[Solved] how can i optimize performance with this query?

How to optimize this SQL query?

In case you have your own slow SQL query, you can optimize it automatically here.

For the query above, the following recommendations will be helpful as part of the SQL tuning process.
You'll find 3 sections below:

  1. Description of the steps you can take to speed up the query.
  2. The optimal indexes for this query, which you can copy and create in your database.
  3. An automatically re-written query you can copy and execute in your database.
The optimization process and recommendations:
  1. Avoid Subqueries In From Clause (modified query below): The database cannot properly optimize subqueries in the FROM clause. Therefore, we recommend to extract the subqueries to temporary tables, index them and join to them in the outer query.
  2. 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.
Optimal indexes for this query:
ALTER TABLE `es_temp1` ADD INDEX `es_temp1_idx_user_id` (`user_id`);
ALTER TABLE `es_temp2` ADD INDEX `es_temp2_idx_user_id` (`user_id`);
ALTER TABLE `es_temp3` ADD INDEX `es_temp3_idx_user_id` (`user_id`);
ALTER TABLE `es_temp4` ADD INDEX `es_temp4_idx_user_id` (`user_id`);
ALTER TABLE `es_temp5` ADD INDEX `es_temp5_idx_user_id` (`user_id`);
The optimized query:
SELECT
        u.id,
        post.post_count,
        updated.update_count,
        print.print_count,
        share.share_count,
        purchase.purchase_count 
    FROM
        users AS u 
    LEFT JOIN
        es_temp1 post 
            ON u.id = post.user_id 
    LEFT JOIN
        es_temp2 updated 
            ON u.id = updated.user_id 
    LEFT JOIN
        es_temp3 print 
            ON u.id = print.user_id 
    LEFT JOIN
        es_temp4 share 
            ON u.id = share.user_id 
    LEFT JOIN
        es_temp5 purchase 
            ON u.id = purchase.user_id

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* original question posted on StackOverflow here.