What is Graphite and what are its top alternatives?
Graphite is a popular open-source tool used for monitoring and graphing the performance of computer systems. It provides a scalable and flexible platform for storing, visualizing, and analyzing time-series data. Key features of Graphite include a powerful graphing system, the ability to create custom dashboards, integration with various data sources, and the capability to scale horizontally. However, Graphite has some limitations such as the complexity of setting up and maintaining the system, lack of out-of-the-box alerting capabilities, and potential performance issues when dealing with large data sets.
- Grafana: Grafana is a leading open-source tool for visualizing and analyzing metrics collected from different data sources. Key features include a rich set of visualization options, support for various data storage backends, alerting capabilities, and an active community. Pros of Grafana include a user-friendly interface and extensive plugin ecosystem, while cons include a steeper learning curve compared to Graphite.
- Prometheus: Prometheus is a monitoring and alerting toolkit designed for reliability and scalability. Key features include a multi-dimensional data model, flexible querying language, powerful alerting system, and integrations with various tools. Pros of Prometheus include native support for Kubernetes monitoring and dynamic service discovery, while cons include a lack of built-in graphing capabilities compared to Graphite.
- InfluxDB: InfluxDB is a time-series database built for handling high write and query loads. Key features include a SQL-like query language, retention policies, continuous queries, and built-in downsampling. Pros of InfluxDB include high performance and scalability, while cons include a steeper learning curve for beginners compared to Graphite.
- Zabbix: Zabbix is an open-source monitoring solution known for its robust feature set, including network monitoring, alerting, and visualization capabilities. Key features include auto-discovery, distributed monitoring, and web monitoring. Pros of Zabbix include a comprehensive set of monitoring features, while cons include a more complex setup process compared to Graphite.
- Elasticsearch: Elasticsearch is a distributed, RESTful search and analytics engine used for real-time data analysis. Key features include full-text search, complex queries, and schema-free JSON documents. Pros of Elasticsearch include high scalability and real-time data indexing, while cons include a higher resource usage compared to Graphite.
- OpenTSDB: OpenTSDB is a scalable, distributed time-series database built on top of Apache HBase. Key features include a robust data model, built-in aggregation functions, and integration with Hadoop and other big data tools. Pros of OpenTSDB include high scalability and performance, while cons include a more complex setup process compared to Graphite.
- Cacti: Cacti is a network monitoring and graphing tool designed for easy data collection and visualization. Key features include SNMP support, templating, and customizable graph layouts. Pros of Cacti include a user-friendly interface and extensive community support, while cons include a lack of advanced monitoring features compared to Graphite.
- Netdata: Netdata is a distributed real-time performance and health monitoring tool for systems and applications. Key features include per-second data collection, interactive real-time dashboards, and alarms. Pros of Netdata include easy installation and configuration, while cons include limited long-term data storage capabilities compared to Graphite.
- Wavefront: Wavefront is a cloud-native monitoring and analytics platform designed for real-time visibility into cloud applications and infrastructure. Key features include high cardinality data ingestion, analytics-driven troubleshooting, and auto-discovery of cloud applications. Pros of Wavefront include cloud-native architecture and automated analytics, while cons include potential cost concerns compared to Graphite.
- Sysdig: Sysdig is a cloud-native visibility and security platform built for monitoring, troubleshooting, and securing containers and microservices. Key features include deep container visibility, system call capture, and vulnerability management. Pros of Sysdig include comprehensive container monitoring capabilities, while cons include a higher learning curve for beginners compared to Graphite.
Top Alternatives to Graphite
- 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 is a Python library for building GraphQL schemas/types fast and easily. ...
- Pencil
A web application microframework for Rust
- 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 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 is a host/service/network monitoring program written in C and released under the GNU General Public License. ...
- Zabbix
Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics. ...
- OpenCensus
It is a set of libraries for various languages that allow you to collect application metrics and distributed traces, then transfer the data to a backend of your choice in real time. This data can be analyzed by developers and admins to understand the health of the application and debug problems. ...
Graphite alternatives & related posts
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
- No interactive query builder1
related Grafana posts
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...
(GitHub : https://github.com/m3db/m3)
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.
- Will replace RESTful interfaces0
- The future of API's0
related Graphene posts
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 .
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 .
related Pencil posts
Prometheus
- Powerful easy to use monitoring47
- Flexible query language38
- Dimensional data model32
- Alerts27
- Active and responsive community23
- Extensive integrations22
- Easy to setup19
- Beautiful Model and Query language12
- Easy to extend7
- Nice6
- Written in Go3
- Good for experimentation2
- Easy for monitoring1
- Just for metrics12
- Bad UI6
- Needs monitoring to access metrics endpoints6
- Not easy to configure and use4
- Supports only active agents3
- Written in Go2
- TLS is quite difficult to understand2
- Requires multiple applications and tools2
- Single point of failure1
related Prometheus posts
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...
(GitHub : https://github.com/m3db/m3)
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.
- Easy to setup88
- Free64
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- Can build dashboards3
- More "user-friendly"3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
- Unintuituve6
- Elasticsearch is huge4
- Hardweight UI3
- Works on top of elastic only3
related Kibana posts
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.
This is my stack in Application & Data
JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
Slack
Nagios
- It just works53
- The standard28
- Customizable12
- The Most flexible monitoring system8
- Huge stack of free checks/plugins to choose from1
related Nagios posts
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...
(GitHub : https://github.com/m3db/m3)
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.
Zabbix
- Free21
- Alerts9
- Service/node/network discovery5
- Templates5
- Base metrics from the box4
- Multi-dashboards3
- SMS/Email/Messenger alerts3
- Grafana plugin available2
- Supports Graphs ans screens2
- Support proxies (for monitoring remote branches)2
- Perform website checking (response time, loading, ...)1
- API available for creating own apps1
- Templates free available (Zabbix Share)1
- Works with multiple databases1
- Advanced integrations1
- Supports multiple protocols/agents1
- Complete Logs Report1
- Open source1
- Supports large variety of Operating Systems1
- Supports JMX (Java, Tomcat, Jboss, ...)1
- The UI is in PHP5
- Puppet module is sluggish2
related Zabbix posts
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!
I am looking for an easy to set up and use monitoring solution for my servers and network infrastructure. What are the main differences between Checkmk and Zabbix? What would you recommend and why?