I have a table of items in the shop, an item may have different entries with same serial number (sn) (but different ids) if the same item was bought again later on with different price (price here is how much did a single item cost the shop)
id | sn | amount | price
----+------+--------+-------
1 | AP01 | 100 | 7
2 | AP01 | 50 | 8
3 | X2P0 | 200 | 12
4 | X2P0 | 30 | 18
5 | STT0 | 20 | 20
6 | PLX1 | 200 | 10
and a table of transactions
id | item_id | price
----+---------+-------
1 | 1 | 10
2 | 1 | 9
3 | 1 | 10
4 | 2 | 11
5 | 3 | 15
6 | 3 | 15
7 | 3 | 15
8 | 4 | 18
9 | 5 | 22
10 | 5 | 22
11 | 5 | 22
12 | 5 | 22
and transaction.item_id references items(id)
I want to group items by serial number (sn), get their sum(amount) and avg(price), and join it with a sold column that counts number of transactions with referenced id
I did the first with
select i.sn, sum(i.amount), avg(i.price) from items i group by i.sn;
sn | sum | avg
------+-----+---------------------
STT0 | 20 | 20.0000000000000000
PLX1 | 200 | 10.0000000000000000
AP01 | 150 | 7.5000000000000000
X2P0 | 230 | 15.0000000000000000
Then when I tried to join it with transactions I got strange results
select i.sn, sum(i.amount), avg(i.price) avg_cost, count(t.item_id) sold, sum(t.price) profit from items i left join transactions t on (i.id=t.item_id) group by i.sn;
sn | sum | avg_cost | sold | profit
------+-----+---------------------+------+--------
STT0 | 80 | 20.0000000000000000 | 4 | 88
PLX1 | 200 | 10.0000000000000000 | 0 | (null)
AP01 | 350 | 7.2500000000000000 | 4 | 40
X2P0 | 630 | 13.5000000000000000 | 4 | 63
As you can see, only the sold and profit columns show correct results, the sum and avg show different results than the expected
I can't separate the statements because I am not sure how can I add the count to the sn group which has the item_id as its id?
select
j.sn,
j.sum,
j.avg,
count(item_id)
from (
select
i.sn,
sum(i.amount),
avg(i.price)
from items i
group by i.sn
) j
left join transactions t
on (j.id???=t.item_id);
There are multiple matches in both tables, so the join multiplies the rows (and eventually produces wron results). I would recommend pre-joining, then aggregating:
select
sn,
sum(amount) total_amount,
avg(price) avg_price,
sum(no_transactions) no_transactions
from (
select
i.*,
(
select count(*)
from transactions t
where t.item_id = i.id
) no_transactions
from items i
) t
group by sn
Related
I've got 2 tables, one with an area, actions and quantities, and the other with prices and the goal is to combine the two in a view
table1
areaid integer
bananaunits integer
kilometers_ran integer
dogecoins integer
areaid | bananaunits | kilometers_ran | dogecoin
1 | 0 | 1 | 10
2 | 4 | 2 | 100
table2
rateid integer
description text
cost_per_unit integer
rateid | description | cost_per_unit
1 | price per banana | 0.5
2 | price per kilometers run | 2
3 | price per doge | 1
The intended outcome is to have a view which has the fields as following:
areaid, rateid, description, cost_per_unit, units, combined_cost
areaid| rateid| description| cost_per_unit| units| total_cost
1 | 1 | price per banana | 0.5 | 0 | 0
1 | 2 | per kilometers run | 2 | 1 | 2
1 | 3 | price per doge | 1 | 10 | 10
2 | 1 | price per banana | 0.5 | 4 | 2
2 | 2 | per kilometers run | 2 | 2 | 4
2 | 3 | price per doge | 1 | 100 | 100
In other words, I need to present all the rates per area in individual rows. how to achieve this?
Edit: current query that doesnt work
select areaid, rateid, description, cost_per_unit, units, combined_cost from table1,table2
Since you don't have a joining key and you want a row for each of combination of the area and rates, you're basically looking for a CROSS JOIN also called cartesian product
I need to subtract a value, found in a different table, from values across different rows.
For example, the tables I have are:
ProductID | Warehouse | Locator | qtyOnHand
-------------------------------------------
100 | A | 123 | 12
100 | A | 124 | 12
100 | A | 124 | 8
101 | A | 126 | 6
101 | B | 127 | 12
ProductID | Sold
----------------
100 | 26
101 | 16
Result:
ProductID | Warehouse | Locator | qtyOnHand | available
-------------------------------------------------------
100 | A | 123 | 12 | 0
100 | A | 123 | 12 | 0
100 | A | 124 | 8 | 6
101 | A | 126 | 6 | 0
101 | B | 127 | 12 | 12
The value should only be subtracted from those in warehouse A.
Im using postgresql. Any help is much appreciated!
If I understand correctly, you want to compare the overall stock to the cumulative amounts in the first table. The rows in the first table appear to be ordered from largest to smallest. Note: This is an interpretation and not 100% consistent with the data in the question.
Use JOIN to bring the data together and then cumulative sums and arithmetic:
select t1.*,
(case when running_qoh < t2.sold then 0
when running_qoh - qtyOnHand < t2.sold then (running_qoh - t2.sold)
else qtyOnHand
end) as available
from (select t1.*,
sum(qtyOnHand) over (partition by productID order by qtyOnHand desc) as running_qoh
from table1 t1
) t1 join
table2 t2
using (ProductID)
I have a Teradata query that generates:
customer | order | amount | days_ago
123 | 1 | 50 | 2
123 | 1 | 50 | 7
123 | 2 | 10 | 19
123 | 3 | 100 | 35
234 | 4 | 20 | 20
234 | 5 | 10 | 10
With performance in mind, what’s the most efficient way to produce an output per customer where orders is the number of distinct orders a customer had within the last 30 days and total is the sum of the amount of the distinct orders regardless of how many days ago the order was placed?
Desired output:
customer | orders | total
123 | 2 | 160
234 | 2 | 30
Given your rules, maybe it takes two steps - de-duplicate first then aggregate:
SELECT customer,
SUM(CASE WHEN days_ago <=30 THEN 1 ELSE 0 END) AS orders,
SUM(amount) AS total
FROM
(SELECT customer, order, MAX-or-MIN(amount) AS amount, MIN-or-MAX(days_ago) AS days_ago
FROM your_relation
GROUP BY 1, 2) AS DistinctCustOrder
GROUP BY 1;
I have a table supplier_account which has five coloumns supplier_account_id(pk),supplier_id(fk),voucher_no,debit and credit. I want to get the sum of debit grouped by supplier_id and then subtract the value of credit of the rows in which voucher_no is not null. So for each subsequent rows the value of sum of debit gets reduced. I have tried using 'with' clause.
with debitdetails as(
select supplier_id,sum(debit) as amt
from supplier_account group by supplier_id
)
select acs.supplier_id,s.supplier_name,acs.purchase_voucher_no,acs.purchase_voucher_date,dd.amt-acs.credit as amount
from supplier_account acs
left join supplier s on acs.supplier_id=s.supplier_id
left join debitdetails dd on acs.supplier_id=dd.supplier_id
where voucher_no is not null
But here the debit value will be same for all rows. After subtraction in the first row I want to get the result in second row and subtract the next credit value from that.
I know it is possible by using temporary tables. The problem is I cannot use temporary tables because the procedure is used to generate reports using Jasper Reports.
What you need is an implementation of the running total. The easiest way to do it with a help of a window function:
with debitdetails as(
select id,sum(debit) as amt
from suppliers group by id
)
select s.id, purchase_voucher_no, dd.amt, s.credit,
dd.amt - sum(s.credit) over (partition by s.id order by purchase_voucher_no asc)
from suppliers s
left join debitdetails dd on s.id=dd.id
order by s.id, purchase_voucher_no
SQL Fiddle
Results:
| id | purchase_voucher_no | amt | credit | ?column? |
|----|---------------------|-----|--------|----------|
| 1 | 1 | 43 | 5 | 38 |
| 1 | 2 | 43 | 18 | 20 |
| 1 | 3 | 43 | 8 | 12 |
| 2 | 4 | 60 | 5 | 55 |
| 2 | 5 | 60 | 15 | 40 |
| 2 | 6 | 60 | 30 | 10 |
shops
id_shop | id_prod_ty | position
and
products
id | price | id_prod_ty | distance
table shops contains 2 shops for each id_prod_ty (product type) and a position.
table products contains many records with different prices and a distance
basically i need to have a query that selects price low and high for each product and for each shops grouping by closer prices (and that are not closer to other shops)
so for example
shops
id_shop | id_prod_ty | position
1 | 1 | 3
2 | 1 | 7
3 | 2 | 8
4 | 2 | 4
....
products
id | price | id_prod_ty | distance
1 | 10 | 1 | 1
2 | 04 | 1 | 2
3 | 02 | 1 | 4
4 | 44 | 1 | 2
5 | 09 | 1 | 1
6 | 13 | 1 | 7
7 | 15 | 1 | 8
8 | 09 | 2 | 5
9 | 12 | 2 | 8
10 | 17 | 2 | 1
11 | 32 | 2 | 13
12 | 22 | 2 | 2
...
result shout be like this
id_prod_ty | id_price_low | id_price_high | id_shop
1 | 3 (02) | 4 (44) | 1
1 | 6 (13) | 7 (15) | 2
2 | 8 (09) | 12 (22) | 4
2 | 9 (12) | 11 (32) | 3
...
thanks
I think I understand the problem. For each price, you are trying to assign the shop that is nearest, based on the distance and the position.
The approach starts by joining the positions and shops. It then calculates the difference between the position and distance -- that seems to be the measure you are using. Each price id will appear twice (once for each shop). Using a window function, it calculates the minimum difference for each id.
With this information, the query can determine which is the closer shop. This version returns the prices, rather than the ids of the lowest prices. Here is the final, untested, query:
select id_prod_ty,
MIN(case when diff = minDiff then price) as price_low,
MAX(case when diff = minDiff then price) as price_high,
s.id_shop
from (select p.id_prod_ty,
s.id_shop,
p.price,
(p.distance - s.position) as diff,
MIN(p.distance - s.position) over (partition by p.id) as minDiff
from products p join
shops s
on p.id_prod_ty = s.id_prod_ty
) ps
group by id_prod_ty, id_shop
Here is a variation that gets the ids as well. It uses a window function to find the min and max price, and then compares the price to these values to get the ids:
select id_prod_ty,
MIN(minPrice) as price_low,
MIN(case when price = minPrice then id end) id_price_low,
MAX(maxprice) as price_high,
MIN(case when price = maxPrice then id end) id_price_high,
id_shop
from (select *,
MIN(IsThisShopPrice) over (partition by id_prod_ty, id_shop) as minPrice,
MIN(IsThisShopPrice) over (partition by id_prod_ty, id_shop) as maxPrice,
from (select p.id_prod_ty, s.id_shop, p.price, p.id,
(p.distance - s.position) as diff,
(case when (p.distance - s.position) = MIN(p.distance - s.position) over (partition by p.id)
then 'Y'
else 'N'
end) as IsThisShop
(case when (p.distance - s.position) = MIN(p.distance - s.position) over (partition by p.id)
then price
end) as IsThisShopPrice
from products p join
shops s
on p.id_prod_ty = s.id_prod_ty
) ps
) ps
group by id_prod_ty, id_shop