Backend Requirements

Docker
Docker Compose

Frontend Requirements

Node.js (with npm)

Backend local development

Start the stack with Docker Compose:

docker-compose up -d

Now you can open your browser and interact with these URLs:

Frontend, built with Docker, with routes handled based on the path: http://localhost

Backend, JSON based web API based on OpenAPI: http://localhost/api/

Automatic interactive documentation with Swagger UI (from the OpenAPI backend): http://localhost/docs

Alternative automatic documentation with ReDoc (from the OpenAPI backend): http://localhost/redoc

Couchbase, web administration: http://localhost:8091

Flower, administration of Celery tasks: http://localhost:5555

Traefik UI, to see how the routes are being handled by the proxy: http://localhost:8090

Note: The first time you start your stack, it might take a minute for it to be ready. While the backend waits for the database to be ready and configures everything. You can check the logs to monitor it.

To check the logs, run:

docker-compose logs

To check the logs of a specific service, add the name of the service, e.g.:

docker-compose logs backend

If your Docker is not running in localhost (the URLs above wouldn't work) check the sections below on Development with Docker Toolbox and Development with a custom IP.

Backend local development, additional details

General workflow

Open your editor at ./backend/app/ (instead of the project root: ./), so that you see an ./app/ directory with your code inside. That way, your editor will be able to find all the imports, etc.

Modify or add Pydantic models in ./backend/app/app/models/, API endpoints in ./backend/app/app/api/, CRUD (Create, Read, Update, Delete) utils in ./backend/app/app/crud/. The easiest might be to copy the ones for Items (models, endpoints, and CRUD utils) and update them to your needs.

Add and modify tasks to the Celery worker in ./backend/app/app/worker.py.

If you need to install any additional package to the worker, add it to the file ./backend/app/celeryworker.dockerfile.

There is an .env file that has some Docker Compose default values that allow you to just run docker-compose up -d and start working, while still being able to use and share the same Docker Compose files for deployment, avoiding repetition of code and configuration as much as possible.

Full Text Search

After starting your stack with Docker Compose, go to the Couchbase admin user interface. For example, at: http://localhost:8091.

Go to the search section, and perform all the steps in the official guide: https://docs.couchbase.com/server/6.0/fts/fts-creating-indexes.html

But before saving the index definition, copy the JSON contents and save them to a .json file inside of ./backend/app/app/search_index_definitions/ (for example, items_01.json), besides users_01.json and users.json.

The same way you can create Full Text Search "aliases", you can copy users.json as an example.

All the index definition .json files in that directory will be automatically created as Full Text Search indexes during the backend startup process.

Docker Compose Override

During development, you can change Docker Compose settings that will only affect the local development environment, in the files docker-compose.dev.*.yml.

The changes to those files only affect the local development environment, not the production environment. So, you can add "temporal" changes that help the development workflow.

For example, the directory with the backend code is mounted as a Docker "host volume" (in the file docker-compose.dev.volumes.yml), mapping the code you change live to the directory inside the container. That allows you to test your changes right away, without having to build the Docker image again. It should only be done during development, for production, you should build the Docker image with a recent version of the backend code. But during development, it allows you to iterate very fast.

There is a command override in the file docker-compose.dev.command.yml that runs /start-reload.sh (included in the base image) instead of the default /start.sh (also included in the base image). It starts a single server process (instead of multiple, as would be for production) and reloads the process whenever the code changes. As it is in docker-compose.dev.command.yml, it only applies to local development. Have in mind that if you have a syntax error and save the Python file, it will break and exit, and the container will stop. After that, you can restart the container by fixing the error and running again:

docker-compose up -d

There is also a commented out command override (in the file docker-compose.dev.command.yml), you can uncomment it and comment the default one. It makes the backend container run a process that does "nothing", but keeps the process running. That allows you to get inside your living container and run commands inside, for example a Python interpreter to test installed dependencies, or start the development server that reloads when it detects changes, or start a Jupyter Notebook session.

To get inside the container with a bash session you can start the stack with:

docker-compose up -d

and then exec inside the running container:

docker-compose exec backend bash

You should see an output like:

root@7f2607af31c3:/app#

that means that you are in a bash session inside your container, as a root user, under the /app directory.

There you use the script /start-reload.sh to run the debug live reloading server. You can run that script from inside the container with:

bash /start-reload.sh

...it will look like:

root@7f2607af31c3:/app# bash /start-reload.sh

and then hit enter. That runs the live reloading server that auto reloads when it detects code changes.

Nevertheless, if it doesn't detect a change but a syntax error, it will just stop with an error. But as the container is still alive and you are in a Bash session, you can quickly restart it after fixing the error, running the same command ("up arrow" and "Enter").

...this previous detail is what makes it useful to have the container alive doing nothing and then, in a Bash session, make it run the live reload server.

Backend tests

To test the backend run:

DOMAIN=backend sh ./scripts/test.sh

The file ./scripts/test.sh has the commands to generate a testing docker-stack.yml file from the needed Docker Compose files, start the stack and test it.

The tests run with Pytest, modify and add tests to ./backend/app/app/tests/.

If you need to install any additional package for the tests, add it to the file ./backend/app/tests.dockerfile.

If you use GitLab CI the tests will run automatically.

Test running stack

If your stack is already up and you just want to run the tests, you can use:

docker-compose exec backend-tests /tests-start.sh

That /tests-start.sh script inside the backend-tests container calls pytest. If you need to pass extra arguments to pytest, you can pass them to that command and they will be forwarded.

For example, to stop on first error:

docker-compose exec backend-tests /tests-start.sh -x

Live development with Python Jupyter Notebooks

If you know about Python Jupyter Notebooks, you can take advantage of them during local development.

The docker-compose.dev.build.yml file sends a variable env with a value dev to the build process of the Docker image (during local development) and the Dockerfile has steps to then install and configure Jupyter inside your Docker container.

So, you can enter into the Docker running container:

docker-compose exec backend bash

And use the environment variable $JUPYTER to run a Jupyter Notebook with everything configured to listen on the public port (so that you can use it from your browser).

It will output something like:

root@73e0ec1f1ae6:/app# $JUPYTER
[I 12:02:09.975 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[I 12:02:10.317 NotebookApp] Serving notebooks from local directory: /app
[I 12:02:10.317 NotebookApp] The Jupyter Notebook is running at:
[I 12:02:10.317 NotebookApp] http://(73e0ec1f1ae6 or 127.0.0.1):8888/?token=f20939a41524d021fbfc62b31be8ea4dd9232913476f4397
[I 12:02:10.317 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 12:02:10.317 NotebookApp] No web browser found: could not locate runnable browser.
[C 12:02:10.317 NotebookApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://(73e0ec1f1ae6 or 127.0.0.1):8888/?token=f20939a41524d021fbfc62b31be8ea4dd9232913476f4397

you can copy that URL and modify the "host" to be localhost or the domain you are using for development (e.g. local.dockertoolbox.tiangolo.com), in the case above, it would be, e.g.:

http://localhost:8888/token=f20939a41524d021fbfc62b31be8ea4dd9232913476f4397

and then open it in your browser.

You will have a full Jupyter Notebook running inside your container, that has direct access to your database by the name container name, etc. So, you can just copy your backend code and run it directly, without needing to modify it.

If you use tools like Hydrogen or Visual Studio Code Jupyter, you can use that same modified URL.

Development with Docker Toolbox

If you are using Docker Toolbox in Windows or macOS instead of Docker for Windows or Docker for Mac, Docker will be running in a VirtualBox Virtual Machine, and it will have a local IP different than 127.0.0.1, which is the IP address for localhost in your machine.

The address of your Docker Toolbox virtual machine would probably be 192.168.99.100 (that is the default).

As this is a common case, the domain local.dockertoolbox.tiangolo.com points to that (private) IP, just to help with development (actually dockertoolbox.tiangolo.com and all its subdomains point to that IP). That way, you can start the stack in Docker Toolbox, and use that domain for development. You will be able to open that URL in Chrome and it will communicate with your local Docker Toolbox directly as if it was a cloud server, including CORS (Cross Origin Resource Sharing).

If you used the default CORS enabled domains while generating the project, local.dockertoolbox.tiangolo.com was configured to be allowed. If you didn't, you will need to add it to the list in the variable BACKEND_CORS_ORIGINS in the .env file.

To configure it in your stack, follow the section Change the development "domain" below, using the domain local.dockertoolbox.tiangolo.com.

After performing those steps you should be able to open: http://local.dockertoolbox.tiangolo.com and it will be server by your stack in your Docker Toolbox virtual machine.

Check all the corresponding available URLs in the section at the end.

Develpment in `localhost` with a custom domain

You might want to use something different than localhost as the domain. For example, if you are having problems with cookies that need a subdomain, and Chrome is not allowing you to use localhost.

In that case, you have two options: you could use the instructions to modify your system hosts file with the instructions below in Development with a custom IP or you can just use localhost.tiangolo.com, it is set up to point to localhost (to the IP 127.0.0.1) and all its subdomains too. And as it is an actual domain, the browsers will store the cookies you set during development, etc.

If you used the default CORS enabled domains while generating the project, localhost.tiangolo.com was configured to be allowed. If you didn't, you will need to add it to the list in the variable BACKEND_CORS_ORIGINS in the .env file.

To configure it in your stack, follow the section Change the development "domain" below, using the domain localhost.tiangolo.com.

After performing those steps you should be able to open: http://localhost.tiangolo.com and it will be server by your stack in localhost.

Check all the corresponding available URLs in the section at the end.

Development with a custom IP

If you are running Docker in an IP address different than 127.0.0.1 (localhost) and 192.168.99.100 (the default of Docker Toolbox), you will need to perform some additional steps. That will be the case if you are running a custom Virtual Machine, a secondary Docker Toolbox or your Docker is located in a different machine in your network.

In that case, you will need to use a fake local domain (dev.{{cookiecutter.domain_main}}) and make your computer think that the domain is is served by the custom IP (e.g. 192.168.99.150).

If you used the default CORS enabled domains, dev.{{cookiecutter.domain_main}} was configured to be allowed. If you want a custom one, you need to add it to the list in the variable BACKEND_CORS_ORIGINS in the .env file.

Open your hosts file with administrative privileges using a text editor:
- Note for Windows: If you are in Windows, open the main Windows menu, search for "notepad", right click on it, and select the option "open as Administrator" or similar. Then click the "File" menu, "Open file", go to the directory c:\Windows\System32\Drivers\etc\, select the option to show "All files" instead of only "Text (.txt) files", and open the hosts file.
- Note for Mac and Linux: Your hosts file is probably located at /etc/hosts, you can edit it in a terminal running sudo nano /etc/hosts.
Additional to the contents it might have, add a new line with the custom IP (e.g. 192.168.99.150) a space character, and your fake local domain: dev.{{cookiecutter.domain_main}}.

The new line might look like:

192.168.99.100    dev.{{cookiecutter.domain_main}}

Save the file.
- Note for Windows: Make sure you save the file as "All files", without an extension of .txt. By default, Windows tries to add the extension. Make sure the file is saved as is, without extension.

...that will make your computer think that the fake local domain is served by that custom IP, and when you open that URL in your browser, it will talk directly to your locally running server when it is asked to go to dev.{{cookiecutter.domain_main}} and think that it is a remote server while it is actually running in your computer.

To configure it in your stack, follow the section Change the development "domain" below, using the domain dev.{{cookiecutter.domain_main}}.

After performing those steps you should be able to open: http://dev.{{cookiecutter.domain_main}} and it will be server by your stack in localhost.

Check all the corresponding available URLs in the section at the end.

Change the development "domain"

If you need to use your local stack with a different domain than localhost, you need to make sure the domain you use points to the IP where your stack is set up. See the different ways to achieve that in the sections above (i.e. using Docker Toolbox with local.dockertoolbox.tiangolo.com, using localhost.tiangolo.com or using dev.{{cookiecutter.domain_main}}).

To simplify your Docker Compose setup, for example, so that the API explorer, Swagger UI, knows where is your API, you should let it know you are using that domain for development. You will need to edit 1 line in 2 files.

Open the file located at ./.env. It would have a line like:

DOMAIN=localhost

Change it to the domain you are going to use, e.g.:

DOMAIN=localhost.tiangolo.com

That variable will be used by some of the local development docker-compose.dev.*.yml files, for example, to tell Swagger UI to use that domain for the API.

Now open the file located at ./frontend/.env. It would have a line like:

VUE_APP_DOMAIN_DEV=localhost

Change that line to the domain you are going to use, e.g.:

VUE_APP_DOMAIN_DEV=localhost.tiangolo.com

That variable will make your frontend communicate with that domain when interacting with your backend API, when the other variable VUE_APP_ENV is set to development.

After changing the two lines, you can re-start your stack with:

docker-compose up -d

and check all the corresponding available URLs in the section at the end.

Frontend development

Enter the frontend directory, install the NPM packages and start the live server using the npm scripts:

cd frontend
npm install
npm run serve

Then open your browser at http://localhost:8080

Notice that this live server is not running inside Docker, it is for local development, and that is the recommended workflow. Once you are happy with your frontend, you can build the frontend Docker image and start it, to test it in a production-like environment. But compiling the image at every change will not be as productive as running the local development server.

Check the file package.json to see other available options.

If you have Vue CLI installed, you can also run vue ui to control, configure, serve and analyse your application using a nice local web user interface.

If you are only developing the frontend (e.g. other team members are developing the backend) and there is a staging environment already deployed, you can make your local development code use that staging API instead of a full local Docker Compose stack.

To do that, modify the file ./frontend/.env, there's a section with:

VUE_APP_ENV=development
# VUE_APP_ENV=staging

Switch the comment, to:

# VUE_APP_ENV=development
VUE_APP_ENV=staging

Deployment

You can deploy the stack to a Docker Swarm mode cluster with a main Traefik proxy, set up using the ideas from DockerSwarm.rocks, to get automatic HTTPS certificates, etc.

And you can use CI (continuous integration) systems to do it automatically.

But you have to configure a couple things first.

Persisting Docker named volumes

You need to make sure that each service (Docker container) that uses a volume is always deployed to the same Docker "node" in the cluster, that way it will preserve the data. Otherwise, it could be deployed to a different node each time, and each time the volume would be created in that new node before starting the service. As a result, it would look like your service was starting from scratch every time, losing all the previous data.

That's specially important for a service running a database. But the same problem would apply if you were saving files in your main backend service (for example, if those files were uploaded by your users, or if they were created by your system).

To solve that, you can put constraints in the services that use one or more data volumes (like databases) to make them be deployed to a Docker node with a specific label. And of course, you need to have that label assigned to one (only one) of your nodes.

Adding services with volumes

For each service that uses a volume (databases, services with uploaded files, etc) you should have a label constraint in your docker-compose.deploy.volumes-placement.yml file.

To make sure that your labels are unique per volume per stack (for examlpe, that they are not the same for prod and stag) you should prefix them with the name of your stack and then use the same name of the volume.

Then you need to have those constraints in your deployment Docker Compose file for the services that need to be fixed with each volume.

To be able to use different environments, like prod and stag, you should pass the name of the stack as an environment variable. Like:

STACK_NAME={{cookiecutter.docker_swarm_stack_name_staging}} sh ./scripts/deploy.sh

To use and expand that environment variable inside the docker-compose.deploy.volumes-placement.yml files you can add the constraints to the services like:

version: '3.3'
services:
  couchbase:
    volumes:
      - app-couchbase-data:/opt/couchbase/var
    deploy:
      placement:
        constraints:
          - node.labels.${STACK_NAME}.app-couchbase-data == true

note the ${STACK_NAME}. In the script ./scripts/deploy.sh, that docker-compose.deploy.volumes-placement.yml would be converted, and saved to a file docker-stack.yml containing:

version: '3.3'
services:
  couchbase:
    volumes:
      - app-couchbase-data:/opt/couchbase/var
    deploy:
      placement:
        constraints:
          - node.labels.{{cookiecutter.docker_swarm_stack_name_main}}.app-couchbase-data == true

If you add more volumes to your stack, you need to make sure you add the corresponding constraints to the services that use that named volume.

Then you have to create those labels in some nodes in your Docker Swarm mode cluster. You can use docker-auto-labels to do it automatically.

`docker-auto-labels`

You can use docker-auto-labels to automatically read the placement constraint labels in your Docker stack (Docker Compose file) and assign them to a random Docker node in your Swarm mode cluster if those labels don't exist yet.

To do that, you can install docker-auto-labels:

pip install docker-auto-labels

And then run it passing your docker-stack.yml file as a parameter:

docker-auto-labels docker-stack.yml

You can run that command every time you deploy, right before deploying, as it doesn't modify anything if the required labels already exist.

(Optionally) adding labels manually

If you don't want to use docker-auto-labels or for any reason you want to manually assign the constraint labels to specific nodes in your Docker Swarm mode cluster, you can do the following:

First, connect via SSH to your Docker Swarm mode cluster.
Then check the available nodes with:

docker node ls

you would see an output like:

ID                            HOSTNAME               STATUS              AVAILABILITY        MANAGER STATUS
nfa3d4df2df34as2fd34230rm *   dog.example.com        Ready               Active              Reachable
2c2sd2342asdfasd42342304e     cat.example.com        Ready               Active              Leader
c4sdf2342asdfasd4234234ii     snake.example.com      Ready               Active              Reachable

then chose a node from the list. For example, dog.example.com.

Add the label to that node. Use as label the name of the stack you are deploying followed by a dot (.) followed by the named volume, and as value, just true, e.g.:

docker node update --label-add {{cookiecutter.docker_swarm_stack_name_main}}.app-couchbase-data=true dog.example.com

Then you need to do the same for each stack version you have. For example, for staging you could do:

docker node update --label-add {{cookiecutter.docker_swarm_stack_name_staging}}.app-couchbase-data=true cat.example.com

Deploy to a Docker Swarm mode cluster

There are 3 steps:

Build your app images
Optionally, push your custom images to a Docker Registry
Deploy your stack

Here are the steps in detail:

Build your app images

Set these environment variables, prepended to the next command:
- TAG=prod
- FRONTEND_ENV=production
Use the provided script-build.sh file with those environment variables:

TAG=prod FRONTEND_ENV=production bash ./scripts/build.sh

Optionally, push your images to a Docker Registry

Note: if the deployment Docker Swarm mode "cluster" has more than one server, you will have to push the images to a registry or build the images in each server, so that when each of the servers in your cluster tries to start the containers it can get the Docker images for them, pulling them from a Docker Registry or because it has them already built locally.

If you are using a registry and pushing your images, you can omit running the previous script and instead using this one, in a single shot.

Set these environment variables:
- TAG=prod
- FRONTEND_ENV=production
Use the provided script-build-push.sh file with those environment variables:

TAG=prod FRONTEND_ENV=production bash ./scripts/build-push.sh

Deploy your stack

Set these environment variables:
- DOMAIN={{cookiecutter.domain_main}}
- TRAEFIK_TAG={{cookiecutter.traefik_constraint_tag}}
- STACK_NAME={{cookiecutter.docker_swarm_stack_name_main}}
- TAG=prod
Use the provided script-deploy.sh file with those environment variables:

DOMAIN={{cookiecutter.domain_main}} \
TRAEFIK_TAG={{cookiecutter.traefik_constraint_tag}} \
STACK_NAME={{cookiecutter.docker_swarm_stack_name_main}} \
TAG=prod \
bash ./scripts/deploy.sh

If you change your mind and, for example, want to deploy everything to a different domain, you only have to change the DOMAIN environment variable in the previous commands. If you wanted to add a different version / environment of your stack, like "preproduction", you would only have to set TAG=preproduction in your command and update these other environment variables accordingly. And it would all work, that way you could have different environments and deployments of the same app in the same cluster.

Deployment Technical Details

For the 3 steps (build, push, deploy) you need a generated docker-stack.yml, it is generated using the docker-compose command with some of the docker-compose.*.yml files. As each of these steps uses different docker-compose.*.yml files, the generated docker-stack.yml file is slightly different. But it's all generated by the scripts.

You can do the process by hand based on those same scripts if you wanted. The general structure of the scripts is like this:

# Use the environment variables passed to this script, as TAG and FRONTEND_ENV
# And re-create those variables as environment variables for the next command
TAG=${TAG} \
# Set the environment variable FRONTEND_ENV to the same value passed to this script with
# a default value of "production" if nothing else was passed
FRONTEND_ENV=${FRONTEND_ENV-production} \
# The actual comand that does the work: docker-compose
docker-compose \
# Pass the files that should be used at this stage, the set of files changes in each script / each stage
-f docker-compose.deploy.build.yml \
-f docker-compose.deploy.images.yml \
# Use the docker-compose sub command named "config", it just uses the docker-compose.*.yml files passed
# to it and prints their combined contents
# Put those contents in a file "docker-stack.yml", with ">"
config > docker-stack.yml

# The previous only generated a docker-stack.yml file, but didn't do anything with it
# Now this command uses that same file and does some operation with it, in this case, build it
docker-compose -f docker-stack.yml build

Continuous Integration / Continuous Delivery

If you use GitLab CI, the included .gitlab-ci.yml can automatically deploy it. You may need to update it according to your GitLab configurations.

If you use any other CI / CD provider, you can base your deployment from that .gitlab-ci.yml file, as all the actual script steps are performed in bash scripts that you can easily re-use.

GitLab CI is configured assuming 2 environments following GitLab flow:

prod (production) from the production branch.
stag (staging) from the master branch.

If you need to add more environments, for example, you could imagine using a client-approved preprod branch, you can just copy the configurations in .gitlab-ci.yml for stag and rename the corresponding variables. All the Docker Compose files are configured to support as many environments as you need, so that you only need to modify .gitlab-ci.yml (or whichever CI system configuration you are using).

Docker Compose files

There are several Docker Compose files, each with a specific purpose.

They are designed to support several "stages", like development, building, testing, and deployment. Also, allowing the deployment to different environments like staging and production (and you can add more environments very easily).

They are designed to have the minimum repetition of code and configurations, so that if you need to change something, you have to change it in the minimum amount of places. That's why several of the files use environment variables that get auto-expanded. That way, if for example, you want to use a different domain, you can call the docker-compose command with a different DOMAIN environment variable instead of having to change the domain in several places inside the Docker Compose files.

Also, if you want to have another deployment environment, say preprod, you just have to change environment variables, but you can keep using the same Docker Compose files.

Because of that, for each "stage" (development, building, testing, deployment) you would use a different set of Docker Compose files.

But you probably don't have to worry about the different files, for building, testing and deployment, you would probably use a CI system (like GitLab CI) and the different configured files would be already set there.

And for development, there's a .env file that will be automatically used by docker-compose locally, with the default configurations already set for local development. Including environment variables. So, for local development you can just run:

docker-compose up -d

and it will do the right thing.

They are also separated by the common tasks and functionalities they solve, and they are named accordinly. So, although there are many Docker Compose files, each one has a name that shows what should be in there, and the contents tend to be small and specific. That makes it easier to modify, or add configurations, as you can go directly to the relevant file.

The docker-compose.deploy.*.yml files are only used at deployment, being it to production or any other environment. They build the images in production mode (not installing debugging packages), set configurations for Docker Swarm mode, etc.

The docker-compose.dev.*.yml files are only used during development. They have overrides and tools for development, as mounting app volumes directly inside the container to iterate fast, map ports directly to your machine, install debugging packages, etc.

The docker-compose.test.yml file is used for testing, during development and in a CI environment running tests, but not used in deployment to production (or staging or any other deployment environment of the final code).

The docker-compose.shared.*.yml files are used at several stages and contain stuff shared by several stages: development, testing, deployment. They have things like the databases or the environment variables, that are used by all the main services / containers, during development, testing and deployment. The file for admin, that has utils needed for development and production, like the Swagger UI interactive API documentation system. But this file is not used during testing (in CI environments) as this is not needed or used in that stage.

The purpose of each Docker Compose file is:

docker-compose.deploy.build.yml: build directories and Dockerfiles, for deployment (the building process for development has a little difference).
docker-compose.deploy.command.yml: command overrides for images only during deployment. Initially only for the main Traefik proxy, making it run in a Docker Swarm mode cluster.
docker-compose.deploy.images.yml: image names to be created, with environment variables for the specific tag.
docker-compose.deploy.labels.yml: labels for deployment, the configurations to make the internal Traefik proxy serve some services on specific URLs, some with basic HTTP auth, etc. Also labels used in the internal Traefik proxy container to make it talk to the public Traefik proxy (outside of this stack) and make it send requests for this domain, generate HTTPS certificates, etc.
docker-compose.deploy.networks.yml: networks that have to be used and shared by containers that need to be able to talk to the public Traefik proxy (when a service requires a domain for itself).
docker-compose.deploy.volumes-placement.yml: volume declarations, volumes used by stateful services (as databases) and volume placement constraints, to make those services always run on the node that has their volumes, even after stack updates.
docker-compose.dev.build.yml: build directories and Dockerfiles, for local development, sets a built-time argument that then is used in the Dockerfiles to install and configure helper tools exclusively for development.
docker-compose.dev.command.yml: command overrides for local development. To tell the internal Traefik proxy to work with a local Docker in the host instead of a Docker Swarm mode cluster. And (commented out but ready to be used) overrides to make the containers run an infinite loop while keeping alive to be able to run the development server manually or do any other interactive work.
docker-compose.dev.env.yml: development environment variable overrides.
docker-compose.dev.labels.yml: local development labels, to be used by the local development Traefik proxy. They have to be declared in a different place than for deployment.
docker-compose.dev.networks.yml: local development networks, to enable interactively talking to the backend.
docker-compose.dev.ports.yml: local development port mappings.
docker-compose.dev.volumes.yml: local development mounted volumes, mainly to map the development code directory inside the container, for fast development without needing to re-build the images.
docker-compose.shared.admin.yml: additional services for administration or utilities with their configurations, like PGAdmin and Swagger, that are not needed during testing and use external images (don't need to be built or create images).
docker-compose.shared.base-images.yml: base Docker images used without modification for shared services, as databases. Used in deployment, development, testing, etc.
docker-compose.shared.depends.yml: dependencies between main services with depends_on, used in deployment, development, testing, etc.
docker-compose.shared.env.yml: environment variables used by services, as database passwords, secret keys, etc.
docker-compose.test.yml: specific additional container to be used only during testing, mainly the container that tests the backend and the APIs.

URLs

These are the URLs that will be used and generated by the project.

Production

Production URLs, from the branch production.

Frontend: https://{{cookiecutter.domain_main}}

Backend: https://{{cookiecutter.domain_main}}/api/

Automatic Interactive Docs (Swagger UI): https://{{cookiecutter.domain_main}}/docs

Automatic Alternative Docs (ReDoc): https://{{cookiecutter.domain_main}}/redoc

Couchbase: https://db.{{cookiecutter.domain_main}}/

Flower: https://flower.{{cookiecutter.domain_main}}

Staging

Staging URLs, from the branch master.

Frontend: https://{{cookiecutter.domain_staging}}

Backend: https://{{cookiecutter.domain_staging}}/api/

Automatic Interactive Docs (Swagger UI): https://{{cookiecutter.domain_staging}}/docs

Automatic Alternative Docs (ReDoc): https://{{cookiecutter.domain_staging}}/redoc

Couchbase: https://db.{{cookiecutter.domain_staging}}/

Flower: https://flower.{{cookiecutter.domain_staging}}

Development

Development URLs, for local development.

Frontend: http://localhost

Backend: http://localhost/api/

Automatic Interactive Docs (Swagger UI): https://localhost/docs

Automatic Alternative Docs (ReDoc): https://localhost/redoc

Couchbase: http://localhost:8091/

Flower: http://localhost:5555

Traefik UI: http://localhost:8090

Development with Docker Toolbox

Development URLs, for local development.

Frontend: http://local.dockertoolbox.tiangolo.com

Backend: http://local.dockertoolbox.tiangolo.com/api/

Automatic Interactive Docs (Swagger UI): https://local.dockertoolbox.tiangolo.com/docs

Automatic Alternative Docs (ReDoc): https://local.dockertoolbox.tiangolo.com/redoc

Couchbase: http://local.dockertoolbox.tiangolo.com:8091

Flower: http://local.dockertoolbox.tiangolo.com:5555

Traefik UI: http://local.dockertoolbox.tiangolo.com:8090

Development with a custom IP

Development URLs, for local development.

Frontend: http://dev.{{cookiecutter.domain_main}}

Backend: http://dev.{{cookiecutter.domain_main}}/api/

Automatic Interactive Docs (Swagger UI): https://dev.{{cookiecutter.domain_main}}/docs

Automatic Alternative Docs (ReDoc): https://dev.{{cookiecutter.domain_main}}/redoc

Couchbase: http://dev.{{cookiecutter.domain_main}}:8091

Flower: http://dev.{{cookiecutter.domain_main}}:5555

Traefik UI: http://dev.{{cookiecutter.domain_main}}:8090

Development in localhost with a custom domain

Development URLs, for local development.

Frontend: http://localhost.tiangolo.com

Backend: http://localhost.tiangolo.com/api/

Automatic Interactive Docs (Swagger UI): https://localhost.tiangolo.com/docs

Automatic Alternative Docs (ReDoc): https://localhost.tiangolo.com/redoc

Couchbase: http://localhost.tiangolo.com:8091

Flower: http://localhost.tiangolo.com:5555

Traefik UI: http://localhost.tiangolo.com:8090

Project generation and updating, or re-generating

This project was generated using https://github.com/tiangolo/full-stack-fastapi-couchbase with:

pip install cookiecutter
cookiecutter https://github.com/tiangolo/full-stack-fastapi-couchbase

You can check the variables used during generation in the file cookiecutter-config-file.yml.

You can generate the project again with the same configurations used the first time.

That would be useful if, for example, the project generator (tiangolo/full-stack-fastapi-couchbase) was updated and you want to integrate or review the changes.

You could generate a new project with the same configurations as this one in a parallel directory. And compare the differences between the two, without having to overwrite your current code but being able to use the same variables used for your current project.

To achieve that, the generated project includes the file cookiecutter-config-file.yml with the current variables used.

You can use that file while generating a new project to reuse all those variables.

For example, run:

cookiecutter --config-file ./cookiecutter-config-file.yml --output-dir ../project-copy https://github.com/tiangolo/full-stack-fastapi-couchbase

That will use the file cookiecutter-config-file.yml in the current directory (in this project) to generate a new project inside a sibling directory project-copy.

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README.md

README.md

{{cookiecutter.project_name}}

Backend Requirements

Frontend Requirements

Backend local development

Backend local development, additional details

General workflow

Full Text Search

Docker Compose Override

Backend tests

Test running stack

Live development with Python Jupyter Notebooks

Development with Docker Toolbox

Develpment in `localhost` with a custom domain

Development with a custom IP

Change the development "domain"

Frontend development

Deployment

Persisting Docker named volumes

Adding services with volumes

`docker-auto-labels`

(Optionally) adding labels manually

Deploy to a Docker Swarm mode cluster

Deployment Technical Details

Continuous Integration / Continuous Delivery

Docker Compose files

URLs

Production

Staging

Development

Development with Docker Toolbox

Development with a custom IP

Development in localhost with a custom domain

Project generation and updating, or re-generating

Files

README.md

Latest commit

History

README.md

File metadata and controls

{{cookiecutter.project_name}}

Backend Requirements

Frontend Requirements

Backend local development

Backend local development, additional details

General workflow

Full Text Search

Docker Compose Override

Backend tests

Test running stack

Live development with Python Jupyter Notebooks

Development with Docker Toolbox

Develpment in localhost with a custom domain

Development with a custom IP

Change the development "domain"

Frontend development

Deployment

Persisting Docker named volumes

Adding services with volumes

docker-auto-labels

(Optionally) adding labels manually

Deploy to a Docker Swarm mode cluster

Deployment Technical Details

Continuous Integration / Continuous Delivery

Docker Compose files

URLs

Production

Staging

Development

Development with Docker Toolbox

Development with a custom IP

Development in localhost with a custom domain

Project generation and updating, or re-generating

Develpment in `localhost` with a custom domain

`docker-auto-labels`