Improving the performance of a MySQL left join sub query - mysql

Question

I have the following MySQL query which calculates the total number of orders for each month within a given date range e.g. a year. The query works correctly, but the performance is slow (around 250ms).
Any ideas on how to rewrite it to make it more efficient?
WITH recursive `dates` AS (
(
SELECT '2019-11-28' AS item
)
UNION
ALL (
SELECT
item + INTERVAL 1 DAY
FROM
`dates`
WHERE
item + INTERVAL 1 DAY <= '2020-11-27'
)
)
SELECT
DATE_FORMAT(`item`, '%b %y') AS `date`,
COUNT(`orders`.`id`) AS `total`
FROM
`dates`
LEFT JOIN (
SELECT
`orders`.`id`,
`orders`.`created_at`
FROM
`orders`
INNER JOIN `locations` ON `orders`.`location_id` = `locations`.`id`
WHERE
`orders`.`shop_id` = 10379184
AND `locations`.`country_id` = 128
AND `orders`.`created_at` >= '2019-11-28 12:01:42'
AND `orders`.`created_at` <= '2020-11-27 12:01:42'
) AS `orders` ON DATE(`orders`.`created_at`) = `dates`.`item`
GROUP BY
`date`
UPDATE: Some have suggested using two left joins, however if I do that then the country_id filter is not applied:
WITH recursive `dates` AS (
(
SELECT
'2019-11-28' AS item
)
UNION
ALL (
SELECT
item + INTERVAL 1 DAY
FROM
`dates`
WHERE
item + INTERVAL 1 DAY <= '2020-11-27'
)
)
SELECT
DATE_FORMAT(`item`, '%b %y') AS `date`,
COUNT(`orders`.`id`) AS `total`
FROM
`dates`
LEFT JOIN `orders` USE INDEX (`orders_created_at_index`) ON DATE(`created_at`) = `dates`.`item`
AND `orders`.`shop_id` = 10379184
AND `orders`.`created_at` >= '2019-11-28 12:22:43'
AND `orders`.`created_at` <= '2020-11-27 12:22:43'
LEFT JOIN `locations` ON `orders`.`location_id` = `locations`.`id`
AND `locations`.`country_id` = 128
GROUP BY
`date`
Thanks!

Answer 1

I would suggest using a correlated subquery:
SELECT DATE_FORMAT(d.item, '%b %y') AS `date`,
(SELECT COUNT(*)
FROM orders o JOIN
locations l
ON o.location_id = l.id
WHERE shop_id = 10379184 AND
country_id = 128 AND
o.created_at >= d.item AND
o.created_at < d.item + interval 1 day
) as total
FROM dates d;
This avoids the outer aggregation, which is often a performance improvement.
In addition, indexes could probably help the query, but it is unclear where columns such as country_id and shop_id are coming from.

Answer 2

After much tinkering, I produced the following which operates in under 40ms, which is good enough for my needs. I still think it's not ideal and would welcome any improvements...
SELECT
`date`,
COUNT(`order`)
FROM
(
WITH recursive `dates` AS (
(
SELECT
'2019-11-28' AS item
)
UNION
ALL (
SELECT
item + INTERVAL 1 DAY
FROM
`dates`
WHERE
item + INTERVAL 1 DAY <= '2020-11-27'
)
)
SELECT
DATE_FORMAT(`item`, '%b %y') AS `DATE`,
`orders`.`id` AS `order`,
`locations`.`id` AS `location`
FROM
`dates`
LEFT JOIN
`orders`
ON
DATE(`created_at`) = `dates`.`item`
AND
`orders`.`shop_id` = 10379184
AND
`orders`.`created_at` >= '2019-11-28 12:22:43'
AND
`orders`.`created_at` <= '2020-11-27 12:22:43'
LEFT JOIN
`locations`
ON
`orders`.`location_id` = `locations`.`id`
AND
`locations`.`country_id` = 209
) AS items
WHERE
(
`order` IS NULL
AND `location` IS NULL
)
OR (
`order` IS NOT NULL
AND `location` IS NOT NULL
)
GROUP BY
`date`