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Grafana
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Munin

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Grafana vs Munin: What are the differences?

Developers describe Grafana as "Open source Graphite & InfluxDB Dashboard and Graph Editor". 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. On the other hand, Munin is detailed as "PnP networked resource monitoring tool that can help to answer the what just happened to kill our performance". Munin is a networked resource monitoring tool that can help analyze resource trends and "what just happened to kill our performance?" problems. It is designed to be very plug and play. A default installation provides a lot of graphs with almost no work.

Grafana and Munin can be categorized as "Monitoring" tools.

Grafana and Munin are both open source tools. It seems that Grafana with 29.3K GitHub stars and 5.55K forks on GitHub has more adoption than Munin with 1.31K GitHub stars and 381 GitHub forks.

According to the StackShare community, Grafana has a broader approval, being mentioned in 559 company stacks & 313 developers stacks; compared to Munin, which is listed in 15 company stacks and 3 developer stacks.

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 Munin?

Munin is a networked resource monitoring tool that can help analyze resource trends and "what just happened to kill our performance?" problems. It is designed to be very plug and play. A default installation provides a lot of graphs with almost no work.
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      What are some alternatives to Grafana and Munin?
      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.
      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.
      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.
      See all alternatives
      Decisions about Grafana and Munin
      StackShare Editors
      StackShare Editors
      Icinga
      Icinga
      Graphite
      Graphite
      Logstash
      Logstash
      Elasticsearch
      Elasticsearch
      Grafana
      Grafana
      Kibana
      Kibana

      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.

      See more
      Conor Myhrvold
      Conor Myhrvold
      Tech Brand Mgr, Office of CTO at Uber · | 9 upvotes · 550.9K 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’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
      StackShare Editors
      StackShare Editors
      Flask
      Flask
      AWS EC2
      AWS EC2
      Celery
      Celery
      Datadog
      Datadog
      PagerDuty
      PagerDuty
      Airflow
      Airflow
      StatsD
      StatsD
      Grafana
      Grafana

      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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      Grafana
      Grafana
      Splunk
      Splunk
      Kibana
      Kibana

      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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      Elasticsearch
      Elasticsearch
      Grafana
      Grafana
      Kibana
      Kibana

      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

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

      See more
      Interest over time
      Reviews of Grafana and Munin
      Review ofGrafanaGrafana

      analyze heap dump and many logging or traces

      How developers use Grafana and Munin
      Avatar of ShadowICT
      ShadowICT uses GrafanaGrafana

      We use Grafana to view live stats relating to our servers such as memory and CPU usage. We also use Grafana to monitor our gaming servers for data such as latency and player counts. This allows us to generate effective analytics and see when problems arise.

      Avatar of Andrew Gatenby
      Andrew Gatenby uses GrafanaGrafana

      Everyone likes graphs, right?! This isn't a tool we actively use right now, but paired with Prometheus we want to use it to have visual monitors on things like API cluster health, status, queue stats, DB/redis query and cache stats etc.

      Avatar of Scrayos UG (haftungsbeschränkt)
      Scrayos UG (haftungsbeschränkt) uses GrafanaGrafana

      Grafana is used in combination with Prometheus to display the gathered stats and to monitor our physical servers aswell as their virtual applications. We also use Grafana to get notifications about irregularities.

      Avatar of sapslaj
      sapslaj uses GrafanaGrafana

      Grafana takes the data from InfluxDB and presents it in a nice flexible format. Bonus points for built-in alerts and playlists (cycles through different dashboards automatically)

      Avatar of Bùi Thanh
      Bùi Thanh uses GrafanaGrafana
      • Graph report with many panels and Dashboard.
      • Easy to deploy, and view performance of system.
      • Intergrating with many datasource: Prometheus, CloudWatch
      • Alerts
      How much does Grafana cost?
      How much does Munin cost?
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