Use sum function in calculated column - tsql

Is it possible to use a sum function in a calculated column?
If yes, I would like to create a calculated column, that calculates the sum of a column in the same table where the date is smaller than the date of this entry. is this possible?
And last, would this optimize repeated calls on this value over the exemplified view below?
SELECT ProductGroup, SalesDate, (
SELECT SUM(Sales)
FROM SomeList
WHERE (ProductGroup= KVU.ProductGroup) AND (SalesDate<= KVU.SalesDate)) AS cumulated
FROM SomeList AS KVU

Is it possible to use a sum function in a calculated column?
Yes, it's possible using a scalar valued function (scalar UDF) for you computed column but this would be a disaster. Using scalar UDFs for computed columns destroy performance. Adding a scalar UDF that accesses data (which would be required here) makes things even worse.
It sounds to me like you just need a good ol' fashioned index to speed things up. First some sample data:
IF OBJECT_ID('dbo.somelist','U') IS NOT NULL DROP TABLE dbo.somelist;
GO
CREATE TABLE dbo.somelist
(
ProductGroup INT NOT NULL,
[Month] TINYINT NOT NULL CHECK ([Month] <= 12),
Sales DECIMAL(10,2) NOT NULL
);
INSERT dbo.somelist
VALUES (1,1,22),(2,1,45),(2,1,25),(2,1,19),(1,2,100),(1,2,200),(2,2,50.55);
and the correct index:
CREATE NONCLUSTERED INDEX nc_somelist ON dbo.somelist(ProductGroup,[Month])
INCLUDE (Sales);
With this index in place this query would be extremely efficient:
SELECT s.ProductGroup, s.[Month], SUM(s.Sales)
FROM dbo.somelist AS s
GROUP BY s.ProductGroup, s.[Month];
If you needed to get a COUNT by month & product group you could create an indexed view like so:
CREATE VIEW dbo.vw_somelist WITH SCHEMABINDING AS
SELECT s.ProductGroup, s.[Month], TotalSales = COUNT_BIG(*)
FROM dbo.somelist AS s
GROUP BY s.ProductGroup, s.[Month];
GO
CREATE UNIQUE CLUSTERED INDEX uq_cl__vw_somelist ON dbo.vw_somelist(ProductGroup, [Month]);
Once that indexed view was in place your COUNTs would be pre-aggregated. You cannot, however, include SUM in an indexed view.

Related

PostgreSQL array of data composite update element using where condition

I have a composite type:
CREATE TYPE mydata_t AS
(
user_id integer,
value character(4)
);
Also, I have a table, uses this composite type as an array of mydata_t.
CREATE TABLE tbl
(
id serial NOT NULL,
data_list mydata_t[],
PRIMARY KEY (id)
);
Here I want to update the mydata_t in data_list, where mydata_t.user_id is 100000
But I don't know which array element's user_id is equal to 100000
So I have to make a search first to find the element where its user_id is equal to 100000 ... that's my problem ... I don't know how to make the query .... in fact, I want to update the value of the array element, where it's user_id is equal to 100000 (Also where the id of tbl is for example 1) ... What will be my query?
Something like this (I know it's wrong !!!)
UPDATE "tbl" SET "data_list"[i]."value"='YYYY'
WHERE "id"=1 AND EXISTS (SELECT ROW_NUMBER() OVER() AS i
FROM unnest("data_list") "d" WHERE "d"."user_id"=10000 LIMIT 1)
For example, this is my tbl data:
Row1 => id = 1, data = ARRAY[ROW(5,'YYYY'),ROW(6,'YYYY')]
Row2 => id = 2, data = ARRAY[ROW(10,'YYYY'),ROW(11,'YYYY')]
Now i want to update tbl where id is 2 and set the value of one of the tbl.data elements to 'XXXX' where the user_id of element is equal to 11
In fact, the final result of Row2 will be this:
Row2 => id = 2, data = ARRAY[ROW(10,'YYYY'),ROW(11,'XXXX')]
If you know the value value, you can use the array_replace() function to make the change:
UPDATE tbl
SET data_list = array_replace(data_list, (11, 'YYYY')::mydata_t, (11, 'XXXX')::mydata_t)
WHERE id = 2
If you do not know the value value then the situation becomes more complex:
UPDATE tbl SET data_list = data_arr
FROM (
-- UPDATE doesn't allow aggregate functions so aggregate here
SELECT array_agg(new_data) AS data_arr
FROM (
-- For the id value, get the data_list values that are NOT modified
SELECT (user_id, value)::mydata_t AS new_data
FROM tbl, unnest(data_list)
WHERE id = 2 AND user_id != 11
UNION
-- Add the values to update
VALUES ((11, 'XXXX')::mydata_t)
) x
) y
WHERE id = 2
You should keep in mind, though, that there is an awful lot of work going on in the background that cannot be optimised. The array of mydata_t values has to be examined from start to finish and you cannot use an index on this. Furthermore, updates actually insert a new row in the underlying file on disk and if your array has more than a few entries this will involve substantial work. This gets even more problematic when your arrays are larger than the pagesize of your PostgreSQL server, typically 8kB. All behind the scene so it will work, but at a performance penalty. Even though array_replace sounds like changes are made in-place (and they indeed are in memory), the UPDATE command will write a completely new tuple to disk. So if you have 4,000 array elements that means that at least 40kB of data will have to be read (8 bytes for the mydata_t type on a typical system x 4,000 = 32kB in a TOAST file, plus the main page of the table, 8kB) and then written to disk after the update. A real performance killer.
As #klin pointed out, this design may be more trouble than it is worth. Should you make data_list as table (as I would do), the update query becomes:
UPDATE data_list SET value = 'XXXX'
WHERE id = 2 AND user_id = 11
This will have MUCH better performance, especially if you add the appropriate indexes. You could then still create a view to publish the data in an aggregated form with a custom type if your business logic so requires.

Computed table column with MAX value between rows containing a shared value

I have the following table
CREATE TABLE T2
( ID_T2 integer NOT NULL PRIMARY KEY,
FK_T1 integer, <--- foreign key to T1(Table1)
FK_DATE date, <--- foreign key to T1(Table1)
T2_DATE date, <--- user input field
T2_MAX_DIFF COMPUTED BY ( (SELECT DATEDIFF (day, MAX(T2_DATE), CURRENT_DATE) FROM T2 GROUP BY FK_T1) )
);
I want T2_MAX_DIFF to display the number of days since last input across all similar entries with a common FK_T1.
It does work, but if another FK_T1 values is added to the table, I'm getting an error about "multiple rows in singleton select".
I'm assuming that I need some sort of WHERE FK_T1 = FK_T1 of corresponding row. Is it possible to add this? I'm using Firebird 3.0.7 with flamerobin.
The error "multiple rows in singleton select" means that a query that should provide a single scalar value produced multiple rows. And that is not unexpected for a query with GROUP BY FK_T1, as it will produce a row per FK_T1 value.
To fix this, you need to use a correlated sub-query by doing the following:
Alias the table in the subquery to disambiguate it from the table itself
Add a where clause, making sure to use the aliased table (e.g. src, and src.FK_T1), and explicitly reference the table itself for the other side of the comparison (e.g. T2.FK_T1)
(optional) remove the GROUP BY clause because it is not necessary given the WHERE clause. However, leaving the GROUP BY in place may uncover certain types of errors.
The resulting subquery then becomes:
(SELECT DATEDIFF (day, MAX(src.T2_DATE), CURRENT_DATE)
FROM T2 src
WHERE src.FK_T1 = T2.FK_T1
GROUP BY src.FK_T1)
Notice the alias src for the table referenced in the subquery, the use of src.FK_T1 in the condition, and the explicit use of the table in T2.FK_T1 to reference the column of the current row of the table itself. If you'd use src.FK_T1 = FK_T1, it would compare with the FK_T1 column of src (as if you'd used src.FK_T1 = src.FK_T2), so that would always be true.
CREATE TABLE T2
( ID_T2 integer NOT NULL PRIMARY KEY,
FK_T1 integer,
FK_DATE date,
T2_DATE date,
T2_MAX_DIFF COMPUTED BY ( (
SELECT DATEDIFF (day, MAX(src.T2_DATE), CURRENT_DATE)
FROM T2 src
WHERE src.FK_T1 = T2.FK_T1
GROUP BY src.FK_T1) )
);

Why grouping method sum in slick returns Option even if column used for sum is mandatory column?

CREATE TABLE orders
(
id bigint NOT NULL,
...
created_on date NOT NULL,
quantity int NOT NULL,
...
CONSTRAINT orders_pkey PRIMARY KEY (id)
)
SELECT DATE(o.created_on) AS date, sum(quantity)
FROM orders o
GROUP BY date
ordersItemsQuery.groupBy(_.createdOn).map{
case (created, group) => (created, group.map(_.quantity).sum)
}
notice quantity is not null column, group.map(_.quantity).sum returns Rep[Option[Int]] but not Rep[Int] why?
The Slick method sum evaluates Option[T], and shouldn't be confused with the standard Scala collections method sum that returns a non-optional value.
Slick's sum is optional because a query may produce no results. That is, if you run SELECT SUM(column) FROM table and there are no rows, you do not get back zero from the database. Instead, you get back no rows. Slick is being consistent with this behaviour. Or rather: the sum is happening in SQL, on the database server, and doesn't produce a result when there are no rows.
In contrast to the way a database works, Scala's sum does allow you to sum an empty list (List[Int]().sum) and get back zero.

How to split array in json using json_query?

I've got a column in a table that's a json. It contains only values without keys like
Now I'm trying to split the data from the json and create new table using every index of each array as new entry like
I've already tried
SELECT JSON_QUERY(abc) as 'Type', Id as 'ValueId' from Table FOR JSON AUTO
Is there any way to handle splitting given that some arrays might be empty and look like
[]
?
A fairly simply approach would be to use outer apply with openjson.
First, create and populate sample table (Please save us this step in your future questions):
DECLARE #T AS TABLE
(
Id int,
Value nvarchar(20)
)
INSERT INTO #T VALUES
(1, '[10]'),
(2, '[20, 200]'),
(3, '[]'),
(4, '')
The query:
SELECT Id, JsonValues.Value
FROM #T As t
OUTER APPLY
OPENJSON( Value ) As JsonValues
WHERE ISJSON(t.Value) = 1
Results:
Id Value
1 10
2 20
2 200
3 NULL
Note the ISJSON condition in the where clause will prevent exceptions in case the Value column contains anything other than a valid json (an empty array [] is still considered valid for this purpose).
If you don't want to return a row where the json array is empty, use cross apply instead of outer apply.
Your own code calling for FOR JSON AUTO tries to create JSON out of tabular data. But what you really needs seems to be the opposite direction: You want to transform JSON to a result set, a derived table. This is done by OPENJSON.
Your JSON seems to be a very minimalistic array.
You can try something along this.
DECLARE #json NVARCHAR(MAX) =N'[1,2,3]';
SELECT * FROM OPENJSON(#json);
The result returns the zero-based ordinal position in key, the actual value in value and a (very limited) type-enum.
Hint: If you want to use this against a table's column you must use APPLY, something along
SELECT *
FROM YourTable t
OUTER APPLY OPENJSON(t.TheJsonColumn);

PostgreSQL hierarchical nested set huge database

I have a database that must store thousands of scenarios (each scenario with a single unix_timestamp value). Each scenario has 1,800,000 registers organized in a Nested Set structure.
The general table structure is given by:
table_skeleton:
- unix_timestamp integer
- lft integer
- rgt integer
- value
Usually, my SELECTs are will perform taking all nested values within an specific scenario, it means for example:
SELECT * FROM table_skeleton WHERE unix_timestamp = 123 AND lft >= 10 AND rgt <= 53
So I hierarchically divided my table into master / children within groups of dates, for example:
table_skeleton_201303 inherits table_skeleton:
- unix_timestamp integer
- lft integer
- ...
and
table_skeleton_201304 inherits table_skeleton:
- unix_timestamp integer
- lft integer
- ...
And also created index for each children according to the usual search I am expecting, it is for example:
Create Index idx_201303
on table_skeleton_201303
using btree(unix_timestamp, lft, rgt)
It improved the retrieval, but it still takes about 1 minute for each select.
I imagined that this was because the index was too big to be loaded into memory always so I tried to create partial index for each timestamp, for example:
Create Index idx_201303_1362981600
on table_skeleton_201303
using btree(lft, rgt)
WHERE unix_timestamp = 1362981600
And in fact the second type of index created is much, much, much smaller than the general one. However, when I run an EXPLAIN ANALYZE for the SELECT I've previously shown here, the query solver ignores my new partial index and keeps using the giant old one.
Is there a reason for that?
Is there any new approach to optimize such type of huge nested set hierarchical database?
When you filter on a table by field_a > x and field_b > y, then an index for field_a, field_b will (actually just may, depending on the distribution and the percentage of rows with field_a > x, as per the statistics collected) only be used for "field_a > x", and field_b > y will be a sequential search.
In the case above, having two indexes, one for each field, could be used and each of the results joined, the internal equivalent of:
SELECT *
FROM table t
JOIN (
SELECT id table field_a > x) ta ON (ta.id = t.id)
JOIN (
SELECT id table field_b > y) tb ON (tb.id = t.id);
There is a change you could benefit from a GIST index, and treating your lft and rgt fields as points:
CREATE INDEX ON table USING GIST (unix_timestamp, point(lft, rgt));
SELECT * table
WHERE unix_timestamp = 123 AND
point(lft,rgt) <# box(point(10,'-inf'), point('inf',53));