Airbrake vs Prometheus: What are the differences?
Airbrake: Airbrake captures and groups errors in Ruby, iOS, Django, PHP & more. Airbrake collects errors for your applications in all major languages and frameworks. We alert you to new errors and give you critical context, trends and details needed to find and fix errors fast; Prometheus: An open-source service monitoring system and time series database, developed by SoundCloud. Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
Airbrake belongs to "Exception Monitoring" category of the tech stack, while Prometheus can be primarily classified under "Monitoring Tools".
Some of the features offered by Airbrake are:
- Dupe Detection- Developers will only receive an email once when an error occurs — rather than getting buried under an error email avalanche.
- Auto Resolve- An action on each application deployment — that way developers can focus on the problems that exist today, not the ones from last month.
- Github Aware- Airbrake is github-aware, and can link code in your Airbrake error stack traces to the corresponding locations on github.
On the other hand, Prometheus provides the following key features:
- a multi-dimensional data model (timeseries defined by metric name and set of key/value dimensions)
- a flexible query language to leverage this dimensionality
- no dependency on distributed storage
"Reliable" is the primary reason why developers consider Airbrake over the competitors, whereas "Powerful easy to use monitoring" was stated as the key factor in picking Prometheus.
Prometheus is an open source tool with 25K GitHub stars and 3.55K GitHub forks. Here's a link to Prometheus's open source repository on GitHub.
According to the StackShare community, Prometheus has a broader approval, being mentioned in 243 company stacks & 85 developers stacks; compared to Airbrake, which is listed in 91 company stacks and 22 developer stacks.
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We recently implemented Thanos alongside Prometheus into our Kubernetes clusters, we had previously used a variety of different metrics systems and we wanted to make life simpler for everyone by just picking one.
Prometheus seemed like an obvious choice due to its powerful querying language, native Kubernetes support and great community. However we found it somewhat lacking when it came to being highly available, something that would be very important if we wanted this to be the single source of all our metrics.
Thanos came along and solved a lot of these problems. It allowed us to run multiple Prometheis without duplicating metrics, query multiple Prometheus clusters at once, and easily back up data and then query it. Now we have a single place to go if you want to view metrics across all our clusters, with many layers of redundancy to make sure this monitoring solution is as reliable and resilient as we could reasonably make it.
If you're interested in a bit more detail feel free to check out the blog I wrote on the subject that's linked.
Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:
By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.
To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...
(GitHub : https://github.com/m3db/m3)
We have Prometheus as a monitoring engine as a part of our stack which contains Kubernetes cluster, container images and other open source tools. Also, I am aware that Sysdig can be integrated with Prometheus but I really wanted to know whether Sysdig or sysdig+prometheus will make better monitoring solution.
We primarily use Prometheus to gather metrics and statistics to display them in Grafana. Aside from that we poll Prometheus for our orchestration-solution "JCOverseer" to determine, which host is least occupied at the moment.
Efficiently trace and detect errors in the stack without having to explicitly setup error tracking for each action.
Gather metrics from systems and applications. Evaluate alerting rules. Alerts are pushed to OpsGenie and Slack.
We primarily use Prometheus to gather metrics and statistics to display them in Grafana.