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Munin vs Prometheus: What are the differences?

Introduction

Prometheus and Munin are two popular monitoring systems used in the IT industry. Both have their own set of features and capabilities. In this comparison, we will highlight the key differences between Munin and Prometheus.

  1. Data Storage: Prometheus uses a custom-built time series database for storing metrics data. It stores data as a series of timestamped values. On the other hand, Munin uses RRDtool (Round-Robin Database) for storing metrics data, which is optimized for performance and disk space efficiency.

  2. Data Collection: Prometheus follows a pull-based model where it actively scrapes data from the targets using HTTP protocols. It relies on the targets exposing a specific endpoint for data collection. In contrast, Munin follows a push-based model where the agents running on the targets send data to the Munin master node over TCP or SSH connections.

  3. Flexibility: Prometheus offers a more flexible and dynamic querying language called PromQL (Prometheus Query Language) that allows users to perform complex queries and aggregations on the collected data. Munin, on the other hand, has a limited set of predefined plugins and doesn't provide as much flexibility in terms of data analysis and querying.

  4. Alerting: Prometheus has a built-in alerting system that allows users to define and configure alerting rules based on the collected data. It provides various notification channels and supports advanced alerting features like silencing, inhibition, and alert grouping. Munin, on the contrary, does not have a native alerting system and would require additional setup and integration with external tools for alerting capabilities.

  5. Scalability: Prometheus is designed to be highly scalable and can handle large-scale deployments with thousands of targets and millions of time series data points. It supports horizontal scaling by setting up a federated setup with multiple Prometheus servers. Munin, although scalable to a certain extent, may face limitations in handling very large infrastructures due to its push-based architecture.

  6. Community and Ecosystem: Prometheus has a thriving and active open-source community, which has led to the development of a rich ecosystem of integrations, exporters, and tooling. It has extensive support for popular technologies and frameworks. Munin, although it has a community, has a comparatively smaller ecosystem and may have fewer integrations and plugins available.

In summary, Prometheus and Munin differ in their approach to data storage, data collection, flexibility in querying, alerting capabilities, scalability, and the size of their respective communities and ecosystems. Each has its own strengths and considerations that need to be taken into account while choosing the right monitoring solution for a specific use case.

Advice on Munin and Prometheus
Susmita Meher
Senior SRE at African Bank · | 4 upvotes · 774.8K views
Needs advice
on
GrafanaGrafanaGraphiteGraphite
and
PrometheusPrometheus

Looking for a tool which can be used for mainly dashboard purposes, but here are the main requirements:

  • Must be able to get custom data from AS400,
  • Able to display automation test results,
  • System monitoring / Nginx API,
  • Able to get data from 3rd parties DB.

Grafana is almost solving all the problems, except AS400 and no database to get automation test results.

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Replies (1)
Sakti Behera
Technical Specialist, Software Engineering at AT&T · | 3 upvotes · 560K views
Recommends
on
GrafanaGrafanaPrometheusPrometheus

You can look out for Prometheus Instrumentation (https://prometheus.io/docs/practices/instrumentation/) Client Library available in various languages https://prometheus.io/docs/instrumenting/clientlibs/ to create the custom metric you need for AS4000 and then Grafana can query the newly instrumented metric to show on the dashboard.

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Sunil Chaudhari
Needs advice
on
MetricbeatMetricbeat
and
PrometheusPrometheus

Hi, We have a situation, where we are using Prometheus to get system metrics from PCF (Pivotal Cloud Foundry) platform. We send that as time-series data to Cortex via a Prometheus server and built a dashboard using Grafana. There is another pipeline where we need to read metrics from a Linux server using Metricbeat, CPU, memory, and Disk. That will be sent to Elasticsearch and Grafana will pull and show the data in a dashboard.

Is it OK to use Metricbeat for Linux server or can we use Prometheus?

What is the difference in system metrics sent by Metricbeat and Prometheus node exporters?

Regards, Sunil.

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Replies (2)
Matthew Rothstein
Recommends
on
PrometheusPrometheus

If you're already using Prometheus for your system metrics, then it seems like standing up Elasticsearch just for Linux host monitoring is excessive. The node_exporter is probably sufficient if you'e looking for standard system metrics.

Another thing to consider is that Metricbeat / ELK use a push model for metrics delivery, whereas Prometheus pulls metrics from each node it is monitoring. Depending on how you manage your network security, opting for one solution over two may make things simpler.

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Recommends
on
InstanaInstana

Hi Sunil! Unfortunately, I don´t have much experience with Metricbeat so I can´t advise on the diffs with Prometheus...for Linux server, I encourage you to use Prometheus node exporter and for PCF, I would recommend using the instana tile (https://www.instana.com/supported-technologies/pivotal-cloud-foundry/). Let me know if you have further questions! Regards Jose

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Mat Jovanovic
Head of Cloud at Mats Cloud · | 3 upvotes · 704.3K views
Needs advice
on
DatadogDatadogGrafanaGrafana
and
PrometheusPrometheus

We're looking for a Monitoring and Logging tool. It has to support AWS (mostly 100% serverless, Lambdas, SNS, SQS, API GW, CloudFront, Autora, etc.), as well as Azure and GCP (for now mostly used as pure IaaS, with a lot of cognitive services, and mostly managed DB). Hopefully, something not as expensive as Datadog or New relic, as our SRE team could support the tool inhouse. At the moment, we primarily use CloudWatch for AWS and Pandora for most on-prem.

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Replies (2)
Lucas Rincon
Recommends
on
InstanaInstana

this is quite affordable and provides what you seem to be looking for. you can see a whole thing about the APM space here https://www.apmexperts.com/observability/ranking-the-observability-offerings/

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Recommends
on
DatadogDatadog

I worked with Datadog at least one year and my position is that commercial tools like Datadog are the best option to consolidate and analyze your metrics. Obviously, if you can't pay the tool, the best free options are the mix of Prometheus with their Alert Manager and Grafana to visualize (that are complementary not substitutable). But I think that no use a good tool it's finally more expensive that use a not really good implementation of free tools and you will pay also to maintain its.

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Decisions about Munin and Prometheus
Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 348.6K views

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

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Pros of Munin
Pros of Prometheus
  • 3
    Good defaults
  • 2
    Extremely fast to install
  • 2
    Alerts can trigger any command line program
  • 2
    Adheres to traditional Linux standards
  • 1
    Easy to write custom plugins
  • 47
    Powerful easy to use monitoring
  • 38
    Flexible query language
  • 32
    Dimensional data model
  • 27
    Alerts
  • 23
    Active and responsive community
  • 22
    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

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Cons of Munin
Cons of Prometheus
    Be the first to leave a con
    • 12
      Just for metrics
    • 6
      Bad UI
    • 6
      Needs monitoring to access metrics endpoints
    • 4
      Not easy to configure and use
    • 3
      Supports only active agents
    • 2
      Written in Go
    • 2
      TLS is quite difficult to understand
    • 2
      Requires multiple applications and tools
    • 1
      Single point of failure

    Sign up to add or upvote consMake informed product decisions

    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.

    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 Munin?
    What companies use Prometheus?
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    What tools integrate with Munin?
    What tools integrate with Prometheus?

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    What are some alternatives to Munin and Prometheus?
    Cacti
    Cacti is a complete network graphing solution designed to harness the power of RRDTool's data storage and graphing functionality. Cacti provides a fast poller, advanced graph templating, multiple data acquisition methods, and user management features out of the box.
    Zabbix
    Zabbix is a mature and effortless enterprise-class open source monitoring solution for network monitoring and application monitoring of millions of metrics.
    Nagios
    Nagios is a host/service/network monitoring program written in C and released under the GNU General Public License.
    Ganglia
    It is a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids. It is based on a hierarchical design targeted at federations of clusters.
    collectd
    collectd gathers statistics about the system it is running on and stores this information. Those statistics can then be used to find current performance bottlenecks (i.e. performance analysis) and predict future system load (i.e. capacity planning). Or if you just want pretty graphs of your private server and are fed up with some homegrown solution you're at the right place, too.
    See all alternatives