Alternatives to Ganglia logo

Alternatives to Ganglia

collectd, Zabbix, Nagios, Munin, and Grafana are the most popular alternatives and competitors to Ganglia.
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What is Ganglia and what are its top alternatives?

It is a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids. It is based on a hierarchical design targeted at federations of clusters.
Ganglia is a tool in the Monitoring Tools category of a tech stack.

Top Alternatives to Ganglia

  • collectd
    collectd

    collectd gathers statistics about the system it is running on and stores this information. Those statistics can then be used to find current performance bottlenecks (i.e. performance analysis) and predict future system load (i.e. capacity planning). Or if you just want pretty graphs of your private server and are fed up with some homegrown solution you're at the right place, too. ...

  • Zabbix
    Zabbix

    Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics. ...

  • Nagios
    Nagios

    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License. ...

  • Munin
    Munin

    Munin is a networked resource monitoring tool that can help analyze resource trends and "what just happened to kill our performance?" problems. It is designed to be very plug and play. A default installation provides a lot of graphs with almost no work. ...

  • Grafana
    Grafana

    Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins. ...

  • Prometheus
    Prometheus

    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. ...

  • Effector
    Effector

    It is an effective multi-store state manager for Javascript apps, that allows you to manage data in complex applications. ...

  • Kibana
    Kibana

    Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...

Ganglia alternatives & related posts

collectd logo

collectd

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143
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System and applications metrics collector
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PROS OF COLLECTD
  • 2
    Open Source
  • 2
    Modular, plugins
  • 1
    KISS
CONS OF COLLECTD
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    related collectd posts

    Łukasz Korecki
    CTO & Co-founder at EnjoyHQ · | 7 upvotes · 267.7K views

    We use collectd because of it's low footprint and great capabilities. We use it to monitor our Google Compute Engine machines. More interestingly we setup collectd as StatsD replacement - all our Clojure services push application-level metrics using our own metrics library and collectd pushes them to Stackdriver

    See more
    Zabbix logo

    Zabbix

    568
    824
    62
    Track, record, alert and visualize performance and availability of IT resources
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    62
    PROS OF ZABBIX
    • 18
      Free
    • 9
      Alerts
    • 5
      Service/node/network discovery
    • 4
      Templates
    • 4
      Base metrics from the box
    • 3
      Multi-dashboards
    • 3
      SMS/Email/Messenger alerts
    • 2
      Grafana plugin available
    • 2
      Supports Graphs ans screens
    • 2
      Support proxies (for monitoring remote branches)
    • 1
      Perform website checking (response time, loading, ...)
    • 1
      API available for creating own apps
    • 1
      Templates free available (Zabbix Share)
    • 1
      Works with multiple databases
    • 1
      Advanced integrations
    • 1
      Supports multiple protocols/agents
    • 1
      Complete Logs Report
    • 1
      Open source
    • 1
      Supports large variety of Operating Systems
    • 1
      Supports JMX (Java, Tomcat, Jboss, ...)
    CONS OF ZABBIX
    • 5
      The UI is in PHP
    • 2
      Puppet module is sluggish

    related Zabbix posts

    Shared insights
    on
    DatadogDatadogZabbixZabbixCentreonCentreon

    My team is divided on using Centreon or Zabbix for enterprise monitoring and alert automation. Can someone let us know which one is better? There is one more tool called Datadog that we are using for cloud assets. Of course, Datadog presents us with huge bills. So we want to have a comparative study. Suggestions and advice are welcome. Thanks!

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    Nagios logo

    Nagios

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    102
    Complete monitoring and alerting for servers, switches, applications, and services
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    PROS OF NAGIOS
    • 53
      It just works
    • 28
      The standard
    • 12
      Customizable
    • 8
      The Most flexible monitoring system
    • 1
      Huge stack of free checks/plugins to choose from
    CONS OF NAGIOS
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      related Nagios posts

      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

      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...

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

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

      See more
      Shared insights
      on
      PrometheusPrometheusNagiosNagios

      I am new to DevOps and looking for training in DevOps. Some institutes are offering Nagios while some Prometheus in their syllabus. Please suggest which one is being used in the industry and which one should I learn.

      See more
      Munin logo

      Munin

      69
      91
      10
      PnP networked resource monitoring tool that can help to answer the what just happened to kill our performance
      69
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      PROS OF MUNIN
      • 3
        Good defaults
      • 2
        Extremely fast to install
      • 2
        Alerts can trigger any command line program
      • 2
        Adheres to traditional Linux standards
      • 1
        Easy to write custom plugins
      CONS OF MUNIN
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        related Munin posts

        Grafana logo

        Grafana

        12.7K
        10.2K
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        Open source Graphite & InfluxDB Dashboard and Graph Editor
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        PROS OF GRAFANA
        • 84
          Beautiful
        • 67
          Graphs are interactive
        • 57
          Free
        • 56
          Easy
        • 33
          Nicer than the Graphite web interface
        • 24
          Many integrations
        • 17
          Can build dashboards
        • 10
          Easy to specify time window
        • 9
          Dashboards contain number tiles
        • 8
          Can collaborate on dashboards
        • 5
          Open Source
        • 5
          Click and drag to zoom in
        • 5
          Integration with InfluxDB
        • 4
          Authentification and users management
        • 4
          Threshold limits in graphs
        • 3
          It is open to cloud watch and many database
        • 3
          Simple and native support to Prometheus
        • 2
          Great community support
        • 2
          Alerts
        • 2
          You can visualize real time data to put alerts
        • 2
          You can use this for development to check memcache
        • 0
          Grapsh as code
        • 0
          Plugin visualizationa
        CONS OF GRAFANA
        • 1
          No interactive query builder

        related Grafana posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

        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...

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

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

        See more
        Matt Menzenski
        Senior Software Engineering Manager at PayIt · | 14 upvotes · 205.8K views

        Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

        See more
        Prometheus logo

        Prometheus

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        An open-source service monitoring system and time series database, developed by SoundCloud
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        PROS OF PROMETHEUS
        • 46
          Powerful easy to use monitoring
        • 38
          Flexible query language
        • 32
          Dimensional data model
        • 27
          Alerts
        • 23
          Active and responsive community
        • 22
          Extensive integrations
        • 19
          Easy to setup
        • 12
          Beautiful Model and Query language
        • 7
          Easy to extend
        • 6
          Nice
        • 3
          Written in Go
        • 2
          Good for experimentation
        • 1
          Easy for monitoring
        CONS OF PROMETHEUS
        • 12
          Just for metrics
        • 6
          Bad UI
        • 6
          Needs monitoring to access metrics endpoints
        • 4
          Not easy to configure and use
        • 3
          Supports only active agents
        • 2
          Written in Go
        • 2
          TLS is quite difficult to understand
        • 2
          Requires multiple applications and tools
        • 1
          Single point of failure

        related Prometheus posts

        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 3.1M views

        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...

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

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

        See more
        Matt Menzenski
        Senior Software Engineering Manager at PayIt · | 14 upvotes · 205.8K views

        Grafana and Prometheus together, running on Kubernetes , is a powerful combination. These tools are cloud-native and offer a large community and easy integrations. At PayIt we're using exporting Java application metrics using a Dropwizard metrics exporter, and our Node.js services now use the prom-client npm library to serve metrics.

        See more
        Effector logo

        Effector

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        22
        18
        Multi-store state manager for Javascript apps
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        22
        + 1
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        PROS OF EFFECTOR
        • 7
          Statically typed
        • 6
          Less boilerplate
        • 3
          Small bundle size
        • 1
          Signal functions
        • 1
          Effects calculation
        CONS OF EFFECTOR
        • 2
          Undocumented methods like setState
        • 1
          Lack of debugging tools

        related Effector posts

        Kibana logo

        Kibana

        16.5K
        13.1K
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        Visualize your Elasticsearch data and navigate the Elastic Stack
        16.5K
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        PROS OF KIBANA
        • 88
          Easy to setup
        • 62
          Free
        • 45
          Can search text
        • 21
          Has pie chart
        • 13
          X-axis is not restricted to timestamp
        • 8
          Easy queries and is a good way to view logs
        • 6
          Supports Plugins
        • 3
          Dev Tools
        • 3
          More "user-friendly"
        • 3
          Can build dashboards
        • 2
          Easy to drill-down
        • 2
          Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
        • 1
          Up and running
        CONS OF KIBANA
        • 5
          Unintuituve
        • 3
          Elasticsearch is huge
        • 3
          Works on top of elastic only
        • 2
          Hardweight UI

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        Tymoteusz Paul
        Devops guy at X20X Development LTD · | 23 upvotes · 5.1M views

        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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        Patrick Sun
        Software Engineer at Stitch Fix · | 11 upvotes · 506K views

        Elasticsearch's built-in visualization tool, Kibana, is robust and the appropriate tool in many cases. However, it is geared specifically towards log exploration and time-series data, and we felt that its steep learning curve would impede adoption rate among data scientists accustomed to writing SQL. The solution was to create something that would replicate some of Kibana's essential functionality while hiding Elasticsearch's complexity behind SQL-esque labels and terminology ("table" instead of "index", "group by" instead of "sub-aggregation") in the UI.

        Elasticsearch's API is really well-suited for aggregating time-series data, indexing arbitrary data without defining a schema, and creating dashboards. For the purpose of a data exploration backend, Elasticsearch fits the bill really well. Users can send an HTTP request with aggregations and sub-aggregations to an index with millions of documents and get a response within seconds, thus allowing them to rapidly iterate through their data.

        See more