Alternatives to Nagios logo

Alternatives to Nagios

Zabbix, Splunk, Icinga, Solarwinds, and AppDynamics are the most popular alternatives and competitors to Nagios.
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What is Nagios and what are its top alternatives?

Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
Nagios is a tool in the Monitoring Tools category of a tech stack.
Nagios is an open source tool with 60 GitHub stars and 36 GitHub forks. Here鈥檚 a link to Nagios's open source repository on GitHub

Nagios alternatives & related posts

Splunk logo

Splunk

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Search, monitor, analyze and visualize machine data
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    Splunk logo
    Splunk
    VS
    Nagios logo
    Nagios

    related Splunk posts

    Kibana
    Kibana
    Splunk
    Splunk
    Grafana
    Grafana

    I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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    Icinga logo

    Icinga

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    A resilient, open source monitoring system
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      Icinga logo
      Icinga
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      Nagios logo
      Nagios

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      StackShare Editors
      StackShare Editors
      Kibana
      Kibana
      Grafana
      Grafana
      Elasticsearch
      Elasticsearch
      Logstash
      Logstash
      Graphite
      Graphite
      Icinga
      Icinga

      One size definitely doesn鈥檛 fit all when it comes to open source monitoring solutions, and executing generally understood best practices in the context of unique distributed systems presents all sorts of problems. Megan Anctil, a senior engineer on the Technical Operations team at Slack gave a talk at an O鈥橰eilly Velocity Conference sharing pain points and lessons learned at wrangling known technologies such as Icinga, Graphite, Grafana, and the Elastic Stack to best fit the company鈥檚 use cases.

      At the time, Slack used a few well-known monitoring tools since it鈥檚 Technical Operations team wasn鈥檛 large enough to build an in-house solution for all of these. Nor did the team think it鈥檚 sustainable to throw money at the problem, given the volume of information processed and the not-insignificant price and rigidity of many vendor solutions. With thousands of servers across multiple regions and millions of metrics and documents being processed and indexed per second, the team had to figure out how to scale these technologies to fit Slack鈥檚 needs.

      On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they鈥檝e tried to build usability into Grafana that reflects the team鈥檚 mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

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      Solarwinds logo

      Solarwinds

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      Unlock powerful workflows, automation, and reporting
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        Solarwinds logo
        Solarwinds
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        Nagios logo
        Nagios
        AppDynamics logo

        AppDynamics

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        Application management for the cloud generation
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        AppDynamics
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        Nagios logo
        Nagios
        PRTG logo

        PRTG

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        A powerful & easy network monitoring software
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          PRTG
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          Nagios
          Prometheus logo

          Prometheus

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          An open-source service monitoring system and time series database, developed by SoundCloud
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          Prometheus
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          Nagios

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          Conor Myhrvold
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber | 11 upvotes 1M views
          atUber TechnologiesUber Technologies
          Prometheus
          Prometheus
          Graphite
          Graphite
          Grafana
          Grafana
          Nagios
          Nagios

          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鈥檚 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鈥檚 metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

          https://eng.uber.com/m3/

          (GitHub : https://github.com/m3db/m3)

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          Raja Subramaniam Mahali
          Raja Subramaniam Mahali
          Prometheus
          Prometheus
          Kubernetes
          Kubernetes
          Sysdig
          Sysdig

          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.

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          related Kibana posts

          Tymoteusz Paul
          Tymoteusz Paul
          Devops guy at X20X Development LTD | 19 upvotes 907.4K views
          Vagrant
          Vagrant
          VirtualBox
          VirtualBox
          Ansible
          Ansible
          Elasticsearch
          Elasticsearch
          Kibana
          Kibana
          Logstash
          Logstash
          TeamCity
          TeamCity
          Jenkins
          Jenkins
          Slack
          Slack
          Apache Maven
          Apache Maven
          Vault
          Vault
          Git
          Git
          Docker
          Docker
          CircleCI
          CircleCI
          LXC
          LXC
          Amazon EC2
          Amazon EC2

          Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

          It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

          I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

          We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

          If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

          The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

          Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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          Tanya Bragin
          Tanya Bragin
          Product Lead, Observability at Elastic | 10 upvotes 206.4K views
          atElasticElastic
          Elasticsearch
          Elasticsearch
          Logstash
          Logstash
          Kibana
          Kibana

          ELK Stack (Elasticsearch, Logstash, Kibana) is widely known as the de facto way to centralize logs from operational systems. The assumption is that Elasticsearch (a "search engine") is a good place to put text-based logs for the purposes of free-text search. And indeed, simply searching text-based logs for the word "error" or filtering logs based on a set of a well-known tags is extremely powerful, and is often where most users start.

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