Need advice about which tool to choose?Ask the StackShare community!
Fluentd vs Prometheus: What are the differences?
Introduction: Fluentd and Prometheus are both popular open-source data collection and monitoring tools used in the field of DevOps. Although they serve similar purposes, there are some key differences between these two tools.
Data Collection Approach: Fluentd is a log collector and aggregator that operates on logs in real-time. It collects logs from various sources, transforms them, and sends them to various destinations for further processing. On the other hand, Prometheus is a time-series database and monitoring system that is primarily used for monitoring and alerting. It collects metrics data from configured targets periodically and stores it for analysis and visualization.
Data Types: Fluentd is more oriented towards collecting and processing unstructured log data. It accepts logs in various formats such as text, JSON, and others. Prometheus, on the other hand, focuses on collecting and analyzing numeric time-series data. It is designed to monitor and analyze metrics related to system performance, resource utilization, and application behavior.
Query Language: Fluentd uses its own query language called Fluent Query Language (FLQL). It allows users to filter and manipulate log data using a SQL-like syntax. Prometheus, on the other hand, uses Prometheus Query Language (PromQL) for querying and analyzing time-series data. PromQL provides a powerful set of operators and functions specifically tailored for time-series analysis.
Monitoring Architecture: Fluentd follows a centralized architecture where logs are collected from various sources and sent to a centralized server for processing and analysis. It provides a unified view of logs across the system. In contrast, Prometheus follows a decentralized architecture where it scrapes metrics data directly from configured targets at regular intervals. Each target maintains its own metrics data, and Prometheus queries these targets individually.
Alerting and Monitoring Capabilities: Fluentd focuses on log aggregation and routing, and does not have built-in support for alerting and monitoring. Prometheus, on the other hand, has powerful alerting and monitoring capabilities. It allows users to define alert rules based on metric conditions and send alerts to various notification channels. It also provides a flexible dashboard for visualizing and analyzing metrics data.
Integration with Ecosystem: Fluentd is highly extensible and can be integrated with various other tools, services, and platforms. It provides plugins for different log sources and destinations, allowing seamless integration with existing infrastructure. Prometheus also has a wide range of integrations with different systems and frameworks. It provides exporters for collecting metrics from various sources and supports integrations with popular monitoring and visualization tools.
**In Summary, Fluentd is a log collector and aggregator that operates on unstructured log data, while Prometheus is a monitoring system that specializes in time-series data analysis and alerting. Fluentd uses FLQL for log querying, follows a centralized architecture, and does not have built-in monitoring capabilities. Prometheus uses PromQL for time-series analysis, follows a decentralized architecture, and provides powerful monitoring and alerting features. Both tools have extensive integrations with different systems and can be used together to create a comprehensive monitoring solution.
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 Fluentd
- Open-source11
- Easy9
- Great for Kubernetes node container log forwarding9
- Lightweight9
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
Sign up to add or upvote prosMake informed product decisions
Cons of Fluentd
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