Thanos and Prometheus- side by side comparison

Thanos and Prometheus are not monitoring software. They are open-source projects that provide scalable, long-term storage for metrics and monitoring data.

Thanos is a highly available Prometheus setup with long-term storage capabilities. It allows users to query data from multiple Prometheus instances, providing a single global view of the system. This can be useful for monitoring distributed systems with large amounts of data.

Prometheus, on the other hand, is a time-series database and monitoring system. It collects metrics from monitored targets by scraping metrics HTTP endpoints on these targets. Prometheus can then store the scraped data and allow users to query it using a flexible query language.

To use Thanos and Prometheus together, you would need to set up a Prometheus instance to scrape metrics from your monitored targets. You would then configure Thanos to query this Prometheus instance and store the scraped data in long-term storage. Finally, you can use Thanos to query the stored data and visualize it using a visualization tool like Grafana.

Here is a brief overview of the steps involved in setting up Thanos and Prometheus:

Install and configure Prometheus on a server. This involves setting up a configuration file that specifies which targets to scrape metrics from and how often to scrape them.

Install and configure Thanos on the same server or on a different server. This involves setting up a configuration file that specifies how to connect to the Prometheus instance and where to store the scraped data.

Set up long-term storage for Thanos. This can be done using a cloud storage provider like Google Cloud Storage or Amazon S3, or using a distributed file system like Minio.

Use Thanos to query the data stored by Prometheus and visualize it using a tool like Grafana. This allows you to see a global view of your system and analyze its performance over time.

Overall, Thanos and Prometheus can be powerful tools for monitoring and analyzing the performance of large and distributed systems. They provide scalable storage for metrics data and allow you to easily query and visualize this data to gain insights into your system.