Are they really tricks or simply basic techniques combined to create a solution. Before writing these mechanics for using native MySQL to create a compound JSON object, let me point out that the easiest way to get one is to use the MySQL Node.js library, as shown recently in my “Is SQL Programming” blog post.

Moving data from a relational model output to a JSON structure isn’t as simple as a delimited list of columns in a SQL query. Let’s look at it in stages based on the MySQL Server 12.18.2 Functions that create JSON values.

Here’s how you return single row as a JSON object, which is quite straightforward:

SELECT JSON_OBJECT('first_name',c.first_name,'last_name',c.last_name) AS json_result
FROM   contact c
WHERE …

I could have also called this article When Pandas meet Dolphins in MySQL Shell.

Some time ago, I wrote a post related on how to collect initial relevant data when trying to seek help for MySQL.

Since then, the MySQL Shell engineering team implemented another powerful native utility that collect all the essential information and more and store them in a single zip file.

This zip archive contains TSV and YAML files that, for example, the MySQL Support Team could use to solve your eventual issue.

For more information regarding MySQL Shell’s util.debug.collectDiagnostics(), I encourage you to check the manual.

This tool is …

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 …

When you use MySQL Shell with extra Python plugins (like these available on GitHub), sometimes, you could need extra Python modules.

Some of my plugins require requests and prettytable for example.

MySQL 8.0.26 uses embedded Python 3.9 and if your system doesn’t have that version (like Oracle Linux 8), you won’t be able to install the missing module.

So how could we install the missing modules ? The easiest method is to use PIP, but if you don’t have PIP for Python 3.9 installed on the system, this will be more complicated…. not really !

This how to install PIP in MySQL Shell:

wget https://bootstrap.pypa.io/get-pip.py
mysqlsh --py -f get-pip.py 

And now you can use PIP to install extra modules:

mysqlsh --pym pip …

    Pandas is a Python software library for data analysis and manipulation.  It allows you to import data from CSV files, SQL, and Excel.  And it is open source.

    Pandas works well with the MySQL Shell.  After installing Pandas with pip install pandas on my Fedora test system, I was able to load Pandas.  The documentation is excellent at pandas.pydata.org   and I was quickly able to get one of their examples working from within mysqlsh.

Did I mention SQL as a source of data? Reading from MySQL is very easy.  

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Sometimes clients want that the information they collected from the user should be encrypted and stored in a database. Data encryption and…

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There was an excellent question from Francis on my MySQL X DevAPI tutorial on how to work with NULL values:

Is it possible to store an attribute with the null value, and how to find it (xxx is null ?

It may sound like a trivial question, but the answer is not all that simple, so instead of just replying directly to the comment, I think it is worth covering the subject of NULL values in the X DevAPI in a separate post.

The Short Story

In short there are two ways to work with “not a value” in the X DevAPI. One way is to not set a value for those fields in which case you can …

These days, I mostly program in PHP with MySQL as the database, which is just fine by me. I have had a long-time interest in MySQL (SQL in general), and after several steady months of programming in PHP, I must say I have really come into the language and developed a fondness for it. All that being said, I still enjoy using and learning the MySQL Shell in Python mode. As Database Developers, we often need the LAST INSERT ID value from a previous INSERT statement on a column that has the AUTO_INCREMENT attribute. MySQL Shell has a get_auto_increment_value() method we can call against a Shell object result and retrieve that value. Continue reading and see examples of the MySQL Shell get_auto_increment_value() method used in Python mode…

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I recently watched a fantastic Python Pandas library tutorial series on YouTube. Without a doubt, Pandas is great for all sorts of data stuff. On the same token, MySQL Shell in Python mode is quite powerful in the sense that Python and the MySQL Shell (version >= 8.0) are somewhat united in the same environment. Although Pandas is in a league all its own when it comes to data analysis, between the power of MySQL and Python, we can also perform some basic analysis easily in MySQL Shell Python mode. In this blog post, I will cover some basic data analysis using Python mode in the MySQL Shell. Continue reading to see examples…

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OS, Software, and DB used:

• OpenSuse Leap …