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Prometheus vs Sentry: What are the differences?
Prometheus is an open-source monitoring and alerting toolkit, while Sentry is an open-source error tracking solution. Let's explore the key differences between them.
Data Collection and Storage: Prometheus collects and stores time series data, including metrics, while Sentry focuses on capturing and managing application-level errors and exceptions. Prometheus uses a pull-based model to scrape data from various targets, while Sentry receives errors directly from applications via SDKs.
Alerting Capabilities: Prometheus has powerful built-in alerting functionalities, allowing users to define and customize alerts based on specified thresholds or conditions. On the other hand, Sentry provides real-time notifications for events and errors, but it does not feature the extensive alerting capabilities like Prometheus.
Metrics Monitoring vs Error Tracking: Prometheus is primarily designed for monitoring metrics, providing detailed insights into system performance and resource utilization. Sentry, on the other hand, is focused on error tracking and reporting, helping developers identify and fix bugs and issues in their applications.
Support for Multiple Data Sources: Prometheus supports a wide range of integrations and can scrape metrics from various sources, such as applications, databases, and APIs. Sentry integrates seamlessly with popular programming languages and frameworks, making it easy to capture and track errors across different platforms.
Visualization and Dashboards: Prometheus offers a built-in visualization tool called Grafana, which allows users to create real-time dashboards and graphs for monitoring metrics. Sentry also provides a simple interface to visualize errors and events, enabling developers to analyze and debug issues effectively.
Scalability and Performance: Prometheus is known for its scalability and ability to handle large-scale deployments, making it suitable for monitoring complex systems. While Sentry can handle a substantial volume of errors, it is primarily focused on error tracking and may not be as scalable as Prometheus in terms of monitoring a vast number of metrics.
In summary, Prometheus is a comprehensive monitoring and alerting tool, primarily used for collecting and analyzing metrics, while Sentry is a specialized error tracking solution that helps in identifying and resolving application-level errors.
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.
I essentially inherited a Shopify theme that was originally created by an agency. After discovering a number of errors being thrown in the Dev Console just by scrolling through the website, I needed more visibility over any errors happening in the field. Having used both Sentry and TrackJS, I always got lost in the TrackJS interface, so I felt more comfortable introducing Sentry. The Sentry free tier is also very generous, although it turns out the theme threw over 15k errors in less than a week.
I highly recommend setting up error tracking from day one. Theoretically, you should never need to upgrade from the free tier if you're keeping on top of the errors...
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 Sentry
- Consolidates similar errors and makes resolution easy237
- Email Notifications121
- Open source108
- Slack integration84
- Github integration71
- Easy49
- User-friendly interface44
- The most important tool we use in production28
- Hipchat integration18
- Heroku Integration17
- Good documentation15
- Free tier14
- Self-hosted11
- Easy setup9
- Realiable7
- Provides context, and great stack trace6
- Feedback form on error pages4
- Love it baby4
- Gitlab integration3
- Filter by custom tags3
- Super user friendly3
- Captures local variables at each frame in backtraces3
- Easy Integration3
- Performance measurements1
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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 Sentry
- Confusing UI12
- Bundle size4
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