I have two tables: shipped (item, qty_shipd, date_shpd) and forecast (item, qty_forecat, date_forecast) and i need something below
Item Forecast Shipped Forecast_date Shipped_date
item1 50 100 2018-01-01 2018-01-15
item2 0 50 - 2018-01-06
item3 100 100 2018-02-01 2018-02-05
item4 150 0 2018-02-01 -
item1 0 20 - 2018-03-15
item1 10 50 2018-04-01 2018-04-28
Is it possibile have something like this table?
Thanks so much
Hmmm. This seems rather complicated. You seem to want to combine the rows from both tables within a month, not losing any values from either one.
If so, I think this does what you want:
select item, max(shipped) as shipped, max(shipped_date) as shipped_date,
max(forecast) as forecast, max(forecast_date) as forecast_date
from ((select Item, Shipped, Shipped_date, null as forecast, null as forecast_date,
row_number() over (partition by item, year(shipped_date), month(shipped_date) order by shipped_date) as seqnum
from shipped
) union all
(select Item, NULL as Shipped, NULL as Shipped_date, null as forecast, null as forecast_date,
row_number() over (partition by item, year(shipped_date), month(shipped_date) order by shipped_date) as seqnum
from shipped
)
) sf
group by item, year(coalesce(shipped_date, forecast_date)),
month(coalesce(shipped_date, forecast_date)), seqnum
I suggest you use Calendar Table (temporary or simply permanent table).
Join the calendar table date with each shipped and forecast date using outer join. Don't forget to join forecast item with shipped item. Also handle if the data from shipped and forecast is NULL
SELECT
ISNULL(f.item, s.item) AS Item,
c.Date,
ISNULL(qty_forecat,0) AS Forecast,
ISNULL(qty_shipd,0) AS Shipped,
date_forecast AS Forecast_date,
date_shpd AS Shipped_date
FROM
Calendar AS c
LEFT OUTER JOIN forecast AS f
ON c.Date=f.date_forecast
LEFT OUTER JOIN shipped AS s
ON c.Date=s.date_shpd
AND f.item=s.item
Related
I have 3 tables:
Buys
ID
Item
qty
price
1
1001
10
1.00
2
1001
10
2.00
3
1001
10
3.00
4
1002
10
2.00
5
1002
10
1.00
6
1003
10
1.00
7
1004
10
1.00
8
1004
10
2.00
Fallback
Item
price
1001
3.00
1002
3.00
1003
4.00
Stock
Item
stock
1001
15
1002
5
1003
25
1004
15
I have to calculate actually price each item. For that, I have to check the table "buys" each row from the biggest ID to smallest ID and take all prices as long as the stock is sufficient. If not enough buys in the table, I have to use the fallback prices for part of stock, I don't have price in first table.
So for item no. 1001, stock is 15. Price for 10 pcs found in ID 3 (3.00 USD); price for rest 5 pieces in row ID 2 (2.00 USD). So correct actually stockprice is 2.66 USD.
For item no. 1002, stock is 5. Price for latest buy is 1.00 USD in row ID 5 with quantity more than 5. So correct actually stockprice is 1.00 USD.
For item no. 1003, stock is 25. Only one entry in row ID 6 with 10 pcs for 1.00 USD each. so price for missing 15pcs have to take from fallback table 4.00 USD. So correct actually stockprice is 2.80 USD.
Result should be like this:
Item
stock
value
1001
15
2.66
1002
5
1.00
1003
25
2.80
But I have no idea how that works. Thank you very much for help.
Using conditional aggregation when comparring stock to buys runnig totals, finally apply the fallback
select t.item, (s + t.qf * f.price) s, stock, (s + t.qf * f.price) / stock price
from (
select s.Item, s.Stock,
sum(coalesce(case when b.qe <= Stock then b.qty else Stock - b.qs end * b.price, 0)) s,
-- qty for fallback
min(case when Stock > coalesce(b.qe,0) then Stock - coalesce(b.qe,0) else 0 end) qf
from Stock s
left join (
select Item, qty, price, ID,
sum(qty) over(partition by Item order by ID desc) - qty qs, -- starting runnig total
sum(qty) over(partition by Item order by ID desc) qe -- ending runnig total
from Buys
) b on s.Item = b.Item and s.Stock > b.qs
group by s.Item, s.Stock
) t
join Fallback f on f.Item = t.Item;
order by t.Item;
Provided a fallback can be missing for an item a minor tweak is requierd.
select t.item, (s + t.qf * coalesce(f.price, 0)) s, stock, (s + t.qf * coalesce(f.price, 0)) / stock price
from (
select s.Item, s.Stock,
sum(coalesce(case when b.qe <= Stock then b.qty else Stock - b.qs end * b.price, 0)) s,
-- qty for fallback
min(case when Stock > coalesce(b.qe,0) then Stock - coalesce(b.qe,0) else 0 end) qf
from Stock s
left join (
select Item, qty, price, ID,
sum(qty) over(partition by Item order by ID desc) - qty qs, -- starting runnig total
sum(qty) over(partition by Item order by ID desc) qe -- ending runnig total
from Buys
) b on s.Item = b.Item and s.Stock > b.qs
group by s.Item, s.Stock
) t
left join Fallback f on f.Item = t.Item
where t.qf = 0 or f.item is not null
order by t.Item;
The query will not return a row if a fallback is required but is missing. Otherwise the row is returned.
db<>fiddle
You need to create a running sum of the quantity in Buys and calculate the price based off that.
This is somewhat complicated by the fact that you may have too many, or not enough, rows in Buys to fulfil the stock.
SELECT
s.Item,
s.stock,
(
ISNULL(b.FoundStockPrice, 0)
+ CASE WHEN s.stock > ISNULL(b.FoundStock, 0)
THEN s.stock - ISNULL(b.FoundStock, 0)
ELSE 0 END * f.price
) / s.stock
FROM Stock s
JOIN Fallback f ON f.Item = s.Item
OUTER APPLY (
SELECT
FoundStock = SUM(b.qty),
FoundStockPrice = SUM(
CASE WHEN b.FullStock > b.RunningSum THEN b.qty
ELSE b.FullStock - (b.RunningSum - b.qty) END
* b.price)
FROM (
SELECT *,
RunningSum = SUM(b.qty) OVER (PARTITION BY b.Item
ORDER BY b.ID DESC ROWS UNBOUNDED PRECEDING),
FullStock = s.stock
FROM Buys b
WHERE b.Item = s.Item
) b
WHERE b.RunningSum - b.qty < s.stock
) b;
Steps are as follows:
For every Stock take all relevant Buys rows.
Calculate a running sum of qty, and then filter to only rows where the running sum includes the final stock (in other words it must up to the previous running sum).
Sum these Buys rows multiplied by their price, taking into account that we need to net off anything over the necessary stock. Take also a total sum of the quantity.
The final price is: the previous calculated total, plus any remaining unfound stock multiplied by the fallback.price, all divided by the total stock.
db<>fiddle
I have a table with historical stocks prices for hundreds of stocks. I need to extract only those stocks that reached $10 or greater for the first time.
Stock
Price
Date
AAA
9
2021-10-01
AAA
10
2021-10-02
AAA
8
2021-10-03
AAA
10
2021-10-04
BBB
9
2021-10-01
BBB
11
2021-10-02
BBB
12
2021-10-03
Is there a way to count how many times each stock hit >= 10 in order to pull only those where count = 1 (in this case it would be stock BBB considering it never reached 10 in the past)?
Since I couldn't figure how to create count I've tried the below manipulations with min/max dates but this looks like a bit awkward approach. Any idea of a simpler solution?
with query1 as (
select Stock, min(date) as min_greater10_dt
from t
where Price >= 10
group by Stock
), query2 as (
select Stock, max(date) as max_greater10_dt
from t
where Price >= 10
group by Stock
)
select Stock
from t a
join query1 b on b.Stock = a.Stock
join query2 c on c.Stock = a.Stock
where not(a.Price < 10 and a.Date between b.min_greater10_dt and c.max_greater10_dt)
This is a type of gaps-and-islands problem which can be solved as follows:
detect the change from < 10 to >= 10 using a lagged price
count the number of such changes
filter in only stock where this has happened exactly once
and take the first row since you only want the stock (you could group by here but a row number allows you to select the entire row should you wish to).
declare #Table table (Stock varchar(3), Price money, [Date] date);
insert into #Table (Stock, Price, [Date])
values
('AAA', 9, '2021-10-01'),
('AAA', 10, '2021-10-02'),
('AAA', 8, '2021-10-03'),
('AAA', 10, '2021-10-04'),
('BBB', 9, '2021-10-01'),
('BBB', 11, '2021-10-02'),
('BBB', 12, '2021-10-03');
with cte1 as (
select Stock, Price, [Date]
, row_number() over (partition by Stock, case when Price >= 10 then 1 else 0 end order by [Date] asc) rn
, lag(Price,1,0) over (partition by Stock order by [Date] asc) LaggedStock
from #Table
), cte2 as (
select Stock, Price, [Date], rn, LaggedStock
, sum(case when Price >= 10 and LaggedStock < 10 then 1 else 0 end) over (partition by Stock) StockOver10
from cte1
)
select Stock
--, Price, [Date], rn, LaggedStock, StockOver10 -- debug
from cte2
where Price >= 10
and StockOver10 = 1 and rn = 1;
Returns:
Stock
BBB
Note: providing DDL+DML as show above makes it much easier of people to assist.
I am writing a Query with SQL and couldn't figure it out yet...
My table looks like this:
Category Price Date
Cat1 20 2019-04
Cat2 12 2019-04
Cat3 5 2019-04
Cat1 23 2020-04
Cat2 17 2020-04
Cat3 8 2020-04
I would like to get a table that shows this:
Cat Pct_change Period
Cat 1 0.15 2019-2020
Cat 2 0.41 "
And so on.
I can get this category by category but I have like 100 categories, cant do this manually. It would be great, too, to see both prices side by side. What I don't (can't) allow is to generate new tables just saving the data to join separate tables...
Thank you!!
Use LEAD() window function to get the price and date of the next date for each category:
SELECT Category,
ROUND(1.0 * next_price / Price - 1, 2) Pct_change,
SUBSTR(Date, 1, 4) || '-' || SUBSTR(next_date, 1, 4) Period
FROM (
SELECT *,
LEAD(Price) OVER (PARTITION BY Category ORDER BY Date) next_price,
LEAD(Date) OVER (PARTITION BY Category ORDER BY Date) next_date
FROM tablename
)
WHERE next_date IS NOT NULL
See the demo.
Results:
Category
Pct_change
Period
Cat1
0.15
2019-2020
Cat2
0.42
2019-2020
Cat3
0.6
2019-2020
You can use first_value():
select distinct category, min(date), max(date),
(-1 + first_value(price) over (partition by category order by date desc) /
first_value(price) over (partition by category order by date asc)
) as percent_change
from t;
TL;DR:
Given this table:
WITH subscriptions AS (SELECT TIMESTAMP("2020-11-01") as date, "premium" as product, 50 as diff
UNION ALL SELECT TIMESTAMP("2020-11-01"), "basic", 100
UNION ALL SELECT TIMESTAMP("2020-11-02"), "basic", -10
UNION ALL SELECT TIMESTAMP("2020-11-03"), "premium", 20
UNION ALL SELECT TIMESTAMP("2020-11-03"), "basic", 40
)
How to do I get a table where the missing date/product combination (2020-11-02 - premium) is included with a fallback value for diff of 0.
Ideally, for multiple products. A list of all products can be get like this:
SELECT ARRAY_AGG(DISTINCT product) FROM subscriptions
I want to be able to get the subscription count per day, either for all products or just for some products.
And the way I think this can be easily achieved is by preparing a database that looks like this:
|---------------------|------------------|------------------|
| date | product | total |
|---------------------|------------------|------------------|
| 2020-11-01 | premium | 100 |
|---------------------|------------------|------------------|
| 2020-11-01 | basic | 50 |
|---------------------|------------------|------------------|
With this table, I can easily group by date and product or just by date and sum the total.
Before I get to the result table I have generated a table where for each day and product I calculate the difference in subscriptions. How many new subscribers for each product are there and how many are no longer subscribed.
This table looks like this:
|---------------------|------------------|------------------|
| date | product | diff |
|---------------------|------------------|------------------|
| 2020-11-01 | premium | 50 |
|---------------------|------------------|------------------|
| 2020-11-01 | basic | -20 |
|---------------------|------------------|------------------|
Meaning on November, 1st the total count of premium subscribers increased by 50, and the total count of basic subscribers decreased by 20.
The problem now is that this temporary table is missing date points if there weren't any changes one product, see the example below.
When I started there was no product table and I only had the date and diff column.
To get from the second to the first table I used this query which worked perfect:
WITH subscriptions AS (SELECT TIMESTAMP("2020-11-01") as date, 150 as diff
UNION ALL SELECT TIMESTAMP("2020-11-02"), -10
UNION ALL SELECT TIMESTAMP("2020-11-03"), 60
)
SELECT
*,
SUM(diff) OVER (ORDER BY date) as total_subscriptions
FROM subscriptions
ORDER BY date
But when I add the product column and try to calculate the sum per day and product there are some data points missing.
WITH subscriptions AS (SELECT TIMESTAMP("2020-11-01") as date, "premium" as product, 50 as diff
UNION ALL SELECT TIMESTAMP("2020-11-01"), "basic", 100
UNION ALL SELECT TIMESTAMP("2020-11-02"), "basic", -10
UNION ALL SELECT TIMESTAMP("2020-11-03"), "premium", 20
UNION ALL SELECT TIMESTAMP("2020-11-03"), "basic", 40
)
SELECT
*,
SUM(diff) OVER (PARTITION BY product ORDER BY date) as total_subscriptions
FROM subscriptions
ORDER BY date
--
|---------------------|------------------|------------------|
| date | product | total |
|---------------------|------------------|------------------|
| 2020-11-01 | basic | 100 |
|---------------------|------------------|------------------|
| 2020-11-01 | premium | 50 |
|---------------------|------------------|------------------|
| 2020-11-02 | basic | 90 |
|---------------------|------------------|------------------|
| 2020-11-03 | basic | 130 |
|---------------------|------------------|------------------|
| 2020-11-03 | premium | 70 |
|---------------------|------------------|------------------|
If I now show the total number of subscriptions per day, I would get:
150 -> 90 -> 200
But I would expect:
150 -> 140 -> 200
Same goes for the total number of premium subscriptions per day:
50 -> 0 -> 70
But I would expect:
50 -> 50 -> 70
I believe the best option to fix this would be to add the missing date/product combinations.
How would I do this?
-- Try this,I am creating a table for list of products and add total product in that list. Joining with your table to get data as per your requirement.
WITH subscriptions AS (SELECT TIMESTAMP("2020-11-01") as date, "premium" as product, 50 as diff
UNION ALL SELECT TIMESTAMP("2020-11-01"), "basic", 100
UNION ALL SELECT TIMESTAMP("2020-11-02"), "basic", -10
UNION ALL SELECT TIMESTAMP("2020-11-03"), "premium", 20
UNION ALL SELECT TIMESTAMP("2020-11-03"), "basic", 40
),
product_name as (
Select product from subscriptions group by 1
union all
Select "Total" as product
)
Select date
,product
,total_subscriptions
from (
Select a.date
,a.product
,diff
,SUM(diff) OVER (PARTITION BY a.product ORDER BY a.date) as total_subscriptions
from
(
Select date,a.product
from product_name A
join subscriptions B
on 1=1
where a.product !='Total'
group by 1,2
) A
left join subscriptions B
on A.product = B.product
and A.date = B.date
group by 1,2,3
) group by 1,2,3
union all
Select date
,product
,total_subscriptions
from
(
Select date,a.product
,diff
,SUM(diff) OVER (PARTITION BY a.product ORDER BY date) as total_subscriptions
from product_name A
join subscriptions B
on 1=1
where a.product ='Total'
group by 1,2,3
) group by 1,2,3
order by 1,2
If I follow you correctly, one approach is to can generate a fixed the list of dates for the period you want, and cross join it with the list of products. This gives you all possible combinations. Then, you can bring the subscriptions table with a left join, and finally perform the window sum:
select d.dt, p.product, sum(s.diff) over(partition by p.product order by d.dt) total
from unnest(generate_timestamp_array(
timestamp('2020-11-01'),
timestamp('2020-11-03'),
interval 1 day)
) dt
cross join (
select 'basic' product
union all select 'premium'
) p
left join subscriptions on s.product = p.product and s.date = dt
We can make the query a more generic by dynamically generating the date range and list of products:
select d.dt, p.product, sum(s.diff) over(partition by p.product order by d.dt) total
from (select min(date) min_dt, max(date) max_dt from subscriptions) d0
cross join unnest(generate_timestamp_array(d0.min_dt, d0.max_dt, interval 1 day)) dt
cross join (select distinct product from subscriptions) p
left join subscriptions on s.product = p.product and s.date = dt
Use GENERATE_TIMESTAMP_ARRAY:
WITH subscriptions AS (SELECT TIMESTAMP("2020-11-01") as date, "premium" as product, 50 as diff
UNION ALL SELECT TIMESTAMP("2020-11-01"), "basic", 100
UNION ALL SELECT TIMESTAMP("2020-11-02"), "basic", -10
UNION ALL SELECT TIMESTAMP("2020-11-03"), "premium", 20
UNION ALL SELECT TIMESTAMP("2020-11-03"), "basic", 40
),
dates AS (
SELECT *
FROM UNNEST(GENERATE_TIMESTAMP_ARRAY('2020-11-01 00:00:00', '2020-11-03 00:00:00', INTERVAL 1 DAY)) as date
),
products AS (
SELECT DISTINCT product FROM subscriptions
)
SELECT dates.date, products.product, subscriptions.diff
FROM dates
CROSS JOIN products
LEFT JOIN subscriptions
ON subscriptions.date = dates.date AND subscriptions.product = products.product
I am trying to find out differences between two rows in a same table. Having trouble to find right query. For example, I have
Year Item Qty Amount
------------------------------
2014 Shoes 500 2500
2014 Ties 300 900
2014 Pants 200 4000
2015 Shoes 600 3000
2015 Ties 200 600
I am trying to find out what was the increased (or decreased) from previous year to this year. I will always have only two years to compare. The query result should look like following:
Items Qty Diff Amount Diff
------------------------------
Shoes 100 500
Ties (-100) (-300)
Pants Null Null
What should be the query look like?
If you want to include everything, then you can use FULL OUTER JOIN, if just the one with the earlier year, LEFT OUTER JOIN, if you want the one with both earlier and subsequent year, then INNER JOIN.
SELECT
T1.Item
, (T2.QTY-T1.QTY) AS [QTY Diff]
, (T2.Amount - T1.Amount) AS [Amount Diff]
FROM
<<Table>> T1
LEFT OUTER JOIN <<Table>> T2
ON T1.Item=T2.Item
AND T1.YEAR=(T2.YEAR-1);
1. Use LAG or LEAD
WITH tb(Year,Item,Qty,Amount) AS (
SELECT 2014,'Shoes',500,2500 UNION
SELECT 2014,'Ties',300,900 UNION
SELECT 2014,'Pants',200,4000 UNION
SELECT 2015,'Shoes',600,3000 UNION
SELECT 2015,'Ties',200,600
)
SELECT *,Qty-LAG(qty)OVER(PARTITION BY Item ORDER BY year) AS QtyDiff ,Amount-LAG(Amount)OVER(PARTITION BY Item ORDER BY year) AS AmountDiff
FROM tb
Year Item Qty Amount QtyDiff AmountDiff
----------- ----- ----------- ----------- ----------- -----------
2014 Pants 200 4000 NULL NULL
2014 Shoes 500 2500 NULL NULL
2015 Shoes 600 3000 100 500
2014 Ties 300 900 NULL NULL
2015 Ties 200 600 -100 -300
2.Cross or Outer Apply
WITH tb(Year,Item,Qty,Amount) AS (
SELECT 2014,'Shoes',500,2500 UNION
SELECT 2014,'Ties',300,900 UNION
SELECT 2014,'Pants',200,4000 UNION
SELECT 2015,'Shoes',600,3000 UNION
SELECT 2015,'Ties',200,600
)
SELECT t1.Year,t1.Item,t1.Qty- t2.qty AS DiffQty,t1.Amount-t2.Amount AS DiffAmount
FROM tb AS t1
OUTER APPLY (SELECT TOP 1 tt.qty,tt.Amount FROM tb AS tt WHERE tt.Year<t1.Year AND t1.Item=tt.Item ORDER BY tt.Year desc) AS t2
ORDER BY t1.Item,t1.Year
Using the lag function is the best approach to this.
SELECT [Year], [Item], [Qty], [Amount],
[Qty] - LAG([Qty]) OVER (PARTITION BY [Item] ORDER BY [Year]) [QtyDiff],
[Amount] - LAG([Amount]) OVER (PARTITION BY [Item] ORDER BY [Year]) [AmountDiff]
FROM [ItemTable] it
order BY [Year] DESC, [Item];
Hope this helps.
Here is the required query:
SET #YEAR1 = '2014';
SET #YEAR2 = '2015';
SELECT
Item,
if(count(*)>1,sum(if(Year=#YEAR2,Qty,-Qty)),NULL) as 'Qty Diff',
if(count(*)>1,sum(if(Year=#YEAR2,Amount,-Amount)),NULL) as 'Amount Diff'
FROM
table
WHERE
Year IN (#YEAR1,#YEAR2)
group by Item;