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 Selecting Unnecessary Columns (query line: 2): Avoid selecting all columns with the '*' wildcard, unless you intend to use them all. Selecting redundant columns may result in unnecessary performance degradation.
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.
Use Numeric Column Types For Numeric Values (query line: 9): Referencing a numeric value (e.g. 123) as a string in a WHERE clause might result in poor performance. Possible impacts of storing numbers as varchars: more space will be used, you won't be able to perform arithmetic operations, the data won't be self-validated, aggregation functions like SUM won't work, the output may sort incorrectly and more. If the column is numeric, remove the quotes from the constant value, to make sure a numeric comparison is done.
Optimal indexes for this query:
ALTER TABLE `inefficient_foreign_key_exclude_a` ADD INDEX `inefficient_key_idx_id` (`id`);
ALTER TABLE `inefficient_foreign_key_exclude_b` ADD INDEX `inefficient_key_idx_name` (`name`);
The optimized query:
ON a.id = b.a_id
NOT (b.name = '123')