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Prometheus vs Sensu vs StatsD: What are the differences?
Introduction: In the realm of monitoring and metrics collection, Prometheus, Sensu, and StatsD are popular tools used by organizations for various purposes. Each of these tools offers unique features and functionalities that cater to different requirements in monitoring and observability. Understanding the key differences between Prometheus, Sensu, and StatsD can help organizations make informed decisions when choosing the appropriate tool for their specific needs.
Metrics collection: Prometheus is primarily a metrics collection and alerting tool that excels in storing and querying time-series data. In contrast, Sensu is more of a monitoring framework that offers flexibility and the ability to collect metrics from a wide range of sources. StatsD, on the other hand, focuses on simple metric aggregation and forwarding, making it suitable for quick and easy metrics collection.
Alerting capabilities: Prometheus has a robust alerting system that allows users to define alerting rules based on metrics thresholds and conditions. Sensu also provides alerting features but is more flexible in terms of integrating with different notification mechanisms. StatsD, however, lacks built-in alerting capabilities and is primarily focused on metric aggregation and reporting.
Scalability: Prometheus is known for its scalability and can handle a high volume of metrics data efficiently. Sensu is also scalable but requires additional configuration for large-scale deployments. StatsD is lightweight and designed for low-overhead metric collection, making it suitable for smaller-scale applications or use cases.
Data retention and storage: Prometheus stores metrics data locally using its built-in time-series database, which is optimized for fast query performance. Sensu relies on external storage solutions for data retention, offering more flexibility in terms of data storage options. StatsD does not store data long-term and is more focused on real-time metric aggregation and reporting.
Integration with other tools: Prometheus has extensive integrations with various monitoring tools, making it a popular choice for DevOps teams seeking a comprehensive monitoring solution. Sensu also offers numerous integrations with external tools, allowing for seamless workflow automation and customization. StatsD, being a simpler tool, may have limited integration options compared to Prometheus and Sensu.
Community support and ecosystem: Prometheus has a thriving community and ecosystem with a wide range of plugins, exporters, and libraries available for users to extend its functionality. Sensu also has a strong community backing and a growing ecosystem of plugins and extensions. StatsD, being a lightweight tool, may have a smaller community compared to Prometheus and Sensu.
In Summary, understanding the key differences between Prometheus, Sensu, and StatsD in terms of metrics collection, alerting capabilities, scalability, data retention, integration options, and community support can help organizations choose the right tool for their monitoring and observability 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 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
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 Sensu
- Plugins1
- Written in Go1
Cons of StatsD
- No authentication; cannot be used over Internet1
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