Reflections on adopting Prometheus as the standard time series data tooling

observability prometheus time series data

The tool Prometheus has been in use within the organisation for approximately 12 months now, with a growing responsibility that now includes determining the health of all of our physical systems, whether systems are “up” to the public and a detailed analysis of software on a subset of those systems. It seems reasonable now to be retrospective, and evaluate the strengths and the weaknesses of the tool as I have used it.

The problem

Broadly, the problem that we were trying to solve with Prometheus is to get insight into how well our applications were performing in a production environment. Specifically:

  • Is the software doing what it is supposed to?

  • If not, why not?

Many possible solutions

There are many different approaches to attempting to answer those question, including logs, traces, time series data or event aggregation.

Time series data as the common diagnostic language

Each of the different methods of introspecting an application have their strengths and their weaknesses:

Technique Tradeoffs


Provide a superb level of detail, but are difficult to parse. Good for debugging a specific issue but near impossible to use to spot patterns over time.

Transaction Tracing

Provide an excellent summary of the path through the stack and latency or issues associated with a given request but require a large amount of investment to setup and are not in common use yet.

Event Aggregation

Events can be aggregated and sliced more easily, but require a structured approach to logging that must be retrofitted across all applications

That leaves "time series data".

Time series data is the collection of a numeric representation of the state of an application over time. A trivial example is the number of HTTP requests that results in a 500 status code:

Time collected Total 500s









In the example above there was a total of 5 500’s; 2 of which happened between 15 seconds and 30 seconds, and 3 of which happened between 30 seconds and 45 seconds.

Time series data is excellent to work with as so much information can be derived from such a simple export. We can determine rate, total number, issue times and by comparing it against other time series we can correlate this issue against other strange anomalies in the infrastructure, such as CPU load or network throughput.

The many tools that adhere to this model

There are quite a few tools that make it easy to capture and store time series data:

Each have their own tradeoffs.

Settling on Prometheus

In the past, I have used Sensu, New Relic and Prometheus. I have also used ElasticSearch for log aggregation, but that’s a story for another day.

Prometheus has a number of characteristics that, for me, mean it’s superior as a monitoring tool:

  • Totally Free: Owned by the CNCF Prometheus is “Free as in beer”. In the past as an agency we have been bitten by being priced out of other services (looking at you, New Relic!)

  • Bizarrely Simple: Prometheus is written in Go, and is extremely simple to get up and running. It’s packaged in Debian 9, or one can simply pick it up from GitHub and drop it in /usr/local/bin/ . Such simplicity is attractive when playing around with it.

  • Reliable: To make Prometheus highly available, you simply add another Prometheus. These’s no consensus or other fuckery; it simply scans all metrics 2x. To scale it, you add another Prometheus which collects and aggregates some metrics, then you point your first Prometheus at that Prometheus and collect only the aggregated data.

  • Opinionated: Prometheus makes certain assumptions about how the data should be exported, and doesn’t forgive violations of those assumptions easily. Though (like seemingly all new tech) initially it felt un-intuitive, the consistency and assumptions of this approach are super nice.

  • Alerts: Prometheus integrations with another free tool called “Alertmanager”, also from the authors of Prometheus. This functions as a reliable alerting tool, integrating with many of the standard alerting mechanisms (pushover being my favorite), and easily extended to more.

  • Standard format for metrics: Prometheus collects metrics by making a GET HTTP request, and expecting a simple, easy to understand text format back. This is nice because applications can support Prometheus easily (there are a host of libraries to do the minimal work required), and it’s extremely easy to instrument other applications that currently do not support time series data.


The road to adoption has definitely not been super simple, however. There have been issues with the adoption of this tool that have only had resolutions with time and considered thought.

A complex, difficult to understand tool

In an insightful moment, a colleague said to me “This feels like the MySQL console! It’s a magic language without help”. This is an astute comparison; In order to use Prometheus effectively, users must learn this language, including it’s arcane rules about rate , comparison between metrics etc.

Prometheus integrates with the graphing tool “Grafana”. The best solution to make Prometheus more accessible was to install Grafana, and a swathe of dashboards for the available metrics. These dashboards are all freely available, and with minimal configuration, can provide much more insight into the metrics for non-specialists.

Self hosting can be hard

The standard joke with monitoring is “What’s monitoring the monitoring?” It reaches beyond the immediate question, and into how to ensure that monitoring is architected for reliability.

We have had just the once incident over the last 12 months where Prometheus became unavailable. It turns out that as you increase the responsibility, it’s disk space requirements also increase (shock, I know). At some point, it filled the disk and corrupted data, then failed to boot. Though annoying, our meta-monitoring picked it up, and it was readjusted.

Practically, if you’re running Kubernetes I recommend installing the excellent stable/prometheus chart from Helm onto your cluster. It might be there already, it’s become the defacto monitoring tool for Kubernetes. If you’re not using Kubernetes, then … consult your sysadmin.

Moving Forward

I hope to see Prometheus grow an even larger role within our Organisation. Further, as we improve our infrastructure architectures I think it will prove progressively more useful.

I think we need to further instrument our software to take advantage of exporting data (such as Magento 2), which will let us get even better insight into our applications.

Additionally, I would consider moving to a hosted model of Prometheus to make it more accessible to members of the team who area not familiar with the helm deployment model. However, I think perhaps the it’s not worth the tradeoffs just yet.

In Summary

Prometheus has become our defacto time series collection and monitoring tool. In combined with Grafana, it is an accessible tool to help developers inspect their applications or infrastructure in a production environment. I’m quite sure it’s not the only tool capable of doing the job, or perhaps even the best — but I’ve enjoyed it.

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