Skip to main content

MySql with Docker

Running a MySQL database in a Docker container is straightforward. Here are the steps:


Pull the Official MySQL Image:

The official MySQL image is available on Docker Hub. You can choose the version you want (e.g., MySQL 8.0): docker pull mysql:8.0


Create a Docker Volume (Optional):

To persist your database data, create a Docker volume or bind mount. Otherwise, data will be lost when the container restarts.

Example using a volume: docker volume create mysql-data


Run the MySQL Container:

Use the following command to start a MySQL container: docker run --name my-mysql -e MYSQL_ROOT_PASSWORD=secret -v mysql-data:/var/lib/mysql -d mysql:8.0

Replace secret with your desired root password.


The MySQL first-run routine will take a few seconds to complete.


Check if the database is up by running: docker logs my-mysql


Look for a line that says “ready for connections.”


Access MySQL Shell:

To interact with MySQL, attach to the container and run the mysql command: docker exec -it my-mysql mysql -p


Enter the root password when prompted.


To import an SQL file from your filesystem: docker exec -it my-mysql mysql -psecret database_name < path-to-file.sql


Access MySQL from Host:

If you want to access MySQL from your host machine, set up a port binding:

Add the following to your docker-compose.yml file (if using Docker Compose):

services:

mysql:

ports:

- 33060:3306


If not using Docker Compose, pass -p 33060:3306 to docker run.

That’s it! You now have a MySQL database running in a Docker container.

Comments

Popular posts from this blog

Financial Engineering

Financial Engineering: Key Concepts Financial engineering is a multidisciplinary field that combines financial theory, mathematics, and computer science to design and develop innovative financial products and solutions. Here's an in-depth look at the key concepts you mentioned: 1. Statistical Analysis Statistical analysis is a crucial component of financial engineering. It involves using statistical techniques to analyze and interpret financial data, such as: Hypothesis testing : to validate assumptions about financial data Regression analysis : to model relationships between variables Time series analysis : to forecast future values based on historical data Probability distributions : to model and analyze risk Statistical analysis helps financial engineers to identify trends, patterns, and correlations in financial data, which informs decision-making and risk management. 2. Machine Learning Machine learning is a subset of artificial intelligence that involves training algorithms t...

Wholesale Customer Solution with Magento Commerce

The client want to have a shop where regular customers to be able to see products with their retail price, while Wholesale partners to see the prices with ? discount. The extra condition: retail and wholesale prices hasn’t mathematical dependency. So, a product could be $100 for retail and $50 for whole sale and another one could be $60 retail and $50 wholesale. And of course retail users should not be able to see wholesale prices at all. Basically, I will explain what I did step-by-step, but in order to understand what I mean, you should be familiar with the basics of Magento. 1. Creating two magento websites, stores and views (Magento meaning of website of course) It’s done from from System->Manage Stores. The result is: Website | Store | View ———————————————— Retail->Retail->Default Wholesale->Wholesale->Default Both sites using the same category/product tree 2. Setting the price scope in System->Configuration->Catalog->Catalog->Price set drop-down to...

How to Prepare for AI Driven Career

  Introduction We are all living in our "ChatGPT moment" now. It happened when I asked ChatGPT to plan a 10-day holiday in rural India. Within seconds, I had a detailed list of activities and places to explore. The speed and usefulness of the response left me stunned, and I realized instantly that life would never be the same again. ChatGPT felt like a bombshell—years of hype about Artificial Intelligence had finally materialized into something tangible and accessible. Suddenly, AI wasn’t just theoretical; it was writing limericks, crafting decent marketing content, and even generating code. The world is still adjusting to this rapid shift. We’re in the middle of a technological revolution—one so fast and transformative that it’s hard to fully comprehend. This revolution brings both exciting opportunities and inevitable challenges. On the one hand, AI is enabling remarkable breakthroughs. It can detect anomalies in MRI scans that even seasoned doctors might miss. It can trans...