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Prometheus vs RRDtool vs StatsD: What are the differences?
Introduction
In the realm of monitoring tools, Prometheus, RRDtool, and StatsD play vital roles in collecting and visualizing metrics. Each tool offers unique features and functions that cater to specific monitoring needs.
Data Storage: Prometheus stores data as time series, allowing for flexible queries and easy retrieval of historical data. RRDtool, on the other hand, uses round-robin databases (RRDs) which have fixed data retention periods and reduce disk space usage. StatsD primarily focuses on aggregating metrics before sending them to a backend system for storage.
Data Collection: Prometheus employs a pull-based model where it scrapes metrics from target endpoints at regular intervals. RRDtool, in contrast, relies on a push-based system by updating RRD files with new data points. StatsD functions as a lightweight daemon that accepts custom metrics from applications and forwards them to backends.
Data Visualization: Prometheus comes with a built-in graphical interface that enables users to create custom dashboards and visualize metrics easily. RRDtool provides graphing capabilities, but users may need additional tools for advanced visualization. StatsD does not offer visualization features and mainly focuses on metric aggregation and forwarding.
Alerting and Monitoring: Prometheus has a powerful alerting system that supports complex queries and integrations with notification channels. RRDtool lacks built-in alerting capabilities, as its main focus is on data storage and graphing. StatsD does not include alerting features but can be integrated with other tools for monitoring purposes.
Ecosystem and Integrations: Prometheus has a rich ecosystem with various exporters, integrations, and community support, making it versatile for different use cases. RRDtool has a more niche focus on time-series data storage and visualization, with fewer integrations compared to Prometheus. StatsD is often used in conjunction with other monitoring tools like Graphite and Prometheus for a complete monitoring solution.
Scalability and Performance: Prometheus is known for its scalability and performance, handling large volumes of metrics efficiently. RRDtool may struggle with scalability due to its fixed database structure and limitations on data retention. StatsD is lightweight and designed for high performance, making it suitable for real-time metric processing but may require additional tools for scalability.
In Summary, Prometheus, RRDtool, and StatsD differ in data storage, collection methods, visualization capabilities, alerting features, ecosystem support, and scalability/performance, catering to a variety of monitoring 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.
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.
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 RRDtool
- Do one thing and do it well6
Pros of StatsD
- Open source9
- Single responsibility7
- Efficient wire format5
- Handles aggregation3
- Loads of integrations3
- Many implementations1
- Scales well1
- Simple to use1
- NodeJS1
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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 RRDtool
Cons of StatsD
- No authentication; cannot be used over Internet1
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