Hello my friend,

This is the third and the last (at least for the time being ) blogpost about monitoring of the infrastructure with Prometheus, one of the most powerful and popular open source time series database and metrics collection framework. In today’s talk we’ll cover the build of our own Prometheus exporter, which performs trace route checks.

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Why to Automate Monitoring?

Many tools nowadays give you possibility not only to collect metrics, but also to act perform a simple )(or complex) analysis and act based on the result of such an analysis. So can Prometheus. With a help of the Alertmanager, it is possible to send a REST API request upon certain condition, which would trigger an automation activity or a workflow to act upon the business logic needed for the condition, such as remediation and/or configuration. This is why you need to know how the network automation works at a good level.

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• Principles of software developments and tools for that.
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Brief Description

In the previous blogpost about Prometheus we have learned how to use an official Prometheus Blackbox exporter in order to validate the remote resource, such as web service. One of the tests, though, which you are not able to conduct with the Blackbox exporter is a trace route. We were thinking, why such an essential test was not included in the list of the checks available in the Blackbox exporter. And we don’t have an answer. However, we have a theory.

It looks like the Blackbox exporter was created to help the DevOps teams to monitor the reachability of the online resource and its basic user-facing characteristics, such as latency, response code, etc. At the same, trace route is not something that they are really interested into. In fact, some developers hardly understand the infrastructure (no office, network engineers often have no clue how applications work). As a result, trace route is just test that they won’t run against the remote online services. Really, why would you run it if you don’t control Internet entirely?

We faced an interesting scenario, where having the constantly running trace routes helped us to improve stability of the remote connections. Originally, we have just temporary increases in the average requests latency of the remote online service. Both ICMP and HTTP GET tests were showing consistent increase in the average response time. First, we thought that it may be related to the application load. However, the metrics collected from the application itself didn’t show anything suspicious; neither did metrics collected from the VMs shown anything wrong. We thought that having a continuously measured trace route would be an interesting metric to have to access if there are any changes in the network path, which can increase latency.

First of all, we decided to take a look, what exists already in the Prometheus community:

• There is one created in Java Script. Eeeck, Java Script and DevOps are just different worlds.
• There is one integration with RIPE Atlas. It is interesting, if you want to rely on the RIPE Atlas; however, if you build your own monitoring mesh, it is not super useful.
• There is one MTR wrapper. Well, wrapping around CLI tools is quite often a dubious idea. Not so long ago we have faced a problem with running MTR in a Docker container (Alpine) on a Linux hosts (we tested both Debian and CentOS), which resulted in literally thousands of stale processes, which, as a consequence, crashed that container.

We were not impressed by capabilities of the existing Prometheus exporters, so we have decided to create our own.

Use Case

Following the same logic we have introduced in the previous blogpost about Prometheus, we want to:

• Constantly run trace route against the number of the endpoints remotely accessible over the Internet.
• Store in the Prometheus database the number of the hops measured during the trace route measurement.
• Be able to signal, which destinations shall be monitored from the Prometheus host itself, without any local configuration on the exporter side itself (if possible).

Solution Description

We are repeating the topology we have created the last time, with a single difference: we are using now our own Prometheus exporter, which is collecting the trace route:

In the previous two blogposts we have shared how to setup the prometheus to interact with already created exporters. So, the communication patterns are the same:

• Orange solid lines show the monitoring flows (trace routes) between Prometheus traceroute exporters and the monitored targets.
• Dashed violet lines show the control plane flows between Prometheus backend and Prometheus traceroute exporters.

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Implementation Details

The main focus of this blogpost is to share some insights, how we build our exporter and why we did it in that way.

Step #1. Develop Prometheus Exporter

In our Zero-to-Hero Network Automation Training, which we encourage you to enrol to, we are sharing a lot of real-world scenarios, how Python is used in network automation scenario. We also share the principles of the software design for infrastructure (network, server, etc) projects. Leveraging that knowledge, we decided to use Python to build our own Prometheus exporter.

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Step #1.1. Mode of Operation

First thing, that we were to solve is the mode of operation. Strictly speaking, there are two possibilities:

• Static targets. In this mode you define in the exporter configuration, what it needs to collect. The configuration file is local to the exporter, which creates an extra administrative overhead for maintenance of such targets. However, this drawback can be neglected if the rollout of the exporter’s configuration is done using some configuration tool (e.g., Ansible). Such a mode for exporter is very easy to implement, though.

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• Dynamic targets. In this mode you define the targets, which are to be polled by the exporter inside the Prometheus configuration, which makes maintenance of the exporter’s configuration much simpler. However, the complexity of the code for the exporter itself is much higher.

We tried to summarise both modes in the following table:

We decided to implement both modes, as some of them may be useful in different circumstances. In fact, jumping ahead, during some different infrastructure setups, we figured out that the second mode can be significantly slower due to various reasons (e.g., presence or absence of application firewall running on the host with Docker, etc).

Here is how it works:

And:

Step #1.2. How to Pass Targets from Prometheus Dynamically

As you can see from provided diagrams, we achieve the dynamic configuration of the exporter from the Prometheus side using the relabelling functionality. The relabelling functionality works in a way that Prometheus, when it performs scrapping of metrics from the device, passes an argument target with a value of each element provided in static_configs[0][targets] list. For example, see the output from the Prometheus Traceroute Exporter working in the dynamic mode:

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2022-04-25T21:52:06.510541470Z 192.168.51.71 - - [25/Apr/2022 21:52:06] "GET /probe?target=wwww.openstack.org HTTP/1.1" 200 703

2022-04-25T21:52:34.441181995Z 192.168.51.71 - - [25/Apr/2022 21:52:34] "GET /probe?target=github.com HTTP/1.1" 200 700

2022-04-25T21:52:35.721759341Z 192.168.51.71 - - [25/Apr/2022 21:52:35] "GET /probe?target=karneliuk.com HTTP/1.1" 200 703

Looking at timestamps, you can see that requests are sent one after another, about the completion of the previous one.

The tricky thing to solve was to use the subtract and process the passed argument on the Prometheus exporter side. Out of the box, the official Python Prometheus client doesn’t have such a functionality. As such, the extra middleware is needed. By middleware we mean some web service. We decided not to overcomplicate setup by adding extra dependency, such as Flask or FastAPI, and used the WSGI (Web Server Gateway Interface) library, which is a part of a standard Python distribution.

It allowed us implement a logic, which calls a Prometheus exporter service, when the corresponding route (URL) is hit. It also allowed us to parse the parameters of the API call, and, as a result, act upon it using the parameters.

For reference, here is the code of the entire Python Prometheus exporter:

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# (c)2022, Karneliuk.com

# Modules

from wsgiref.simple_server import make_server

from urllib.parse import parse_qs

from prometheus_client import make_wsgi_app

from prometheus_client.core import GaugeMetricFamily, REGISTRY

import os

import jinja2





# Classes

class DynamicTargetExporter(object):

    def __init__(self, args, application_port: int, path_default_page: str):

        self._args = args

        self._application_port = application_port

        self._path_default_page = path_default_page



        # Prometheus metrics

        self._metrics_app = make_wsgi_app()

        REGISTRY.register(CustomCollector())



    def start(self):

        # WSGI server

        httpd = make_server("", self._application_port, self._middleware_wsgi)

        httpd.serve_forever()



    def _middleware_wsgi(self, environ, start_response):

        if environ["PATH_INFO"] in {"/probe", "/metrics"}:

            query_parameters = parse_qs(environ["QUERY_STRING"])



            if query_parameters:

                try:

                    os.environ["PROMETHEUS_TARGET"] = query_parameters["target"][0]



                except (IndexError, KeyError) as e:

                    print(f"Failed to identify target: {e}. Using 'localhost' as destination.")

                    os.environ["PROMETHEUS_TARGET"] = "localhost"



            return self._metrics_app(environ, start_response)



        template = jinja2.Template(open(self._path_default_page, "r").read())

        rendered_page = template.render(args=self._args)



        response_body = str.encode(rendered_page)



        response_status = "200 OK"

        response_headers = [

            ('Content-Type', 'text/html'),

            ('Content-Length', str(len(response_body)))

        ]



        start_response(response_status, response_headers)

        return [response_body]

Step #1.3. Performing Traceroute Measurement

The last piece in our journey of building exporter, was a mechanism to conduct the traceroute measurement itself. For that, we used a ready icmplib library, which offers a pure Python implementation of there traceroute and ping functionality. We wrapped into a custom class needed for the Prometheus scrapping:

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# (c)2022, Karneliuk.com

# Modules

from prometheus_client.core import GaugeMetricFamily, REGISTRY

import icmplib

import time





# Classes

class CustomCollector(object):

    def collect(self):

        # Set target

        target = os.getenv("PROMETHEUS_TARGET") if os.getenv("PROMETHEUS_TARGET") else "localhost"



        # Perfrom measurement

        try:

            timestamp_start = time.time()

            measured_hops = len(icmplib.traceroute(target))

            timestamp_finish = time.time()

            is_successfull = 1



        except icmplib.exceptions.NameLookupError:

            timestamp_finish = time.time()

            measured_hops = 0

            is_successfull = 0



        # Report metrics

        yield GaugeMetricFamily("probe_success",

                                "Result of the probe execution",

                                is_successfull)



        yield GaugeMetricFamily("probe_traceroute_hops_amount",

                                "Amount of hops towards destination host",

                                measured_hops)



        yield GaugeMetricFamily("probe_execution_duration_seconds",

                                "Duration of the measurement",

                                timestamp_finish - timestamp_start)

Find all the details of this exporter in our Github repository.

Step #2. Host with Exporter

Once the Prometheus Exporter was developed with a Python and we have published it to the GitHub Container Registry using GitHub Action (CI/CD pipeline), we are able to deploy a new Prometheus Traceroute Exporter anywhere.

We are showing the Dynamic Targets Exporter mode. Refer to the GitHub repository for a Static example.

Step #2.1. Traceroute Exporter Docker-compose File

We have published two Docker images: for x86_64 architecture and for arm32v7, what runs on Raspberry PI (yes, you can code even from PI). As such, the docker-compose.yaml contains the filed for the architecture:

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---

version: "3.9"

services:

  traceroute_exporter:

    image: "ghcr.io/akarneliuk/traceroute-exporter:${PLATFORM}"

    privileged: true

    healthcheck:

      test:

        - "CMD"

        - "curl"

        - "-f"

        - "http://localhost:9101"

      interval: 1m

      timeout: 10s

      retries: 3

      start_period: 30s

    ports:

      - "9101:9101"

    command:

      - "--dynamic"

...

Step #2.2. Launch Docker Container with Prometheus Traceroute Exporter

As the docker-compose.yaml includes variables you need to launch it accordingly:

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$ sudo PLATFORM=$(uname -m) docker-compose up -d

Such a launch will create a valuable with a platform type and pass it to the docker-compose tool. We also added the health check in the container to make sure it operates properly in order to bring it down, if something crashed in the app:

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$ sudo docker container ls

CONTAINER ID   IMAGE                                           COMMAND                  CREATED       STATUS                PORTS                                       NAMES

1a92941a5b5d   ghcr.io/akarneliuk/traceroute-exporter:x86_64   "python3 main.py --d…"   2 days ago    Up 2 days (healthy)   0.0.0.0:9101->9101/tcp, :::9101->9101/tcp   python-traceroute-exporter_traceroute_exporter_1

You can see that the image name contains the platform, what suggests it was pulled and used properly to match your platform.

Step #3. Host with Prometheus

Refer to the previous blogpost for a detailed information.

Step #3.1. Prometheus Configuration File

As explained above, as we rely on the relabelling, the key part is to include relabelling in the Prometheus job in the prometheus.yaml configuration file:

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$ cat prometheus.yaml

!

! OUTPUT IS TRUNCATED FOR BREVITY

!

scrape_configs:

!

! OUTPUT IS TRUNCATED FOR BREVITY

!

  - job_name: 'prometheus python traceroute exporter'

    metrics_path: /probe

    static_configs:

      - targets:

        - karneliuk.com

        - github.com

        - www.openstack.org

    relabel_configs:

      - source_labels: [__address__]

        target_label: __param_target

      - source_labels: [__param_target]

        target_label: instance

      - target_label: __address__

        replacement: 192.168.51.72:9101

...

The key fields here are:

• metrics_path has a value “/probe“, which is different to a default one “/metrics“.
• static_configs include targets with a list of hosts to run traceroute against.
• relable_configs contains the target IP address and a port of the exporter running traceroute.

Step #3.2. Start or Restart Prometheus

The docker-compose.yaml file for Prometheus isn’t changed since the previous blogpost. As such, you just need to re-start the container with it:

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$ sudo docker-compose restart

Check that it is properly started afterwards:

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sudo docker container ls

[sudo] password for aaa: 

CONTAINER ID   IMAGE               COMMAND                  CREATED      STATUS        PORTS                                       NAMES

1e2db6eda83a   prom/prometheus     "/bin/prometheus --c…"   8 days ago   Up 26 hours   0.0.0.0:9090->9090/tcp, :::9090->9090/tcp   prometheus_prometheus_1

Validation

Step #1. New Prometheus Targets

Once you have restarted the Prometheus and let it a few minutes to settle, in the target list you will see a new job and the status of the polled destinations:

Step #2. Traceroute Hops Resuls

Go to “Graphs” tab and collect information about the success of probes using probe_success metric, which shows whether the traceroute measurement was successful or not:

Finally, collect the probe_traceroute_hops_amount metric, which contains a value with the number of hops towards the destination from the exporter:

Examples in GitHub

You can find this and other examples in our GitHub repository.

Lessons Learned

There were there key lessons learned for us, which, on the one hand, cost us a lot of time, but, on the other hand, allowed us to advance quite a bit in the software development in general and for network automation in particular:

• For the first time we have used automated Docker image build as a part of GitHub Actions (CI/CD platform) and GitHub Container Registry (GHCR). It is not that we won’t be able to complete our project without that, as we used to use Docker Hub a lot beforehand. Moreover, it is possible to publish images in the Docker Hub from GitHub Actions as well. But, the experience itself was an interesting and useful one.
• For the first time we have build multiple Docker images for different platforms. It was quite unexpected to see that a Linux image with Alpine, which we used for a standard deployment didn’t work on Raspberry PI. Luckily, with the help of some threads in GitHub and StackExachange, we were able to figure out an issue and to build our exporter using a different base image.
• For the first time we have used WSGI, a Python built-in API gateway/web server. Compared to fastAPI, which we typically use for our projects, it was very tiny and awkward to a degree. However, its built-in nature and a simple functionality was perfect for us to achieve our goal: to be able to process API call parameters and convert them to variables, which are used to conditionally execute the script further.

Conclusion

Prometheus is rightfully one of the most popular and useful open source tools these days for metrics collection, which can be very useful in the network automation as well. For example, it is possible to create custom exporters, which will be polling the network devices or applications, which so far doesn’t have the streaming telemetry functionality neither with GNMI or NETCONF, process collected metrics and to store them in the time series database for further visualisation or processing alerting/remediation in an automated way. Take care and good bye.

Need Help? Contract Us

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P.S.

If you have further questions or you need help with your networks, we are happy to assist you, just send us a message. Also don’t forget to share the article on your social media, if you like it.

BR,

Anton Karneliuk