Alternatives to Graphite logo

Alternatives to Graphite

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

Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand
Graphite is a tool in the Monitoring Tools category of a tech stack.
Graphite is an open source tool with 5.3K GitHub stars and 1.3K GitHub forks. Here’s a link to Graphite's open source repository on GitHub

Top Alternatives to Graphite

  • 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. ...

  • Graphene

    Graphene

    Graphene is a Python library for building GraphQL schemas/types fast and easily. ...

  • Pencil

    Pencil

    A web application microframework for Rust

  • 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. ...

  • 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. ...

  • Nagios

    Nagios

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

  • Zabbix

    Zabbix

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

  • StatsD

    StatsD

    It is a network daemon that runs on the Node.js platform and listens for statistics, like counters and timers, sent over UDP or TCP and sends aggregates to one or more pluggable backend services (e.g., Graphite). ...

Graphite alternatives & related posts

Grafana logo

Grafana

10.3K
8K
396
Open source Graphite & InfluxDB Dashboard and Graph Editor
10.3K
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396
PROS OF GRAFANA
  • 83
    Beautiful
  • 67
    Graphs are interactive
  • 56
    Free
  • 54
    Easy
  • 33
    Nicer than the Graphite web interface
  • 24
    Many integrations
  • 16
    Can build dashboards
  • 10
    Easy to specify time window
  • 9
    Dashboards contain number tiles
  • 8
    Can collaborate on dashboards
  • 5
    Integration with InfluxDB
  • 5
    Click and drag to zoom in
  • 5
    Open Source
  • 4
    Authentification and users management
  • 4
    Threshold limits in graphs
  • 3
    It is open to cloud watch and many database
  • 2
    You can visualize real time data to put alerts
  • 2
    Great community support
  • 2
    Alerts
  • 2
    Simple and native support to Prometheus
  • 2
    You can use this for development to check memcache
  • 0
    Plugin visualizationa
  • 0
    Grapsh as code
CONS OF GRAFANA
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    related Grafana posts

    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.8M 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 · | 11 upvotes · 46.9K 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
    Graphene logo

    Graphene

    89
    121
    0
    GraphQL framework for Python
    89
    121
    + 1
    0
    PROS OF GRAPHENE
    • 0
      Will replace RESTful interfaces
    • 0
      The future of API's
    CONS OF GRAPHENE
      Be the first to leave a con

      related Graphene posts

      Malthe Jørgensen

      We recently switched from MongoDB and the Python library MongoEngine to PostgreSQL and Django in order to:

      • Better leverage GraphQL (using the Graphene library)
      • Allow us to use the autogenerated Django admin interface
      • Allow better performance due to the way some of our pages present data
      • Give us more a mature stack in the form of Django replacing MongoEngine, which we had some issues with in the past.

      MongoDB was hosted on mlab, and we now host Postgres on Amazon RDS .

      See more
      Michael Mota
      Founder at AlterEstate · | 6 upvotes · 51K views

      We recently implemented GraphQL because we needed to build dynamic reports based on the user preference and configuration, this was extremely complicated with our actual RESTful API, the code started to get harder to maintain but switching to GraphQL helped us to to build beautiful reports for our clients that truly help them make data-driven decisions.

      Our goal is to implemented GraphQL in the whole platform eventually, we are using Graphene , a python library for Django .

      See more
      Pencil logo

      Pencil

      2
      10
      0
      A Microframework Inspired by Flask for Rust
      2
      10
      + 1
      0
      PROS OF PENCIL
        Be the first to leave a pro
        CONS OF PENCIL
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          related Pencil posts

          Prometheus logo

          Prometheus

          2.5K
          2.8K
          235
          An open-source service monitoring system and time series database, developed by SoundCloud
          2.5K
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          PROS OF PROMETHEUS
          • 44
            Powerful easy to use monitoring
          • 39
            Flexible query language
          • 32
            Dimensional data model
          • 27
            Alerts
          • 22
            Active and responsive community
          • 21
            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
          • 11
            Just for metrics
          • 6
            Needs monitoring to access metrics endpoints
          • 6
            Bad UI
          • 3
            Not easy to configure and use
          • 2
            Requires multiple applications and tools
          • 2
            Written in Go
          • 2
            Supports only active agents
          • 1
            TLS is quite difficult to understand

          related Prometheus posts

          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber · | 14 upvotes · 2.8M 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

          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
          Kibana logo

          Kibana

          14.2K
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          Visualize your Elasticsearch data and navigate the Elastic Stack
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          PROS OF KIBANA
          • 88
            Easy to setup
          • 61
            Free
          • 44
            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
            Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
          • 2
            Easy to drill-down
          • 1
            Up and running
          CONS OF KIBANA
          • 5
            Unintuituve
          • 3
            Elasticsearch is huge
          • 3
            Works on top of elastic only
          • 2
            Hardweight UI

          related Kibana posts

          Tymoteusz Paul
          Devops guy at X20X Development LTD · | 23 upvotes · 4.5M 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.

          See more
          Patrick Sun
          Software Engineer at Stitch Fix · | 11 upvotes · 452.3K 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
          Nagios logo

          Nagios

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          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 · 2.8M 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
            Zabbix logo

            Zabbix

            521
            718
            57
            Track, record, alert and visualize performance and availability of IT resources
            521
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            PROS OF ZABBIX
            • 16
              Free
            • 7
              Alerts
            • 5
              Service/node/network discovery
            • 4
              Templates
            • 4
              Base metrics from the box
            • 3
              Multi-dashboards
            • 3
              SMS/Email/Messenger alerts
            • 2
              Supports Graphs ans screens
            • 2
              Support proxies (for monitoring remote branches)
            • 2
              Grafana plugin available
            • 1
              API available for creating own apps
            • 1
              Templates free available (Zabbix Share)
            • 1
              Works with multiple databases
            • 1
              Supports large variety of Operating Systems
            • 1
              Supports multiple protocols/agents
            • 1
              Complete Logs Report
            • 1
              Advanced integrations
            • 1
              Supports JMX (Java, Tomcat, Jboss, ...)
            • 1
              Perform website checking (response time, loading, ...)
            CONS OF ZABBIX
            • 5
              The UI is in PHP
            • 2
              Puppet module is sluggish

            related Zabbix posts

            Shared insights
            on
            Datadog
            Zabbix
            Centreon

            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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            StatsD logo

            StatsD

            257
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            Simple daemon for easy stats aggregation
            257
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            PROS OF STATSD
            • 9
              Open source
            • 7
              Single responsibility
            • 5
              Efficient wire format
            • 3
              Loads of integrations
            • 3
              Handles aggregation
            • 1
              Many implementations
            • 1
              Scales well
            • 1
              Simple to use
            • 1
              NodeJS
            CONS OF STATSD
            • 1
              No authentication; cannot be used over Internet

            related StatsD posts

            Łukasz Korecki
            CTO & Co-founder at EnjoyHQ · | 7 upvotes · 244.6K 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

            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’re looking to migrate all of these to our internal monitoring system soon).

            See more