Storage Engines like InnoDB, Aria and MyISAM are Row Stores. They store rows one after the other in blocks or even directly in a single file (MyISAM). On the other hand a Column Store like MariaDB ColumnStore stores all the same attributes (columns) of the rows together in chunks.

This is how the table sales_fact looks like:

CREATE TABLE `sales_fact` (
  `product_id` int(11) NOT NULL,
  `time_id` int(11) NOT NULL,
  `customer_id` int(11) NOT NULL,
  `promotion_id` int(11) NOT NULL,
  `store_id` int(11) NOT …

How could the following SQL queries be improved performance wise and otherwise and can you also explain why your change is more optimal?

Please consider, when testing, that your results are not confused by the Query Cache or by reading data from your I/O system which are an order of magnitude faster (Query Cache) or slower (I/O) than the in-memory behaviour.

Table of Contents

• Covering function
• LIKE search
• Covering function twice
• Compare 2 indexed columns
• Function and 2 indexed columns
• Time series problem …

When it comes to SQL Query tuning with customers we often get the slow running SQL query and possibly, in good cases, also the table structure. But very often, for various reasons, we do not get the data.

SQL query tuning on an empty table or a table with only little data is not really fun because either the results of the optimizer have nothing to do with reality or the response times do not really show if your change has improved anything. For example if your query response time before the change was 2 ms …

MySQL single query performance - the truth!

As suggested by morgo I did a little test for the same query and the same data-set mentioned in Impact of column types on MySQL JOIN performance but looking into an other dimension: the time (aka MySQL versions).

The answer

To make it short. As a good consultant the answer must be: “It depends!” :-)

The test

The query was again the following:

SELECT *
  FROM a
  JOIN b ON b.a_id = a.id
 WHERE a.id BETWEEN 10000 AND 15000
;

The Query Execution Plan was …

In our MySQL trainings and consulting engagements we tell our customers always to use the smallest possible data type to get better query performance. Especially for the JOIN columns. This advice is supported as well by the MySQL documentation in the chapter Optimizing Data Types:

Use the most efficient (smallest) data types possible. MySQL has many specialized types that save disk space and memory. For example, use the smaller integer types if possible to get smaller tables. MEDIUMINT is often a better …