---
title: Monitoring DreamHack - the World's Largest Digital Festival
created_at: 2015-06-24
kind: article
author_name: Christian Svensson (DreamHack Network Team)
---

*Editor's note: This article is a guest post written by a Prometheus user.*

**If you are operating the network for 10,000's of demanding gamers, you need to
really know what is going on inside your network. Oh, and everything needs to be
built from scratch in just five days.**

If you have never heard about [DreamHack](http://www.dreamhack.se/) before, here
is the pitch: Bring 20,000 people together and have the majority of them bring
their own computer.  Mix in professional gaming (eSports), programming contests,
and live music concerts. The result is the world's largest festival dedicated
solely to everything digital.

To make such an event possible, there needs to be a lot of infrastructure in
place. Ordinary infrastructures of this size take months to build, but the crew
at DreamHack builds everything from scratch in just five days. This of course
includes stuff like configuring network switches, but also building the
electricity distribution, setting up stores for food and drinks, and even
building the actual tables.

The team that builds and operates everything related to the network is
officially called the Network team, but we usually refer to ourselves as *tech*
or *dhtech*. This post is going to focus on the work of dhtech and how we used
Prometheus during DreamHack Summer 2015 to try to kick our monitoring up another
notch.

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## The equipment
Turns out that to build a highly performant network for 10,000+
computers, you need at least the same number of network ports. In our case these
come in the form of ~400 Cisco 2950 switches. We call these the access switches.
These are everywhere in the venue where participants will be seated with their
computers.

[![Access switches](https://c1.staticflickr.com/9/8487/8206439882_4739d39a9c_c.jpg)](https://www.flickr.com/photos/dreamhack/8206439882)
<center>*Dutifully standing in line, the access switches are ready to greet the
DreamHackers with high-speed connectivity.*</center>

Obviously just connecting all these computers to a switch is not enough. That
switch needs to be connected to the other switches as well. This is where the
distribution switches (or dist switches) come into play. These are switches that
take the hundreds of links from all access switches and aggregate them into
more manageable 10-Gbit/s high-capacity fibre. The dist switches are then
further aggregated into our core, where the traffic is routed to its
destination.

On top of all of this, we operate our own WiFi networks, DNS/DHCP servers, and
other infrastructure. When completed, our core looks something like the image
below.

[![Network planning map](/assets/dh_network_planning_map.png)](/assets/dh_network_planning_map.png)
<center>*The planning map for the distribution and core layers. The core is
clearly visible in "Hall D"*</center>

All in all this is becoming a lengthy list of stuff to monitor, so let's get to
the reason you're here: How do we make sure we know what's going on?

## Introducing: dhmon
dhmon is the collective name of the systems that not only
monitor the network, but also allow other teams to collect metrics on whatever
they want.

Since the network needs to be built in five days, it's essential that the
monitoring systems are easy to set up and keep in sync if we need to do last
minute infrastructural changes (like adding or removing devices). When we start
to build the network, we need monitoring as soon as possible to be able to
discover any problems with the equipment or other issues we hadn't foreseen.

In the past we have tried to use a mix of commonly available software such as
Cacti, SNMPc, and Opsview among others. While these have worked they have focused on
being closed systems and only provided the bare minimum. A few years back a few
people from the team said "Enough, we can do better ourselves!" and started
writing a custom monitoring solution.

At the time the options were limited. Over the years the system went from using
Graphite (scalability issues), a custom Cassandra store (high complexity), and
InfluxDB (immature software) to finally land on using Prometheus. I first
learned about Prometheus back in 2014 when I met Julius Volz and I had been
eager to try it ever since. This summer we finally replaced the custom
InfluxDB-based metrics store that we had written with Prometheus. Spoiler: We're
not going back.

## The architecture
The monitoring solution consists of three layers:
collection, storage, presentation. Our most critical collectors are
snmpcollector (SNMP) and ipplan-pinger (ICMP), closely followed by dhcpinfo
(DHCP lease stats). We also have some scripts that dump stats about other
systems into [node_exporter](https://github.com/prometheus/node_exporter)'s
textfile collector.

[![dhmon Architecture](/assets/dh_dhmon_architecture.png)](/assets/dh_dhmon_architecture.png)
<center>*The current architecture plan of dhmon as of Summer 2015*</center>

We use Prometheus as a central timeseries storage and querying engine, but we
also use Redis and memcached to export snapshot views of binary information that
we collect but cannot store in Prometheus in any sensible way, or when we need
to access very fresh data.

One such case is in our presentation layer. We use our dhmap web application to
get an overview of the overall health of the access switches. In order to be
effective at resolving errors, we need a latency of ~10 seconds from data
collection to presentation. Our goal is to have fixed the problem before the
customer notices, or at least before they have walked over to the support people
to report an issue. For this reason, we have been using memcached since the
beginning to access the latest snapshot of the network.

We continued to use memcached this year for our low-latency data, while using
Prometheus for everything that's historical or not as latency-sensitive. This
decision was made simply because we were unsure how Prometheus would perform at
very short sampling intervals. In the end, we found no reason for why we can't
use Prometheus for this data as well - we will definitely try to replace our
memcached with Prometheus at the next DreamHack.

[![dhmon Visualization](/assets/dh_dhmon_visualization.png)](/assets/dh_dhmon_visualization.png)
<center>*The overview of our access layer visualized by dhmon*</center>

## Prometheus setup
The block that so far has been referred to as *Prometheus*
really consists of three products:
[Prometheus](https://github.com/prometheus/prometheus),
[PromDash](https://github.com/prometheus/promdash), and
[Alertmanager](https://github.com/prometheus/alertmanager). The setup is fairly
basic and all three components are running on the same host. Everything is
served by an Apache web server that just acts as a reverse proxy.

    ProxyPass /prometheus http://localhost:9090/prometheus
    ProxyPass /alertmanager http://localhost:9093/alertmanager
    ProxyPass /dash http://localhost:3000/dash

## Exploring the network
Prometheus has a powerful querying engine that allows
you to do pretty cool things with the streaming information collected from all
over your network. However, sometimes the queries need to process too much data
to finish within a reasonable amount of time. This happened to us when we wanted
to graph the top 5 utilized links out of ~18,000 in total. While the query
worked, it would take roughly the amount of time we set our timeout limit to,
meaning it was both slow and flaky. We decided to use Prometheus' [recording
rules](/docs/prometheus/latest/configuration/recording_rules/) for precomputing heavy queries.

    precomputed_link_utilization_percent = rate(ifHCOutOctets{layer!='access'}[10m])*8/1000/1000
                                             / on (device,interface,alias)
                                           ifHighSpeed{layer!='access'}

After this, running `topk(5, precomputed_link_utilization_percent)` was
blazingly fast.

## Being reactive: alerting
So at this stage we had something we could query for
the state of the network. Since we are humans, we don't want to spend our time
running queries all the time to see if things are still running as they should,
so obviously we need alerting.

For example: we know that all our access switches use GigabitEthernet0/2 as an
uplink. Sometimes when the network cables have been in storage for too long they
oxidize and are not able to negotiate the full 1000 Mbps that we want.

The negotiated speed of a network port can be found in the SNMP OID
`IF-MIB::ifHighSpeed`. People familiar with SNMP will however recognize that
this OID is indexed by an arbitrary interface index. To make any sense of this
index, we need to cross-reference it with data from SNMP OID `IF-MIB::ifDescr`
to retrieve the actual interface name.

Fortunately, our snmpcollector supports this kind of cross-referencing while
generating Prometheus metrics. This allows us in a simple way to not only query
data, but also define useful alerts. In our setup we configured the SNMP
collection to annotate any metric under the `IF-MIB::ifTable` and
`IF-MIB::ifXTable` OIDs with `ifDescr`. This will come in handy now when we need
to specify that we are only interested in the `GigabitEthernet0/2` port and no
other interface.

Let's have a look at what such an alert definition looks like.

    ALERT BadUplinkOnAccessSwitch
      IF ifHighSpeed{layer='access', interface='GigabitEthernet0/2'} < 1000 FOR 2m
      SUMMARY "Interface linking at {{$value}} Mbps"
      DESCRIPTION "Interface {{$labels.interface}} on {{$labels.device}} linking at {{$value}} Mbps"

Done! Now we will get an alert if a switch's uplink suddenly links at a
non-optimal speed.

Let's also look at how an alert for an almost full DHCP scope looks like:

    ALERT DhcpScopeAlmostFull
      IF ceil((dhcp_leases_current_count / dhcp_leases_max_count)*100) > 90 FOR 2m
      SUMMARY "DHCP scope {{$labels.network}} is almost full"
      DESCRIPTION "DHCP scope {{$labels.network}} is {{$value}}% full"

We found the syntax to define alerts easy to read and understand even if you had
no previous experience with Prometheus or time series databases.

[![Prometheus alerts for DreamHack](/assets/dh_prometheus_alerts.png)](/assets/dh_prometheus_alerts.png)
<center>*Oops! Turns out we have some bad uplinks, better run out and fix
it!*</center>

## Being proactive: dashboards
While alerting is an essential part of
monitoring, sometimes you just want to have a good overview of the health of
your network. To achieve this we used [PromDash](/docs/visualization/promdash/).
Every time someone asked us something about the network, we crafted a query to
get the answer and saved it as a dashboard widget. The most interesting ones
were then added to an overview dashboard that we proudly displayed.

[![dhmon Dashboard](/assets/dh_dhmon_dashboard.png)](/assets/dh_dhmon_dashboard.png)
<center>*The DreamHack Overview dashboard powered by PromDash*</center>

## The future
While changing an integral part of any system is a complex job and
we're happy that we managed to integrate Prometheus in just one event, there are
without a doubt a lot of areas to improve. Some areas are pretty basic: using
more precomputed metrics to improve performance, adding more alerts, and tuning
the ones we have. Another area is to make it easier for operators: creating an
alert dashboard suitable for our network operations center (NOC), figuring out
if we want to page the people on-call, or just let the NOC escalate alerts.

Some bigger features we're planning on adding: syslog analysis (we have a lot of
syslog!), alerts from our intrusion detection systems, integrating with our
Puppet setup, and also integrating more across the different teams at DreamHack.
We managed to create a proof-of-concept where we got data from one of the
electrical current sensors into our monitoring, making it easy to see if a
device is faulty or if it simply doesn't have any electricity anymore. We're
also working on integrating with the point-of-sale systems that are used in the
stores at the event. Who doesn't want to graph the sales of ice cream?

Finally, not all services that the team operates are on-site, and some even run
24/7 after the event. We want to monitor these services with Prometheus as well,
and in the long run when Prometheus gets support for federation, utilize the
off-site Prometheus to replicate the metrics from the event Prometheus.

## Closing words
We're really excited about Prometheus and how easy it makes
setting up scalable monitoring and alerting from scratch.

A huge shout-out to everyone that helped us in `#prometheus` on
[FreeNode](https://freenode.net/) during the event. Special thanks to Brian
Brazil, Fabian Reinartz and Julius Volz. Thanks for helping us even in the cases
where it was obvious that we hadn't read the documentation thoroughly enough.

Finally, dhmon is all open-source, so head over to https://github.com/dhtech/
and have a look if you're interested. If you feel like you would like to be a
part of this, just head over to `#dreamhack` on
[QuakeNet](https://www.quakenet.org/) and have a chat with us. Who knows, maybe
you will help us build the next DreamHack?
