Last week, we faced a network outage at my client's.
We tried to gather facts to know more precisely when it started, to help the netops diagnose what was wrong.
And when we got all we could, it was mainly stats and figures from our application point of view, which is insufficient (and often considered as unreliable) for netops.
And needless to say that we have a proper monitoring but as we do not run it, it’s not for us to analyze ...
That’s where we, the project team, decided to setup our own monitoring.
And as I recently read a lot about Prometheus and Grafana, I decided it was high time to play with these new promising tools.
I simply installed Prometheus on my machine and node exporters on the machines I wanted to monitor. Then I plugged Grafana on top and using some dashboards available from the Grafana community, I quickly displayed relevant information about my servers.
But we were missing something, important to us : how to monitor the route our packets were following ?
Traceroute is obviously the answer, but I didn’t want to run a traceroute manually and on top of that, I had to be capable of saying when the route had changed.
Install and configure Prometheus
Just as a reminder on how Prometheus works, here is the diagram taken from the official documentation
We’re not going to use the docker image since I didn't find in the documentation how to mount a volume to be able to keep my data.
No need to configure something special, but the general config file.
Source : https://prometheus.io/docs/prometheus/latest/installation/
The prometheus.yml should look like that :
# my global config
global:
scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s).
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
# - alertmanager:9093
# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
# - "first_rules.yml"
# - "second_rules.yml"
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
# The job name is added as a label `job=` to any timeseries scraped from this config.
- job_name: 'prometheus'
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'.
static_configs:
- targets: ['localhost:9090']
Once started, you should have the following log :
level=info ts=2017-11-18T16:40:40.3794356Z caller=main.go:215 msg="Starting Prometheus" version="(version=2.0.0, branch=HEAD)" level=info ts=2017-11-18T16:40:40.3794809Z caller=main.go:216 build_context="(go=go1.9.2, user=root@615b82cb36b6, date=20171108-07:11:59)" level=info ts=2017-11-18T16:40:40.3794976Z caller=main.go:217 host_details="(Linux 4.9.49-moby #1 SMP Wed Sep 27 23:17:17 UTC 2017 x86_64 (none))" level=info ts=2017-11-18T16:40:40.3819182Z caller=web.go:380 component=web msg="Start listening for connections" address=0.0.0.0:9090 level=info ts=2017-11-18T16:40:40.3819422Z caller=main.go:314 msg="Starting TSDB" level=info ts=2017-11-18T16:40:40.381969Z caller=targetmanager.go:71 component="target manager" msg="Starting target manager..." level=info ts=2017-11-18T16:40:40.387602Z caller=main.go:326 msg="TSDB started" level=info ts=2017-11-18T16:40:40.3876796Z caller=main.go:394 msg="Loading configuration file" filename=/etc/prometheus/prometheus.yml level=info ts=2017-11-18T16:40:40.389357Z caller=main.go:371 msg="Server is ready to receive requests."
Configuring metrics scraping
Now it’s time to collect metrics.
The most usual way to do it is to use exporter (node exporter is a standard way to do it) or to code a collection point. This is the principle where Prometheus asks for metrics.
I've then setup a "node_exporter" on one of my Rasp (you can follow Alex Ellis' article if you want to know how) and I simply added an entry in my prometheus.yml, such as :
- job_name: rpi
static_configs:
- targets: ['192.168.0.69:9100']
After a restart, Prometheus starts gathering information about the machine you're collecting data from.You can verify this by using the Prometheus console, which now shows lots of metrics starting with "node"
Now, what we are going to do is slightly different : we want to monitor the traceroute from the local machine (also the machine where Prometheus server is running) to the destination machine.
Thus, we are not going to « scrape » a distant machine but the local machine thanks to the Pushgateway, in its dockerized form.
docker run -d -p 9091:9091 --name pushgateway prom/pushgatewayThen let's configure Prometheus to scrape from the "pushgateway". Just add the following section at the end of your prometheus.yml and restart Prometheus.
- job_name: traceroute
static_configs:
- targets: ['localhost:9091']
But in order to be able to use the keyword "instance", you will have to add an option to the job section, in your prometheus.yml
- job_name: traceroute
honor_labels: true
static_configs:
- targets: ['localhost:9091']
To know why using the keyword "instance" is important, I strongly encourage you to read the doc, by following the link below !(And there's an explanation at the end of the article)
Source : https://prometheus.io/docs/prometheus/latest/configuration/configuration/#scrape_config
We are going to test our system by sending random values.
max$ echo "fake_number 16" | curl --data-binary @- http://localhost:9091/metrics/job/traceroute max$ echo "fake_number 11" | curl --data-binary @- http://localhost:9091/metrics/job/traceroute max$ echo "fake_number 31" | curl --data-binary @- http://localhost:9091/metrics/job/traceroute max$ echo "fake_number 1" | curl --data-binary @- http://localhost:9091/metrics/job/tracerouteNote that you have to define the same job name at the end of the URL and in the prometheus.yml.
Now, if you look at the Prometheus console, you can see your graph with the information we added thanks to our HTTP requests.
echo "traceroute_hops{instance=\"192.168.0.69:9100\"} $(traceroute 192.168.0.69 | grep -e "^ [0-9]" -e "^[0-9]" | wc -l)" \
| curl --data-binary @- http://localhost:9091/metrics/job/traceroute
This line adds a metric to our job with the number of hops to reach our destination.I'm aware that it is not useful to monitor a Rasp on the same LAN :) it's just for demonstration purposes
And in order to have some history, just add this line to your crontab, with the desired frequency of execution.
Pushing the monitoring to the next level with Grafana
First, let's install Grafana in its dockerized form
docker run -d --name=grafana -p 3000:3000 grafana/grafanaSource : https://hub.docker.com/r/grafana/grafana/
Log in to the Grafana console (admin/admin) and click on "Add data source"
To do so, go to the end of the page and click "+ Add row"
Then click on the panel title and choose "Edit"
To make it even more clear on our dashboard, we will add a "single stat" panel.
In the "Options" tab, define "Stat" to "Current", check the "Background" box and set the thresholds to "1,1", which means that the only valid value is 1.
KO :
If more than one server is being monitored, we need to modify the metric query, so we only view information from the Grafana current node (the machine(s) selected from the top dropdown menu):
traceroute_hops{instance=~'$node'}
That's the reason why we had to add the option "honor_labels: true". And if you enter "{{instance}}" in the "Legend format" textfield, it will display the name of the current node.
