Case:
I select an initial date and an end date, it should bring me the movements of all the products in that date range, but if there were movements before the initial date (records in table), I want to obtain the previous sum (prevData)
if the first move is exit 5 and the second move is income 2.
I would have in the first row (prevData-5), second row would have (prevData-5 + 2) and thus have a cumulative.
The prevData would be calculated as the sum of the above, validating the product id of the record, I made the query but if the product has 10 movements, I would do the query 10 times, and how would I identify the sum of another product_id?
SELECT
ik.id,
ik.quantity,
ik.date,
ik.product_id,
#balance = (SELECT SUM(quantity) FROM table_kardex WHERE product_id = ik.product_id AND id < ik.id)
from table_kardex ik
where ik.date between '2021-11-01' and '2021-11-15'
order by ik.product_id,ik.id asc
I hope you have given me to understand, I will be attentive to any questions.
#table_kardex
id|quantity|date|product_id
1 8 2020-10-12 2
2 15 2020-10-12 1
3 5 2021-11-01 1
4 10 2021-11-01 2
5 -2 2021-11-02 1
6 -4 2021-11-02 2
#result
id|quantity|date|product_id|saldo
3 5 2021-11-01 1 20 (15+5)
5 -2 2021-11-02 1 18 (15+5-2)
4 10 2021-11-01 2 18 (8+10-4)
6 -4 2021-11-02 2 14 (15+5-2)
Use MySQL 5.7
If you're using MySQL 8+, then analytic functions can be used here:
WITH cte AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY date) rn,
SUM(quantity) OVER (PARTITION BY product_id ORDER BY date) saldo
FROM table_kardex
WHERE date BETWEEN '2021-11-01' AND '2021-11-15'
)
SELECT id, quantity, date, product_id, saldo
FROM cte
WHERE rn > 1
ORDER BY product_id, date;
MySQL 5.7
Try this:
SELECT *
FROM (
SELECT product_id,
t1.`date`,
SUM(t2.quantity) - t1.quantity cumulative_quantity_before,
SUM(t2.quantity) cumulative_quantity_after
FROM table t1
JOIN table t2 USING (product_id)
WHERE t1.`date` >= t2.`date`
AND t1.`date` <= #period_end
GROUP BY product_id, t1.`date`, t1.quantity
) prepare_data
WHERE `date` >= #period_start;
The easiest solution is to use the window function SUM OVER to get the running total. In the second step reduce this to the date you want to have this started:
SELECT id, quantity, date, product_id, balance
FROM
(
SELECT
id,
quantity,
date,
product_id,
SUM(quantity) OVER (PARTITION BY product_id ORDER BY id) AS balance
from table_kardex ik
where date < DATE '2021-11-16'
) cumulated
WHERE date >= DATE '2021-11-01'
ORDER BY product_id, id;
UPDATE: You have changed your request to mention that you are using an old MySQL version (5.7). This doesn't support window functions. In that case use your original query. If I am not mistaken, though, #balance = (...) is invalid syntax for MySQL. And according to your explanation you want id <= ik.id, not id < ik.id:
SELECT
ik.id,
ik.quantity,
ik.date,
ik.product_id,
(
SELECT SUM(quantity)
FROM table_kardex
WHERE product_id = ik.product_id AND id <= ik.id
) AS balance
FROM table_kardex ik
WHERE ik.date >= DATE '2021-11-01' AND ik.date < DATE '2021-11-16'
ORDER BY ik.product_id, ik.id;
The appropriate indexes for this query are:
create index idx1 on table_kardex (date, product_id, id);
create index idx2 on table_kardex (product_id, id, quantity);
Good Day,
I am using the following code to calculate the 9 Day Moving average.
SELECT SUM(close)
FROM tbl
WHERE date <= '2002-07-05'
AND name_id = 2
ORDER BY date DESC
LIMIT 9
But it does not work because it first calculates all of the returned fields before the limit is called. In other words it will calculate all the closes before or equal to that date, and not just the last 9.
So I need to calculate the SUM from the returned select, rather than calculate it straight.
IE. Select the SUM from the SELECT...
Now how would I go about doing this and is it very costly or is there a better way?
If you want the moving average for each date, then try this:
SELECT date, SUM(close),
(select avg(close) from tbl t2 where t2.name_id = t.name_id and datediff(t2.date, t.date) <= 9
) as mvgAvg
FROM tbl t
WHERE date <= '2002-07-05' and
name_id = 2
GROUP BY date
ORDER BY date DESC
It uses a correlated subquery to calculate the average of 9 values.
Starting from MySQL 8, you should use window functions for this. Using the window RANGE clause, you can create a logical window over an interval, which is very powerful. Something like this:
SELECT
date,
close,
AVG (close) OVER (ORDER BY date DESC RANGE INTERVAL 9 DAY PRECEDING)
FROM tbl
WHERE date <= DATE '2002-07-05'
AND name_id = 2
ORDER BY date DESC
For example:
WITH t (date, `close`) AS (
SELECT DATE '2020-01-01', 50 UNION ALL
SELECT DATE '2020-01-03', 54 UNION ALL
SELECT DATE '2020-01-05', 51 UNION ALL
SELECT DATE '2020-01-12', 49 UNION ALL
SELECT DATE '2020-01-13', 59 UNION ALL
SELECT DATE '2020-01-15', 30 UNION ALL
SELECT DATE '2020-01-17', 35 UNION ALL
SELECT DATE '2020-01-18', 39 UNION ALL
SELECT DATE '2020-01-19', 47 UNION ALL
SELECT DATE '2020-01-26', 50
)
SELECT
date,
`close`,
COUNT(*) OVER w AS c,
SUM(`close`) OVER w AS s,
AVG(`close`) OVER w AS a
FROM t
WINDOW w AS (ORDER BY date DESC RANGE INTERVAL 9 DAY PRECEDING)
ORDER BY date DESC
Leading to:
date |close|c|s |a |
----------|-----|-|---|-------|
2020-01-26| 50|1| 50|50.0000|
2020-01-19| 47|2| 97|48.5000|
2020-01-18| 39|3|136|45.3333|
2020-01-17| 35|4|171|42.7500|
2020-01-15| 30|4|151|37.7500|
2020-01-13| 59|5|210|42.0000|
2020-01-12| 49|6|259|43.1667|
2020-01-05| 51|3|159|53.0000|
2020-01-03| 54|3|154|51.3333|
2020-01-01| 50|3|155|51.6667|
Use something like
SELECT
sum(close) as sum,
avg(close) as average
FROM (
SELECT
(close)
FROM
tbl
WHERE
date <= '2002-07-05'
AND name_id = 2
ORDER BY
date DESC
LIMIT 9 ) temp
The inner query returns all filtered rows in desc order, and then you avg, sum up those rows returned.
The reason why the query given by you doesn't work is due to the fact that the sum is calculated first and the LIMIT clause is applied after the sum has already been calculated, giving you the sum of all the rows present
an other technique is to do a table:
CREATE TABLE `tinyint_asc` (
`value` tinyint(3) unsigned NOT NULL default '0',
PRIMARY KEY (value)
) ;
INSERT INTO `tinyint_asc` VALUES (0),(1),(2),(3),(4),(5),(6),(7),(8),(9),(10),(11),(12),(13),(14),(15),(16),(17),(18),(19),(20),(21),(22),(23),(24),(25),(26),(27),(28),(29),(30),(31),(32),(33),(34),(35),(36),(37),(38),(39),(40),(41),(42),(43),(44),(45),(46),(47),(48),(49),(50),(51),(52),(53),(54),(55),(56),(57),(58),(59),(60),(61),(62),(63),(64),(65),(66),(67),(68),(69),(70),(71),(72),(73),(74),(75),(76),(77),(78),(79),(80),(81),(82),(83),(84),(85),(86),(87),(88),(89),(90),(91),(92),(93),(94),(95),(96),(97),(98),(99),(100),(101),(102),(103),(104),(105),(106),(107),(108),(109),(110),(111),(112),(113),(114),(115),(116),(117),(118),(119),(120),(121),(122),(123),(124),(125),(126),(127),(128),(129),(130),(131),(132),(133),(134),(135),(136),(137),(138),(139),(140),(141),(142),(143),(144),(145),(146),(147),(148),(149),(150),(151),(152),(153),(154),(155),(156),(157),(158),(159),(160),(161),(162),(163),(164),(165),(166),(167),(168),(169),(170),(171),(172),(173),(174),(175),(176),(177),(178),(179),(180),(181),(182),(183),(184),(185),(186),(187),(188),(189),(190),(191),(192),(193),(194),(195),(196),(197),(198),(199),(200),(201),(202),(203),(204),(205),(206),(207),(208),(209),(210),(211),(212),(213),(214),(215),(216),(217),(218),(219),(220),(221),(222),(223),(224),(225),(226),(227),(228),(229),(230),(231),(232),(233),(234),(235),(236),(237),(238),(239),(240),(241),(242),(243),(244),(245),(246),(247),(248),(249),(250),(251),(252),(253),(254),(255);
After you can used it like that:
select
date_add(tbl.date, interval tinyint_asc.value day) as mydate,
count(*),
sum(myvalue)
from tbl inner
join tinyint_asc.value <= 30 -- for a 30 day moving average
where date( date_add(o.created_at, interval tinyint_asc.value day ) ) between '2016-01-01' and current_date()
group by mydate
This query is fast:
select date, name_id,
case #i when name_id then #i:=name_id else (#i:=name_id)
and (#n:=0)
and (#a0:=0) and (#a1:=0) and (#a2:=0) and (#a3:=0) and (#a4:=0) and (#a5:=0) and (#a6:=0) and (#a7:=0) and (#a8:=0)
end as a,
case #n when 9 then #n:=9 else #n:=#n+1 end as n,
#a0:=#a1,#a1:=#a2,#a2:=#a3,#a3:=#a4,#a4:=#a5,#a5:=#a6,#a6:=#a7,#a7:=#a8,#a8:=close,
(#a0+#a1+#a2+#a3+#a4+#a5+#a6+#a7+#a8)/#n as av
from tbl,
(select #i:=0, #n:=0,
#a0:=0, #a1:=0, #a2:=0, #a3:=0, #a4:=0, #a5:=0, #a6:=0, #a7:=0, #a8:=0) a
where name_id=2
order by name_id, date
If you need an average over 50 or 100 values, it's tedious to write, but
worth the effort. The speed is close to the ordered select.