I have these rows in my product table:
product_name | product_code | percentage.
prod1#00X | 1 | 50
prod2#00X | 2 | 20
prod3#00X | 3 | 30
I wanna select all the elements of my table but I wanna show 1/100 of the percentage
The result should be:
prod1#00X | 1 | 0.50
prod2#00X | 2 | 0.20
prod3#00X | 3 | 0.30
How can I do?
I wanna find another solution not this:
SELECT product_name, product_code, (percentage/100) as percentage FROM product
Note: I have several columns in my table, not only product_name | product_code | percentage.
Try this way:
SELECT (percentage/100) as percentage,*
FROM product
OR
Using another alias:
SELECT *,(percentage/100) as NewPercentage
FROM product
you can use select statement as
SELECT *
, (percentage/100) as newpercentage
FROM product
where (your condition )
Related
I have a table called example that looks as follows:
ID | MIN | MAX |
1 | 1 | 5 |
2 | 34 | 38 |
I need to take each ID and loop from it's min to max, incrementing by 2 and thus get the following WITHOUT using INSERT statements, thus in a SELECT:
ID | INDEX | VALUE
1 | 1 | 1
1 | 2 | 3
1 | 3 | 5
2 | 1 | 34
2 | 2 | 36
2 | 3 | 38
Any ideas of how to do this?
The set-returning function generate_series does exactly that:
SELECT
id,
generate_series(1, (max-min)/2+1) AS index,
generate_series(min, max, 2) AS value
FROM
example;
(online demo)
The index can alternatively be generated with RANK() (example, see also #a_horse_with_no_name's answer) if you don't want to rely on the parallel sets.
Use generate_series() to generate the numbers and a window function to calculate the index:
select e.id,
row_number() over (partition by e.id order by g.value) as index,
g.value
from example e
cross join generate_series(e.min, e.max, 2) as g(value);
Say I have a table like the following table that represents a path from 1 -> 2 -> 3 -> 4 -> 5:
+------+----+--------+
| from | to | weight |
+------+----+--------+
| a | b | 1 |
| b | c | 2 |
| c | d | 1 |
| d | e | 1 |
| e | f | 3 |
+------+----+--------+
Each row knows where it came from and where it is going
I would like to union a total row that takes the starting name, ending name, and a total weight like so:
+------+----+--------+
| from | to | weight |
+------+----+--------+
| a | f | 8 |
+------+----+--------+
The first table is a result of a CTE expression, and I can easily get the total of the previous query with SUM, but I'm unable to get the LAST_VALUE to work in a similar way to:
WITH RECURSIVE cte AS (
...
)
SELECT *
FROM cte
UNION ALL
SELECT 'total', FIRST_VALUE(from), LAST_VALUE(to), SUM(weight)
FROM cte
The FIRST_VALUE and LAST_VALUE functions require OVER clauses which seem to add unnecessary complications to what I would expect, so I think I am going the wrong direction with that. Any ideas on how to achieve this?
So I made a strange solution that:
Selects the first from value (partitioned by TRUE)
Selects the last to value (partitioned by TRUE again)
Cross joins the sum of all weights, limited to 1
WITH RECURSIVE cte AS (
...
)
SELECT *
FROM cte
UNION ALL (
SELECT FIRST_VALUE(from) OVER (PARTITION BY TRUE), LAST_VALUE(to) OVER (PARTITION BY TRUE), total
FROM cte
CROSS JOIN (
SELECT SUM(weight) as total
FROM cte
) tmp
LIMIT 1
);
Is it hacky? Yes. Does it work? Also yes. I'm sure there are better solutions, and I would love to hear them.
I'm working on a problem, involving these two tables.
books
isbn | title | author
------------+-----------------------------------------+------------------
1840918626 | Hogwarts: A History | Bathilda Bagshot
3458400871 | Fantastic Beasts and Where to Find Them | Newt Scamander
9136884926 | Advanced Potion-Making | Libatius Borage
transactions
id | patron_id | isbn | checked_out_date | checked_in_date
----+-----------+------------+------------------+-----------------
1 | 1 | 1840918626 | 2012-05-05 | 2012-05-06
2 | 4 | 9136884926 | 2012-05-05 | 2012-05-06
3 | 2 | 3458400871 | 2012-05-05 | 2012-05-06
4 | 3 | 3458400871 | 2018-04-29 | 2018-05-02
5 | 2 | 9136884926 | 2018-05-03 | NULL
6 | 1 | 3458400871 | 2018-05-03 | 2018-05-05
7 | 5 | 3458400871 | 2018-05-05 | NULL
the query "Make a list of all book titles and denote whether or not a copy of that book is checked out." so pretty much just the first table with a checked out column.
im trying to SELECT DISTINCT on a sub query with the checkout books first, but that doesn't work. I've researched and others say to accomplish this use a GROUP BY clause instead of DISTINCT but the examples they provide are one column queries and when more columns are added it doesn't work.
this is my closest attempt
SELECT DISTINCT ON (title)
title, checked_out
FROM(
SELECT b.title, t.checked_in_date IS NULL AS checked_out
FROM transactions t
natural join books b
ORDER BY checked_out DESC
) t;
or you can join only transactions where books are not checked in:
SELECT b.title, t.isbn IS NOT NULL AS checked_out
, t.checked_out_date
FROM books b
LEFT JOIN transactions t ON t.isbn = b.isbn AND t.checked_in_date IS NULL
ORDER BY checked_out DESC
I adjusted your attempt a little bit. Basically I changed the way your data is joined
SELECT DISTINCT ON (title)
title, checked_out
FROM(
SELECT b.title, t.checked_in_date IS NULL AS checked_out
FROM books b
LEFT OUTER JOIN transactions t USING (isbn)
ORDER BY checked_out DESC
) t;
Situation
Using Python 3, Django 1.9, Cubes 1.1, and Postgres 9.5.
These are my datatables in pictorial form:
The same in text format:
Store table
------------------------------
| id | code | address |
|-----|------|---------------|
| 1 | S1 | Kings Row |
| 2 | S2 | Queens Street |
| 3 | S3 | Jacks Place |
| 4 | S4 | Diamonds Alley|
| 5 | S5 | Hearts Road |
------------------------------
Product table
------------------------------
| id | code | name |
|-----|------|---------------|
| 1 | P1 | Saucer 12 |
| 2 | P2 | Plate 15 |
| 3 | P3 | Saucer 13 |
| 4 | P4 | Saucer 14 |
| 5 | P5 | Plate 16 |
| and many more .... |
|1000 |P1000 | Bowl 25 |
|----------------------------|
Sales table
----------------------------------------
| id | product_id | store_id | amount |
|-----|------------|----------|--------|
| 1 | 1 | 1 |7.05 |
| 2 | 1 | 2 |9.00 |
| 3 | 2 | 3 |1.00 |
| 4 | 2 | 3 |1.00 |
| 5 | 2 | 5 |1.00 |
| and many more .... |
| 1000| 20 | 4 |1.00 |
|--------------------------------------|
The relationships are:
Sales belongs to Store
Sales belongs to Product
Store has many Sales
Product has many Sales
What I want to achieve
I want to use cubes to be able to do a display by pagination in the following manner:
Given the stores S1-S3:
-------------------------
| product | S1 | S2 | S3 |
|---------|----|----|----|
|Saucer 12|7.05|9 | 0 |
|Plate 15 |0 |0 | 2 |
| and many more .... |
|------------------------|
Note the following:
Even though there were no records in sales for Saucer 12 under Store S3, I displayed 0 instead of null or none.
I want to be able to do sort by store, say descending order for, S3.
The cells indicate the SUM total of that particular product spent in that particular store.
I also want to have pagination.
What I tried
This is the configuration I used:
"cubes": [
{
"name": "sales",
"dimensions": ["product", "store"],
"joins": [
{"master":"product_id", "detail":"product.id"},
{"master":"store_id", "detail":"store.id"}
]
}
],
"dimensions": [
{ "name": "product", "attributes": ["code", "name"] },
{ "name": "store", "attributes": ["code", "address"] }
]
This is the code I used:
result = browser.aggregate(drilldown=['Store','Product'],
order=[("Product.name","asc"), ("Store.name","desc"), ("total_products_sale", "desc")])
I didn't get what I want.
I got it like this:
----------------------------------------------
| product_id | store_id | total_products_sale |
|------------|----------|---------------------|
| 1 | 1 | 7.05 |
| 1 | 2 | 9 |
| 2 | 3 | 2.00 |
| and many more .... |
|---------------------------------------------|
which is the whole table with no pagination and if the products not sold in that store it won't show up as zero.
My question
How do I get what I want?
Do I need to create another data table that aggregates everything by store and product before I use cubes to run the query?
Update
I have read more. I realised that what I want is called dicing as I needed to go across 2 dimensions. See: https://en.wikipedia.org/wiki/OLAP_cube#Operations
Cross-posted at Cubes GitHub issues to get more attention.
This is a pure SQL solution using crosstab() from the additional tablefunc module to pivot the aggregated data. It typically performs better than any client-side alternative. If you are not familiar with crosstab(), read this first:
PostgreSQL Crosstab Query
And this about the "extra" column in the crosstab() output:
Pivot on Multiple Columns using Tablefunc
SELECT product_id, product
, COALESCE(s1, 0) AS s1 -- 1. ... displayed 0 instead of null
, COALESCE(s2, 0) AS s2
, COALESCE(s3, 0) AS s3
, COALESCE(s4, 0) AS s4
, COALESCE(s5, 0) AS s5
FROM crosstab(
'SELECT s.product_id, p.name, s.store_id, s.sum_amount
FROM product p
JOIN (
SELECT product_id, store_id
, sum(amount) AS sum_amount -- 3. SUM total of product spent in store
FROM sales
GROUP BY product_id, store_id
) s ON p.id = s.product_id
ORDER BY s.product_id, s.store_id;'
, 'VALUES (1),(2),(3),(4),(5)' -- desired store_id's
) AS ct (product_id int, product text -- "extra" column
, s1 numeric, s2 numeric, s3 numeric, s4 numeric, s5 numeric)
ORDER BY s3 DESC; -- 2. ... descending order for S3
Produces your desired result exactly (plus product_id).
To include products that have never been sold replace [INNER] JOIN with LEFT [OUTER] JOIN.
SQL Fiddle with base query.
The tablefunc module is not installed on sqlfiddle.
Major points
Read the basic explanation in the reference answer for crosstab().
I am including with product_id because product.name is hardly unique. This might otherwise lead to sneaky errors conflating two different products.
You don't need the store table in the query if referential integrity is guaranteed.
ORDER BY s3 DESC works, because s3 references the output column where NULL values have been replaced with COALESCE. Else we would need DESC NULLS LAST to sort NULL values last:
PostgreSQL sort by datetime asc, null first?
For building crosstab() queries dynamically consider:
Dynamic alternative to pivot with CASE and GROUP BY
I also want to have pagination.
That last item is fuzzy. Simple pagination can be had with LIMIT and OFFSET:
Displaying data in grid view page by page
I would consider a MATERIALIZED VIEW to materialize results before pagination. If you have a stable page size I would add page numbers to the MV for easy and fast results.
To optimize performance for big result sets, consider:
SQL syntax term for 'WHERE (col1, col2) < (val1, val2)'
Optimize query with OFFSET on large table
I have a table in SQL Server 2008 R2 which contains product orders. For the most part, it is one entry per product
ID | Prod | Qty
------------
1 | A | 1
4 | B | 1
7 | A | 1
8 | A | 1
9 | A | 1
12 | C | 1
15 | A | 1
16 | A | 1
21 | B | 1
I want to create a view based on the table which looks like this
ID | Prod | Qty
------------------
1 | A | 1
4 | B | 1
9 | A | 3
12 | C | 1
16 | A | 2
21 | B | 1
I've written a query using a table expression, but I am stumped on how to make it work. The sql below does not actually work, but is a sample of what I am trying to do. I've written this query multiple different ways, but cannot figure out how to get the right results. I am using row_number to generate a sequential id. From that, I can order and compare consecutive rows to see if the next row has the same product as the previous row since ReleaseId is sequential, but not necessarily contiguous.
;with myData AS
(
SELECT
row_number() over (order by a.ReleaseId) as 'Item',
a.ReleaseId,
a.ProductId,
a.Qty
FROM OrdersReleased a
UNION ALL
SELECT
row_number() over (order by b.ReleaseId) as 'Item',
b.ReleaseId,
b.ProductId,
b.Qty
FROM OrdersReleased b
INNER JOIN myData c ON b.Item = c.Item + 1 and b.ProductId = c.ProductId
)
SELECT * from myData
Usually you drop the ID out of something like this, since it is a summary.
SELECT a.ProductId,
SUM(a.Qty) AS Qty
FROM OrdersReleased a
GROUP BY a.ProductId
ORDER BY a.ProductId
-- if you want to do sub query you can do it as a column (if you don't have a very large dataset).
SELECT a.ProductId,
SUM(a.Qty) AS Qty,
(SELECT COUNT(1)
FROM OrdersReleased b
WHERE b.ReleasedID - 1 = a.ReleasedID
AND b.ProductId = b.ProductId) as NumberBackToBack
FROM OrdersReleased a
GROUP BY a.ProductId
ORDER BY a.ProductId