Alternatives to Graphite logo

Alternatives to Graphite

Graphene, Grafana, Pencil, Kibana, and Prometheus 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 4.8K GitHub stars and 1.2K GitHub forks. Here鈥檚 a link to Graphite's open source repository on GitHub

Graphite alternatives & related posts

Graphene logo

Graphene

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GraphQL framework for Python
Graphene logo
Graphene
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Graphite logo
Graphite

related Graphene posts

Malthe J酶rgensen
Malthe J酶rgensen
CTO at Peergrade | 13 upvotes 83.1K views
atPeergradePeergrade
PostgreSQL
PostgreSQL
Django
Django
GraphQL
GraphQL
Graphene
Graphene
Amazon RDS
Amazon RDS

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 .

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Michael Mota
Michael Mota
CEO & Founder at AlterEstate | 6 upvotes 24.9K views
atAlterEstateAlterEstate
Graphene
Graphene
Django
Django
GraphQL
GraphQL

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 .

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related Grafana posts

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)

See more
Grafana
Grafana
Kibana
Kibana

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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Pencil logo

Pencil

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A Microframework Inspired by Flask for Rust
    Be the first to leave a pro
    Pencil logo
    Pencil
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    Graphite logo
    Graphite

    related Kibana posts

    Tymoteusz Paul
    Tymoteusz Paul
    Devops guy at X20X Development LTD | 19 upvotes 875.2K 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 199.9K 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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    Prometheus logo

    Prometheus

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

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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)

    See more
    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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    Nagios logo

    Nagios

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

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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)

    See more

    related StatsD posts

    艁ukasz Korecki
    艁ukasz Korecki
    CTO & Co-founder at EnjoyHQ | 6 upvotes 144.4K views
    atEnjoyHQEnjoyHQ
    collectd
    collectd
    Google Compute Engine
    Google Compute Engine
    StatsD
    StatsD
    Clojure
    Clojure
    Stackdriver
    Stackdriver

    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

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

    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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