citus/src/test/regress/expected/intermediate_result_pruning_queries_1.out

SET search_path TO "intermediate result pruning";
-- a very basic case, where the intermediate result
-- should go to both workers
WITH some_values_1 AS MATERIALIZED
	(SELECT key FROM table_1 WHERE value IN ('3', '4'))
SELECT
	count(*)
FROM
	some_values_1 JOIN table_2 USING (key);
 count
---------------------------------------------------------------------
     2
(1 row)

-- a very basic case, where the intermediate result
-- should only go to one worker because the final query is a router
-- we use random() to prevent postgres inline the CTE(s)
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4'))
SELECT
	count(*)
FROM
	some_values_1 JOIN table_2 USING (key) WHERE table_2.key = 3;
 count
---------------------------------------------------------------------
     1
(1 row)

-- a similar query, but with a reference table now
-- given that reference tables are replicated to all nodes
-- we have to broadcast to all nodes
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4'))
SELECT
	count(*)
FROM
	some_values_1 JOIN ref_table USING (key);
 count
---------------------------------------------------------------------
     2
(1 row)

-- a similar query as above, but this time use the CTE inside
-- another CTE
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1)
SELECT
	count(*)
FROM
	some_values_2 JOIN table_2 USING (key) WHERE table_2.key = 3;
 count
---------------------------------------------------------------------
     1
(1 row)

-- the second CTE does a join with a distributed table
-- and the final query is a router query
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key))
SELECT
	count(*)
FROM
	some_values_2 JOIN table_2 USING (key) WHERE table_2.key = 3;
 count
---------------------------------------------------------------------
     1
(1 row)

-- the first CTE is used both within second CTE and the final query
-- the second CTE does a join with a distributed table
-- and the final query is a router query
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key))
SELECT
	count(*)
FROM
	(some_values_2 JOIN table_2 USING (key)) JOIN some_values_1 USING (key) WHERE table_2.key = 3;
 count
---------------------------------------------------------------------
     1
(1 row)

-- the first CTE is used both within second CTE and the final query
-- the second CTE does a join with a distributed table but a router query on a worker
-- and the final query is another router query on another worker
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE table_2.key = 4)
SELECT
	count(*)
FROM
	(some_values_2 JOIN table_2 USING (key)) JOIN some_values_1 USING (key) WHERE table_2.key = 4;
 count
---------------------------------------------------------------------
     1
(1 row)

-- the first CTE is used both within second CTE and the final query
-- the second CTE does a join with a distributed table but a router query on a worker
-- and the final query is a router query on the same worker, so the first result is only
-- broadcasted to a single node
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE table_2.key = 3)
SELECT
	count(*)
FROM
	(some_values_2 JOIN table_2 USING (key)) JOIN some_values_1 USING (key) WHERE table_2.key = 3;
 count
---------------------------------------------------------------------
     1
(1 row)

-- the same query with the above, but the final query is hitting all shards
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key))
SELECT
	count(*)
FROM
	(some_values_2 JOIN table_2 USING (key)) JOIN some_values_1 USING (key) WHERE table_2.key != 3;
 count
---------------------------------------------------------------------
     1
(1 row)

-- even if we add a filter on the first query and make it a router query,
-- the first intermediate result still hits all workers because of the final
-- join is hitting all workers
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE table_2.key = 3)
SELECT
	count(*)
FROM
	(some_values_2 JOIN table_2 USING (key)) JOIN some_values_1 USING (key) WHERE table_2.key != 4;
 count
---------------------------------------------------------------------
     1
(1 row)

-- the reference table is joined with a distributed table and an intermediate
-- result, but the distributed table hits all shards, so the intermediate
-- result is sent to all nodes
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM ref_table WHERE value IN ('3', '4'))
SELECT
	count(*)
FROM
	(some_values_1 JOIN ref_table USING (key)) JOIN table_2 USING (key);
 count
---------------------------------------------------------------------
     2
(1 row)

-- similar query as above, but this time the whole query is a router
-- query, so no intermediate results
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM ref_table WHERE value IN ('3', '4'))
SELECT
	count(*)
FROM
	(some_values_1 JOIN ref_table USING (key)) JOIN table_2 USING (key) WHERE table_2.key = 4;
 count
---------------------------------------------------------------------
     1
(1 row)

-- now, the second CTE has a single shard join with a distributed table
-- so the first CTE should only be broadcasted to that node
-- since the final query doesn't have a join, it should simply be broadcasted
-- to one node
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE key = 1)
SELECT
	count(*)
FROM
	some_values_2;
 count
---------------------------------------------------------------------
     0
(1 row)

-- the same query inlined inside a CTE, and the final query has a
-- join with a distributed table
WITH top_cte as MATERIALIZED (
		WITH some_values_1 AS MATERIALIZED
		(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
		some_values_2 AS MATERIALIZED
		(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE key = 4)
	SELECT
		DISTINCT key
	FROM
		some_values_2
)
SELECT
	count(*)
FROM
	top_cte JOIN table_2 USING (key);
 count
---------------------------------------------------------------------
     1
(1 row)

-- very much the same query, but this time the top query is also a router query
-- on a single worker, so all intermediate results only hit a single node
WITH top_cte as MATERIALIZED (
		WITH some_values_1 AS MATERIALIZED
		(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
		some_values_2 AS MATERIALIZED
		(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE key = 1)
	SELECT
		DISTINCT key
	FROM
		some_values_2
)
SELECT
	count(*)
FROM
	top_cte JOIN table_2 USING (key) WHERE table_2.key = 2;
 count
---------------------------------------------------------------------
     0
(1 row)

-- some_values_1 is first used by a single shard-query, and than with a multi-shard
-- CTE, finally a cartesian product join
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('6', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1 JOIN table_2 USING (key) WHERE key = 4),
	some_values_3 AS MATERIALIZED
	(SELECT key FROM (some_values_2 JOIN table_2 USING (key)) JOIN some_values_1 USING (key))
SELECT * FROM some_values_3 JOIN ref_table ON (true) ORDER BY 1,2,3;
 key | key | value
---------------------------------------------------------------------
   4 |   1 | 1
   4 |   2 | 2
   4 |   3 | 3
   4 |   4 | 4
   4 |   5 | 5
   4 |   6 | 6
(6 rows)

-- join on intermediate results, so should only
-- go to a single node
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM table_2 WHERE value IN ('3', '4'))
SELECT count(*) FROM some_values_2 JOIN some_values_1 USING (key);
 count
---------------------------------------------------------------------
     2
(1 row)

-- same query with WHERE false make sure that we're not broken
-- for such edge cases
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM table_2 WHERE value IN ('3', '4'))
SELECT count(*) FROM some_values_2 JOIN some_values_1 USING (key) WHERE false;
 count
---------------------------------------------------------------------
     0
(1 row)

-- do not use some_values_2 at all, so only 2 intermediate results are
-- broadcasted
WITH some_values_1 AS MATERIALIZED
	(SELECT key, random() FROM table_1 WHERE value IN ('3', '4')),
	some_values_2 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1),
	some_values_3 AS MATERIALIZED
	(SELECT key, random() FROM some_values_1)
SELECT
	count(*)
FROM
	some_values_3;
 count
---------------------------------------------------------------------
     2
(1 row)

-- lets have some deeper intermediate results
-- the inner most two results and the final query (which contains only intermediate results)
-- hitting single worker, others hitting all workers
-- (see below query where all intermediate results hit a single node)
SELECT count(*) FROM
(
	SELECT avg(min::int) FROM
	(
		SELECT min(table_1.value) FROM
		(
			SELECT avg(value::int) as avg_ev_type FROM
			(
				SELECT max(value) as mx_val_1 FROM
				(
					SELECT avg(value::int) as avg FROM
					(
						SELECT cnt FROM
						(
							SELECT count(*) as cnt, value
							FROM table_1
							WHERE key = 1
							GROUP BY value
						) as level_1, table_1
						WHERE table_1.key = level_1.cnt AND key = 3
					) as level_2, table_2
					WHERE table_2.key = level_2.cnt AND key = 5
					GROUP BY level_2.cnt
				) as level_3, table_1
				WHERE value::numeric = level_3.avg AND key = 6
				GROUP BY level_3.avg
			) as level_4, table_2
			WHERE level_4.mx_val_1::int = table_2.key
			GROUP BY level_4.mx_val_1
		) as level_5, table_1
		WHERE level_5.avg_ev_type = table_1.key AND key > 111
		GROUP BY level_5.avg_ev_type
	) as level_6, table_1 WHERE table_1.key::int = level_6.min::int
	GROUP BY table_1.value
) as bar;
 count
---------------------------------------------------------------------
     0
(1 row)

-- the same query where all intermediate results hits one
-- worker because each and every query is a router query -- but on different nodes
SELECT count(*) FROM
(
	SELECT avg(min::int) FROM
	(
		SELECT min(table_1.value) FROM
		(
			SELECT avg(value::int) as avg_ev_type FROM
			(
				SELECT max(value) as mx_val_1 FROM
				(
					SELECT avg(value::int) as avg FROM
					(
						SELECT cnt FROM
						(
							SELECT count(*) as cnt, value
							FROM table_1
							WHERE key = 1
							GROUP BY value
						) as level_1, table_1
						WHERE table_1.key = level_1.cnt AND key = 3
					) as level_2, table_2
					WHERE table_2.key = level_2.cnt AND key = 5
					GROUP BY level_2.cnt
				) as level_3, table_1
				WHERE value::numeric = level_3.avg AND key = 6
				GROUP BY level_3.avg
			) as level_4, table_2
			WHERE level_4.mx_val_1::int = table_2.key AND table_2.key = 1
			GROUP BY level_4.mx_val_1
		) as level_5, table_1
		WHERE level_5.avg_ev_type = table_1.key AND key = 111
		GROUP BY level_5.avg_ev_type
	) as level_6, table_1
	WHERE table_1.key::int = level_6.min::int AND table_1.key = 4
	GROUP BY table_1.value
) as bar;
 count
---------------------------------------------------------------------
     0
(1 row)

-- sanity checks for set operations
-- the intermediate results should just hit a single worker
(SELECT key FROM table_1 WHERE key = 1)
INTERSECT
(SELECT key FROM table_1 WHERE key = 2);
 key
---------------------------------------------------------------------
(0 rows)

-- the intermediate results should just hit a single worker
WITH cte_1 AS MATERIALIZED
(
	(SELECT key FROM table_1 WHERE key = 1)
	INTERSECT
	(SELECT key FROM table_1 WHERE key = 2)
),
cte_2 AS MATERIALIZED
(
	(SELECT key FROM table_1 WHERE key = 3)
	INTERSECT
	(SELECT key FROM table_1 WHERE key = 4)
)
SELECT * FROM cte_1
	UNION
SELECT * FROM cte_2;
 key
---------------------------------------------------------------------
(0 rows)

-- one final test with SET operations, where
-- we join the results with distributed tables
-- so cte_1 should hit all workers, but still the
-- others should hit single worker each
WITH cte_1 AS MATERIALIZED
(
	(SELECT key FROM table_1 WHERE key = 1)
	INTERSECT
	(SELECT key FROM table_1 WHERE key = 2)
),
cte_2 AS MATERIALIZED
(
	SELECT count(*) FROM table_1 JOIN cte_1 USING (key)
)
SELECT * FROM cte_2;
 count
---------------------------------------------------------------------
     0
(1 row)

-- sanity checks for non-colocated subquery joins
-- the recursively planned subquery (bar) should hit all
-- nodes
SELECT
	count(*)
FROM
	(SELECT key, random() FROM table_1) as foo,
	(SELECT key, random() FROM table_2) as bar
WHERE
	foo.key != bar.key;
 count
---------------------------------------------------------------------
    14
(1 row)

-- the recursively planned subquery (bar) should hit one
-- node because foo goes to a single node
SELECT
	count(*)
FROM
	(SELECT key, random() FROM table_1 WHERE key = 1) as foo,
	(SELECT key, random() FROM table_2) as bar
WHERE
	foo.key != bar.key;
 count
---------------------------------------------------------------------
     4
(1 row)

SELECT *
FROM
(
    WITH accounts_cte AS MATERIALIZED (
        SELECT id AS account_id
        FROM accounts
    ),
    joined_stats_cte_1 AS MATERIALIZED (
        SELECT spent, account_id
        FROM stats
        INNER JOIN accounts_cte USING (account_id)
    ),
    joined_stats_cte_2 AS MATERIALIZED (
        SELECT spent, account_id
        FROM joined_stats_cte_1
        INNER JOIN accounts_cte USING (account_id)
    )
    SELECT SUM(spent) OVER (PARTITION BY coalesce(account_id, NULL))
    FROM accounts_cte
    INNER JOIN joined_stats_cte_2 USING (account_id)
) inner_query;
 sum
---------------------------------------------------------------------
 100
(1 row)

-- Testing a having clause that could have been a where clause between a distributed table
-- and a reference table. This query was the cause for intermediate results not being
-- available during the replace of the planner for the master query with the standard
-- planner.
-- Since the having clause could have been a where clause the having clause on the grouping
-- on the coordinator is replaced with a Result node containing a One-time filter if the
-- having qual (one-time filter works because the query doesn't change with the tuples
-- returned from below).
SELECT count(*),
       spent
FROM stats
GROUP BY 2
HAVING (
           SELECT count(*)
           FROM accounts
       ) > 0;
 count | spent
---------------------------------------------------------------------
     1 |   100
(1 row)