I have one table where I dump all records from different sources (x, y, z) like below
+----+------+--------+
| id | source |
+----+--------+
| 1 | x |
| 2 | y |
| 3 | x |
| 4 | x |
| 5 | y |
| 6 | z |
| 7 | z |
| 8 | x |
| 9 | z |
| 10 | z |
+----+--------+
Then I have one mapping table where I map values between sources based on my usecase like below
+----+-----------+
| id | mapped_id |
+----+-----------+
| 1 | 2 |
| 1 | 9 |
| 3 | 7 |
| 4 | 10 |
| 5 | 1 |
+----+-----------+
I want merged results where I can see only unique results like
+-----+------------+
| id | mapped_ids |
+-----+------------+
| 1 | 2,9,5 |
| 3 | 7 |
| 4 | 10 |
| 6 | null |
| 8 | null |
+-----+------------+
I am trying different options but could not figure this out, is there way I can write joins to do this. I have to use the mapping table where associations are stored and identify unique records along with records which are not mapped anywhere.
My understanding is, you want to see all dump_table IDs that do not appear in the mapping_id column and then aggregate the mapped_ids for those that are left:
select d1.id,
array_agg(m1.mapped_id order by m1.mapped_id) filter (where m1.mapped_id is not null) as mapped_ids
from dump_table d1
left join mapping_table m1 using (id)
where not exists (select *
from mapping_table m2
where m2.mapped_id = d1.id)
group by d1.id;
Online example: https://rextester.com/JQZ17650
Try something like this:
SELECT id, name, ARRAY_AGG(mapped_id) AS mapped_ids
FROM table1 AS t1
LEFT JOIN table2 AS t2 USING (id)
GROUP BY id, name
Related
Suppose I have the following two tables:
foo:
| id | goober | value |
|----|--------|-------|
| 1 | a1 | 25 |
| 2 | a1 | 125 |
| 3 | b2 | 500 |
bar:
| id | foo_id | value |
|----|--------|-------|
| 1 | 1 | 4 |
| 2 | 3 | 19 |
| 3 | 3 | 42 |
| 4 | 3 | 22 |
| 5 | 3 | 56 |
Note the n:1 relationship of bar.foo_id : foo.id.
My goal is to sum the value columns for tables foo and bar, joining on bar.foo_id=foo.id, and finally grouping by goober from foo. Then performing a calculation if possible, though not critical.
Resulting in a final output looking something like:
| goober | foo_value_sum | bar_value_sum | foo_bar_diff |
|--------|---------------|---------------|--------------|
| a1 | 150 | 4 | 146 |
| b2 | 500 | 139 | 361 |
This should be rather simple by the following query that creates two CTEs and then joins them afterwards:
with bar_agg as
(
select foo.goober
,sum(bar.value) bar_value_sum
from foo
join bar
on bar.foo_id = foo.id
group by foo.goober
)
,foo_agg as
(
select foo.goober
,sum(foo.value) foo_value_sum
from foo
group by foo.goober
)
select foo.goober
,foo_value_sum
,bar_value_sum
,foo_value_sum - bar_value_sum foo_bar_diff
from foo_agg foo
left join bar_agg bar
on bar.goober = foo.goober
order by foo.goober
I have a table like the following one:
+---------+-------+-------+-------------+--+
| Section | Group | Level | Fulfillment | |
+---------+-------+-------+-------------+--+
| A | Y | 1 | 82.2 | |
| A | Y | 2 | 23.2 | |
| A | M | 1 | 81.1 | |
| A | M | 2 | 28.2 | |
| B | Y | 1 | 89.1 | |
| B | Y | 2 | 58.2 | |
| B | M | 1 | 32.5 | |
| B | M | 2 | 21.4 | |
+---------+-------+-------+-------------+--+
And this would be my desired output:
+---------+-------+--------------------+--------------------+
| Section | Group | Level1_Fulfillment | Level2_Fulfillment |
+---------+-------+--------------------+--------------------+
| A | Y | 82.2 | 23.2 |
| A | M | 81.1 | 28.2 |
| B | Y | 89.1 | 58.2 |
| B | M | 32.5 | 21.4 |
+---------+-------+--------------------+--------------------+
Thus, for each section and group I'd like to obtain their percents of fulfillment for level 1 and level 2. To achieve this, I've tried crosstab(), but using this function returns me an error ("The provided SQL must return 3 columns: rowid, category, and values.") because I'm using more than three columns (I need to maintain section and group as identifiers for each row). Is possible to use crosstab in this case?
Regards.
I find crosstab() unnecessary complicated to use and prefer conditional aggregation:
select section,
"group",
max(fulfillment) filter (where level = 1) as level_1,
max(fulfillment) filter (where level = 2) as level_2
from the_table
group by section, "group"
order by section;
Online example
I have two subqueries. Here is the output of subquery A....
id | date_lat_lng | stat_total | rnum
-------+--------------------+------------+------
16820 | 2016_10_05_10_3802 | 9 | 2
15701 | 2016_10_05_10_3802 | 9 | 3
16821 | 2016_10_05_11_3802 | 16 | 2
17861 | 2016_10_05_11_3802 | 16 | 3
16840 | 2016_10_05_12_3683 | 42 | 2
17831 | 2016_10_05_12_3767 | 0 | 2
17862 | 2016_10_05_12_3802 | 11 | 2
17888 | 2016_10_05_13_3683 | 35 | 2
17833 | 2016_10_05_13_3767 | 24 | 2
16823 | 2016_10_05_13_3802 | 24 | 2
and subquery B, in which date_lat_lng and stat_total has commonality with subquery A, but id does not.
id | date_lat_lng | stat_total | rnum
-------+--------------------+------------+------
17860 | 2016_10_05_10_3802 | 9 | 1
15702 | 2016_10_05_11_3802 | 16 | 1
17887 | 2016_10_05_12_3683 | 42 | 1
15630 | 2016_10_05_12_3767 | 20 | 1
16822 | 2016_10_05_12_3802 | 20 | 1
16841 | 2016_10_05_13_3683 | 35 | 1
15632 | 2016_10_05_13_3767 | 23 | 1
17863 | 2016_10_05_13_3802 | 3 | 1
16842 | 2016_10_05_14_3683 | 32 | 1
15633 | 2016_10_05_14_3767 | 12 | 1
Both subquery A and B pull data from the same table. I want to delete the rows in that table that share the same ID as subquery A but only where date_lat_lng and stat_total have a shared match in subquery B.
Effectively I need:
DELETE FROM table WHERE
id IN
(SELECT id FROM (subqueryA) WHERE
subqueryA.date_lat_lng=subqueryB.date_lat_lng
AND subqueryA.stat_total=subqueryB.stat_total)
Except I'm not sure where to place subquery B, or if I need an entirely different structure.
Something like this,
DELETE FROM table WHERE
id IN (
SELECT DISTINCT id
FROM subqueryA
JOIN subqueryB
USING (id,date_lat_lng,stat_total)
)
I'm relatively new to PostgreSQL and trying to figure out how to solve the following scenario. Let's say I have three tables:
stores
| store_id |
|----------|
| 1 |
| 2 |
| 3 |
products
| product_id |
|------------|
| 1 |
| 2 |
| 3 |
store_has_product
| store_id | product_id |
|----------|------------|
| 1 | 3 |
| 1 | 2 |
| 2 | 1 |
| 3 | 3 |
| 1 | 1 |
| 3 | 1 |
| 3 | 2 |
And now I'm trying to build a query to join all products to the stores table and group them in an array, so that I have an output like this:
| store_id | products |
|----------|-----------|
| 1 | {3, 2, 1} |
| 2 | {2} |
| 3 | {3, 1, 2} |
I know that Arrays are possible with PostgreSQL, but I don't get how to write such a query and probably already spent too much time thinking about a solution.
Thanks for your help!
If you are using version 8.4 or later you can use array_agg:
SELECT store_id, array_agg(product_id::text) as products
FROM store_has_product
GROUP BY store_id
I'm using SQL server 2008R2 and I have a view which returns the following:
+----+-------+-------+-------+-------+-------+-------+
| ID | col1A | col1B | col2A | col2B | col3A | col3B |
+----+-------+-------+-------+-------+-------+-------+
| 1 | 1 | 1 | 3 | 5 | 4 | 4 |
| 2 | 1 | 1 | 5 | 5 | 5 | 4 |
| 3 | 3 | 4 | 5 | 5 | 4 | 4 |
| 4 | 1 | 2 | 5 | 5 | 4 | 3 |
| 5 | 1 | 1 | 2 | 2 | 3 | 3 |
+----+-------+-------+-------+-------+-------+-------+
As you can see this view contains column pairs (col1A and col1B), (col2A and col2B), (col3A and col3B).
I need to query this view and find rows where the column pairs contain different values.
So I would be looking to return:
+----+------------+---+-----+
| ID | ColumnType | A | B |
+----+------------+---+-----+
| 1 | Col2 | 3 | 5 |
| 2 | Col3 | 5 | 4 |
| 3 | Col1 | 3 | 4 |
| 4 | Col1 | 1 | 2 |
| 4 | Col3 | 4 | 3 |
+----+------------+---+-----+
I think I need to use UNPIVOT but not sure how – appreciate any suggestions?
Since you are using SQL Server 2008+ you can use CROSS APPLY to unpivot the pair of columns and then you can easily compare the values in the A and B to return the rows that don't match:
select t.ID,
c.ColumnType,
c.A,
c.B
from [dbo].[yourview] t
cross apply
(
values
('Col1', Col1A, Col1B),
('Col2', Col2A, Col2B),
('Col3', Col3A, Col3B)
) c (ColumnType, A, B)
where c.A <> c.B;
If you have different datatypes in your columns, then you'll need to convert the data to the same type. You can do this conversion within the VALUES clause:
select t.ID,
c.ColumnType,
c.A,
c.B
from [dbo].[yourview] t
cross apply
(
values
('Col1', cast(Col1A as varchar(50)), Col1B),
('Col2', cast(Col2A as varchar(50)), Col2B),
('Col3', cast(Col3A as varchar(50)), Col3B)
) c (ColumnType, A, B)
where c.A <> c.B