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Prometheus vs collectd: What are the differences?
Introduction
In this article, we will explore the key differences between Prometheus and collectd. Both Prometheus and collectd are popular monitoring tools used in the IT industry for collecting and visualizing metrics. However, they differ in several important aspects.
- Data storage:
Prometheus stores data in a time series database, allowing users to query and analyze metrics over time. On the other hand, collectd does not have built-in data storage capabilities and requires integration with other databases or monitoring systems for data storage.
- Collection method:
Prometheus uses a pull-based model for metric collection, where it actively queries targets at regular intervals to gather metric data. In contrast, collectd uses a push-based model, where it relies on agents installed on target systems to send metric data to a centralized server.
- Metrics format:
Prometheus uses its own exposition format called Prometheus exposition format (text-based format), which is simple and human-readable. In contrast, collectd uses a binary format for transmitting metrics, which is more efficient for data transport but not as easily readable by humans.
- Monitoring system compatibility:
Prometheus is designed to be a standalone monitoring system and includes its own visualization and alerting capabilities. Collectd, on the other hand, is often used as a data collection agent for other monitoring systems like Graphite or InfluxDB, which handle visualization and alerting.
- Scalability and architecture:
Prometheus uses a federated architecture, allowing multiple Prometheus servers to be connected and form a highly scalable monitoring solution. Collectd, on the other hand, does not have native support for federation and may require additional tools or configurations for achieving similar scalability.
- Community and ecosystem:
Prometheus has a large and active community, with a wide range of exporters and integrations available. It also has strong integration with Kubernetes for containerized environments. Collectd also has a community and ecosystem, but it may not be as extensive and mature as Prometheus.
In summary, Prometheus and collectd differ in terms of data storage, collection method, metrics format, monitoring system compatibility, scalability and architecture, and community/ecosystem support. Each tool has its own strengths and use cases, so it is important to consider these differences when choosing the right monitoring solution for your specific needs.
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.
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/
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.
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.
Pros of collectd
- Open Source2
- Modular, plugins2
- KISS1
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
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Cons of collectd
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
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