Pandora's Flask

Pandora's Flask: Monitoring a Python web app with Prometheus

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We eat lots of our own dog food at MetricFire, monitoring our services with a dedicated cluster running the same software.

This has worked out really well for us over the years: as our own customer, we quickly spot issues in our various ingestion, storage, and rendering services. It also drives the service status transparency our customers love.

Our customers include large multinational coffee brewers, game companies, and other data science/SaaS companies. Without MetricFire, companies install their own monitoring software and hire engineers to maintain and process that information. MetricFire saves companies money by doing all of that engineering work for them, so they don’t have to hire their own team. 

MetricFire allows for on-premises setups, as well as our typical hosted platform, depending on the customer's needs.

Eating the right dog food here means integrating Prometheus monitoring with our (mostly Python) backend stack.

This post describes how we’ve done that in one instance, with a fully worked example of monitoring a simple Flask application running under uWSGI + nginx. We’ll also discuss why it remains surprisingly involved to get this right.


Key Takeaways

  • MetricFire monitors services using its own tools, ensuring quick issue detection and customer transparency.

  • Container orchestration like Kubernetes is seen as a potential solution for seamless integration.

  • MetricFire foresees smoother Prometheus integration as more services move to container orchestration.


A little history

Prometheus' ancestor and main inspiration are Google's Borgmon.

In its native environment, Borgmon relies on ubiquitous and straightforward service discovery: monitored services are managed by Borg, so it’s easy to find e.g. all jobs running on a cluster for a particular user; or for more complex deployments, all sub-tasks that together make up a job.

Each of these might become a single target for Borgmon to scrape data from via /varz endpoints, analogous to Prometheus’ /metrics. Each is typically a multi-threaded server written in C++, Java, Go, or (less commonly) Python.

Prometheus inherits many of Borgmon's assumptions about its environment. In particular, client libraries assume that metrics come from various libraries and subsystems, in multiple threads of execution, running in a shared address space. On the server side, Prometheus assumes that one target is one (probably) multi-threaded program.

Here at MetricFire, we know how well these tools work to give insight into collecting, storing, and visualizing time series data from any source. Let us help you with your monitoring needs by signing up for the MetricFire free trial to set up Graphite and build Grafana dashboards. You can also book a demo and talk to the MetricFire team about how you can best set up your monitoring stack. 


Why did it have to be snakes?

These assumptions break in many non-Google deployments, particularly in the Python world. Here it is common (e.g. using Django or Flask) to run under a WSGI application server that spreads requests across multiple workers, each of which is a process rather than a thread.

In a naive deployment of the Prometheus Python client for a Flask app running under uWSGI, each request from the Prometheus server to /metrics can hit a different worker process, each of which exports its own counters, histograms, etc. The resulting monitoring data is garbage.

For example, each scrape of a specific counter will return the value for one worker rather than the whole job. The value jumps all over the place and tells you nothing useful about the application as a whole.



A solution

Amit Saha discusses the same problems and various solutions in a detailed writeup. We follow option #2: the Prometheus Python client includes a multiprocess mode intended to handle this situation, with gunicorn being the motivating example of an application server.

This works by sharing a directory of mmap()'d dictionaries across all the processes in an application. Each process then does the maths to return a shared view of the whole application's metrics when it is scraped by Prometheus.

This has some "headline" disadvantages listed in the docs: no per-process Python metrics for free, lack of full support for certain metric types, a slightly complicated Gauge type, etc.

It's also difficult to configure end-to-end. Here's what's necessary & how we achieved each part in our environment; hopefully, this full example will help anyone doing similar work in the future.

  1. The shared directory must be passed to the process as an environment variable, prometheus_multiproc_dir.

    No problem: we use uWSGI's env option to pass it in: see uwsgi.ini.

  2. The client’s shared directory must be cleared across application restarts.

    This was a little tricky to figure out. We use one of uWSGI's hardcoded hooks, exec-asap, to exec a shell script right after reading the configuration file and before doing anything else. See uwsgi.ini.

    Our script removes & recreates the Prometheus client's shared data directory.

    In order to be sure of the right permissions, we run uwsgi under supervisor as root and drop privs within uwsgi.

  3. The application must set up the Python client’s multiprocess mode.

    This is mostly a matter of following the docs, which we did via Saha's post: see

    Note that this includes some neat middleware exporting Prometheus metrics for response status and latency.

  4. uWSGI must set up the application environment so that applications load after fork().

    By default, uWSGI attempts to save memory by loading the application and then fork()'ing. This indeed has copy-on-write advantages and might save a significant amount of memory.

    However, it appears to interfere with the operation of the client's multiprocess mode - possibly because there's some locking prior to fork() this way.

    uWSGI's lazy-apps option allows us to load the application after forking, which gives us a cleaner environment.

So altogether, this results in a working /metrics endpoint for our Flask app running under uWSGI. You can try out the full worked example in our pandoras_flask demo.

Note that in our demo we expose the metrics endpoint on a different port to the app proper - this makes it easy to allow access for our monitoring without users being able to hit it.For your own look at how things work, you can also book a demo and talk to the MetricFire team directly about how to best set up your monitoring system.

In our deployments, we also use the uwsgi_exporter to get more stats out of uWSGI itself.



Saha's blog post lays out a series of alternatives, with pushing metrics via a local statsd as the favored solution. That’s not really a hop we prefer to take.

Ultimately, running everything under container orchestration like kubernetes would provide the native environment in which Prometheus shines, but that’s a big step just to get its other advantages in an existing Python application stack.

Probably the most Promethean intermediate step is to register each sub-process separately as a scraping target. This is the approach taken by django-prometheus, though the suggested “port range” approach is a bit hacky.

In our environment, we could (and may yet) implement this idea with something like:

  1. Running a webserver inside a thread in each process, listening on an ephemeral port and serving /metrics queries;
  2. Having the webserver register and regularly refresh its address (e.g. hostname:32769) in a short-TTL etcd path—we use etcd already for most of our service discovery needs;
  3. Using file-based service discovery in Prometheus to locate these targets and scrape them as individuals.

We think this approach is less involved than using the Python client’s multiprocess mode, but it comes with its own complexities.

It’s worth noting that having one target per worker contributes to something of a time series explosion. For example, in this case, a single default Histogram metric to track response times from the Python client across 8 workers would produce around 140 individual time series, before multiplying by other labels we might include. That’s not a problem for Prometheus to handle, but it does add up (or likely, multiply) as you scale, so be careful!



For now, exporting metrics to Prometheus from a standard Python web app stack is a bit involved no matter which road you take. We hope this post will help people who just want to get going with their existing nginx + uwsgi + Flask apps.

As we run more services under container orchestration—something we intend to do—we expect it will become easier to integrate Prometheus monitoring with them.

If you are looking for a hosted monitoring solution, Hosted Graphite may be the right option for you, and you can get a free trial here — if you’d like a demo, please get in touch!

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