose	Honda Civic	5

city	car_model	sum
NULL	NULL	78
NULL	Honda Accord	33
NULL	Honda CRV	10
NULL	Honda Civic	35
Dublin	NULL	33
Dublin	Honda Accord	10
Dublin	Honda CRV	3
Dublin	Honda Civic	20
Fremont	NULL	32
Fremont	Honda Accord	15
Fremont	Honda CRV	7
Fremont	Honda Civic	10
San Jose	NULL	13
San Jose	Honda Accord	8
San Jose	Honda Civic	5

Known Issues¶

No issues were found

Snowpark Migration Accelerator: Group By¶

Description¶

Syntax¶

Sample Source Patterns¶

Setup data¶

Databricks¶

Snowflake¶

Pattern code¶

Databricks¶

Snowflake¶

Known Issues¶

Related EWIs¶