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A production Falcon deployment on Ubuntu normally uses Falcon’s WSGI callable, Gunicorn as the application server, a systemd service and Unix socket, and Nginx as the public reverse proxy. The sequence is: install the app in an isolated virtual environment, run Gunicorn as a non-root account, let Nginx reach its socket, then add HTTPS, firewall rules, secrets handling, monitoring, and a rollback plan.
What the finished deployment looks like
A browser or API client connects to Nginx on ports 80 or 443. Nginx handles the public listener, TLS termination, request headers, and optional static files. It forwards dynamic requests over /run/gunicorn.sock to Gunicorn. Gunicorn imports the callable exposed by your Falcon project and dispatches each request to Falcon middleware and routes.
Falcon 4.4.0 supports CPython 3.9 and newer and installs with pip install falcon. Falcon conforms to WSGI, so a WSGI server such as Gunicorn can load it. Keep the source tree owned by a deployment account and execute the service with a separate runtime account that cannot modify the source. If an upload or process-restart vulnerability is ever found, removing source write access limits what that compromise can change.
Prerequisites and account separation
Install operating-system packages
Use an Ubuntu host with administrative access and a DNS name pointing to it. Install Python, virtual-environment support, and Nginx:
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sudo apt update
sudo apt install python3 python3-venv python3-pip nginxCheck that the interpreter meets Falcon 4.4.0’s requirement:
python3 --versionCreate deployment and runtime identities
The deployment account owns releases and installs dependencies. The runtime account only reads the application and runs Gunicorn:
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sudo adduser --disabled-password --gecos '' deploy
sudo addgroup --system falconapp
sudo adduser --system --ingroup falconapp --no-create-home falconrun
sudo usermod -aG www-data falconrun
sudo mkdir -p /srv/falconapp
sudo chown deploy:falconapp /srv/falconapp
sudo chmod 750 /srv/falconappThe falconapp group gives the service read and directory-traversal access without making falconrun the owner. The supplementary www-data membership lets the process and Nginx share a socket whose group is www-data. If your organization uses different identities, substitute them consistently in the file permissions and unit files.
Put the Falcon project in a virtual environment
Install code and dependencies
Copy or check out the project as deploy under /srv/falconapp. Create the environment and install the locked application dependencies:
sudo -u deploy python3 -m venv /srv/falconapp/venv
sudo -u deploy /srv/falconapp/venv/bin/python -m pip install --upgrade pip
sudo -u deploy /srv/falconapp/venv/bin/pip install falcon gunicorn
sudo -u deploy /srv/falconapp/venv/bin/pip install -r /srv/falconapp/requirements.txt
sudo chown -R deploy:falconapp /srv/falconapp
sudo chmod -R g+rX,o-rwx /srv/falconappUse a pinned requirements file for repeatable releases. The final permission command grants the runtime group read and execute access but no write access. Any directory that must be writable at runtime, such as a deliberately configured upload or cache directory, should be a separate path with its own ownership and size controls rather than making the whole source tree writable.
Expose an importable WSGI callable
Gunicorn needs a Python module and an object name. A minimal /srv/falconapp/wsgi.py could be:
import falcon
class HealthResource:
def on_get(self, req, resp):
resp.media = {'status': 'ok'}
application = falcon.App()
application.add_route('/health', HealthResource())The target wsgi:application means “import application from wsgi.py.” If your module is named differently, change both the file path and Gunicorn target. Keep configuration and route construction import-safe: an import error should fail the service clearly rather than leaving a process that appears healthy but cannot serve requests.
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Smoke-test Gunicorn before adding Nginx
Run the exact virtual-environment executable from the project directory:
cd /srv/falconapp
sudo -u falconrun /srv/falconapp/venv/bin/gunicorn --bind 127.0.0.1:8000 wsgi:applicationIn another shell, request the loopback endpoint:
curl -i http://127.0.0.1:8000/healthGunicorn’s default bind is loopback port 8000; explicitly specifying it makes the test unambiguous and keeps the temporary listener off the public interface. Stop the foreground process with Ctrl-C. Resolve import errors, missing packages, and Falcon route exceptions here, before introducing systemd or Nginx.
Create systemd units for Gunicorn
A service unit gives Gunicorn supervision, restart behavior, a known working directory, and an explicit interpreter. A socket unit reserves the Unix-socket path and defines the permissions Nginx needs. Type=notify lets Gunicorn report readiness to systemd, so a start can be distinguished from a process that merely exists.
Gunicorn socket unit
Create /etc/systemd/system/gunicorn.socket:
[Unit]
Description=Falcon Gunicorn socket
[Socket]
ListenStream=/run/gunicorn.sock
SocketUser=falconrun
SocketGroup=www-data
SocketMode=0660
[Install]
WantedBy=sockets.targetThe socket is owned by the runtime user and group-readable by Nginx. Mode 0660 avoids exposing the application socket to unrelated local users. If your Nginx worker runs under another account, use that account’s group instead of www-data.
Gunicorn service unit
Create /etc/systemd/system/gunicorn.service:
[Unit]
Description=Falcon Gunicorn service
Requires=gunicorn.socket
After=network.target
[Service]
Type=notify
User=falconrun
Group=falconapp
SupplementaryGroups=www-data
WorkingDirectory=/srv/falconapp
Environment=PATH=/srv/falconapp/venv/bin
ExecStart=/srv/falconapp/venv/bin/gunicorn --workers 3 --bind unix:/run/gunicorn.sock wsgi:application
ExecReload=/bin/kill -s HUP $MAINPID
KillMode=mixed
TimeoutStopSec=5
PrivateTmp=true
[Install]
WantedBy=multi-user.targetThe three-worker value is an example configuration, not a universal capacity rule. Worker count, request timeout, and memory limits must be selected from your workload measurements. The important production properties here are the non-root identity, the virtual-environment executable, the project directory, the socket bind, and the matching callable name.
Load, enable, and inspect the units
-
Ask systemd to read the new files:
sudo systemctl daemon-reload -
Enable the socket at boot and start it now:
sudo systemctl enable --now gunicorn.socket -
Enable and start the service:
sudo systemctl enable gunicorn.service sudo systemctl start gunicorn.service -
Inspect both units and recent logs before exposing traffic:
sudo systemctl status gunicorn.socket gunicorn.service sudo journalctl -u gunicorn.service -e --no-pager -
Confirm the socket exists and has the expected owner and mode:
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sudo stat /run/gunicorn.sock sudo ss -xl | grep gunicorn.sock
After a code or unit-file change, use sudo systemctl daemon-reload and then restart the service. A socket unit can remain active while the service is restarted, which lets Nginx keep a stable endpoint.
Configure Nginx as the reverse proxy
Create a server block
Create a site file such as /etc/nginx/sites-available/falconapp. Replace the example domain and static directory with your values:
server {
listen 80;
listen [::]:80;
server_name api.example.com;
location /static/ {
alias /srv/falconapp/static/;
}
location / {
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
proxy_pass http://unix:/run/gunicorn.sock;
}
}Only use the /static/ location if the directory exists and contains files intended for direct serving. Otherwise remove it and let Falcon handle that path. The proxy headers preserve the original host, client address, and scheme for application logging and URL generation.
Enable and validate Nginx
-
Enable the site and remove the default site if it would catch the same hostname:
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Check syntax before reloading:
sudo nginx -t -
Enable Nginx at boot and start or reload it:
sudo systemctl enable --now nginx sudo systemctl reload nginx -
Request the public hostname and watch both logs:
curl -i http://api.example.com/health sudo journalctl -u nginx -e --no-pager sudo journalctl -u gunicorn.service -e --no-pager
A successful response proves the complete path—DNS, Nginx, socket permissions, Gunicorn import, and Falcon routing—rather than only a local application process.
Make the host production-ready
HTTPS and network exposure
Use a trusted certificate for the real hostname and redirect plain HTTP to HTTPS. Keep Gunicorn on the Unix socket (or loopback) rather than publishing port 8000. Permit only required ports in the host firewall; a typical policy allows SSH for administration and TCP 80 and 443 for web traffic, while denying direct access to internal application ports.
Secrets and configuration
Do not commit credentials, signing keys, or database passwords to the source tree. Store them in a root-controlled environment file or a secret manager and reference that configuration from the service. If you use an environment file, restrict it to the runtime account or its dedicated group and verify the permissions after every deployment. Avoid printing secret values in startup logs.
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Logs, monitoring, and backups
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Send access and error output to a centrally collected destination or configure rotation so an active service cannot fill the disk.
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Monitor systemd service state, Nginx error rates, response latency, memory use, and the health endpoint. A process that is running but repeatedly returning application errors is not healthy.
-
Back up the data Falcon actually needs—database contents, uploaded objects, and configuration—not just a replaceable virtual environment.
-
Document who can deploy, who can read secrets, and how to restore the previous release.
The Falcon deployment guidance describes its snippets as starting points rather than a perfectly tuned or secure environment. Add TLS, access controls, secret management, rotation, monitoring, backups, and rollback procedures for your threat model and availability target.
Deploy updates and roll back safely
Use release directories
For repeatable updates, place each release in its own directory, for example /srv/falconapp/releases/2026-10-15-1200, create its virtual environment, install the pinned dependencies, and run the local smoke test against that directory. Point /srv/falconapp/current at the tested release and make WorkingDirectory and ExecStart reference stable paths that resolve through the link.
Restart and verify
-
Install the new release as
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Run the same
wsgi:applicationsmoke test using the new environment. -
Switch the
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Run
sudo systemctl restart gunicorn.serviceand wait for an active state.The Tool Desk
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Call the health endpoint through Nginx and inspect Gunicorn and Nginx logs.
If validation fails, repoint current to the previous release, restart Gunicorn, and verify the endpoint again. Keep enough previous releases to cover the recovery window and remove old environments only after the new release has proved stable.
Choosing the deployment variant
| Decision | Recommended choice for this guide | When another choice fits |
|---|---|---|
| Application interface | WSGI with Falcon and Gunicorn | Use an ASGI stack only when the application and its middleware are designed for ASGI; do not point a WSGI callable at an ASGI-only server. |
| Gunicorn endpoint | Unix socket at /run/gunicorn.sock |
TCP loopback such as 127.0.0.1:8000 is simpler for diagnostics or a separately managed proxy, but it should not be opened publicly without a deliberate network policy. |
| Process supervision | systemd service plus socket unit | Manual foreground execution is useful for a smoke test, not for reboot recovery or long-term supervision. |
| Public-server duties | Nginx handles TLS, static files, and proxying | A different edge proxy can work if it supplies equivalent TLS, header, buffering, timeout, and access-control behavior. |
| Scaling model | One Ubuntu host with measured Gunicorn workers | Multiple hosts, shared state, health checks, and coordinated releases are needed for horizontal scaling and high availability. |
The Linux examples are a foundation that can evolve into a horizontally scalable design. Scaling out does not remove the need for immutable releases, externalized state, health checks, and a controlled deployment process.
Troubleshoot the failures you are most likely to see
| Symptom | Likely cause | Checks and fix |
|---|---|---|
| Nginx returns 502 Bad Gateway | Gunicorn is stopped, the socket path differs, or Nginx cannot access the socket. | Run systemctl status gunicorn.service gunicorn.socket, verify /run/gunicorn.sock, compare the proxy_pass path with --bind, and inspect journalctl -u nginx. Correct ownership or mode if access is denied. |
| Gunicorn exits immediately | Import error, missing dependency, invalid configuration, or an incorrect callable name. | Read journalctl -u gunicorn.service -e and run the exact virtual-environment Gunicorn command manually from /srv/falconapp. Fix the package, module path, or wsgi:application target. |
| Permission denied while importing code | A parent directory or file is not traversable/readable by falconrun. |
Use namei -l /srv/falconapp/wsgi.py and sudo -u falconrun test -r /srv/falconapp/wsgi.py. Restore group read and execute permissions without granting source write access. |
| Static files return 404 | The Nginx alias does not match the URL prefix or the directory is absent. | Confirm the file exists under the alias path, include the trailing slash in both location and alias, then run nginx -t and reload. |
| Changes are not visible | The service still uses an old working directory or was not restarted. | Check systemctl cat gunicorn.service, verify the release link, run daemon-reload after unit edits, and restart Gunicorn. |
| Nginx configuration test fails | Syntax error, duplicate server name, or a bad include. | Run sudo nginx -t; its error includes the file and line. Fix that line before reloading the running configuration. |
Worker count and capacity planning
There is no authoritative throughput or worker-count figure for every Falcon, Gunicorn, and Nginx deployment. CPU-bound handlers, blocking database calls, request size, response time, memory footprint, and traffic shape all change the result. Start with a conservative worker count, measure under a representative load, watch memory and tail latency, and adjust one variable at a time. Treat any value in an example unit file as a configuration starting point, never as a published performance guarantee.
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