.. | ||
.env | ||
build_and_install_libolm.sh | ||
docker-compose.yml | ||
Dockerfile | ||
Dockerfile.dev | ||
my-project-name.service | ||
README.md | ||
start-dev.sh |
Docker
The docker image will run my-project-name with a SQLite database and end-to-end encryption dependencies included. For larger deployments, a connection to a Postgres database backend is recommended.
Setup
The /data
volume
The docker container expects the config.yaml
file to exist at
/data/config.yaml
. To easily configure this, it is recommended to create a
directory on your filesystem, and mount it as /data
inside the container:
mkdir data
We'll later mount this directory into the container so that its contents persist across container restarts.
Creating a config file
Copy sample.config.yaml
to a file named config.yaml
inside of your newly
created data
directory. Fill it out as you normally would, with a few minor
differences:
-
The bot store directory should reside inside of the data directory so that it is not wiped on container restart. Change it from the default to
/data/store
. There is no need to create this directory yourself, it will be created on startup if it does not exist. -
Choose whether you want to use SQLite or Postgres as your database backend. Postgres has increased performance over SQLite, and is recommended for deployments with many users.
If using SQLite, ensure your database file is stored inside the
/data
directory:database: "sqlite:///data/bot.db"
If using postgres, point to your postgres instance instead:
database: "postgres://username:password@postgres/my-project-name?sslmode=disable"
Note: a postgres container is defined in
docker-compose.yaml
for your convenience. If you would like to use it, set your database connection string to:database: "postgres://postgres:somefancypassword@postgres/postgres?sslmode=disable"
The password
somefancypassword
is defined in the docker compose file.
Change any other config values as necessary. For instance, you may also want to
store log files in the /data
directory.
Running
First, create a volume for the data directory created in the above section:
docker volume create \
--opt type=none \
--opt o=bind \
--opt device="/path/to/data/dir" data_volume
Optional: If you want to use the postgres container defined in
docker-compose.yaml
, start that first:
docker-compose up -d postgres
Start the bot with:
docker-compose up my-project-name
This will run the bot and log the output to the terminal. You can instead run
the container detached with the -d
flag:
docker-compose up -d my-project-name
(Logs can later be accessed with the docker logs
command).
This will use the latest
tag from
Docker Hub.
If you would rather run from the checked out code, you can use:
docker-compose up local-checkout
This will build an optimized, production-ready container. If you are developing
instead and would like a development container for testing local changes, use
the start-dev.sh
script and consult CONTRIBUTING.md.
Note: If you are trying to connect to a Synapse instance running on the
host, you need to allow the IP address of the docker container to connect. This
is controlled by bind_addresses
in the listeners
section of Synapse's
config. If present, either add the docker internal IP address to the list, or
remove the option altogether to allow all addresses.
Updating
To update the container, navigate to the bot's docker
directory and run:
docker-compose pull my-project-name
Then restart the bot.
Systemd
A systemd service file is provided for your convenience at
my-project-name.service. The service uses
docker-compose
to start and stop the bot.
Copy the file to /etc/systemd/system/my-project-name.service
and edit to
match your setup. You can then start the bot with:
systemctl start my-project-name
and stop it with:
systemctl stop my-project-name
To run the bot on system startup:
systemctl enable my-project-name
Building the image
To build a production image from source, use the following docker build
command
from the repo's root:
docker build -t somebody/my-project-name:latest -f docker/Dockerfile .