Automate Your Server Platform Using The Leaseweb API

This blog is about using the Leaseweb API to automate the management of your dedicated servers running with Leaseweb- including examples of how to deploy your dedicated servers without even logging in once! While this article focuses solely on dedicated servers, we also have many API calls for specifically managing your cloud(s) at Leaseweb.


Did you know Leaseweb provides access to more than 150 backend features to manage and control your platform? We develop our systems with an ‘API-first’ philosophy, meaning that anything you do via our Customer Portal can also be done via the API (or more!). Many of these functions will help you when deploying a new dedicated server.

Getting Started

Let’s started by exploring what we can do with the API. Full disclaimer: it doesn’t matter if you are a newbie in the world of API calls or an expert – we have documented everything clearly and with plenty of examples to make your life easier. Detailed API information and examples of five different programming languages (including Ruby, PHP and Python) can be found on the Leaseweb developer website. We have also documented how to access the API on our developer website.

Once you have created your API key you can begin exploring different possibilities. Some options we provide that programmatically control your dedicated server platform via our API are:

  • Server management: fully manage your dedicated servers, including OS install, power cycle, hardware scan, etc.
  • Private Network: add or remove servers from the Private Network.
  • Floating IPs: control which servers the Floating IPs are routed to.
  • IP Management: see which IP is assigned to which server, null route IPs, or set reverse DNS entries.


Assume I have a server farm that consists of several dedicated servers, each with its own Internet uplink public IP. The servers are interconnected on the backend using Private Networking, while the Floating IPs are used to quickly redirect traffic to a different server for disaster recovery purposes.

What are some of the basic API calls I could use to control my farm? Let me give you some examples.

(Re)Installation of an Operating System

Using a POST operation, I can request a reinstall of the OS:

I also need to know the different parameters (payload) like serverID and OperatingSystemID, which can be retrieved via different API calls. In the payload area I can specify what the partition layout should be so that the OS will be installed to my exact specifications.

Custom Operating System Image

Alternatively, it is possible to install your own custom installation image using PXE boot while using the API to set the DHCP option. This will include it in the DHCP lease for the server.

Once the server boots, it will get a DHCP lease and will retrieve the installation image from the URL specified in the DHCP option. For more information, check out our Knowledge Base article that explains this feature in more detail.

Other Features

Some other nice features to control the server farm include possibilities to:

  •  Power cycle a server

This will turn the power off and on for the PDU that the server is connected to.

  • Show which IPs (including Floating IPs) are assigned to a server or are null-routed

  • Perform a hardware scan (reboots the server) and get hardware inventory of a server

  • Inspect how much data traffic a server is doing, both up and downstream

Private Networking

Servers in the same metro data center area (which in most cases means Leaseweb data centers that are relatively close to each other) have the possibility to be connected to the Leaseweb Private Network. This backend network provides a secure and unmetered layer-2 connection between all servers with port speeds of up to 10Gbps.

I am able to check if a server is already connected to my assigned Private Network by using the Inspect Private Network operation. This gives me a list (array) of server IDs that are connected.

If needed, I can add or remove a server from the PN or change the port speed.

Floating IPs

Floating IPs provide the possibility to dynamically reroute traffic to a different server (anchor IP). It is possible automate this process using the API. Assuming a Floating IP definition has already been defined and traffic is routed to my first server AnchorIP, I can use a PUT request to change the AnchorIP to my 2nd server.

A good example would be to embed this in a monitoring system. Once the monitoring system detects a ‘server unavailable’, it can automatically redirect traffic to the standby server using this API call.


While this blog is too short to sum up all the possibilities and features that are possible, it should give you some idea of what a powerful mechanism the API can be to automate and manage your environment.


Set up Private DNS-over-TLS/HTTPS

Domain Name System (DNS) is a crucial part of Internet infrastructure. It is responsible for translating a human-readable, memorizable domain (like into a numeric IP address (such as

In order to translate a domain into an IP address, your device sends a DNS request to a special DNS server called a resolver (which is most likely managed by your Internet provider). The DNS requests are sent in plain text so anyone who has access to your traffic stream can see which domains you visit.

There are two recent Internet standards that have been designed to solve the DNS privacy issue:

  • DNS over TLS (DoT):
  • DNS over HTTPS (DoH)

Both of them provide secure and encrypted connections to a DNS server.

DoT/DoH feature compatibility matrix:

Firefox Chrome Android 9+ iOS 14+

iOS 14 will be released later this year.

In this article, we will setup a private DoH and DoT recursor using pihole in a docker container, and dnsdist as a DNS frontend with Letsencrypt SSL certificates. As a bonus, our DNS server will block tracking and malware while resolving domains for us.


In this example we use Ubuntu 20.04 with docker and docker-compose installed, but you can choose your favorite distro (you might need to adapt a bit).

You may also need to disable systemd-resolved because it occupies port 53 of the server:

# Check which DNS resolvers your server is using:
systemd-resolve --status
# look for "DNS servers" field in output

# Stop systemd-resolved
systemctl stop systemd-resolved

# Then mask it to prevent from further starting
systemctl mask systemd-resolved

# Delete the symlink systemd-resolved used to manage
rm /etc/resolv.conf

# Create /etc/resolv.conf as a regular file with nameservers you've been using:
cat <<EOF > /etc/resolv.conf
nameserver <ip of the first DNS resolver>
nameserver <ip of the second DNS resolver>

Install dnsdist and certbot (for letsencrypt certificates):

# Install dnsdist repo
echo "deb [arch=amd64] focal-dnsdist-15 main" > /etc/apt/sources.list.d/pdns.list
cat <<EOF > /etc/apt/preferences.d/dnsdist
Package: dnsdist*
Pin: origin
Pin-Priority: 600
curl | apt-key add -

apt update
apt install dnsdist certbot


Now we create our docker-compose project:

mkdir ~/pihole
touch ~/pihole/docker-compose.yml

The contents of docker-compose.yml file:

version: '3'
    container_name: pihole
    image: 'pihole/pihole:latest'
    # The DNS server will listen on localhost only, the ports 5300 tcp/udp.
    # So the queries from the Internet won't be able to reach pihole directly.
    # The admin web interface, however, will be reachable from the Internet.
      - ''
      - ''
      - '8081:80/tcp'
      TZ: Europe/Amsterdam
      VIRTUAL_HOST: # domain name we'll use for our DNS server
      WEBPASSWORD: super_secret # Pihole admin password
      - './etc-pihole/:/etc/pihole/'
      - './etc-dnsmasq.d/:/etc/dnsmasq.d/'
    restart: unless-stopped

Start the container:

docker-compose up -d

After the container is fully started (it may take several minutes) check that it is able to resolve domain names:

dig +short @ -p5300
# Excpected output

Letsencrypt Configuration

Issue the certificate for our domain:

certbot certonly

Follow the instructions on the screen (i.e. select the proper authentication method suitable for you, and fill the domain name).

After the certificate is issued it can be found by the following paths:

  • /etc/letsencrypt/live/ – certificate chain
  • /etc/letsencrypt/live/ – private key

By default only the root user can read certificates and keys. Dnsdist, however, is running as user and group _dnsdist, so permissions need to be adjusted:

chgrp _dnsdist /etc/letsencrypt/live/{fullchain.pem,privkey.pem}
chmod g+r /etc/letsencrypt/live/{fullchain.pem,privkey.pem}

# We should also make archive and live directories readable.
# That will not expose the keys since the private key isn't world-readable
chmod 755 /etc/letsencrypt/{live,archive}

The certificates are periodically renewed by Certbot, so dnsdist should be restarted after that happens since it is not able to detect the new certificate. In order to do so, we put a so-called deploy script into /etc/letsencrypt/renewal-hooks/deploy directory:

mkdir -p /etc/letsencrypt/renewal-hooks/deploy
cat <<EOF > /etc/letsencrypt/renewal-hooks/deploy/
systemctl restart dnsdist
chmod +x /etc/letsencrypt/renewal-hooks/deploy/

Dnsdist Configuration

Create dnsdist configuration file /etc/dnsdist/dnsdist.conf with the following content:


-- path for certs and listen address for DoT ipv4,
-- by default listens on port 853.
-- Set X(int) for tcp fast open queue size.
addTLSLocal("", "/etc/letsencrypt/live/", "/etc/letsencrypt/live/", { doTCP=true, reusePort=true, tcpFastOpenSize=64 })

-- path for certs and listen address for DoH ipv4,
-- by default listens on port 443.
-- Set X(int) for tcp fast open queue size.
-- In this example we listen directly on port 443. However, since the DoH queries are simple HTTPS requests, the server can be hidden behind Nginx or Haproxy.
addDOHLocal("", "/etc/letsencrypt/live/", "/etc/letsencrypt/live/", "/dns-query", { doTCP=true, reusePort=true, tcpFastOpenSize=64 })

-- set X(int) number of queries to be allowed per second from a IP
addAction(MaxQPSIPRule(50), DropAction())

--  drop ANY queries sent over udp
addAction(AndRule({QTypeRule(DNSQType.ANY), TCPRule(false)}), DropAction())

-- set X number of entries to be in dnsdist cache by default
-- memory will be preallocated based on the X number
pc = newPacketCache(10000, {maxTTL=86400})

-- server policy to choose the downstream servers for recursion

-- Here we define our backend, the pihole dns server
newServer({address="", name=""})

setMaxTCPConnectionsPerClient(1000)    -- set X(int) for number of tcp connections from a single client. Useful for rate limiting the concurrent connections.
setMaxTCPQueriesPerConnection(100)    -- set X(int) , similiar to addAction(MaxQPSIPRule(X), DropAction())

Checking if DoH and DoT Works

Check if DoH works using curl with doh-url flag:

curl --doh-url

Check if DoT works using kdig program from the knot-dnsutils package:

apt install knot-dnsutils

kdig -d +tls-ca

Setting up Private DNS on Android

Currently only Android 9+ natively supports encrypted DNS queries by using DNS-over-TLS technology.

In order to use it go to: Settings -> Connections -> More connection settings -> Private DNS -> Private DNS provider hostname ->


In this article we’ve set up our own DNS resolving server with the following features:

  • Automatic TLS certificates using Letsencrypt.
  • Supports both modern encrypted protocols: DNS over TLS, and DNS over HTTPS.
  • Implements rate-limit of incoming queries to prevent abuse.
  • Automatically updated blacklist of malware, ad, and tracking domains.
  • Easily upgradeable by simply pulling a new version of Docker image.

Using Correlation IDs in API Calls

Over the years, the IT industry has moved from a single domain, monolithic architecture to a microservice architecture. In a microservice architecture, complex processes are split into smaller and simpler sub-processes. While this kind of architecture has many benefits, there are also some downsides – for example, if you send one request to a Leaseweb API, it ends up in multiple requests in other backend systems [FIGURE 1]. How do you keep track of requests and responses processed by multiple systems? This is where Correlation IDs come into play.

[FIGURE 1: Example request/response flow]

Using a Correlation ID

A Correlation ID is a unique, randomly generated identifier value that is added to every request and response. In a microservice architecture, the initial Correlation ID is passed to your sub-processes. If a sub-system also makes sub-requests, it will also pass the Correlation ID to those systems.

How you pass the Correlation ID to other systems depends on your architecture. At Leaseweb we are using REST APIs a lot, with HTTP headers to pass on the Correlation ID. As a rule, we assign a Correlation ID as soon as possible, and always use a Correlation ID if it is passed on. Our public API only accepts Correlation IDs from internally trusted clients. For any other client (such as an employee or customer API clients) a new Correlation ID is generated for the request.

Real Value of Correlation IDs

The real value of Correlation IDs is realized when you also log the Correlation IDs. Debugging or tracing requests becomes much easier, as you can search all of your logs for the same Correlation ID. Combined with central logging solutions (such as the ELK stack), searching logs becomes even easier and can be done by non-technical colleagues. Providing tools to your colleagues to troubleshoot issues allows them to have more responsibility and gives you more time to work on more technical projects.

We mainly use Correlation IDs at Leaseweb for debugging purposes. When an error occurs, we provide the Correlation ID to the client/customer. If users provide the Correlation ID when submitting a support ticket, we can visualize the entire process needed to fulfil the client’s initial intent. This has significantly improved the time it takes us to fix bugs.

[FIGURE 2: Example of one Correlation ID with multiple requests]

Debugging issues is a time-consuming process if Correlation IDs are not used. When your environment scales, you will need to find solutions to group transactions happening in your systems. By using a Correlation ID, you can easily group requests and events in your systems, allowing you to spend more time fixing the problem and less time trying to find it.

Practical examples on how to implement Correlation IDs

The following examples use Symfony, a popular web application framework. These concepts can also be applied to any other framework, such as Laravel, Django, Flask or Ruby on Rails.

If you are unfamiliar with the concept of Service Containers and Dependency Injection, we recommend reading the excellent Symfony documentation about it here:

Using Monolog to append Correlation IDs to your application logs

When processing a HTTP request your application often logs some information – such as when an error occurred, or an important change made in your system that you want to keep track of. When using the Monolog logging library in PHP (, you can use the concept of “Processors” (read more about that here on

One way to do this is by creating a Monolog Processor class:


namespace App\Monolog\Processor;

use Symfony\Component\HttpFoundation\RequestStack;

class CorrelationIdProcessor
    protected $requestStack;

    public function __construct(RequestStack $requestStack)

       $this->requestStack = $requestStack;

    public function processRecord(array $record)
        $request = $this->requestStack->getCurrentRequest();

        if (!$request) {

        $correlationId = $request->headers->get(‘X-My-Correlation-ID');

        if (empty($correlationId)) {

        // If we have a correlation id include it in every monolog line
        $record['extra']['correlation_id'] = $correlationId;
        return $record;

Then register this class on the service container as a monolog processor in services.yml:

# app/config/services.yml

    arguments: ["@request_stack"]
      - name: monolog.processor
        method: processRecord

Now, every time you log something in your application with Monolog:

$this->logger->info('shopping_cart_emptied', [‘cart_id’ => 123]);

You will see the Correlation ID of the HTTP Request in your log files:

$ grep ‘shopping_cart_emptied’ var/logs/prod.log

[2020-07-03 12:14:45] app.INFO: shopping_cart_emptied {“cart_id”: 123} {"correlation_id":"d135d5f1-3dd0-45fa-8f26-55d8d6a44876"}

You can utilize the same pattern to log the name of the user that is currently logged in, the remote IP address of the API client, or anything else that makes troubleshooting faster for you.

Using Guzzle to append Correlation IDs when making sub-requests

If your API makes API calls to other microservices (and you use Guzzle to do this) you can make use of Handlers and Middleware.

Some teams at Leaseweb depend on many downstream microservices, and can therefore have multiple guzzle clients as services on the service container. While each Guzzle client is configured with its own base URL and/or authentication, it is possible for all of the Guzzle clients to share the same HandlerStack.

First, create the middleware:


namespace App\Guzzle\Middleware;

use Symfony\Component\HttpFoundation\RequestStack;
use Psr\Http\Message\RequestInterface;

class CorrelationIdMiddleware
    protected $requestStack;
    public function __construct(RequestStack $requestStack)
        $this->requestStack = $requestStack;

    public function __invoke(callable $handler)
        return function (RequestInterface $request, array $options = []) use ($handler) {
            $request = $this->requestStack->getCurrentRequest();

            if (!$request) {
                return $handler($request, $options);

            $correlationId = $request->headers->get(‘X-My-Correlation-ID');

            if (empty($correlationId)) {
                 return $handler($request, $options);
            $request = $request->withHeader(‘X-My-Correlation-ID’, $correlationId);
            return $handler($request, $options);

Define this middleware as service on the service container and create a HandlerStack:

# app/config/services.yml

    class: App\Guzzle\Middleware:
    arguments: ["@request_stack"]

    class: GuzzleHttp\HandlerStack
    factory: ['GuzzleHttp\HandlerStack', 'create']
      - [push, ["@correlation_id_middleware", "correlation_id_forwarder"]]

With these two services defined, you can now configure all your Guzzle clients using the HandlerStack so that the Correlation ID of the current HTTP request is forwarded to downstream HTTP requests:

# app/config/services.yml

      - base_uri:
        handler: "@correlation_id_handler_stack”

Now every API call that you make to will include the HTTP request header ‘X-My-Correlation-ID’ and have the same value as the Correlation ID of the current HTTP request. You can also apply the same Monolog and Guzzle tricks described here to the downstream API.

Expose Correlation IDs in error responses

The missing link between these processes is to now expose your Correlation IDs to your users so they can also log them or use them in support cases they report to your organization.

Symfony makes this easy using Event Listeners. You can define Event Listeners in Symfony to pre-process HTTP requests as well as to post-process HTTP Responses just before they are returned by Symfony to the API caller. In this example, we will create a HTTP Response listener and add the Correlation ID of the current HTTP request as a HTTP Header in the HTTP Response.

First, we create a service on the Service Container:

namespace App\Listener;
use Symfony\Component\HttpFoundation\RequestStack;
use Symfony\Component\HttpKernel\Event\FilterResponseEvent;

class CorrelationIdResponseListener
    protected $requestStack;
    public function __construct(RequestStack $requestStack)
        $this->requestStack = $requestStack;

    public function onKernelResponse(FilterResponseEvent $event)
        $request = $this->requestStack->getCurrentRequest();

        if (!$request) {

        $correlationId = $request->headers->get(‘X-My-Correlation-ID');

        if (empty($correlationId)) {

        $event->getResponse()->headers->set(‘X-My-Correlation-ID’, $correlationId);

Now configure it as a Symfony Event Listener:

# app/config/services.yml

    class: App\Listener\CorrelationIdResponseListener
    arguments: ["@request_stack"]
      - { name: kernel.event_listener, event: kernel.response, method: onKernelResponse }

Every response that is generated by your Symfony application will now include a X-My-Correlation-ID HTTP response header with the same Correlation ID as the HTTP request.

The Value of Correlation IDs

Using Correlation IDs throughout your whole stack gives you more insight into all (sub)requests during a transaction. Using the right tools allows others to debug issues, giving your developers more time to work on new awesome features.

Implementing Correlation IDs isn’t hard to do, and can be achieved quickly depending on your software stack. At Leaseweb, the use of Correlation IDs has saved us hours of time while debugging issues on numerous occasions.

Technical Careers at Leaseweb

We are searching for the next generation of engineers and developers to help us build infrastructure to automate our global hosting services! If you are interested in finding out more, check out our Careers at Leaseweb.


Measuring IP Connectivity with RIPE Atlas

One of the most important but often overlooked criteria when choosing a hosting provider is the performance of its network. Poor network performance for any kind of online service leads to low customer satisfaction. What is the best way to measure network performance that most accurately simulates a customers experience with a network? We found this question to be an interesting idea to explore during a Leaseweb hackathon!

RIPE Atlas

RIPE Atlas is the RIPE NCC’s main Internet data collection system. Its a global network of devices, called probes and anchors, that actively measure Internet connectivity. Anyone can access this data via Internet traffic maps, streaming data visualizations, and an API. RIPE Atlas users can also perform customized measurements to gain valuable data about their own networks.

Due to the size and reach of the Atlas project, it’s one of the most important internet measurement initiatives internationally. What’s great is that the project is run by RIPE, but driven by the global community of RIPE members (and non-members!) who contribute some of their resources to help the project. This improves visibility into the inner-workings of the global Internet. It’s used by Internet professionals all over the world to improve quality of their network, debug issues, and learn. Leaseweb contributes to the RIPE Atlas by hosting 7 ‘anchors’ in various data centers all over the globe.

Since Leaseweb already contributes to RIPE Atlas with these anchors, it’s an obvious choice as a source of random probes to be used against those anchors and compared with other infrastructure providers’ anchors. (By the way, if you would like to do your own measurements and contribute to the RIPE Atlas project at the same time, you can request a probe for your home or office right here!). You can read more on the structure of the Atlas network (and how probes, anchors and the RIPE backend work together) in various posts on the RIPE labs pages.

The main elements needed to use RIPE Atlas are measurements, sources and targets. These elements all have their own function to facilitate experiments.

Getting The Data

Triggering one-off measurements and fetching the results of those measurements can be done using the RIPE Atlas API. RIPE-NCC also developed a wrapper around the RIPE Atlas API to allow anyone to communicate with the RIPE Atlas API using Python. It is maintained by RIPE Atlas developers and is therefore the best choice for consuming their API. Using it requires an API key. Wrapper is open-source and available on GitHub.

Installation is very simple using pip:

$ pip install ripe.atlas.cousteau

Included classes in the Python script:

from ripe.atlas.cousteau import (

Measurement Types

RIPE Atlas offers several measurement types – Ping, Traceroute, DNS, HTTP, SSL, NTP, and WiFi. Creating a measurement allows you to specify a type of test (or ‘experiment’) to perform. Typically, these have something to do with latency for a service, but there are also options to check things like resolving a domain name and checking DNS propagation. These measurements can be one-off or recurring. For our Hackathon project we used one-off measurements.


Probes defined as sources are used to trigger the measurement. They can be defined explicitly or taken from a pool (area, country, prefix or AS number). Below, the defined source takes 50 random probes from Europe to be used as a source of measurement.

source = AtlasSource(
    value= "North-Central",


Targeted IPs that the measurements are run against. In this example, ping is run against one of Leaseweb’s anchors.

pingLSW = Ping(

Create Request

Defining a request combines all of the elements together: measurements and sources. It also requires a start time, API key and – in this case – a one-off flag.

atlas_request = AtlasCreateRequest(
    measurements=[pingLSW, pingOVH, pingAzure, pingAWS, pingUni],

To summarize what this request will do:

  • We specify a number of ping tests to various endpoints
  • We specify where we want to have those request come from and how many we want
  • …and we bundle those tests into a single one-off request.

Calling RIPE Atlas API now is simple

response = atlas_request.create()

After calling this function, Atlas will launch tests towards 50 random probes in the area we designated, and will store the results.

Returned values, stored in response, are measurement counts. In this case, there are 5 values as there are 5 measurements defined. The actual results have to be retrieved separately, so the next step is fetching the measurement data. Here, class AtlasLatestRequest is used:

results = AtlasLatestRequest(msm_id=measurement_id).create()

The results variable now has stored all of the measurement details needed to calculate and compare latencies. It’s clear to see how powerful Atlas is. In a few simple lines we’ve generated latency information from 50 end points to multiple targets!

Visualizing Data

Visualizing data that was fetched from RIPE Atlas API was done with pygal Python library that supports various chart types and styles. For this Hackathon project pygal.Bar() was used to draw out the comparison results. (Pygal usage is out of the scope of this blog post). Two charts below show the data from measurements taken from Europe and from Russia.

Conclusion and Going Forward

This Hackathon project showed the basic features of RIPE Atlas and what it can accomplish.  RIPE Atlas also maintains a parsing library name Sagan, available in GitHub, that handles format changes in measurement results and always returns a native Python object.

RIPE Atlas has a huge amount of functionality and can be easily used in your own experiments and measurements. Remember to always use the Atlas infrastructure responsibly.

Other tools for using permanent measurementsstreaming data, and building dashboards are RIPE Prometheus exporter, working as a metric exporter of RIPE Atlas measurement results that exports collected metrics to Prometheus. Grafana is common tool that works well with Prometheus to create dashboards with useful metrics, gathered from RIPE Atlas measurements.  

Comments? Questions? Other ways to use RIPE Atlas and receive measurements? I’d love to hear your feedback! 


Measuring and Monitoring With Prometheus and Alertmanager

As one of the most successful projects of the Cloud Native Computing Foundation (CNCF), it is highly likely that you have heard of Prometheus. Initially built at SoundCloud in 2012 to fulfil their monitoring needs, Prometheus is now one of the most popular solutions for time-series based monitoring.

At Leaseweb, we use Prometheus for a variety of purposes – from basic system monitoring of our internal systems, to blackbox monitoring from several of our network locations, to cloud data usage and capacity monitoring.

Whether you have one or several servers, it is always good to have insight into what your systems are doing and how they are performing. In this article, we will show you how to set up a basic Prometheus server and expose system metrics using node_exporter.

For later blogs in this series, we will add Alertmanager to our Prometheus server and use Grafana to graph our recorded metrics.

This is an overview of the components involved and their role:

  • Prometheus: Scrapes metrics on external data sources (or ‘exporters’), stores metrics in time-series databases, and exposes metrics through API.
  • node_exporter: Exposes several system metrics, such as CPU & disk usage
  • Alertmanager: Handles alerts generated by the Prometheus server. Takes care of deduplicating, grouping, and routing alerts to the correct alert channel such as email, Telegram, PagerDuty, Slack, etc.
  • Grafana: Uses Prometheus as a datasource to graph the recorded metrics.

For this tutorial, we are going to use three servers running Ubuntu 18.04 LTS. However, the instructions can be easily adapted for any other recent Linux distribution. These can either be bare metal servers or cloud instances. When your Prometheus setup grows and you start to scrape more and more metrics, it is advisable to have SSD based storage in your Prometheus server.

If you want to start out small or experiment, you can also combine several components on one system.

A Note on Security

Since Prometheus was designed to be run in a private network/cloud setting, it does not offer any authentication or access control out of the box. Because of this, be careful not to expose any of the services to the outside world. There are several ways you can achieve this (implementation of which is outside of the scope of this tutorial).

To achieve this, you could use the Leaseweb private networking feature and bind the Prometheus related services to your private networking interface. Other options are to use a reverse proxy that implements basic authentication, or using firewall rules to only allow certain IP addresses to connect to your Prometheus-related services.

Installing Prometheus

To start off, we will install the Prometheus server. The prometheus package is part of the standard Ubuntu distribution repositories, but unfortunately the version (2.1.0) is quite old. At the time of writing this blog post, the latest version is 2.16.0, which is what we will be using.

On the system that will be our Prometheus server, we start off by creating a user and group called prometheus:

useradd -M -r -s /bin/false prometheus

Next, we create the directories that will contain the configuration and the data of Prometheus:

mkdir /etc/prometheus /var/lib/prometheus

Download Prometheus server and verify its integrity:

cd /tmp
wget -O - -q | grep linux-amd64 | shasum -c -

The last command should result in  prometheus-2.16.0.linux-amd64.tar.gz: OK. If it doesn’t, the downloaded file is corrupted. Next we unpack the file and move the various components into place:

tar xzf prometheus-2.16.0.linux-amd64.tar.gz
cp prometheus-2.16.0.linux-amd64/{prometheus,promtool} /usr/local/bin/
chown prometheus:prometheus /usr/local/bin/{prometheus,promtool}
cp -r prometheus-2.16.0.linux-amd64/{consoles,console_libraries} /etc/prometheus/
cp prometheus-2.16.0.linux-amd64/prometheus.yml /etc/prometheus/prometheus.yml

chown -R prometheus:prometheus /etc/prometheus
chown prometheus:prometheus /var/lib/prometheus

And clean up our downloaded files in /tmp

rm -f /tmp/prometheus-2.16.0.linux-amd64.tar.gz
rm -rf /tmp/prometheus-2.16.0.linux-amd64

Add prometheus itself to the config for scraping initially.

To be able to start and stop our prometheus server, we will create a systemd unit file.Use you favorite editor to create the file /etc/systemd/system/prometheus.service and add the following to it:

Description=Prometheus Time Series Collection and Processing Server

ExecStart=/usr/local/bin/prometheus \
    --config.file /etc/prometheus/prometheus.yml \
    --storage.tsdb.path /var/lib/prometheus \
    --web.console.templates=/etc/prometheus/consoles \


Activate and start the service with the following commands:

systemctl daemon-reload
systemctl start prometheus
systemctl enable prometheus

The command systemctl status prometheus should now indicate that our service is up and running:

You should be able to access the web interface of the prometheus server now on http://<server IP>:9090:

If we go to Status > Targets we can see that the Prometheus server itself has already been added as a scraping target for metrics. This default target collects metrics about the performance of the Prometheus server. You can view the metrics that are being recorded under http://<server IP>:9090/metrics.

Prometheus provides two convenient endpoints for monitoring its health and status. You can use these to add to any other monitoring system you might have.

root@HRA-blogtest:~# curl localhost:9090/-/healthy
Prometheus is Healthy.
root@HRA-blogtest:~# curl localhost:9090/-/ready
Prometheus is Ready.

Monitor System Metrics with the Node Exporter

To make things a little more interesting, we are going to add a target to obtain system metrics of the Prometheus server. For this, we need to install the node exporter first.

Installing the node exporter

Download Prometheus node exporter and verify its integrity:

cd /tmp
wget -O - -q | grep linux-amd64 | shasum -c -

The last command should result in node_exporter-0.18.1.linux-amd64.tar.gz: OK. If it doesn’t, the downloaded file is corrupted.

Next we unpack the file and move the node exporter into place:

tar xzf node_exporter-0.18.1.linux-amd64.tar.gz
cp node_exporter-0.18.1.linux-amd64/node_exporter /usr/local/bin/
chown prometheus:prometheus /usr/local/bin/node_exporter

And clean up our downloaded files in /tmp

rm -f /tmp/node_exporter-0.18.1.linux-amd64.tar.gz
rm -rf /tmp/node_exporter-0.18.1.linux-amd64

Create a unit file /etc/systemd/system/node_exporter.service for the node exporter using your favorite editor.

Description=Prometheus Node Exporter



Reload the systemd configuration to activate our unit file, start the service, and enable the service to start at boot time:

systemctl daemon-reload
systemctl start node_exporter.service
systemctl enable node_exporter.service

The node exporter should now be running. You can verify this with systemctl status node_exporter

The node exporter listens on TCP port 9100. You should be able to see the node exporter metrics now at http://<server IP>:9100/metrics.

Adding the node exporter target to Prometheus

Now that the node exporter is running, we need to adapt the configuration of the Prometheus server so it can start scraping our node exporter metrics.

Open /etc/prometheus/prometheus.yml in your editor and adapt the scrape config section to look like the following:

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

    # metrics_path defaults to '/metrics'
    # scheme defaults to 'http'.

    - targets: ['localhost:9090']

  - job_name: 'node'
    scrape_interval: 5s
    - targets: ['localhost:9100']

Save the changes and restart the prometheus server configuration with systemctl restart prometheus

The Prometheus server web interface should show a new target now under Status > Targets:

Querying and Graphing the Recorded Metrics

Now that everything is set up, it is time to start looking into some of the things we are now measuring! Switch to the Graph tab in the Prometheus server web interface.

Enter node_memory_MemAvailable_bytes and click Execute. The Console tab will show you the current amount of memory free in bytes.

Switch to the Graph tab and you will see a graph of the amount of bytes of free memory there were over the course of the last hour. You can increase and decrease the time range with the plus and minus on the top left of the graph.

There is another metric that records the total amount of memory in the system. It is called node_memory_MemTotal_bytes. We can use this to calculate the percentage of memory free in the system. Enter the following in the query area and click execute:

(node_memory_MemAvailable_bytes / node_memory_MemTotal_bytes) * 100

The graph will now show the percentage of free memory over time.

We can make this even more accurate by taking into account buffered and cached memory:

((node_memory_MemFree_bytes + node_memory_Buffers_bytes + node_memory_Cached_bytes) / node_memory_MemTotal_bytes) * 100

Or turn it around and show the percentage of used memory instead:

(node_memory_MemTotal_bytes - node_memory_MemFree_bytes - node_memory_Buffers_bytes - node_memory_Cached_bytes) / node_memory_MemTotal_bytes * 100

The CPU usage is recorded in the metrics under node_cpu_seconds_total. This metric has several modes of the CPU recorded:

  • user: Time spent in userland
  • system: Time spent in the kernel
  • iowait: Time spent waiting for I/O
  • idle: Time the CPU had nothing to do
  • irq&softirq: Time servicing interrupts
  • guest: If you are running VMs, the CPU they use
  • steal: If you are a VM, time other VMs “stole” from your CPUs

These metrics are recorded as counters, so to get the per second values we will use the irate function:


As you can see, when you have multiple CPU’s in your server, it will return metrics for each CPU individually. To get the overall value across all CPU’s we can use PromQL’s aggregation features using sum by:

sum by (mode, instance) (irate(node_cpu_seconds_total{job="node"}[5m]))

We can also calculate the percentage of CPU used by taking the per second idle rate and multiplying it by 100 (to get the percent CPU idle), and then subtracting it from 100%:

100 - (avg by (instance) (irate(node_cpu_seconds_total{job="node",mode="idle"}[5m])) * 100)

And finally, to get the amount of data sent or received by our server, we can use irate(node_network_transmit_bytes_total{device!="lo"}[1m]) and irate(node_network_receive_bytes_total{device!="lo"}[1m]). This will give us a bytes-per-minute graph. The device!="lo" makes sure we exclude the local loopback interface.

To turn this into megabits, we will have to do some math:

(sum(irate(node_network_receive_bytes_total{device!="lo"}[1m])) by (instance, device) * 8 / 1024 / 1024)

To get a full idea of the possibilities of the PromQL querying language, see the documentation. By investigating the metrics available in the node exporter, you can create a lot more graphs like these – for example, for the amount of available disk space, the amount of file descriptors used, and a lot more.

In the next part of this blog, we will go deeper into visualizing the metrics using Grafana, and will also define alerting rules to receive alerts through Alertmanager.