---
aliases:
- /2022/06/mariadb
categories:
- kubernetes
- jetstream2
date: '2022-06-06'
layout: post
slug: mariadb-jetstream2-kubernetes
title: Deploy MariaDB on Jetstream 2 on top of Kubernetes

---

In this tutorial we will install a MariaDB instance backed by a persistent volume
on Jetstream 2.
It will be in the `jhub` namespace, so that it can be accessed by the JupyterHub users and from
no other namespace.

As usual all configuration files and scripts are in my reference repository:

    gh repo clone zonca/jupyterhub-deploy-kubernetes-jetstream
    cd jupyterhub-deploy-kubernetes-jetstream/mariadb

## Install via Helm

Bitnami provides a nicely prepackaged MariaDB instance via Helm,
modify the `mariadb/values.yaml` file, in particular set all the passwords to randomly generated values.

I have configured the recipe so that:

* database name is `mariadbk8s`
* non root username is `mariadbuser`

Install it with

    bash install_mariadb.sh

## Load data from a SQL dump

Once the database is running, follow the printout of the Helm recipe on how to get a temporary pod to connect to the database.

Once that is running, you will have a terminal running, there you can get your SQL dump for example from gist:

    cd /tmp
    curl https://gist.github.com/zonca/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx/raw/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx/dump.sql --output dump.sql -L

Finally ingest the data (will need to paste the `root` password from `values.yaml`):

    mysql -h mariadb.jhub.svc.cluster.local -uroot -p mariadbk8s < dump.sql

## Add support in user containers

Finally you need to make sure that the `mariadb-client` package is installed in the Jupyter single user OS, and in the Python environment you will need the `mariadb` package and possibly `sqlalchemy`.

For example Centos 7 needs the MariaDB custom repositories and the packages:

    MariaDB-devel MariaDB-connect-engine

The connection string for SQLAlchemy will be:

    from urllib.parse import quote_plus as urlquote
    pw = urlquote('xxxxxxxxxxxxxxxxxxxx')
    engine = sqlalchemy.create_engine(f"mariadb+mariadbconnector://mariadbuser:{pw}@mariadb.jhub.svc.cluster.local:3306/mariadbk8s")