A student wanted a better example of writing a MySQL Persistent Stored Module (PSM) that maintains transactional scope across a couple tables. Here’s the one I wrote about ten years ago to build the MySQL Video Store model. It looks I neglected to put it out there before, so here it is for reference.

-- Conditionally drop procedure if it exists.
DROP PROCEDURE IF EXISTS contact_insert;

-- Reset the delimiter so that a semicolon can be used as a statement and block terminator.
DELIMITER $$

SELECT 'CREATE PROCEDURE contact_insert' AS "Statement";
CREATE PROCEDURE contact_insert
( pv_member_type         CHAR(12)
, pv_account_number      CHAR(19)
, pv_credit_card_number  CHAR(19)
, pv_credit_card_type    CHAR(12)
, pv_first_name          CHAR(20)
, pv_middle_name         CHAR(20)
, pv_last_name           CHAR(20)
, pv_contact_type        CHAR(12)
, pv_address_type        CHAR(12)
, pv_city                CHAR(30)
, pv_state_province      CHAR(30)
, …

In MySQL, the @@sql_mode parameter should generally use ONLY_FULL_GROUP_BY. If it doesn’t include it and you don’t have the ability to change the database parameters, you can use a MySQL PSM (Persistent Stored Module), like:

Create the set_full_group_by procedure:

-- Drop procedure conditionally on whether it exists already.
DROP PROCEDURE IF EXISTS set_full_group_by;

-- Reset delimter to allow semicolons to terminate statements.
DELIMITER $$

-- Create a procedure to verify and set connection parameter.
CREATE PROCEDURE set_full_group_by()
  LANGUAGE SQL
  NOT DETERMINISTIC
  SQL SECURITY DEFINER
  COMMENT 'Set connection parameter when not set.'
BEGIN

  /* Check whether full group by is set in the connection and
     if unset, set it in the scope of the connection. */
  IF NOT EXISTS
    (SELECT NULL
     WHERE  REGEXP_LIKE(@@SQL_MODE,'ONLY_FULL_GROUP_BY'))
  THEN
    SET SQL_MODE=(SELECT CONCAT(@@sql_mode,',ONLY_FULL_GROUP_BY'));
  END IF; …

Adminer is a full-featured database management tool written in PHP. In this tutorial, I will explain how to install Adminer on Alma Linux 8.

Azure SQL Database is a fully managed platform as a service  (PaaS). Azure handles database management functions such as upgrading, patching, backups, and monitoring and we do not need to worry about these operations. In this article, we will see how to create an SQL database, connect to it, create a table in it and delete it at the end.

I created the first draft of this post many years ago.  At that time, I was working with physical servers having 192 GB of RAM or more.  On such systems, doing memory pressure tests with MySQL is complicated.  I used a trick to simulate a Linux server with less RAM (also works with vms, probably not with Kubernetes or containers).  I recently needed the trick again and as I

In 2009, I showed an example of how to use the MySQL LOAD DATA INFILE command. Last year, I updated the details to reset the secure_file-priv privilege to use the LOAD DATA INFILE command, but you can avoid that approach with a simple Python 3 program like the one in this example. You also can use MySQL Shell’s new parallel table import feature, introduced in 8.0.17, as noted in a comment on this blog post.

The example requires creating an avenger table, avenger.csv file, a readWriteData.py Python script, run the readWriteData.py Python script, and a query that validates the insertion of the avenger.csv file’s data into the avenger table. The complete code in five steps using the sakila demonstration database:

• Creating the …

Working through our chapter on MySQL views, I wrote the query two ways to introduce the idea of SQL tuning. That’s one of the final topics before introducing JSON types.

I gave the students this query based on the Sakila sample database after explaining how to use the EXPLAIN syntax. The query only uses only inner joins, which are generally faster and more efficient than subqueries as a rule of thumb than correlated subqueries.

SELECT   ctry.country AS country_name
,        SUM(p.amount) AS tot_payments
FROM     city c INNER JOIN address a
ON       c.city_id = a.city_id INNER JOIN customer cus
ON       a.address_id = cus.address_id INNER JOIN payment p
ON       cus.customer_id = p.customer_id INNER JOIN country ctry
ON       c.country_id = ctry.country_id
GROUP BY ctry.country;

It generated the following tabular explain plan output:

 …

While exporting the table with MySQL native utility, we don’t have any control on the process, and also there will be no progress update as well on the process completion. So when exporting the larger table will consume high resource utilization and also the disk space usage will also be high.

MySQL shell utility will make the process easier. It will export the table and we can import the data back with a parallel thread and also will provide the current progress status on export/import progress.

util.exportTable() utility was introduced in Shell – 8.0.22 version, will export the data in a controlled manner. We can store the data in either local or Cloud Infrastructure Object Storage bucket as well.

We will see about the compression ratio along with the time taken for native MySQL vs Shell utility

Feature :

• Compression
• Progress status
• Supported output …

When I went over my example of using the WITH clause to solve how to use a series of literal values in data sets, some students got it right away and some didn’t. The original post showed how to solve a problem where one value in the data set is returned in the SELECT-list and two values are used as the minimum and maximum values with a BETWEEN operator. It used three approaches with literal values:

• A list of Python dictionaries that require you to filter the return set from the database through a range loop and if statement that mimics a SQL BETWEEN operator.
• A WITH clause that accepts the literals as bind variables to filter the query results inside the query.
• A table design that holds the literals values that an analyst might use for reporting.

It was the last example that required elaboration. I explained you might build a web form that uses a table, and the table could allow a …

As an example for my class on the usefulness of Common Table Expressions (CTEs), I created three examples with Python. They extend an exercise in Chapter 9 on subqueries from Learning SQL by Alan Beaulieu. All of the examples work with the sakila sample database.

These bullets describe the examples:

1. Uses local variables and a range for loop and if statement that uses the variables to evaluate and add an element to the derived table (or query result set) from MySQL.
2. Uses a CTE with substitution variables from the Python program, which eliminates the need to evaluate and add an element to the query result set because the query does that.
3. Uses a table to hold the variables necessary to evaluate and add the element to the query result set.

This is the first Python program:

# Import the library.
import sys
import …