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Nagios vs Prometheus vs Sensu: What are the differences?
<Write Introduction here>
Monitoring Protocol: Nagios primarily uses an active checking method where the Nagios server actively queries servers and devices at regular intervals to check their status. Prometheus, on the other hand, uses a pull method where the Prometheus server scrapes metrics from targeted endpoints at defined intervals. Sensu also employs a pull-based approach but allows for greater flexibility in terms of choosing the monitoring protocol.
Data Storage: Nagios stores monitoring data in flat text files, which can lead to performance issues when handling large amounts of data. Prometheus stores data in a time-series database, making it more efficient for querying and analyzing data. Sensu allows users to choose different storage backends, such as Redis or InfluxDB, providing more flexibility in data storage.
Scalability: Nagios can become difficult to scale horizontally due to its architecture, which may require additional effort to set up a distributed monitoring system. Prometheus is designed to be highly scalable and can effortlessly handle large-scale deployments and high-volume data collection. Sensu also offers good scalability, making it easier to manage and monitor various resources in a distributed environment.
Alerting and Notification: Nagios provides basic alerting and notification features through email and SMS notifications. Prometheus includes a robust alerting manager that can send alerts via various channels like email, Slack, and PagerDuty, making it more versatile in notifying users of issues. Sensu also offers advanced alerting capabilities, allowing users to define complex alerting rules and integrate with multiple notification channels.
Metrics Monitoring: Nagios is more focused on monitoring the status of various services and hosts, providing basic metrics. Prometheus specializes in metrics monitoring, offering extensive support for collecting, storing, and querying metrics from different endpoints. Sensu, like Prometheus, emphasizes metrics monitoring, allowing for detailed analysis and visualization of performance data across the infrastructure.
Community and Support: Nagios has a large and established user community, providing extensive documentation and resources for troubleshooting and customization. Prometheus has also gained significant traction in the monitoring community, with active development and a growing user base. Sensu, though newer compared to Nagios and Prometheus, has a strong community backing and offers responsive support for users in need. ```
In Summary, Nagios, Prometheus, and Sensu differ in terms of monitoring protocol, data storage, scalability, alerting capabilities, metrics monitoring, and community support.
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.
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.
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.
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.
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
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.
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.
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/
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.
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.
- free open source
- modern interface and architecture
- large community
- extendable I knew Nagios for decades but it was really outdated (by its architecture) at some point. That's why Icinga started first as a fork, not with Icinga2 it is completely built from scratch but backward-compatible with Nagios plugins. Now it has reached a state with which I am confident.
Pros of Nagios
- It just works53
- The standard28
- Customizable12
- The Most flexible monitoring system8
- Huge stack of free checks/plugins to choose from1
Pros of 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
Pros of Sensu
- Support for almost anything13
- Easy setup11
- Message routing9
- Devs can code their own checks7
- Ease of use5
- Price4
- Nagios plugin compatibility3
- Easy configuration, scales well and performance is good3
- Written in Go1
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Cons of Nagios
Cons of Prometheus
- 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
Cons of Sensu
- Plugins1
- Written in Go1
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