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Applies to: 
Databricks SQL Databricks Runtime
Defines a temporary result set that you can reference possibly multiple times within the scope of a SQL statement. A CTE is used mainly in a SELECT statement.
WITH [ RECURSIVE ] common_table_expression [, ...]
common_table_expression
view_identifier [ ( column_identifier [, ...] ) ] [ AS ] ( query | recursive_query )
recursive_query
base_case_query UNION ALL step_case_query
RECURSIVE
Important
This feature is in Public Preview.
Applies to:
Databricks Runtime 17.0 and laterWhen specified, the common table expressions can contain a
recursive_query.view_identifier
An identifier by which the
common_table_expressioncan be referencedcolumn_identifier
An optional identifier by which a column of the
common_table_expressioncan be referenced.If
column_identifiers are specified their number must match the number of columns returned by thequery. If no names are specified the column names are derived from thequery.-
A query producing a result set.
recursive_query
Applies to:
Databricks Runtime 17.0 and laterWhen
RECURSIVEis specified, a query of a CTE can refer to itself. This makes a CTE a recursive CTE.-
This subquery provides the seed or anchor for the recursion. It must not reference
view_identifier. -
This subquery produces a new set of rows based on the previous step. To be recursive it must reference
view_identifier.
-
- Recursive CTEs are not supported for
UPDATE,DELETE, andMERGEstatements. step_subquerymust not include correlated column references toview_identifier.- Invoking random number generators from the recursive part can generate the same random number in each iteration.
- The number of recursive steps is limited to 100.
- The total size of the resultset is limited to 1,000,000 rows.
-- CTE with multiple column aliases
> WITH t(x, y) AS (SELECT 1, 2)
SELECT * FROM t WHERE x = 1 AND y = 2;
1 2
-- CTE in CTE definition
> WITH t AS (
WITH t2 AS (SELECT 1)
SELECT * FROM t2)
SELECT * FROM t;
1
-- CTE in subquery
> SELECT max(c) FROM (
WITH t(c) AS (SELECT 1)
SELECT * FROM t);
1
-- CTE in subquery expression
> SELECT (WITH t AS (SELECT 1)
SELECT * FROM t);
1
-- CTE in CREATE VIEW statement
> CREATE VIEW v AS
WITH t(a, b, c, d) AS (SELECT 1, 2, 3, 4)
SELECT * FROM t;
> SELECT * FROM v;
1 2 3 4
-- CTE names are scoped
> WITH t AS (SELECT 1),
t2 AS (
WITH t AS (SELECT 2)
SELECT * FROM t)
SELECT * FROM t2;
2
The following example shows how to find all destinations from New York to other cities, including direct routes and those routed through other cities:
> DROP VIEW IF EXISTS routes;
> CREATE TEMPORARY VIEW routes(origin, destination) AS VALUES
('New York', 'Washington'),
('New York', 'Boston'),
('Boston', 'New York'),
('Washington', 'Boston'),
('Washington', 'Raleigh');
> WITH RECURSIVE destinations_from_new_york AS (
SELECT 'New York' AS destination, ARRAY('New York') AS path, 0 AS length
UNION ALL
SELECT r.destination, CONCAT(d.path, ARRAY(r.destination)), d.length + 1
FROM routes AS r
JOIN destinations_from_new_york AS d
ON d.destination = r.origin AND NOT ARRAY_CONTAINS(d.path, r.destination)
)
SELECT * FROM destinations_from_new_york;
destination path length
----------- ----------------------------------- ------
New York ["New York"] 0
Washington ["New York","Washington"] 1
Boston ["New York","Boston"] 1
Boston ["New York","Washington","Boston"] 2
Raleigh ["New York","Washington","Raleigh"] 2
The following example demonstrates how to traverse a directed graph and check for the presence of an infinite loop in the traversal:
> DROP TABLE IF EXISTS graph;
> CREATE TABLE IF NOT EXISTS graph( f int, t int, label string );
> INSERT INTO graph VALUES
(1, 2, 'arc 1 -> 2'),
(1, 3, 'arc 1 -> 3'),
(2, 3, 'arc 2 -> 3'),
(1, 4, 'arc 1 -> 4'),
(4, 5, 'arc 4 -> 5'),
(5, 1, 'arc 5 -> 1');
> WITH RECURSIVE search_graph(f, t, label, path, cycle) AS (
SELECT *, array(struct(g.f, g.t)), false FROM graph g
UNION ALL
SELECT g.*, path || array(struct(g.f, g.t)), array_contains(path, struct(g.f, g.t))
FROM graph g, search_graph sg
WHERE g.f = sg.t AND NOT cycle
)
SELECT path FROM search_graph;
The result is a set of paths of increasing length while avoiding cycles and ending up with a non-infinite path
[{"f":1,"t":4},{"f":4,"t":5},{"f":5,"t":1},{"f":1,"t":2},{"f":2,"t":3}]`
that visits all nodes with a clear stop at sink "3".
A depth-first search is achieved by modifying the last line to order by the path column:
SELECT * FROM search_graph ORDER BY path;
The following examples demonstrate basic recursive techniques:
-- Simple loop-like example.
> WITH RECURSIVE r(col) AS (
SELECT 'a'
UNION ALL
SELECT col || char(ascii(substr(col, -1)) + 1) FROM r WHERE LENGTH(col) < 10
)
SELECT * FROM r;
col
----------
a
ab
abc
abcd
abcde
abcdef
abcdefg
abcdefgh
abcdefghi
abcdefghij
-- Another loop-like example.
> WITH RECURSIVE t(n) AS (
SELECT 'foo'
UNION ALL
SELECT n || ' bar' FROM t WHERE length(n) < 20
)
SELECT n AS is_text FROM t;
is_text
-------
foo
foo bar
foo bar bar
foo bar bar bar
foo bar bar bar bar
foo bar bar bar bar bar
-- Invoking a recursive CTE from another CTE.
> WITH RECURSIVE x(id) AS (VALUES (1) UNION ALL SELECT id+1 FROM x WHERE id < 5),
y(id) AS (VALUES (1) UNION ALL SELECT id+1 FROM x WHERE id < 10)
SELECT y.*, x.* FROM y LEFT JOIN x USING (id);
id id
-- --
1 1
4 4
6 null
3 3
5 5
2 2
-- An example of invoking a non-recursive CTE from a recursive CTE.
> WITH RECURSIVE
y (id) AS (VALUES (1)),
x (id) AS (SELECT * FROM y UNION ALL SELECT id+1 FROM x WHERE id < 5)
SELECT * FROM x;
id
--
1
2
3
4
5
-- Temporary views
> CREATE OR REPLACE TEMPORARY VIEW nums AS
WITH RECURSIVE nums (n) AS (
VALUES (1)
UNION ALL
SELECT n+1 FROM nums WHERE n < 6
)
SELECT * FROM nums;
> SELECT * FROM nums;
n
__
1
2
3
4
5
6
> INSERT into a table
> CREATE TABLE rt(level INT);
> WITH RECURSIVE r(level) AS (
VALUES (0)
UNION ALL
SELECT level + 1 FROM r WHERE level < 9
)
INSERT INTO rt SELECT * FROM r;
> SELECT * from rt;
level
-----
0
1
2
3
4
5
6
7
8
9
-- Nested recursive CTE are supported (only inside anchor)
-- Returns a triangular half - total of 55 rows - of a 10x10 square of numbers 1-10
> WITH RECURSIVE t(j) AS (
WITH RECURSIVE s(i) AS (
VALUES (1)
UNION ALL
SELECT i+1 FROM s WHERE i < 10
)
SELECT i FROM s
UNION ALL
SELECT j+1 FROM t WHERE j < 10
)
SELECT * FROM t;
j
--
1
2
2
3
3
3
4
...
9
10
10
10
10
10
10
10
10
10
10
-- Honoring LIMIT
> WITH RECURSIVE t(n) AS (
VALUES (1)
UNION ALL
SELECT n+1 FROM t
)
SELECT * FROM t LIMIT 10;
n
--
1
2
3
4
5
6
7
8
9
10
-- Organizational tree examples
-- Extracting all departments under 'A'
> CREATE TABLE department (
id INTEGER, -- department ID
parent_department INTEGER, -- upper department ID
name string -- department name
);
> INSERT INTO department VALUES
(0, NULL, 'ROOT'),
(1, 0, 'A'),
(2, 1, 'B'),
(3, 2, 'C'),
(4, 2, 'D'),
(5, 0, 'E'),
(6, 4, 'F'),
(7, 5, 'G');
> WITH RECURSIVE subdepartment AS (
SELECT name as root_name, * FROM department WHERE name = 'A'
UNION ALL
SELECT sd.root_name, d.* FROM department AS d, subdepartment AS sd
WHERE d.parent_department = sd.id
)
SELECT * FROM subdepartment ORDER BY name;
root_name id parent_department name
--------- -- ----------------- ----
A 1 0 A
A 2 1 B
A 3 2 C
A 4 2 D
A 6 4 F
-- The same as above but extracting the level number:
> WITH RECURSIVE subdepartment(level, id, parent_department, name) AS (
SELECT 1, * FROM department WHERE name = 'A'
UNION ALL
SELECT sd.level + 1, d.* FROM department AS d, subdepartment AS sd
WHERE d.parent_department = sd.id
)
SELECT * FROM subdepartment ORDER BY name;
level id parent_department name
----- -- ----------------- ----
1 1 0 A
2 2 1 B
3 3 2 C
3 4 2 D
4 6 4 F
-- The same as above but only shows level 2 or more
> WITH RECURSIVE subdepartment(level, id, parent_department, name) AS (
SELECT 1, * FROM department WHERE name = 'A'
UNION ALL
SELECT sd.level + 1, d.* FROM department AS d, subdepartment AS sd
WHERE d.parent_department = sd.id
)
SELECT * FROM subdepartment WHERE level >= 2 ORDER BY name;
level id parent_department name
----- -- ----------------- ----
2 2 1 B
3 3 2 C
3 4 2 D
4 6 4 F
-- Binary tree examples
-- Another tree example is to find all paths from the second level nodes "2" and "3" to all other nodes.
> CREATE TABLE tree(id INTEGER, parent_id INTEGER);
> INSERT INTO tree
VALUES (1, NULL), (2, 1), (3, 1), (4, 2), (5, 2), ( 6, 2), ( 7, 3), ( 8, 3),
(9, 4), (10,4), (11,7), (12,7), (13,7), (14, 9), (15,11), (16,11);
> WITH RECURSIVE t(id, path) AS (
VALUES(1,cast(array() as array<Int>))
UNION ALL
SELECT tree.id, t.path || array(tree.id)
FROM tree JOIN t ON (tree.parent_id = t.id)
)
SELECT t1.*, t2.*
FROM t AS t1 JOIN t AS t2
ON (t1.path[0] = t2.path[0] AND
size(t1.path) = 1 AND
size(t2.path) > 1)
ORDER BY t1.id, t2.id;
id path id path
-- ---- -- ------------
2 [2] 4 [2,4]
2 [2] 5 [2,5]
2 [2] 6 [2,6]
2 [2] 9 [2,4,9]
2 [2] 10 [2,4,10]
2 [2] 14 [2,4,9,14]
3 [3] 7 [3,7]
3 [3] 8 [3,8]
3 [3] 11 [3,7,11]
3 [3] 12 [3,7,12]
3 [3] 13 [3,7,13]
3 [3] 15 [3,7,11,15]
3 [3] 16 [3,7,11,16]
-- Another tree example counts the leaf nodes reachable from nodes "2" and "3"
> CREATE TABLE tree(id INTEGER, parent_id INTEGER);
> INSERT INTO tree
VALUES (1, NULL), ( 2,1), (3,1 ), ( 4,2), ( 5,2), ( 6,2), ( 7, 3), ( 8, 3),
(9, 4), (10,4), (11,7), (12,7), (13,7), (14,9), (15,11), (16,11);
> WITH RECURSIVE t(id, path) AS (
VALUES(1,cast(array() as array<Int>))
UNION ALL
SELECT tree.id, t.path || array(tree.id)
FROM tree JOIN t ON (tree.parent_id = t.id)
)
SELECT t1.id, count(*)
FROM t AS t1 JOIN t AS t2
ON (t1.path[0] = t2.path[0] AND
size(t1.path) = 1 AND
size(t2.path) > 1)
GROUP BY t1.id
ORDER BY t1.id;
id count(1)
-- --------
2 6
3 7
-- Another tree example that is listing the paths to leaf node:
> CREATE TABLE tree(id INTEGER, parent_id INTEGER);
> INSERT INTO tree
VALUES (1, NULL), ( 2,1), ( 3,1), ( 4,2), ( 5,2), ( 6,2), ( 7, 3), ( 8, 3),
(9, 4), (10,4), (11,7), (12,7), (13,7), (14,9), (15,11), (16,11);
> WITH RECURSIVE t(id, path) AS (
VALUES(1,cast(array() as array<Int>))
UNION ALL
SELECT tree.id, t.path || array(tree.id)
FROM tree JOIN t ON (tree.parent_id = t.id)
)
SELECT id, path FROM t
ORDER BY id;
id path
-- -----------
1 []
2 [2]
3 [3]
4 [2,4]
5 [2,5]
6 [2,6]
7 [3,7]
8 [3,8]
9 [2,4,9]
10 [2,4,10]
11 [3,7,11]
12 [3,7,12]
13 [3,7,13]
14 [2,4,9,14]
15 [3,7,11,15]
16 [3,7,11,16]
-- Sudoku solver (https://sqlite.org)
-- Highlighted text represent the row-wise scan of a 9x9 Sudoku grid where missing values are represented with spaces.
> WITH RECURSIVE generate_series(gs) AS (
SELECT 1
UNION ALL
SELECT gs+1 FROM generate_series WHERE gs < 9
),
x( s, ind ) AS
( SELECT sud, position( ' ' IN sud )
FROM (SELECT '4 8725 5 64 213 29 8 6 73 1 8 2 4 97 15 2 3 2 1 659 28 2 37946'::STRING
AS sud) xx
UNION ALL
SELECT substr( s, 1, ind - 1 ) || z || substr( s, ind + 1 ),
position(' ' in repeat('x',ind) || substr( s, ind + 1 ) )
FROM x,
(SELECT gs::STRING AS z FROM generate_series) z
WHERE ind > 0
AND NOT EXISTS ( SELECT *
FROM generate_series
WHERE z.z = substr( s, ( (ind - 1 ) DIV 9 ) * 9 + gs, 1 )
OR z.z = substr( s, mod( ind - 1, 9 ) - 8 + gs * 9, 1 )
OR z.z = substr( s, mod( ( ( ind - 1 ) DIV 3 ), 3 ) * 3
+ ( ( ind - 1 ) DIV 27 ) * 27 + gs
+ ( ( gs - 1 ) DIV 3 ) * 6, 1 )
)
)
SELECT s FROM x WHERE ind = 0;
431872569587649213629351874245968731168723495973415628394286157716594382852137946
-- You can underspecify the problem, for example by replacing the leading '4' with space ' ' which will yield two solutions
431872569587649213629351874245968731168723495973415628394286157716594382852137946
631872594587649213429351867245968731168723459973415628394286175716594382852137946
-- Finding prime numbers (Sieve of Eratosthenes, https://wiki.postgresql.org)
-- This is an implementation of the Sieve of Erastothenes algorithm for finding the prime numbers up to 150.
> WITH RECURSIVE t1(n) AS (
VALUES(2)
UNION ALL
SELECT n+1 FROM t1 WHERE n < 100
),
t2 (n, i) AS (
SELECT 2*n, 2
FROM t1 WHERE 2*n <= 150
UNION ALL
(
WITH t3(k) AS (
SELECT max(i) OVER () + 1 FROM t2
),
t4(k) AS (
SELECT DISTINCT k FROM t3
)
SELECT k*n, k
FROM t1 CROSS JOIN t4
WHERE k*k <= 150
)
)
SELECT n FROM t1 EXCEPT
SELECT n FROM t2
ORDER BY 1;
2
3
5
7
11
13
17
19
...