Alternatives to Hosted Graphite logo

Alternatives to Hosted Graphite

Librato, Kibana, Grafana, Prometheus, and Nagios are the most popular alternatives and competitors to Hosted Graphite.
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What is Hosted Graphite and what are its top alternatives?

Hosted Graphite is the powerful open-source application metrics system used by hundreds of companies. We take away the headaches of scaling, maintenance, and upgrades and let you do what you do best - write great software.
Hosted Graphite is a tool in the Monitoring Tools category of a tech stack.

Hosted Graphite alternatives & related posts

Librato logo

Librato

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Real-Time Cloud Monitoring
Librato logo
Librato
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Hosted Graphite logo
Hosted Graphite

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Tymoteusz Paul
Tymoteusz Paul
Devops guy at X20X Development LTD | 12 upvotes 196.6K views
Amazon EC2
Amazon EC2
LXC
LXC
CircleCI
CircleCI
Docker
Docker
Git
Git
Vault
Vault
Apache Maven
Apache Maven
Slack
Slack
Jenkins
Jenkins
TeamCity
TeamCity
Logstash
Logstash
Kibana
Kibana
Elasticsearch
Elasticsearch
Ansible
Ansible
VirtualBox
VirtualBox
Vagrant
Vagrant

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 34.6K views
atElasticElastic
Kibana
Kibana
Logstash
Logstash
Elasticsearch
Elasticsearch

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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related Grafana posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 9 upvotes 491.5K views
atUber TechnologiesUber Technologies
Nagios
Nagios
Grafana
Grafana
Graphite
Graphite
Prometheus
Prometheus

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)

See more
Kibana
Kibana
Grafana
Grafana

For our Predictive Analytics platform, we have used both Grafana and Kibana

Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

  • Creating and organizing visualization panels
  • Templating the panels on dashboards for repetetive tasks
  • Realtime monitoring, filtering of charts based on conditions and variables
  • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
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Prometheus logo

Prometheus

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An open-source service monitoring system and time series database, developed by SoundCloud
Prometheus logo
Prometheus
VS
Hosted Graphite logo
Hosted Graphite

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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 9 upvotes 491.5K views
atUber TechnologiesUber Technologies
Nagios
Nagios
Grafana
Grafana
Graphite
Graphite
Prometheus
Prometheus

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)

See more
Raja Subramaniam Mahali
Raja Subramaniam Mahali
Sysdig
Sysdig
Kubernetes
Kubernetes
Prometheus
Prometheus

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.

See more
Nagios logo

Nagios

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Complete monitoring and alerting for servers, switches, applications, and services
Nagios logo
Nagios
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Hosted Graphite logo
Hosted Graphite

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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 9 upvotes 491.5K views
atUber TechnologiesUber Technologies
Nagios
Nagios
Grafana
Grafana
Graphite
Graphite
Prometheus
Prometheus

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)

See more

related Graphite posts

Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 9 upvotes 491.5K views
atUber TechnologiesUber Technologies
Nagios
Nagios
Grafana
Grafana
Graphite
Graphite
Prometheus
Prometheus

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)

See more
Trey Tacon
Trey Tacon
Amazon CloudWatch
Amazon CloudWatch
PagerDuty
PagerDuty
Grafana
Grafana
Graphite
Graphite
StatsD
StatsD
Sentry
Sentry

A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we鈥檙e looking to migrate all of these to our internal monitoring system soon).

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艁ukasz Korecki
艁ukasz Korecki
CTO & Co-founder at EnjoyHQ | 6 upvotes 30.3K views
atEnjoyHQEnjoyHQ
Stackdriver
Stackdriver
Clojure
Clojure
StatsD
StatsD
Google Compute Engine
Google Compute Engine
collectd
collectd

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
Trey Tacon
Trey Tacon
Amazon CloudWatch
Amazon CloudWatch
PagerDuty
PagerDuty
Grafana
Grafana
Graphite
Graphite
StatsD
StatsD
Sentry
Sentry

A huge part of our continuous deployment practices is to have granular alerting and monitoring across the platform. To do this, we run Sentry on-premise, inside our VPCs, for our event alerting, and we run an awesome observability and monitoring system consisting of StatsD, Graphite and Grafana. We have dashboards using this system to monitor our core subsystems so that we can know the health of any given subsystem at any moment. This system ties into our PagerDuty rotation, as well as alerts from some of our Amazon CloudWatch alarms (we鈥檙e looking to migrate all of these to our internal monitoring system soon).

See more
Jaeger logo

Jaeger

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Distributed tracing system released as open source by Uber
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Jaeger
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Hosted Graphite
Supervisord logo

Supervisord

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A client/server system that allows its users to monitor and control a number of processes
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    collectd logo

    collectd

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    System and applications metrics collector
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    艁ukasz Korecki
    艁ukasz Korecki
    CTO & Co-founder at EnjoyHQ | 6 upvotes 30.3K views
    atEnjoyHQEnjoyHQ
    Stackdriver
    Stackdriver
    Clojure
    Clojure
    StatsD
    StatsD
    Google Compute Engine
    Google Compute Engine
    collectd
    collectd

    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
    Telegraf logo

    Telegraf

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    The plugin-driven server agent for collecting & reporting metrics
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      Hosted Graphite logo
      Hosted Graphite
      Munin logo

      Munin

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      PnP networked resource monitoring tool that can help to answer the what just happened to kill our performance
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      Icinga logo

      Icinga

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      A resilient, open source monitoring system
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        StackShare Editors
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        Icinga
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        Grafana
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        Kibana
        Kibana

        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.

        See more
        OpenTracing logo

        OpenTracing

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        Consistent, expressive, vendor-neutral APIs for distributed tracing and context propagation.
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          Hosted Graphite
          Cacti logo

          Cacti

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          Cacti stores all of the necessary information to create graphs and populate them with data in a MySQL...
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          Hosted Graphite
          NGINX Amplify logo

          NGINX Amplify

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          Monitoring and management tool for NGINX
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            Sysdig logo

            Sysdig

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            Open source container monitoring for all Linux container technologies, including Docker, LXC, etc
            Sysdig logo
            Sysdig
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            Raja Subramaniam Mahali
            Sysdig
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            Prometheus
            Prometheus

            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.

            See more