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/mariadbInstall 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.shLoad 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 -LFinally ingest the data (will need to paste the root password from values.yaml):
mysql -h mariadb.jhub.svc.cluster.local -uroot -p mariadbk8s < dump.sqlAdd 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-engineThe 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")