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Graphite vs Prometheus: What are the differences?
Introduction
This Markdown code provides a comparison between Graphite and Prometheus, highlighting their key differences. Graphite and Prometheus are both popular monitoring tools used in different ways to collect and visualize metric data. Below are six key differences between these monitoring tools.
Data Model: Graphite uses a hierarchical data model where metrics are organized into a structure resembling a file system, with metrics stored as dotted strings. On the other hand, Prometheus uses a multidimensional data model where metrics are identified by key-value pairs called labels, allowing flexible querying and filtering based on different dimensions.
Data Storage: Graphite stores metric data as time-series in round-robin databases (RRD), which aggregates the data over time intervals. Prometheus, in contrast, uses its custom time-series database with a flexible storage model that allows for more complex queries and retention policies.
Data Collection: Graphite relies on a pull-based approach, where applications or systems push metrics to the Graphite server periodically. Prometheus, however, uses a pull-based approach, where it scrapes metric endpoints exposed by monitored applications or systems at regular intervals, making it more resilient to network failures and scalable when monitoring large environments.
Query Language: Graphite uses its own query language called Graphite Query Language (GQL) for data retrieval and manipulation. GQL supports various wildcard expressions and mathematical operations. In contrast, Prometheus uses a more powerful query language called PromQL (Prometheus Query Language), which provides a rich set of functions, operators, and aggregations to easily explore and analyze metric data.
Alerting: Graphite lacks native alerting capabilities and relies on third-party tools for alerting. Prometheus, on the other hand, includes a built-in alerting system that allows defining alert rules based on custom query expressions, sending notifications to various channels (like email, Slack, PagerDuty) when specific conditions are met.
Ecosystem and Integrations: Graphite has a rich ecosystem of compatible tools and integrations, including Grafana for visualization, Carbon for data forwarding, and many others. Prometheus also has a growing ecosystem and integrations with popular tools like Grafana, Alertmanager, and exporters, which allow for seamless integration with existing monitoring and notification setups.
In summary, Graphite and Prometheus differ in their data models, storage mechanisms, collection methods, query languages, alerting capabilities, and ecosystem/integrations, making them suitable for different monitoring use cases and environments.
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 Graphite
- Render any graph16
- Great functions to apply on timeseries9
- Well supported integrations8
- Includes event tracking6
- Rolling aggregation makes storage managable3
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 Graphite
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