Fill Rows with future dates even when there's no value - sql

Story:
My dataset looks like this:
+---------+------+-----------------+---------+
| Date | Cost | Revenue Month | Revenue |
+---------+------+-----------------+---------+
| 2018-01 | 20 | 2018-02 | 20 |
| 2018-01 | 20 | 2018-03 | 100 |
| 2018-02 | 5 | 2018-03 | 15 |
| 2018-02 | 5 | 2018-04 | 25 |
+---------+------+-----------------+---------+
Basically the Date Column represents initial investment and the Revenue Month is for money generated due to the investment month. I would like to fill rows for the revenue month for each subsequent month until current month and force the Revenue to show 0 (i.e August 2020)
Objective:
+---------+------+-----------------+---------+---------+
| Date | Cost | Returning Month | Revenue | Product |
+---------+------+-----------------+---------+---------+
| 2018-01 | 20 | 2018-02 | 20 | A |
| 2018-01 | 20 | 2018-03 | 100 | A |
| 2018-01 | 20 | 2018-04 | 0 | A |
| 2018-01 | 20 | 2018-05 | 0 | A |
| 2018-02 | 5 | 2018-03 | 15 | A |
| 2018-02 | 5 | 2018-04 | 25 | A |
| 2018-02 | 5 | 2018-03 | 0 | A |
| 2018-02 | 5 | 2018-03 | 0 | A |
What I tried:
I built this tally date table
DROP TABLE IF EXISTS ##dates
CREATE TABLE ##dates ([date] Date)
DECLARE #dIncr DATE = '01/01/2018'
DECLARE #dEnd DATE = cast(getdate() as date)
WHILE (#dIncr <= #dEnd)
BEGIN
INSERT INTO ##dates ([date]) VALUES (#dIncr)
SELECT #dIncr = DATEADD(month,1,#dIncr)
END
But I'm stuck with this.


If you want to add two months to the data, you can use union all:
select Date, Cost, Returning_Month, Revenue, Product
from t
union all
select Date, Cost, dateadd(month, v.n, Returning_Month), 0 as Revenue, Product
from (select date, cost, max(returning_month) as returning_month, revenue, product
from t
group by date, cost, revenue, product
) t cross apply
(values (1), (2)) v(n);
EDIT:
Use a recursive CTE:
with cte as (
select date, cost, max(returning_month) as returning_month, revenue, product, 0 as lev
from t
group by date, cost, revenue, product
union all
select date, cost, dateadd(month, 1, returning_month), revenue, product, lev + 1
from cte
where returning_month < getdate()
)
select date, cost, returning_month, revenue, product
from cte
where lev > 0;

Related

Subtracting previous row value from current row

I'm doing an aggregation like this:
select
date,
product,
count(*) as cnt
from
t1
where
yyyy_mm_dd in ('2020-03-31', '2020-07-31', '2020-09-30', '2020-12-31')
group by
1,2
order by
product asc, date asc
This produces data which looks like this:
| date | product | cnt | difference |
|------------|---------|------|------------|
| 2020-03-31 | p1 | 100 | null |
| 2020-07-31 | p1 | 1000 | 900 |
| 2020-09-30 | p1 | 900 | -100 |
| 2020-12-31 | p1 | 1100 | 200 |
| 2020-03-31 | p2 | 200 | null |
| 2020-07-31 | p2 | 210 | 10 |
| ... | ... | ... | x |
But without the difference column. How could I make such a calculation? I could pivot the date column and subtract that way but maybe there's a better way

Was able to use lag with partition by and order by to get this to work:
select
date,
product,
count,
count - lag(count) over (partition by product order by date, product) as difference
from(
select
date,
product,
count(*) as count
from
t1
where
yyyy_mm_dd in ('2020-03-31', '2020-07-31', '2020-09-30', '2020-12-31')
group by
1,2
) t

Slicing account balance data in BigQuery to generate a debit report

I have a collection of account balances over time:
+-----------------+------------+-------------+-----------------------+
| account_balance | department | customer_id | timestamp |
+-----------------+------------+-------------+-----------------------+
| 5 | A | 1 | 2019-02-12T00:00:00 |
| -10 | A | 1 | 2019-02-13T00:00:00 |
| -35 | A | 1 | 2019-02-14T00:00:00 |
| 20 | A | 1 | 2019-02-15T00:00:00 |
+-----------------+------------+-------------+-----------------------+
Each record shows the total account balance of a customer at a specified timestamp. The account balance increases e.g. to 20 from -35, when a customer tops-up his account with 55. As a customer uses a services, his account balances decreases e.g. from 5 to -10.
I want to aggregate this data in two ways:
1) Get the debit, credit and balance (credit-debit) of a department per month and year. The results from April should be a summary of all previous months:
+---------+--------+-------+------------+-------+--------+
| balance | credit | debit | department | month | year |
+---------+--------+-------+------------+-------+--------+
| 5 | 10 | -5 | A | 1 | 2019 |
| 20 | 32 | -12 | A | 2 | 2019 |
| 35 | 52 | -17 | A | 3 | 2019 |
| 51 | 70 | -19 | A | 4 | 2019 |
+---------+--------+-------+------------+-------+--------+
A customer's account balance might not change every month. There might be account balance records of customer 1 in February, but not March.
Notes towards the solution:
use EXTRACT(MONTH from timestamp) month
use EXTRACT(YEAR from timestamp) year
GROUP BY month, year, department
2) Get the change of debit, credit and balance of a department by date.
+---------+--------+-------+------------+-------------+
| balance | credit | debit | department | date |
+---------+--------+-------+------------+-------------+
| 5 | 10 | -5 | A | 2019-01-15 |
| 15 | 22 | -7 | A | 2019-02-15 |
| 15 | 20 | -5 | A | 2019-03-15 |
| 16 | 18 | -2 | A | 2019-04-15 |
+---------+--------+-------+------------+-------------+
51 70 -19
When I create a SUM of the deltas, I should get the same values as the last row from results in 1).
Notes towards the solution:
use account_balance - LAG(account_balance) OVER(PARTITION BY department ORDER BY timestamp ASC) delta to compute deltas

Your question is unclear, but it sounds like you want to get the outstanding balance at any given point in time.
The following query does this for 1 point in time.
with calendar as (
select cast('2019-06-01' as timestamp) as balance_calc_ts
),
most_recent_balance as (
select customer_id, balance_calc_ts,max(timestamp) as most_recent_balance_ts
from <table>
cross join calendar
where timestamp < balance_calc_ts -- or <=
group by 1,2
)
select t.customer_id, t.account_balance, mrb.balance_calc_ts
from <table> t
inner join most_recent_balance mrb on t.customer_id = mrb.customer_id and t.timestamp = mrb.balance_calc_ts
If you need to calculate it at a series of points in time, you will need to modify the calendar CTE to return more dates. This is the beauty of CROSS JOINS in BQ!

Gaps And Islands: Splitting Islands Based On External Table

My scenario started off similar to a Island and Gaps problem, where I needed to find consecutive days of work. My current SQL query answers "ProductA was produced at LocationA from DateA through DateB, totaling X quantity".
However, this does not suffice when I needed to throw prices into the mix. Prices are in a separate table and handled in C# after the fact. Price changes are essentially a list of records that say "ProductA from LocationA is now Y value per unit effective DateC".
The end result is it works as long as the island does not overlap with a price-change date, but if it does overlap, I get a "close" answer, but it's not precise.
The C# code can handle applying the prices efficiently, what I need to do though is split the islands based on price changes. My goal is to make the SQL's partioning take into account the ranking of days from the other table, but I'm having trouble applying what I want to do.
The current SQL that generates my island is as follows
SELECT MIN(ScheduledDate) as StartDate, MAX(ScheduledDate) as
EndDate, ProductId, DestinationId, SUM(Quantity) as TotalQuantity
FROM (
SELECT ScheduledDate, DestinationId, ProductId, PartitionGroup = DATEADD(DAY ,-1 * DENSE_RANK() OVER (ORDER BY ScheduledDate), ScheduledDate), Quantity
FROM History
) tmp
GROUP BY PartitionGroup, DestinationId, ProductId;
The current SQL that takes from the PriceChange table and ranks the dates is as follows
DECLARE #PriceChangeDates TABLE(Rank int, SplitDate Date);
INSERT INTO #PriceChangeDates
SELECT DENSE_RANK() over (ORDER BY EffectiveDate) as Rank, EffectiveDate as SplitDate
FROM ProductPriceChange
GROUP BY EffectiveDate;
My thought is to somehow update the first queries inner SELECT statement to somehow take advantage of the #PriceChangeDates table created by the second query. I would think we can multiply the DATEADD's increment parameter by the rank from the declared table, but I am struggling to write it.
If I was to somehow do this with loops, my thought process would be to determine which rank the ScheduledDate would be from the #PriceChangeDates table, where its rank is the rank of the closest Date that is smaller than itself it can find. Then take whatever rank that gives and, I would think, multiply it by the increment parameter being passed in (or some math, for example doing a *#PriceChangeDates.Count() on the existing parameter and then adding in the new rank to avoid collisions). However, that's "loop" logic not "set" logic, and in SQL I need to think in sets.
Any and all help/advice is greatly appreciated. Thank you :)
UPDATE:
Sample data & example on SQLFiddle: http://www.sqlfiddle.com/#!18/af568/1
Where the data is:
CREATE TABLE History
(
ProductId int,
DestinationId int,
ScheduledDate date,
Quantity float
);
INSERT INTO History (ProductId, DestinationId, ScheduledDate, Quantity)
VALUES
(0, 1000, '20180401', 5),
(0, 1000, '20180402', 10),
(0, 1000, '20180403', 7),
(3, 5000, '20180507', 15),
(3, 5000, '20180508', 23),
(3, 5000, '20180509', 52),
(3, 5000, '20180510', 12),
(3, 5000, '20180511', 14);
CREATE TABLE PriceChange
(
ProductId int,
DestinationId int,
EffectiveDate date,
Price float
);
INSERT INTO PriceChange (ProductId, DestinationId, EffectiveDate, Price)
VALUES
(0, 1000, '20180201', 1),
(0, 1000, '20180402', 2),
(3, 5000, '20180101', 5),
(3, 5000, '20180510', 20);
The desired results would be to have a SQL statement that generates the result:
StartDate EndDate ProductId DestinationId TotalQuantity
2018-04-01 2018-04-01 0 1000 5
2018-04-02 2018-04-03 0 1000 17
2018-05-07 2018-05-09 3 5000 90
2018-05-10 2018-05-11 3 5000 26
To clarify, the end result does need the TotalQuantity of each split amount, so the procedural code that manipulates the results and applies the pricing knows how much of each product was one on each side of the price change to accurately determine the values.

Here is one more variant that is likely to perform better than my first answer. I decided to put it as a second answer, because the approach is rather different and the answer would be too long. You should compare performance of all variants with your real data on your hardware, and don't forget about indexes.
In the first variant I was using APPLY to pick a relevant price for each row in the History table. For each row from the History table the engine is searching for a relevant row from the PriceChange table. Even with appropriate index on the PriceChange table when this is done via a single seek, it still means 3.7 million seeks in a loop join.
We can simply join History and PriceChange tables together and with appropriate indexes on both tables it will be an efficient merge join.
Here I'm also using an extended sample data set to illustrate the gaps. I added these rows to the sample data from the question.
INSERT INTO History (ProductId, DestinationId, ScheduledDate, Quantity)
VALUES
(0, 1000, '20180601', 5),
(0, 1000, '20180602', 10),
(0, 1000, '20180603', 7),
(3, 5000, '20180607', 15),
(3, 5000, '20180608', 23),
(3, 5000, '20180609', 52),
(3, 5000, '20180610', 12),
(3, 5000, '20180611', 14);
Intermediate query
We do a FULL JOIN here, not a LEFT JOIN because it is possible that the date on which the price changed doesn't appear in the History table at all.
WITH
CTE_Join
AS
(
SELECT
ISNULL(History.ProductId, PriceChange.ProductID) AS ProductID
,ISNULL(History.DestinationId, PriceChange.DestinationId) AS DestinationId
,ISNULL(History.ScheduledDate, PriceChange.EffectiveDate) AS ScheduledDate
,History.Quantity
,PriceChange.Price
FROM
History
FULL JOIN PriceChange
ON PriceChange.ProductID = History.ProductID
AND PriceChange.DestinationId = History.DestinationId
AND PriceChange.EffectiveDate = History.ScheduledDate
)
,CTE2
AS
(
SELECT
ProductID
,DestinationId
,ScheduledDate
,Quantity
,Price
,MAX(CASE WHEN Price IS NOT NULL THEN ScheduledDate END)
OVER (PARTITION BY ProductID, DestinationId ORDER BY ScheduledDate
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS grp
FROM CTE_Join
)
SELECT *
FROM CTE2
ORDER BY
ProductID
,DestinationId
,ScheduledDate
Create the following indexes
CREATE UNIQUE NONCLUSTERED INDEX [IX_History] ON [dbo].[History]
(
[ProductId] ASC,
[DestinationId] ASC,
[ScheduledDate] ASC
)
INCLUDE ([Quantity])
CREATE UNIQUE NONCLUSTERED INDEX [IX_Price] ON [dbo].[PriceChange]
(
[ProductId] ASC,
[DestinationId] ASC,
[EffectiveDate] ASC
)
INCLUDE ([Price])
and the join will be an efficient MERGE join in the execution plan (not a LOOP join)
Intermediate result
+-----------+---------------+---------------+----------+-------+------------+
| ProductID | DestinationId | ScheduledDate | Quantity | Price | grp |
+-----------+---------------+---------------+----------+-------+------------+
| 0 | 1000 | 2018-02-01 | NULL | 1 | 2018-02-01 |
| 0 | 1000 | 2018-04-01 | 5 | NULL | 2018-02-01 |
| 0 | 1000 | 2018-04-02 | 10 | 2 | 2018-04-02 |
| 0 | 1000 | 2018-04-03 | 7 | NULL | 2018-04-02 |
| 0 | 1000 | 2018-06-01 | 5 | NULL | 2018-04-02 |
| 0 | 1000 | 2018-06-02 | 10 | NULL | 2018-04-02 |
| 0 | 1000 | 2018-06-03 | 7 | NULL | 2018-04-02 |
| 3 | 5000 | 2018-01-01 | NULL | 5 | 2018-01-01 |
| 3 | 5000 | 2018-05-07 | 15 | NULL | 2018-01-01 |
| 3 | 5000 | 2018-05-08 | 23 | NULL | 2018-01-01 |
| 3 | 5000 | 2018-05-09 | 52 | NULL | 2018-01-01 |
| 3 | 5000 | 2018-05-10 | 12 | 20 | 2018-05-10 |
| 3 | 5000 | 2018-05-11 | 14 | NULL | 2018-05-10 |
| 3 | 5000 | 2018-06-07 | 15 | NULL | 2018-05-10 |
| 3 | 5000 | 2018-06-08 | 23 | NULL | 2018-05-10 |
| 3 | 5000 | 2018-06-09 | 52 | NULL | 2018-05-10 |
| 3 | 5000 | 2018-06-10 | 12 | NULL | 2018-05-10 |
| 3 | 5000 | 2018-06-11 | 14 | NULL | 2018-05-10 |
+-----------+---------------+---------------+----------+-------+------------+
You can see that the Price column has a lot of NULL values. We need to "fill" these NULL values with the preceding non-NULL value.
Itzik Ben-Gan wrote a nice article showing how to solve this efficiently The Last non NULL Puzzle. Also see Best way to replace NULL with most recent non-null value.
This is done in CTE2 using MAX window function and you can see how it populates the grp column. This requires SQL Server 2012+. After the groups are determined we should remove rows where Quantity is NULL, because these rows are not from the History table.
Now we can do the same gaps-and-islands step using the grp column as an additional partitioning.
The rest of the query is pretty much the same as in the first variant.
Final query
WITH
CTE_Join
AS
(
SELECT
ISNULL(History.ProductId, PriceChange.ProductID) AS ProductID
,ISNULL(History.DestinationId, PriceChange.DestinationId) AS DestinationId
,ISNULL(History.ScheduledDate, PriceChange.EffectiveDate) AS ScheduledDate
,History.Quantity
,PriceChange.Price
FROM
History
FULL JOIN PriceChange
ON PriceChange.ProductID = History.ProductID
AND PriceChange.DestinationId = History.DestinationId
AND PriceChange.EffectiveDate = History.ScheduledDate
)
,CTE2
AS
(
SELECT
ProductID
,DestinationId
,ScheduledDate
,Quantity
,Price
,MAX(CASE WHEN Price IS NOT NULL THEN ScheduledDate END)
OVER (PARTITION BY ProductID, DestinationId ORDER BY ScheduledDate
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS grp
FROM CTE_Join
)
,CTE_RN
AS
(
SELECT
ProductID
,DestinationId
,ScheduledDate
,grp
,Quantity
,ROW_NUMBER() OVER (PARTITION BY ProductId, DestinationId, grp ORDER BY ScheduledDate) AS rn1
,DATEDIFF(day, '20000101', ScheduledDate) AS rn2
FROM CTE2
WHERE Quantity IS NOT NULL
)
SELECT
ProductId
,DestinationId
,MIN(ScheduledDate) AS StartDate
,MAX(ScheduledDate) AS EndDate
,SUM(Quantity) AS TotalQuantity
FROM
CTE_RN
GROUP BY
ProductId
,DestinationId
,grp
,rn2-rn1
ORDER BY
ProductID
,DestinationId
,StartDate
;
Final result
+-----------+---------------+------------+------------+---------------+
| ProductId | DestinationId | StartDate | EndDate | TotalQuantity |
+-----------+---------------+------------+------------+---------------+
| 0 | 1000 | 2018-04-01 | 2018-04-01 | 5 |
| 0 | 1000 | 2018-04-02 | 2018-04-03 | 17 |
| 0 | 1000 | 2018-06-01 | 2018-06-03 | 22 |
| 3 | 5000 | 2018-05-07 | 2018-05-09 | 90 |
| 3 | 5000 | 2018-05-10 | 2018-05-11 | 26 |
| 3 | 5000 | 2018-06-07 | 2018-06-11 | 116 |
+-----------+---------------+------------+------------+---------------+
This variant doesn't output the relevant price (as the first variant), because I simplified the "last non-null" query. It wasn't required in the question. In any case, it is pretty easy to add the price if needed.

The straight-forward method is to fetch the effective price for each row of History and then generate gaps and islands taking price into account.
It is not clear from the question what is the role of DestinationID. Sample data is of no help here.
I'll assume that we need to join and partition on both ProductID and DestinationID.
The following query returns effective Price for each row from History.
You need to add index to the PriceChange table
CREATE NONCLUSTERED INDEX [IX] ON [dbo].[PriceChange]
(
[ProductId] ASC,
[DestinationId] ASC,
[EffectiveDate] DESC
)
INCLUDE ([Price])
for this query to work efficiently.
Query for Prices
SELECT
History.ProductId
,History.DestinationId
,History.ScheduledDate
,History.Quantity
,A.Price
FROM
History
OUTER APPLY
(
SELECT TOP(1)
PriceChange.Price
FROM
PriceChange
WHERE
PriceChange.ProductID = History.ProductID
AND PriceChange.DestinationId = History.DestinationId
AND PriceChange.EffectiveDate <= History.ScheduledDate
ORDER BY
PriceChange.EffectiveDate DESC
) AS A
ORDER BY ProductID, ScheduledDate;
For each row from History there will be one seek in this index to pick the correct price.
This query returns:
Prices
+-----------+---------------+---------------+----------+-------+
| ProductId | DestinationId | ScheduledDate | Quantity | Price |
+-----------+---------------+---------------+----------+-------+
| 0 | 1000 | 2018-04-01 | 5 | 1 |
| 0 | 1000 | 2018-04-02 | 10 | 2 |
| 0 | 1000 | 2018-04-03 | 7 | 2 |
| 3 | 5000 | 2018-05-07 | 15 | 5 |
| 3 | 5000 | 2018-05-08 | 23 | 5 |
| 3 | 5000 | 2018-05-09 | 52 | 5 |
| 3 | 5000 | 2018-05-10 | 12 | 20 |
| 3 | 5000 | 2018-05-11 | 14 | 20 |
+-----------+---------------+---------------+----------+-------+
Now a standard gaps-and-island step to collapse consecutive days with the same price together. I use a difference of two row number sequences here.
I've added some more rows to your sample data to see the gaps within the same ProductId.
INSERT INTO History (ProductId, DestinationId, ScheduledDate, Quantity)
VALUES
(0, 1000, '20180601', 5),
(0, 1000, '20180602', 10),
(0, 1000, '20180603', 7),
(3, 5000, '20180607', 15),
(3, 5000, '20180608', 23),
(3, 5000, '20180609', 52),
(3, 5000, '20180610', 12),
(3, 5000, '20180611', 14);
If you run this intermediate query you'll see how it works:
WITH
CTE_Prices
AS
(
SELECT
History.ProductId
,History.DestinationId
,History.ScheduledDate
,History.Quantity
,A.Price
FROM
History
OUTER APPLY
(
SELECT TOP(1)
PriceChange.Price
FROM
PriceChange
WHERE
PriceChange.ProductID = History.ProductID
AND PriceChange.DestinationId = History.DestinationId
AND PriceChange.EffectiveDate <= History.ScheduledDate
ORDER BY
PriceChange.EffectiveDate DESC
) AS A
)
,CTE_rn
AS
(
SELECT
ProductId
,DestinationId
,ScheduledDate
,Quantity
,Price
,ROW_NUMBER() OVER (PARTITION BY ProductId, DestinationId, Price ORDER BY ScheduledDate) AS rn1
,DATEDIFF(day, '20000101', ScheduledDate) AS rn2
FROM
CTE_Prices
)
SELECT *
,rn2-rn1 AS Diff
FROM CTE_rn
Intermediate result
+-----------+---------------+---------------+----------+-------+-----+------+------+
| ProductId | DestinationId | ScheduledDate | Quantity | Price | rn1 | rn2 | Diff |
+-----------+---------------+---------------+----------+-------+-----+------+------+
| 0 | 1000 | 2018-04-01 | 5 | 1 | 1 | 6665 | 6664 |
| 0 | 1000 | 2018-04-02 | 10 | 2 | 1 | 6666 | 6665 |
| 0 | 1000 | 2018-04-03 | 7 | 2 | 2 | 6667 | 6665 |
| 0 | 1000 | 2018-06-01 | 5 | 2 | 3 | 6726 | 6723 |
| 0 | 1000 | 2018-06-02 | 10 | 2 | 4 | 6727 | 6723 |
| 0 | 1000 | 2018-06-03 | 7 | 2 | 5 | 6728 | 6723 |
| 3 | 5000 | 2018-05-07 | 15 | 5 | 1 | 6701 | 6700 |
| 3 | 5000 | 2018-05-08 | 23 | 5 | 2 | 6702 | 6700 |
| 3 | 5000 | 2018-05-09 | 52 | 5 | 3 | 6703 | 6700 |
| 3 | 5000 | 2018-05-10 | 12 | 20 | 1 | 6704 | 6703 |
| 3 | 5000 | 2018-05-11 | 14 | 20 | 2 | 6705 | 6703 |
| 3 | 5000 | 2018-06-07 | 15 | 20 | 3 | 6732 | 6729 |
| 3 | 5000 | 2018-06-08 | 23 | 20 | 4 | 6733 | 6729 |
| 3 | 5000 | 2018-06-09 | 52 | 20 | 5 | 6734 | 6729 |
| 3 | 5000 | 2018-06-10 | 12 | 20 | 6 | 6735 | 6729 |
| 3 | 5000 | 2018-06-11 | 14 | 20 | 7 | 6736 | 6729 |
+-----------+---------------+---------------+----------+-------+-----+------+------+
Now simply group by the Diff to get one row per interval.
Final query
WITH
CTE_Prices
AS
(
SELECT
History.ProductId
,History.DestinationId
,History.ScheduledDate
,History.Quantity
,A.Price
FROM
History
OUTER APPLY
(
SELECT TOP(1)
PriceChange.Price
FROM
PriceChange
WHERE
PriceChange.ProductID = History.ProductID
AND PriceChange.DestinationId = History.DestinationId
AND PriceChange.EffectiveDate <= History.ScheduledDate
ORDER BY
PriceChange.EffectiveDate DESC
) AS A
)
,CTE_rn
AS
(
SELECT
ProductId
,DestinationId
,ScheduledDate
,Quantity
,Price
,ROW_NUMBER() OVER (PARTITION BY ProductId, DestinationId, Price ORDER BY ScheduledDate) AS rn1
,DATEDIFF(day, '20000101', ScheduledDate) AS rn2
FROM
CTE_Prices
)
SELECT
ProductId
,DestinationId
,MIN(ScheduledDate) AS StartDate
,MAX(ScheduledDate) AS EndDate
,SUM(Quantity) AS TotalQuantity
,Price
FROM
CTE_rn
GROUP BY
ProductId
,DestinationId
,Price
,rn2-rn1
ORDER BY
ProductID
,DestinationId
,StartDate
;
Final result
+-----------+---------------+------------+------------+---------------+-------+
| ProductId | DestinationId | StartDate | EndDate | TotalQuantity | Price |
+-----------+---------------+------------+------------+---------------+-------+
| 0 | 1000 | 2018-04-01 | 2018-04-01 | 5 | 1 |
| 0 | 1000 | 2018-04-02 | 2018-04-03 | 17 | 2 |
| 0 | 1000 | 2018-06-01 | 2018-06-03 | 22 | 2 |
| 3 | 5000 | 2018-05-07 | 2018-05-09 | 90 | 5 |
| 3 | 5000 | 2018-05-10 | 2018-05-11 | 26 | 20 |
| 3 | 5000 | 2018-06-07 | 2018-06-11 | 116 | 20 |
+-----------+---------------+------------+------------+---------------+-------+

Not sure that i understand correctly, but this is just my idea:
Select concat_ws(',',view2.StartDate, string_agg(view1.splitDate, ','),
view2.EndDate), view2.productId, view2.DestinationId from (
SELECT DENSE_RANK() OVER (ORDER BY EffectiveDate) as Rank, EffectiveDate as
SplitDate FROM PriceChange GROUP BY EffectiveDate) view1 join
(
SELECT MIN(ScheduledDate) as StartDate, MAX(ScheduledDate) as
EndDate,ProductId, DestinationId, SUM(Quantity) as TotalQuantity
FROM (
SELECT ScheduledDate, DestinationId, ProductId, PartitionGroup =
DATEADD(DAY ,-1 * DENSE_RANK() OVER (ORDER BY ScheduledDate),
ScheduledDate), Quantity
FROM History
) tmp
GROUP BY PartitionGroup, DestinationId, ProductId
) view2 on view1.SplitDate >= view2.StartDate
and view1.SplitDate <=view2.EndDate
group by view2.startDate, view2.endDate, view2.productId,
view2.DestinationId
The result from this query will be:
| ranges | productId | DestinationId |
|---------------------------------------------|-----------|---------------|
| 2018-04-01,2018-04-02,2018-04-03 | 0 | 1000 |
| 2018-05-07,2018-05-10,2018-05-11 | 3 | 5000 |
Then, with any procedure language, for each row, you can split the string (with appropriate inclusive or exclusive rule for each boundary) to find out a list of condition (:from, :to, :productId, :destinationId).
And finally, you can loop through the list of conditions and use Union all clause to build one query (which is the union of all queries, which states a condition) to find out the final result. For example,
Select * from History where ScheduledDate >= '2018-04-01' and ScheduledDate <'2018-04-02' and productId = 0 and destinationId = 1000
union all
Select * from History where ScheduledDate >= '2018-04-02' and ScheduledDate <'2018-04-03' and productId = 0 and destinationId = 1000
----Update--------
Just based on above idea, i do some quick changes to provide your resultset. Maybe you can optimize it later
with view3 as
(Select concat_ws(',',view2.StartDate, string_agg(view1.splitDate, ','),
dateadd(day, 1, view2.EndDate)) dateRange, view2.productId, view2.DestinationId from (
SELECT DENSE_RANK() OVER (ORDER BY EffectiveDate) as Rank, EffectiveDate as
SplitDate FROM PriceChange GROUP BY EffectiveDate) view1 join
(
SELECT MIN(ScheduledDate) as StartDate, MAX(ScheduledDate) as
EndDate,ProductId, DestinationId, SUM(Quantity) as TotalQuantity
FROM (
SELECT ScheduledDate, DestinationId, ProductId, PartitionGroup =
DATEADD(DAY ,-1 * DENSE_RANK() OVER (ORDER BY ScheduledDate),
ScheduledDate), Quantity
FROM History
) tmp
GROUP BY PartitionGroup, DestinationId, ProductId
) view2 on view1.SplitDate >= view2.StartDate
and view1.SplitDate <=view2.EndDate
group by view2.startDate, view2.endDate, view2.productId,
view2.DestinationId
),
view4 as
(
select productId, destinationId, value from view3 cross apply string_split(dateRange, ',')
),
view5 as(
select *, row_number() over(partition by productId, destinationId order by value) rn from view4
),
view6 as (
select v52.value fr, v51.value t, v51.productid, v51. destinationid from view5 v51 join view5 v52
on v51.productid = v52.productid
and v51.destinationid = v52.destinationid
and v51.rn = v52.rn+1
)
select min(h.ScheduledDate) StartDate, max(h.ScheduledDate) EndDate, v6.productId, v6.destinationId, sum(h.quantity) TotalQuantity from view6 v6 join History h
on v6.destinationId = h.destinationId
and v6.productId = h.productId
and h.ScheduledDate >= v6.fr
and h.ScheduledDate <v6.t
group by v6.fr, v6.t, v6.productId, v6.destinationId
And the result is exactly the same with what you gave.
| StartDate | EndDate | productId | destinationId | TotalQuantity |
|------------|------------|-----------|---------------|---------------|
| 2018-04-01 | 2018-04-01 | 0 | 1000 | 5 |
| 2018-04-02 | 2018-04-03 | 0 | 1000 | 17 |
| 2018-05-07 | 2018-05-09 | 3 | 5000 | 90 |
| 2018-05-10 | 2018-05-11 | 3 | 5000 | 26 |

Use outer apply to choose the nearest price, then do a group by:
Live test: http://www.sqlfiddle.com/#!18/af568/65
select
StartDate = min(h.ScheduledDate),
EndDate = max(h.ScheduledDate),
h.ProductId,
h.DestinationId,
TotalQuantity = sum(h.Quantity)
from History h
outer apply
(
select top 1 pc.*
from PriceChange pc
where
pc.ProductId = h.ProductId
and pc.Effectivedate <= h.ScheduledDate
order by pc.EffectiveDate desc
) UpToDate
group by UpToDate.EffectiveDate,
h.ProductId,
h.DestinationId
order by StartDate, EndDate, ProductId
Output:
| StartDate | EndDate | ProductId | DestinationId | TotalQuantity |
|------------|------------|-----------|---------------|---------------|
| 2018-04-01 | 2018-04-01 | 0 | 1000 | 5 |
| 2018-04-02 | 2018-04-03 | 0 | 1000 | 17 |
| 2018-05-07 | 2018-05-09 | 3 | 5000 | 90 |
| 2018-05-10 | 2018-05-11 | 3 | 5000 | 26 |

Create calculated value based on calculated value inside previous row

I'm trying to find a way to apply monthly percentage changes to forecast pricing. I set my problem up in excel to make it a bit more clear. I'm using SQL Server 2017.
We'll say all months before 9/1/18 are historical and 9/1/18 and beyond are forecasts. I need to calculate the forecast price (shaded in yellow on the sample data) using...
Forecast Price = (Previous Row Forecast Price * Pct Change) + Previous Row Forecast Price
Just to be clear, the yellow shaded prices do not exist in my data yet. That is what I am trying to have my query calculate. Since this is monthly percentage change, each row depends on the row before and goes beyond a single ROW_NUMBER/PARTITION solution because we have to use the previous calculated price. Clearly what is an easy sequential calculation in excel is a bit more difficult here. Any idea how to create forecasted price column in SQL?

You need to use a recursive CTE. That is one of the easier ways to look at the value of a calculated value from previous row:
DECLARE #t TABLE(Date DATE, ID VARCHAR(10), Price DECIMAL(10, 2), PctChange DECIMAL(10, 2));
INSERT INTO #t VALUES
('2018-01-01', 'ABC', 100, NULL),
('2018-01-02', 'ABC', 150, 50.00),
('2018-01-03', 'ABC', 130, -13.33),
('2018-01-04', 'ABC', 120, -07.69),
('2018-01-05', 'ABC', 110, -08.33),
('2018-01-06', 'ABC', 120, 9.09),
('2018-01-07', 'ABC', 120, 0.00),
('2018-01-08', 'ABC', 100, -16.67),
('2018-01-09', 'ABC', NULL, -07.21),
('2018-01-10', 'ABC', NULL, 1.31),
('2018-01-11', 'ABC', NULL, 6.38),
('2018-01-12', 'ABC', NULL, -30.00),
('2019-01-01', 'ABC', NULL, 14.29),
('2019-01-02', 'ABC', NULL, 5.27);
WITH ncte AS (
-- number the rows sequentially without gaps
SELECT *, ROW_NUMBER() OVER (PARTITION BY ID ORDER BY Date) AS rn
FROM #t
), rcte AS (
-- find first row in each group
SELECT *, Price AS ForecastedPrice
FROM ncte AS base
WHERE rn = 1
UNION ALL
-- find next row for each group from prev rows
SELECT curr.*, CAST(prev.ForecastedPrice * (1 + curr.PctChange / 100) AS DECIMAL(10, 2))
FROM ncte AS curr
INNER JOIN rcte AS prev ON curr.ID = prev.ID AND curr.rn = prev.rn + 1
)
SELECT *
FROM rcte
ORDER BY ID, rn
Result:
| Date | ID | Price | PctChange | rn | ForecastedPrice |
|------------|-----|--------|-----------|----|-----------------|
| 2018-01-01 | ABC | 100.00 | NULL | 1 | 100.00 |
| 2018-01-02 | ABC | 150.00 | 50.00 | 2 | 150.00 |
| 2018-01-03 | ABC | 130.00 | -13.33 | 3 | 130.01 |
| 2018-01-04 | ABC | 120.00 | -7.69 | 4 | 120.01 |
| 2018-01-05 | ABC | 110.00 | -8.33 | 5 | 110.01 |
| 2018-01-06 | ABC | 120.00 | 9.09 | 6 | 120.01 |
| 2018-01-07 | ABC | 120.00 | 0.00 | 7 | 120.01 |
| 2018-01-08 | ABC | 100.00 | -16.67 | 8 | 100.00 |
| 2018-01-09 | ABC | NULL | -7.21 | 9 | 92.79 |
| 2018-01-10 | ABC | NULL | 1.31 | 10 | 94.01 |
| 2018-01-11 | ABC | NULL | 6.38 | 11 | 100.01 |
| 2018-01-12 | ABC | NULL | -30.00 | 12 | 70.01 |
| 2019-01-01 | ABC | NULL | 14.29 | 13 | 80.01 |
| 2019-01-02 | ABC | NULL | 5.27 | 14 | 84.23 |
Demo on DB Fiddle

In SQL Server you can access values of previous / next rows by using the windowing functions LAG and LEAD. You need to define the order of the rows by specifying it in the OVER clause. You may need to wrap the select query, that returns prev/next values in a derived table or CTE, and then select from it and calculate your forecasts.
with cte as (SELECT [Date], Price, LAG(Price, 1) over(order by [Date]) as PrevPrice from TABLE)
select [Date], Price, Price - PrevPrice as PriceChange from cte

SQL Get previous stocks based on modified dates

I have a pretty strange business requirement that I need to fulfill with the following two tables:
STOCK_TB (As of 20150319)
PRODUCT_ID STOCK_QTY
A 20
B 15
STOCK_MODIFIED_TB
PRODUCT_ID MODIFIED_QTY MODIFIED_DATE_FROM MODIFIED_DATE_TO
A 10 20150315 20150318
B -5 20150314 20150316
A -2 20150314 20150316
STOCK_TB represents the current stock of inventory, while STOCK_MODIFIED_TB represents the quantity of stocks modified in a date range. I need to select results of stocks for previous dates. Suppose the result was retrieved on 20150319 for dates 20150314-20150319. This is what the result should look like:
DATE PRODUCT_ID STOCK_QTY
20150314 A 18
20150314 B 10
20150315 A 28
20150315 B 10
20150316 A 28
20150316 B 10
20150317 A 30
20150317 B 15
20150318 A 30
20150318 B 15
20150319 A 20
20150319 B 15
In other words, the stocks for previous dates would be added/subtracted based on the date range given in STOCK_MODIFIED_TB
Is selecting data like this possible without cursors?

I'll try with this answer, of course my subquery in select looking not so well with performance I guess... :
SQLFIddleExample
SELECT cast(a.Date as date) Date,
st.PRODUCT_ID,
st.STOCK_QTY + isnull((SELECT SUM(MODIFIED_QTY)
FROM STOCK_MODIFIED_TB
WHERE MODIFIED_DATE_FROM <= CONVERT(VARCHAR(10), a.Date, 112)
AND MODIFIED_DATE_TO >= CONVERT(VARCHAR(10), a.Date, 112)
AND PRODUCT_ID = st.PRODUCT_ID ),0) STOCK_QTY
FROM STOCK_TB st,
(select DATEADD(day, number, '2015-01-01') Date
from master..spt_values
where type = 'p' ) a
WHERE a.Date between '2015-03-14' and '2015-03-19'
ORDER BY a.Date, st.PRODUCT_ID
Result:
| Date | PRODUCT_ID | STOCK_QTY |
|------------|------------|-----------|
| 2015-03-14 | A | 18 |
| 2015-03-14 | B | 10 |
| 2015-03-15 | A | 28 |
| 2015-03-15 | B | 10 |
| 2015-03-16 | A | 28 |
| 2015-03-16 | B | 10 |
| 2015-03-17 | A | 30 |
| 2015-03-17 | B | 15 |
| 2015-03-18 | A | 30 |
| 2015-03-18 | B | 15 |
| 2015-03-19 | A | 20 |
| 2015-03-19 | B | 15 |