I have specific task, and don't know how to realize it. I hope someone can help me =)
I have stock_move table:
product_id |location_id |location_dest_id |product_qty |date_expected |
-----------|------------|-----------------|------------|--------------------|
327 |80 |84 |10 |2014-05-28 00:00:00 |
327 |80 |84 |10 |2014-05-23 00:00:00 |
327 |80 |84 |10 |2014-02-26 00:00:00 |
327 |80 |85 |10 |2014-02-21 00:00:00 |
327 |80 |84 |10 |2014-02-12 00:00:00 |
327 |84 |85 |20 |2014-02-06 00:00:00 |
322 |84 |80 |120 |2015-12-16 00:00:00 |
322 |80 |84 |30 |2015-12-10 00:00:00 |
322 |80 |84 |30 |2015-12-04 00:00:00 |
322 |80 |84 |15 |2015-11-26 00:00:00 |
i.e. it's table of product moves from one warehouse to second.
I can calculate stock at custom date if I use something like this:
select
coalesce(si.product_id, so.product_id) as "Product",
(coalesce(si.stock, 0) - coalesce(so.stock, 0)) as "Stock"
from
(
select
product_id
,sum(product_qty * price_unit) as stock
from stock_move
where
location_dest_id = 80
and date_expected < now()
group by product_id
) as si
full outer join (
select
product_id
,sum(product_qty * price_unit) as stock
from stock_move
where
location_id = 80
and date_expected < now()
group by product_id
) as so
on si.product_id = so.product_id
Result I have current stock:
Product |Stock |
--------|------|
325 |1058 |
313 |34862 |
304 |2364 |
BUT what to do if I need stock per month?
something like this?
Month |Total Stock |
--------|------------|
Jan |130238 |
Feb |348262 |
Mar |2323364 |
How can I sum product qty from start period to end of each month?
I have just one idea - it's use 24 sub queries for get stock per each month (ex. below)
Jan |Feb | Mar |
----|----|-----|
123 |234 |345 |
End after this rotate rows and columns?
I think this's stupid, but I don't know another way... Help me pls =)
Something like this could give you monthly "ending" inventory snapshots. The trick is your data may omit certain months for certain parts, but that part will still have a balance (ie 50 received in January, nothing happened in February, but you still want to show February with a running total of 50).
One way to handle this is to come up with all possible combinations part/dates. I assumed 1/1/14 + 24 months in this example, but that's easily changed in the all_months subquery. For example, you may only want to start with the minimum date from the stock_move table.
with all_months as (
select '2014-01-01'::date + interval '1 month' * generate_series(0, 23) as month_begin
),
stock_calc as (
select
product_id, date_expected,
date_trunc ('month', date_expected)::date as month_expected,
case
when location_id = 80 then -product_qty * price_unit
when location_dest_id = 80 then product_qty * price_unit
else 0
end as qty
from stock_move
union all
select distinct
s.product_id, m.month_begin::date, m.month_begin::date, 0
from
stock_move s
cross join all_months m
),
running_totals as (
select
product_id, date_expected, month_expected,
sum (qty) over (partition by product_id order by date_expected) as end_qty,
row_number() over (partition by product_id, month_expected
order by date_expected desc) as rn
from stock_calc
)
select
product_id, month_expected, end_qty
from running_totals
where
rn = 1
Related
I'm on Postgres 13 and have a table like this
| key | from | to
-------------------------------------------
| A | 2022-11-27T08:00 | 2022-11-27T09:00
| B | 2022-11-27T09:00 | 2022-11-27T10:00
| C | 2022-11-27T08:30 | 2022-11-27T10:30
I want to calculate the duration of each record, but without overlaps. So the desired result would be
| key | from | to | duration
----------------------------------------------------------
| A | 2022-11-27T08:00 | 2022-11-27T09:00 | '1 hour'
| B | 2022-11-27T09:00 | 2022-11-27T09:45 | '45 minutes'
| C | 2022-11-27T08:30 | 2022-11-27T10:00 | '15 minutes'
I guess, I need a subquery and subtract the overlap somehow, but how would I factor in multiple overlaps? In the example above C overlaps A and B, so I must subtract 30 minutes from A and then 45 minute from B... But I'm stuck here:
SELECT key, (("to" - "from")::interval - s.overlap) as duration
FROM time_entries, (
SELECT (???) as overlap
) s
select
key,
fromDT,
toDT,
(toDT-fromDT)::interval -
COALESCE((SELECT SUM(LEAST(te2.toDT,te1.toDT)-GREATEST(te2.fromDT,te1.fromDT))::interval
FROM time_entries te2
WHERE (te2.fromDT<te1.toDT or te2.toDT>te1.fromDT)
AND te2.key<te1.key),'0 minutes') as duration
from time_entries te1;
output:
key
fromdt
todt
duration
A
2022-11-27 08:00:00
2022-11-27 09:00:00
01:00:00
B
2022-11-27 09:00:00
2022-11-27 10:00:00
01:00:00
C
2022-11-27 08:30:00
2022-11-27 10:30:00
00:30:00
I renamed the columns from and to to fromDT and toDT to avoid using reserved words.
a, step by step, explanation is in the DBFIDDLE
Another approach.
WITH DATA AS
(SELECT KEY,
FROMDT,
TODT,
MIN(FROMDT) OVER(PARTITION BY FROMDT::DATE
ORDER BY KEY) AS START_DATE,
MAX(TODT) OVER(PARTITION BY FROMDT::DATE
ORDER BY KEY) AS END_DATE
FROM TIME_ENTRIES
ORDER BY KEY) ,STAGING_DATA AS
(SELECT KEY,
FROMDT,
TODT,
COALESCE(LAG(START_DATE) OVER (PARTITION BY FROMDT::DATE
ORDER BY KEY),FROMDT) AS T1_DATE,
COALESCE(LAG(END_DATE) OVER (PARTITION BY FROMDT::DATE
ORDER BY KEY),TODT) AS T2_DATE
FROM DATA)
SELECT KEY,
FROMDT,
TODT,
CASE
WHEN FROMDT = T1_DATE
AND TODT = T2_DATE THEN (TODT - FROMDT) ::Interval
WHEN T2_DATE < TODT THEN (TODT - T2_DATE)::Interval
ELSE (T2_DATE - TODT)::interval
END
FROM STAGING_DATA;
In need of help to get the total balances of customers on a daily basis if I backtracked the data.
I have the following table structures in a Postgres database:
Table1: accounts (acc)
|id|acc_created|
|1 |2019-01-01 |
|2 |2019-01-01 |
|3 |2019-01-01 |
Table2: transactions
|transaction_id|acc_id|balance|txn_created |
|1 |1 |100 |2019-01-01 07:00:00|
|2 |1 |50 |2019-01-01 16:32:10|
|3 |1 |25 |2019-01-01 22:10:59|
|4 |2 |200 |2019-01-02 18:34:22|
|5 |3 |150 |2019-01-02 15:09:43|
|6 |1 |125 |2019-01-04 04:52:31|
|7 |1 |0 |2019-01-05 05:10:00|
|8 |2 |300 |2019-01-05 12:34:56|
|9 |3 |120 |2019-01-06 23:59:59|
The transactions table shows the balance after a transaction is made on the account.
To be honest, I am unsure how to write the query, or whether I am overthinking the situation. I know it would involve last_value() and coalesce(), and possibly lag() and lead(). Essentially the criterias I would like to fulfill are:
It takes the last balance value of that day, for that account.
(i.e. the balance for acc_id = '1' on 2019-01-01 would be $25, acc_id ='2' and '3' would be $0)
For days where there are no transaction made by an account, the balance would take from the previous balance of that account.
(i.e. the balance for acc_id = '1' on 2019-01-03 would be $25)
Lastly, I would like the total balance of all accounts aggregated by date.
(i.e. At end of 2019-01-02, the total balance should be $375 (=25+200+150)
I have tried the query below:
SELECT date_trunc('day',date), sum(balance_of_day) FROM (
SELECT txn.created as date,
acc_id,
row_number() over (partition BY acc_id ORDER BY txn_created ASC) as order_of_created,
last_value(balance) over (partition by acc_id ORDER BY txn_created RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) as balance_of_day
FROM transactions) X
where X.order_of_created = 1
GROUP BY 1
However, this only gives me the total balance if a transaction was made by any account on a certain day.
The expected end result (based on the example) should be:
|date |total_balance|
|2019-01-01 |25 |
|2019-01-02 |375 |
|2019-01-03 |375 |
|2019-01-04 |475 |
|2019-01-05 |450 |
|2019-01-06 |420 |
I won't need to present the different account numbers, just the total accumulated balance from all customers at the end of the day. Please let me know how I can solve this! Many thanks!
You can use a few cool postgres feature to accomplish this. First, to get the last balance per day, use DISTINCT ON:
SELECT DISTINCT on(acc_id, txn_created::date)
transaction_id, acc_id, balance, txn_created::date as day
FROM transactions
ORDER BY acc_id, txn_created::date, txn_created desc;
To figure out the balance on any given day, we'll use a daterange per row that includes the current row and excludes the next row, partitioned by acc_id:
SELECT transaction_id, acc_id, balance, daterange(day, lead(day, 1) OVER (partition by acc_id order by day), '[)')
FROM (
SELECT DISTINCT on(acc_id, txn_created::date)
transaction_id, acc_id, balance, txn_created::date as day
FROM transactions
ORDER BY acc_id, txn_created::date, txn_created desc
) sub;
Lastly, join to generate_series. We can join where the date in generate_series is contained by the daterange we created in the last step. The dateranges are intentionally not overlapping, so we can query on any date safely.
WITH balances as (
SELECT transaction_id, acc_id, balance, daterange(day, lead(day, 1) OVER (partition by acc_id order by day), '[)') as drange
FROM (
SELECT DISTINCT on(acc_id, txn_created::date)
transaction_id, acc_id, balance, txn_created::date as day
FROM transactions
ORDER BY acc_id, txn_created::date, txn_created desc
) sub
)
SELECT d::date, sum(balance)
FROM generate_series('2019-01-01'::date, '2019-01-06'::date, '1 day') as g(d)
JOIN balances ON d::date <# drange
GROUP BY d::date;
d | sum
------------+-----
2019-01-01 | 25
2019-01-02 | 375
2019-01-03 | 375
2019-01-04 | 475
2019-01-05 | 450
2019-01-06 | 420
(6 rows)
Here's a fiddle.
This is TSQL and I'm trying to calculate repeat purchase rate for last 12 months. This is achieved by looking at sum of customers who have bought more than 1 time last 12 months and the total number of customers last 12 months.
The SQL code below will give me just that; but i would like to dynamically do this for the last 12 months. This is the part where i'm stuck and not should how to best achieve this.
Each month should include data going back 12 months. I.e. June should hold data between June 2018 and June 2018, May should hold data from May 2018 till May 2019.
[Order Date] is a normal datefield (yyyy-mm-dd hh:mm:ss)
DECLARE #startdate1 DATETIME
DECLARE #enddate1 DATETIME
SET #enddate1 = DATEADD(MONTH, DATEDIFF(MONTH, 0, GETDATE())-1, 0) -- Starting June 2018
SET #startdate1 = DATEADD(mm,DATEDIFF(mm,0,GETDATE())-13,0) -- Ending June 2019
;
with dataset as (
select [Phone No_] as who_identifier,
count(distinct([Order No_])) as mycount
from [MyCompany$Sales Invoice Header]
where [Order Date] between #startdate1 and #enddate1
group by [Phone No_]
),
frequentbuyers as (
select who_identifier, sum(mycount) as frequentbuyerscount
from dataset
where mycount > 1
group by who_identifier),
allpurchases as (
select who_identifier, sum(mycount) as allpurchasescount
from dataset
group by who_identifier
)
select sum(frequentbuyerscount) as frequentbuyercount, (select sum(allpurchasescount) from allpurchases) as allpurchasecount
from frequentbuyers
I'm hoping to achieve end result looking something like this:
...Dec, Jan, Feb, March, April, May, June each month holding both values for frequentbuyercount and allpurchasescount.
Here is the code. I made a little modification for the frequentbuyerscount and allpurchasescount. If you use a sumif like expression you don't need a second cte.
if object_id('tempdb.dbo.#tmpMonths') is not null drop table #tmpMonths
create table #tmpMonths ( MonthID datetime, StartDate datetime, EndDate datetime)
declare #MonthCount int = 12
declare #Month datetime = DATEADD(MONTH, DATEDIFF(MONTH, 0, GETDATE()), 0)
while #MonthCount > 0 begin
insert into #tmpMonths( MonthID, StartDate, EndDate )
select #Month, dateadd(month, -12, #Month), #Month
set #Month = dateadd(month, -1, #Month)
set #MonthCount = #MonthCount - 1
end
;with dataset as (
select m.MonthID as MonthID, [Phone No_] as who_identifier,
count(distinct([Order No_])) as mycount
from [MyCompany$Sales Invoice Header]
inner join #tmpMonths m on [Order Date] between m.StartDate and m.EndDate
group by m.MonthID, [Phone No_]
),
buyers as (
select MonthID, who_identifier
, sum(iif(mycount > 1, mycount, 0)) as frequentbuyerscount --sum only if count > 1
, sum(mycount) as allpurchasescount
from dataset
group by MonthID, who_identifier
)
select
b.MonthID
, max(tm.StartDate) StartDate, max(tm.EndDate) EndDate
, sum(b.frequentbuyerscount) as frequentbuyercount
, sum(b.allpurchasescount) as allpurchasecount
from buyers b inner join #tmpMonths tm on tm.MonthID = b.MonthID
group by b.MonthID
Be aware, that the code was tested only syntax-wise.
After the test data, this is the result:
MonthID | StartDate | EndDate | frequentbuyercount | allpurchasecount
-----------------------------------------------------------------------------
2018-08-01 | 2017-08-01 | 2018-08-01 | 340 | 3702
2018-09-01 | 2017-09-01 | 2018-09-01 | 340 | 3702
2018-10-01 | 2017-10-01 | 2018-10-01 | 340 | 3702
2018-11-01 | 2017-11-01 | 2018-11-01 | 340 | 3702
2018-12-01 | 2017-12-01 | 2018-12-01 | 340 | 3703
2019-01-01 | 2018-01-01 | 2019-01-01 | 340 | 3703
2019-02-01 | 2018-02-01 | 2019-02-01 | 2 | 8
2019-03-01 | 2018-03-01 | 2019-03-01 | 2 | 3
2019-04-01 | 2018-04-01 | 2019-04-01 | 2 | 3
2019-05-01 | 2018-05-01 | 2019-05-01 | 2 | 3
2019-06-01 | 2018-06-01 | 2019-06-01 | 2 | 3
2019-07-01 | 2018-07-01 | 2019-07-01 | 2 | 3
Is there a function that calculates the total count of the complete month like below? I am not sure if postgres. I am looking for the grand total value.
2012-08=# select date_trunc('day', time), count(distinct column) from table_name group by 1 order by 1;
date_trunc | count
---------------------+-------
2012-08-01 00:00:00 | 22
2012-08-02 00:00:00 | 34
2012-08-03 00:00:00 | 25
2012-08-04 00:00:00 | 30
2012-08-05 00:00:00 | 27
2012-08-06 00:00:00 | 31
2012-08-07 00:00:00 | 23
2012-08-08 00:00:00 | 28
2012-08-09 00:00:00 | 28
2012-08-10 00:00:00 | 28
2012-08-11 00:00:00 | 24
2012-08-12 00:00:00 | 36
2012-08-13 00:00:00 | 28
2012-08-14 00:00:00 | 23
2012-08-15 00:00:00 | 23
2012-08-16 00:00:00 | 30
2012-08-17 00:00:00 | 20
2012-08-18 00:00:00 | 30
2012-08-19 00:00:00 | 20
2012-08-20 00:00:00 | 24
2012-08-21 00:00:00 | 20
2012-08-22 00:00:00 | 17
2012-08-23 00:00:00 | 23
2012-08-24 00:00:00 | 25
2012-08-25 00:00:00 | 35
2012-08-26 00:00:00 | 18
2012-08-27 00:00:00 | 16
2012-08-28 00:00:00 | 11
2012-08-29 00:00:00 | 22
2012-08-30 00:00:00 | 26
2012-08-31 00:00:00 | 17
(31 rows)
--------------------------------
Total | 12345
As best I can guess from your question and comments you want sub-totals of the distinct counts by month. You can't do this with group by date_trunc('month',time) because that'll do a count(distinct column) that's distinct across all days.
For this you need a subquery or CTE:
WITH day_counts(day,day_col_count) AS (
select date_trunc('day', time), count(distinct column)
from table_name group by 1
)
SELECT 'Day', day, day_col_count
FROM day_counts
UNION ALL
SELECT 'Month', date_trunc('month', day), sum(day_col_count)
FROM day_counts
GROUP BY 2
ORDER BY 2;
My earlier guess before comments was: Group by month?
select date_trunc('month', time), count(distinct column)
from table_name
group by date_trunc('month', time)
order by time
Or are you trying to include running totals or subtotal lines? For running totals you need to use sum as a window function. Subtotals are just a pain, as SQL doesn't really lend its self to them; you need to UNION two queries then wrap them in an outer ORDER BY.
select
date_trunc('day', time)::text as "date",
count(distinct column) as count
from table_name
group by 1
union
select
'Total',
count(distinct column)
from table_name
group by 1, date_trunc('month', time)
order by "date" = 'Total', 1
I need to use a where clause within an over clause. How?
SELECT SUM(amount) OVER(WHERE dateval > 12)
Or something like that.
--EDIT--
More details
My table is formatted with a year, month, and amount column.
I want to select all the year, month, and amount rows AND create a fourth 'virtual column' that has the sum of the past 12 months of amount column.
For example:
YEAR | MONTH | AMOUNT
2001 | 03 | 10
2001 | 05 | 25
2001 | 07 | 10
Should create:
YEAR | MONTH | AMOUNT | ROLLING 12 MONTHS
2001 | 03 | 10 | 10
2001 | 05 | 25 | 35
2001 | 07 | 10 | 45
Given a query against your three-column resultset, does the below work for you?
SELECT
SUM(amount) OVER(ORDER BY YEAR ASC, MONTH ASC
ROWS BETWEEN 11 PRECEDING AND CURRENT ROW)
...
select a,(select sum(a) from foo fa where fa.a > fb.a) from foo fb;
Doesn't use over, is pretty inefficient since it is running new sub-query for each query, but it works.