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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The MySQL DROP statement is one of many powerful DDL commands. Be it ALTER TABLE some_table DROP some_column or DROP some_table, this type of command can drastically change your data landscape because in executing MySQL DROP, you are completely removing objects from the database! If you are using the phpMyAdmin web interface, you can execute the MySQL DROP statement with just a few mouse clicks. Continue reading to see how…

Drops of water.

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This is the announcement blogpost and this is my overview taken from medium:

The one where MySQL 8.0 lands on Google Cloud SQL

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There are many things that makes me happy. Puppies (see picture), food, wine and databases… (not particularly in that order). And things that makes me even happier such as a well designed schema and proper usage of ORM (Object Relational Mapping).

MySQL was the database I used to love to hate. It grew on me and the fact that long strides were made to make it more consistent and more modern kept me away from …

Introduction In this article, we are going to see how we can implement an audit logging mechanism using MySQL database triggers to store the old and new row states in JSON column types. Database tables Let’s assume we have a library application that has the following two tables: The book table stores all the books that are found in our library, and the book_audit_log table stores the CDC (Change Data Capture) events that happened to a given book record via an INSERT, UPDATE, or DELETE DML statement. The book_audit_log table is created... Read More

The post MySQL audit logging using triggers appeared first on Vlad Mihalcea.

We all know as SQL professionals that the NULL marker is a special case. Oftentimes, you have NULL‘s stored in your tables, and that’s fine. Suppose you want to present an alternative value in query results where some of the columns have NULL? This is a perfect opportunity (but not the only) to use the IFNULL() function. I find IFNULL() quite useful when exporting query results to a CSV file or other type of flat file, providing something more meaningful than the NULL word itself. However, you only have one substitute for the NULL value when using IFNULL(), so keep that in mind. Continue reading and see examples using IFNULL() in SELECT queries…

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There may come a time you need to rename one or more columns in an existing MySQL table for a variety of reasons. Using ALTER TABLE, to rename a column is an easy enough command. But, suppose there are multiple tables in the same database/schema that have the same-named column and all of those columns need to be renamed. That could be a lot of ALTER TABLE statements to type out. Save your energy and time, avoiding all of those ALTER TABLE commands all-together. If you are lucky enough to be working with a MySQL version > 8.0 then the Shell is your salvation. With just a few lines of Python code in \py mode, all of your trouble(s) and headache(s) are no more…

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Date and time values are some of the most important datatypes in an RDBMS. From tracking order dates to payroll hours, DATE and DATETIME datatypes are used in all types of applications. At times as a Developer, you may need only certain portions of a DATE or DATETIME value. In MySQL, the EXTRACT() function can provide you with a specific component of a DATE or DATETIME value depending on which INTERVAL is given as a parameter. Continue reading to see example EXTRACT() queries for understanding…

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Have you ever had to type out a massive CREATE TABLE statement by hand? One with dozens of columns? Maybe several dozens of columns? There are likely some GUI tools to help with large CREATE TABLE commands. Or, other drag-n-drop types of software that I am not familiar with. What if you could write a few lines of Python code and take care of a huge CREATE TABLE statement with way less effort than typed manually? Interested? Continue reading and see how using pandas, pyodbc, and MySQL…

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Oracle MySQL is in need of a columnar storage engine for analytics workloads.  A columnar engine (or column store) stores data vertically, that is, it stores all the data associated with a column together, instead of the traditional RDBMS storage method of storing entire rows together, either in a index organized manner, like InnoDB, or in a heap, like MyISAM.  

Columnar storage has the benefit of reducing IO when only a subset of the row is accessed in a query, because only the data for the accessed rows must be read from disk (or cache) instead of having to read entire rows.  Most columnar stores do not support indexes, but WARP does.

WARP is open source

You can find the WARP source code release on GitHub.  Binaries can be provided upon request.  Simply open an issue for your desired Linux distribution, and I will make them available as soon as I can.

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If you are a developer working in a MySQL environment, this blog post is for you. I share 3 MySQL commands or statements that you should know. That is a bold statement, I know. Turns out, once you do know (of) these commands, you will use them all the time. They minimize guesswork which leads to better productivity in other facets of your programming and querying workflow. I use them myself almost daily and am sure you will too once you see how simple they are to use. So why should you know them? Continue reading and find out…

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…