A minimal monitoring system
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A minimal monitoring system


This has been more or less replaced by a version written in Go. Check out minitor-go. There are no known issues with this version, but it is not really maintained anymore as I've migrated to the Go version since it uses fewer system resources.

What does it do?

Minitor accepts a YAML configuration file with a set of commands to run and a set of alerts to execute when those commands fail. It is designed to be as simple as possible and relies on other command line tools to do checks and issue alerts.

But why?

I'm running a few small services and found Sensu, Consul, Nagios, etc. to all be far too complicated for my usecase.

So how do I use it?


Install and execute with:

pip install minitor

If locally developing you can use:

make run

It will read the contents of config.yml and begin its loop. You could also run it directly and provide a new config file via the --config argument.


You can pull this repository directly from Docker:

docker pull iamthefij/minitor

The Docker image uses a default config.yml that is copied from sample-config.yml. This won't really do anything for you, so when you run the Docker image, you should supply your own config.yml file:

docker run -v $PWD/config.yml:/app/config.yml iamthefij/minitor

Images are provided for amd64, arm, and arm64 architechtures, but the Python package should be compatible with anything that supports Python.


In this repo, you can explore the sample-config.yml file for an example, but the general structure is as follows. It should be noted that environment variable interpolation happens on load of the YAML file.

The global configurations are:

key value
check_interval Maximum frequency to run checks for each monitor
monitors List of all monitors. Detailed description below
alerts List of all alerts. Detailed description below


All monitors should be listed under monitors.

Each monitor allows the following configuration:

key value
name Name of the monitor running. This will show up in messages and logs.
command Specifies the command that should be executed, either in exec or shell form. This command's exit value will determine whether the check is successful
alert_down A list of Alerts to be triggered when the monitor is in a "down" state
alert_up A list of Alerts to be triggered when the monitor moves to an "up" state
check_interval The interval at which this monitor should be checked. This must be greater than the global check_interval value
alert_after Allows specifying the number of failed checks before an alert should be triggered
alert_every Allows specifying how often an alert should be retriggered. There are a few magic numbers here. Defaults to -1 for an exponential backoff. Setting to 0 disables re-alerting. Positive values will allow retriggering after the specified number of checks


Alerts exist as objects keyed under alerts. Their key should be the name of the Alert. This is used in your monitor setup in alert_down and alert_up.

Eachy alert allows the following configuration:

key value
command Specifies the command that should be executed, either in exec or shell form. This is the command that will be run when the alert is executed. This can be templated with environment variables or the variables shown in the table below

Also, when alerts are executed, they will be passed through Python's format function with arguments for some attributes of the Monitor. The following monitor specific variables can be referenced using Python formatting syntax:

token value
{alert_count} Number of times this monitor has alerted
{alert_message} The exception message that was raised
{failure_count} The total number of sequential failed checks for this monitor
{last_output} The last returned value from the check command to either stderr or stdout
{last_success} The ISO datetime of the last successful check
{monitor_name} The name of the monitor that failed and triggered the alert


As of v0.3.0, Minitor supports exporting metrics for Prometheus. Prometheus is an open source tool for reading and querying metrics from different sources. Combined with another tool, Grafana, it allows building of charts and dashboards. You could also opt to just use Minitor to log check results, and instead do your alerting with Grafana.

It is also possible to use the metrics endpoint for monitoring Minitor itself! This allows setting up multiple instances of Minitor on different servers and have them monitor each-other so that you can detect a minitor outage.

To run minitor with metrics, use the --metrics (or -m) flag. The metrics will be served on port 8080 by default, though it can be overriden using --metrics-port (or -p)

minitor --metrics
# or
minitor --metrics --metrics-port 3000


Whether you're looking to submit a patch or just tell me I broke something, you can contribute through the Github mirror and I can merge PRs back to the source repository.

Primary Repo: https://git.iamthefij.com/iamthefij/minitor.git

Github Mirror: https://github.com/IamTheFij/minitor.git