Grafana vs Nagios vs Prometheus

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Grafana
Grafana

3.1K
2K
+ 1
307
Nagios
Nagios

580
404
+ 1
94
Prometheus
Prometheus

1K
725
+ 1
183

What is 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.

What is Nagios?

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

What is 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.
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      What companies use Grafana?
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      What are some alternatives to Grafana, Nagios, and Prometheus?
      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.
      Graphite
      Graphite does two things: 1) Store numeric time-series data and 2) Render graphs of this data on demand
      Splunk
      Splunk Inc. provides the leading platform for Operational Intelligence. Customers use Splunk to search, monitor, analyze and visualize machine data.
      NetData
      Netdata is distributed, real-time, performance and health monitoring for systems and applications. It is a highly optimized monitoring agent you install on all your systems and containers.
      New Relic
      New Relic is the all-in-one web application performance tool that lets you see performance from the end user experience, through servers, and down to the line of application code.
      See all alternatives
      Decisions about Grafana, Nagios, and Prometheus
      StackShare Editors
      StackShare Editors
      Kibana
      Kibana
      Grafana
      Grafana
      Elasticsearch
      Elasticsearch
      Logstash
      Logstash
      Graphite
      Graphite
      Icinga
      Icinga

      One size definitely doesn’t 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’Reilly 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’s use cases.

      At the time, Slack used a few well-known monitoring tools since it’s Technical Operations team wasn’t large enough to build an in-house solution for all of these. Nor did the team think it’s 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’s needs.

      On the backend, they experimented with multiple clusters in both Graphite and ELK, distributed Icinga nodes, and more. At the same time, they’ve tried to build usability into Grafana that reflects the team’s mental models of the system and have found ways to make alerts from Icinga more insightful and actionable.

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      Joseph Irving
      Joseph Irving
      DevOps Engineer at uSwitch · | 4 upvotes · 32.1K views
      atuSwitchuSwitch
      Thanos
      Thanos
      Prometheus
      Prometheus
      Kubernetes
      Kubernetes

      We recently implemented Thanos alongside Prometheus into our Kubernetes clusters, we had previously used a variety of different metrics systems and we wanted to make life simpler for everyone by just picking one.

      Prometheus seemed like an obvious choice due to its powerful querying language, native Kubernetes support and great community. However we found it somewhat lacking when it came to being highly available, something that would be very important if we wanted this to be the single source of all our metrics.

      Thanos came along and solved a lot of these problems. It allowed us to run multiple Prometheis without duplicating metrics, query multiple Prometheus clusters at once, and easily back up data and then query it. Now we have a single place to go if you want to view metrics across all our clusters, with many layers of redundancy to make sure this monitoring solution is as reliable and resilient as we could reasonably make it.

      If you're interested in a bit more detail feel free to check out the blog I wrote on the subject that's linked.

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      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 10 upvotes · 675.2K 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’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)

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      StackShare Editors
      StackShare Editors
      Grafana
      Grafana
      StatsD
      StatsD
      Airflow
      Airflow
      PagerDuty
      PagerDuty
      Datadog
      Datadog
      Celery
      Celery
      AWS EC2
      AWS EC2
      Flask
      Flask

      Data science and engineering teams at Lyft maintain several big data pipelines that serve as the foundation for various types of analysis throughout the business.

      Apache Airflow sits at the center of this big data infrastructure, allowing users to “programmatically author, schedule, and monitor data pipelines.” Airflow is an open source tool, and “Lyft is the very first Airflow adopter in production since the project was open sourced around three years ago.”

      There are several key components of the architecture. A web UI allows users to view the status of their queries, along with an audit trail of any modifications the query. A metadata database stores things like job status and task instance status. A multi-process scheduler handles job requests, and triggers the executor to execute those tasks.

      Airflow supports several executors, though Lyft uses CeleryExecutor to scale task execution in production. Airflow is deployed to three Amazon Auto Scaling Groups, with each associated with a celery queue.

      Audit logs supplied to the web UI are powered by the existing Airflow audit logs as well as Flask signal.

      Datadog, Statsd, Grafana, and PagerDuty are all used to monitor the Airflow system.

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      Kibana
      Kibana
      Splunk
      Splunk
      Grafana
      Grafana

      I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

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      Kibana
      Kibana
      Grafana
      Grafana
      Elasticsearch
      Elasticsearch

      I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics

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      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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      Sentry
      Sentry
      StatsD