For the query above, the following recommendations will be helpful as part of the SQL tuning process. You'll find 3 sections below:
Description of the steps you can take to speed up the query.
The optimal indexes for this query, which you can copy and create in your database.
An automatically re-written query you can copy and execute in your database.
The optimization process and recommendations:
Avoid Subqueries (query line: 4): We advise against using subqueries as they are not optimized well by the optimizer. Therefore, it's recommended to join a newly created temporary table that holds the data, which also includes the relevant search index.
Create Optimal Indexes (modified query below): The recommended indexes are an integral part of this optimization effort and should be created before testing the execution duration of the optimized query.
Explicitly ORDER BY After GROUP BY (modified query below): By default, the database sorts all 'GROUP BY col1, col2, ...' queries as if you specified 'ORDER BY col1, col2, ...' in the query as well. If a query includes a GROUP BY clause but you want to avoid the overhead of sorting the result, you can suppress sorting by specifying 'ORDER BY NULL'.
Replace In Subquery With Correlated Exists (modified query below): In many cases, an EXISTS subquery with a correlated condition will perform better than a non correlated IN subquery.
Optimal indexes for this query:
ALTER TABLE `table` ADD INDEX `table_idx_id_product_id_datelast` (`id`,`product_id`,`datelast`);
The optimized query:
UNIX_TIMESTAMP(max(table.datelast)) - UNIX_TIMESTAMP(min(table.datestart)) AS time
table AS table1
table1.product_id = 12394
AND table1.datelast > '2011-04-13 00:26:59'
table.id = table1.id
NULL) AS T