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Prometheus vs Wavefront: What are the differences?
Introduction
Prometheus and Wavefront are both popular monitoring tools used for collecting, storing, and visualizing time series data. While they have similar functionalities, there are several key differences that set them apart.
Data Model: Prometheus uses a pull-based model, where it periodically scrapes metrics from target systems, while Wavefront uses a push-based model, where applications and systems actively send metrics to Wavefront. This difference in data collection method can impact the scalability and flexibility of monitoring in different environments.
Data Storage: Prometheus stores its collected metrics in a local, on-disk time series database. On the other hand, Wavefront leverages cloud-based storage for its metrics, allowing for unlimited scalability and easy integration with other cloud services. This difference in data storage approach affects the long-term retention and scalability of metrics data.
Query Language: Prometheus offers PromQL, a powerful and flexible query language tailored for time series analysis and monitoring. Wavefront, on the other hand, supports a rich query language called Wavefront Query Language (WQL), which includes advanced features like analytics functions and outlier detection. These differences in query languages enable users to perform different types of complex analyses and data manipulations.
Alerting and Notification: Prometheus has a built-in alerting system that supports alert rule definitions and alert manager integration for configurable notifications. Wavefront also provides alerting capabilities but offers more advanced features like anomaly detection, anomaly alerting, and smart alert routing based on dynamic baselines. The differences in alerting and notification capabilities enable more sophisticated monitoring and alert management strategies with Wavefront.
Integration Ecosystem: Prometheus has a rich ecosystem of exporters and integrations, making it easy to collect metrics from different types of systems and applications. Wavefront also offers integrations with various systems and supports multiple ingestion methods, including agents and APIs. However, Wavefront's integration ecosystem is more oriented towards cloud-native environments and supports seamless integration with popular cloud platforms like Kubernetes and AWS. These differences in integration ecosystem cater to different monitoring requirements and environments.
Visualization and Dashboards: Prometheus provides a basic web interface for visualizing collected metrics and building custom dashboards using PromQL queries. Wavefront, on the other hand, offers a highly intuitive and interactive visualization platform with pre-built dashboards, charts, and rich visual analysis capabilities. Wavefront's focus on data visualization and exploration enables users to gain deeper insights from their metrics data with ease.
In summary, Prometheus and Wavefront differ in their data collection models, data storage approaches, query languages, alerting and notification capabilities, integration ecosystem, and visualization options. Choosing the right tool depends on the specific monitoring requirements, scalability needs, and desired feature set for effective monitoring and observability.
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.
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 Wavefront
- Custom Visualization1
- Advanced Math1
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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