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Grafana vs Graphite vs Prometheus: What are the differences?
Introduction
When it comes to monitoring and visualization tools, Grafana, Graphite, and Prometheus are commonly used solutions in the DevOps world. Each tool has its own strengths and weaknesses that make them suitable for different use cases.
Data Storage: Grafana acts as a front end for various data sources, including Graphite and Prometheus. Graphite is primarily a time-series database that stores numeric time-series data, while Prometheus is a metrics-based monitoring system with its own time-series database. Each tool has its own data storage mechanism and querying capabilities, which can influence the choice based on specific requirements.
Data Model: Graphite uses a simple tree-like data model where data is stored under predefined metrics. Prometheus, on the other hand, uses a key-value pair data model with dimensional labels for metrics. This difference in data models can impact how data is organized, queried, and visualized within each tool.
Data Collection: Graphite relies on the Carbon daemon for data ingestion, while Prometheus uses its own built-in data collection service called Prometheus server. Grafana integrates with both tools to provide a unified visualization layer. The way data is collected and processed in each tool can affect real-time monitoring and alerting capabilities.
Query Language: Graphite uses its own query language called Graphite Query Language (GQL) for data retrieval and manipulation. Prometheus utilizes its own query language called PromQL for querying time-series data. The differences in query languages can influence the learning curve for users and the complexity of formulating queries.
Alerting and Notifications: Both Grafana and Prometheus provide alerting and notification features, allowing users to set up alerts based on specific thresholds and conditions. Grafana supports multi-channel notifications through various integrations, while Prometheus has native support for alert manager. The way alerts are configured and managed can vary between the tools.
Community and Ecosystem: Grafana has a vibrant community and a rich ecosystem of plugins and dashboards, making it a popular choice for visualizing data from different sources. Graphite and Prometheus also have strong communities, but Grafana's flexibility and extensibility through plugins give it an edge when it comes to customization and integration with other tools.
In Summary, Grafana, Graphite, and Prometheus offer unique features in terms of data storage, data model, data collection, query language, alerting, and community support, making them suitable for different monitoring and visualization requirements in the DevOps landscape.
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/
From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."
For our Predictive Analytics platform, we have used both Grafana and Kibana
- Grafana based demo video: https://www.youtube.com/watch?v=tdTB2AcU4Sg
- Kibana based reporting screenshot: https://imgur.com/vuVvZKN
Kibana has predictions
and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).
For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:
- Creating and organizing visualization panels
- Templating the panels on dashboards for repetetive tasks
- Realtime monitoring, filtering of charts based on conditions and variables
- Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics
After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .
I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.
Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).
@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.
Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.
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.
I learned a lot from Grafana, especially the issue of data monitoring, as it is easy to use, I learned how to create quick and simple dashboards. InfluxDB, I didn't know any other types of DBMS, I only knew about relational DBMS or not, but the difference was the scalability of both, but with influxDB, I knew how a time series DBMS works and finally, Telegraf, which is from the same company as InfluxDB, as I used the Windows Operating System, Telegraf tools was the first in the industry, in addition, it has complete documentation, facilitating its use, I learned a lot about connections, without having to make scripts to collect the data.
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 Grafana
- Beautiful89
- Graphs are interactive68
- Free57
- Easy56
- Nicer than the Graphite web interface34
- Many integrations26
- Can build dashboards18
- Easy to specify time window10
- Can collaborate on dashboards10
- Dashboards contain number tiles9
- Open Source5
- Integration with InfluxDB5
- Click and drag to zoom in5
- Authentification and users management4
- Threshold limits in graphs4
- Alerts3
- It is open to cloud watch and many database3
- Simple and native support to Prometheus3
- Great community support2
- You can use this for development to check memcache2
- You can visualize real time data to put alerts2
- Grapsh as code0
- Plugin visualizationa0
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 Grafana
- No interactive query builder1
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