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Cacti vs Prometheus: What are the differences?
Introduction
Cacti and Prometheus are both monitoring solutions widely used in the IT industry. Although they serve a similar purpose, there are several key differences between the two.
1. Data Storage: One of the primary differences between Cacti and Prometheus lies in their approach to data storage. Cacti relies on RRDtool, a round-robin database, to store and graph data over time. On the other hand, Prometheus uses a custom time-series database specifically designed for monitoring purposes, allowing for more efficient storage and retrieval of data.
2. Data Collection: Cacti primarily relies on SNMP (Simple Network Management Protocol) for data collection. It polls devices at regular intervals to gather metrics. In contrast, Prometheus follows a pull-based model where it scrapes metrics from the monitored targets using HTTP. This approach provides more flexibility in terms of target discovery and monitoring various types of applications.
3. Metric Filtering and Aggregation: Cacti offers limited options for metric filtering and aggregation. It mainly focuses on presenting the raw data collected. In contrast, Prometheus provides a powerful querying language called PromQL, allowing users to filter, aggregate, and manipulate metrics based on specific conditions. This capability enables complex analysis and alerting mechanisms.
4. Alerting: Cacti lacks built-in alerting capabilities. Users need to rely on external plugins or scripts to set up alerting rules. Prometheus, on the other hand, comes with a robust alerting system. It allows users to define alerting rules based on metric thresholds or specific conditions and sends notifications when these rules are violated.
5. Ecosystem and Integration: Cacti offers a wide range of plugins and extensions to extend its functionality. It integrates well with various third-party tools and services. Prometheus, on the other hand, has a vast ecosystem with numerous exporters, integrations, and visualization tools developed by the community. Its native integration with Kubernetes and extensive exporter library makes it a popular choice in containerized environments.
6. Scalability and High Availability: Cacti's architecture is generally suitable for small and medium-scale deployments. It can struggle to handle large amounts of data or high-frequency monitoring. In contrast, Prometheus is designed with scalability and high availability in mind. It supports federation and horizontal scaling, allowing users to handle massive amounts of data and ensure reliable monitoring even in distributed environments.
In summary, Cacti relies on RRDtool for data storage, uses SNMP for data collection, lacks advanced filtering, aggregation, and native alerting capabilities, offers a rich ecosystem of plugins but may have limitations in scalability. Prometheus, on the other hand, uses a custom time-series database, employs a pull-based data collection model, offers powerful metric filtering and aggregation features with PromQL, has built-in alerting capabilities, a robust ecosystem, and is designed for scalability and high availability.
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.
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 Cacti
- Free3
- Rrdtool based3
- Fast poller2
- Graphs from snmp1
- Graphs from language independent scripts1
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
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Cons of Cacti
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