Grafana and Prometheus are two of the most important tools for monitoring and observability. When comparing these two tools, it’s easy to fall into the trap of treating them as direct competitors and evaluating which would be better for your needs. These two tools, while often mentioned together, are not direct competitors. They’re complimentary, serving related functions within the monitoring and observability ecosystem.
Prometheus is a monitoring system designed to collect, store, and query metrics from applications and infrastructure. Its main ability lies in alerting and handling large amounts of data, making it an invaluable tool for monitoring and alerting. Grafana on the other hand, excels in visualizing and analyzing data collected by Prometheus or other data sources. Its main ability lies in creating dynamic and interactive dashboards that can display data from multiple sources.
In this article, you will learn about Grafana and Prometheus. You'll also see a detailed comparison of these two tools based on various stages of a data pipeline.
Grafana facilitates creating interactive and dynamic dashboards that you can shared with your team. It supports many different sources of data and has lots of customization options available. This makes Grafana an important choice in several fields.
Typical use cases and industries that use Grafana:
Prometheus is an open-source monitoring and alerting toolkit. It was created to be robust in dynamic surroundings that need reliability and scalability. It gathers time-series data and then stores them. This makes it perfect for real-time monitoring and alerting, especially within cloud-native settings.
Typical use cases and industries that use Prometheus:
Grafana and Prometheus have different jobs in handling data, which involve key steps like collecting data, bringing the data into the system (ingestion), and storing it.
Grafana and Prometheus have different jobs in handling data, which involve key steps like collecting data, bringing the data into the system (ingestion), and storing it. These tools can also show data visually, asking questions about the data (querying) and sending alerts based on certain conditions. Finally, they can also connect with other tools.
When we talk about collecting data, it's all about getting metrics from different places. Data ingestion makes sure this collected information is brought into the system to be processed further. Data storage keeps old records for looking at in the future, which can be shown using charts and dashboards during data visualization.
Asking questions about the data helps users find particular information they need. Alerting informs important people when significant events happen or certain limits are reached. Ecosystem integrations make these steps better by ensuring these tools work well with many other tools and services, making a smooth and complete monitoring system. Together, Grafana and Prometheus fit very well as they mix strong data gathering and keeping abilities with advanced visualization and alerting features.
Grafana is not made to gather data. It is very good for showing and studying data from different places where it is collected. It depends on information from databases, time-series databases, and other data sources to make dynamic and interactive dashboards. This means Grafana requires external tools for collecting data because it does not have the features to gather metrics directly.
Grafana works with many different data sources, making it possible to collect information from various systems. It supports well-known databases like MySQL, PostgreSQL, and Microsoft SQL Server. Grafana also connects with time-series databases such as InfluxDB and Prometheus. Additionally, you can use cloud services like AWS CloudWatch or even simple flat files as data sources for Grafana.
Prometheus is made for gathering data, and it uses a pull-based way to get metrics from services that have instruments installed. It especially collects data and keeps track of things. It uses a pull model to gather metrics from applications and services at regular times. Prometheus works by making HTTP requests to specific endpoints where metrics are available. These metrics need to be presented in a way that Prometheus can understand and use.
Prometheus can work with many exporters. These are tools that help show metrics from different sources. Some well-known exporters include Node Exporter, which is used for hardware and OS metrics, and Blackbox Exporter, which checks endpoints. There are also specific exporters made for particular applications like databases (MySQL Exporter) or web servers (Nginx Exporter), among others. This extensible exporter ecosystem allows Prometheus to gather metrics from virtually any system.
The main difference is that Grafana depends on other tools for gathering data, while Prometheus has its own ways of collecting metrics. Grafana works with many different sources of data to show visualizations, but Prometheus aims at collecting real-time metrics by scraping them itself.
Prometheus is chosen often when there is a need for collecting detailed and frequent metrics, like in monitoring microservices within a Kubernetes setup. It excels at tracking performance data closely and efficiently. On the other hand, Grafana shines when you want to visualize information from various sources all together, creating one unified display across multiple systems. This ability makes it very useful for seeing the bigger picture by combining diverse sets of data into clear visual presentations.
Grafana itself does not keep data; it instead connects with different outside storage options. This connection lets Grafana ask for and show data that is kept in databases, cloud storage services, and other backend systems.
Grafana works well with many different storage options, like InfluxDB, Elasticsearch, AWS CloudWatch, Google BigQuery, and more. This flexibility lets users pick the best storage solution for what they need while using Grafana’s strong visualization tools.
Prometheus has a built-in time-series database that is specially made for saving and getting metrics. This database can deal with many writes at once and efficiently keeps time-series data.
Prometheus lets users set up rules for how long they keep their metric data. It uses smart ways to store information, like compressing time-series data, so that storage is used wisely but still works well without losing speed or performance.
Grafana depends on outside storage solutions, giving a lot of choices for where to keep data. This is flexible but means you have to do more work setting it up and looking after it. On the other hand, Prometheus has its own built-in time-series database made especially for storing metrics. This makes the whole system easier because everything is in one place, though it might limit what kinds of storage you can use compared to Grafana.
Grafana is famous for its strong and adaptable visualization features. It can show many kinds of graphs, such as time-series graphs, bar charts, heatmaps, and others. Users can create complex, interactive dashboards tailored to their specific needs.
Grafana provides many choices for custom visualizations. Users can change how graphs look, add notes, and set thresholds. There are also advanced options like templates and variables to make dashboards more dynamic and interactive.
Prometheus has simple graphing features with its Prometheus Console. This lets users see query results right inside Prometheus, and they don’t need another tool to do this.
Grafana has very advanced visualization features with many ways to customize, making it perfect for building detailed and interactive dashboards. Prometheus gives basic graphing tools that are okay for simple charts but do not have the same level of detail or flexibility as Grafana.
People often use Prometheus together with Grafana to take advantage of Grafana's advanced visualization capabilities.
People often use Prometheus together with Grafana to take advantage of Grafana's advanced visualization capabilities. Grafana can connect to Prometheus as a data source, allowing for detailed and visually appealing displays of the metrics that Prometheus collects.
Grafana gives an easy-to-use interface for getting data from different connected sources. It can work with many query languages depending on the source of data, like SQL when using relational databases or PromQL if you are connecting to Prometheus.
Grafana is very flexible because it works with many different query languages. Users can make queries using SQL, PromQL, InfluxQL, and other languages based on the type of data source they have. This lets users get complex and customized data exactly how they need it.
Prometheus uses PromQL, which is a strong and flexible query language made especially for time-series data. With PromQL, users can do complex queries, aggregations, and changes to the collected metrics.
PromQL is very good for efficiency and flexible use, letting users do real-time analysis on big amounts of time-series data. Its syntax and functions are made especially for metric queries, so it works well for monitoring systems and creating alerts.
Grafana can work with many query languages, making it flexible for different data sources and giving one interface to ask about various kinds of information. On the other hand, Prometheus uses PromQL, which is made specifically for time-series data. It has strong abilities to question metrics but only works with Prometheus data.
Grafana with SQL: SELECT * FROM metrics WHERE value > 100
Prometheus with PromQL: rate(http_requests_total[5m])
Grafana has built-in alerting features that let users make alerts using data from any linked source. Users can set up these alerts right inside their Grafana dashboards and choose specific conditions for when the alerts should go off.
Users can make special alert rules and set limits, deciding when alerts should go off. Grafana can work together with many different alerting systems like PagerDuty, Slack, and email for sending out notifications.
Prometheus uses Alertmanager for alerting. Alertmanager takes care of alerts from Prometheus servers, dealing with deduplication, grouping similar alerts, and sending them to the right places as notifications.
Prometheus's Alertmanager can work with many outside alert systems, like email, PagerDuty, Slack, and more. This gives lots of options for how alerts are sent out and taken care of.
Grafana gives many ways to set up alerts, and it works with different data sources. This makes Grafana good for complicated alerting needs. Prometheus's Alertmanager has strong tools for managing alerts, which work well in big and changing setups.
Grafana's alerting works best for cases where alerts should come from visualized data that includes various sources. On the other hand, Prometheus's alerting fits better in situations needing quick metric-based alerts, particularly in cloud-native and microservices setups.
Grafana contains a big environment with several integrations and plugins, which increase its functionality. It merges smoothly with many data sources, alert systems, and tools outside of itself, making it more flexible and improving user experience.
Prometheus is involved in the Cloud Native Computing Foundation (CNCF) and has a robust ecosystem with many connections to cloud-native tools and services. It functions effectively alongside container orchestration systems such as Kubernetes while also providing support for various exporters suitable for diverse applications and infrastructure elements.
Grafana's ecosystem is more flexible in terms of the types of data sources and visualization plugins it can work with. The ecosystem of Prometheus concentrates on monitoring and alerting inside cloud-native surroundings, giving a strong connection to orchestration tools and exporters.
Grafana's power is in being very flexible and having a lot of ways to show data, making it good for different types of data sources and detailed dashboards. Prometheus is good at collecting metrics and sending alerts in cloud-based settings, giving strong monitoring for systems that are always changing and spread out over many places.
Yes, Grafana and Prometheus often work together. Prometheus collects and stores data, while Grafana offers advanced features for visualizing this data on dashboards. This combination uses the best features of both tools, building a full monitoring system that gives strong data collection, storage, and visualization.
After reading this article, you now know about the roles of Grafana and Prometheus in today's IT and DevOps settings. You have seen their jobs and characteristics like gathering data, taking it in, keeping it stored, showing it visually, making inquiries or alerts, and how they fit into an ecosystem of different tools. Knowing the strong points and boundaries of each tool aids you in selecting one for specific monitoring or observability requirements or how best to combine them for a strong full-coverage monitoring approach.
This post was written by Gourav Bais. Gourav is an applied machine learning engineer skilled in computer vision/deep learning pipeline development, creating machine learning models, retraining systems, and transforming data science prototypes into production-grade solutions.
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