Prometheus vs Thanos: What are the differences?

Prometheus and Thanos are two popular tools used for monitoring and observability in modern infrastructure and cloud-native applications. Let's explore the key differences between Prometheus and Thanos:

1. Data Storage: Prometheus is designed to store metrics locally on each individual node or server. It uses a time-series database for efficient storage and retrieval of metrics data. On the other hand, Thanos extends Prometheus by providing a robust distributed storage layer. It allows metrics to be stored in a highly available and scalable manner across multiple clustered instances, enabling long-term retention and global query views.

2. Data Retention: Prometheus retains metrics data on a short-term basis, typically up to a few weeks. While it offers local data storage and efficient querying, it lacks the capability for long-term retention and global querying of historical metrics data. Thanos, on the other hand, provides long-term data retention by leveraging its distributed storage layer, allowing organizations to retain metrics data for months or even years. This enables deep insights and analysis of historical trends and patterns.

3. High Availability: Prometheus is designed to be deployed as a single instance, making it susceptible to single points of failure. In case of a failure, metrics data can be lost or become inaccessible. Thanos addresses this issue by providing a highly available architecture. It allows data replication and redundancy across multiple instances, ensuring continuous availability even if individual nodes fail. This makes Thanos well-suited for mission-critical monitoring and observability requirements.

4. Federation and Global Queries: Prometheus supports a federation feature that allows multiple Prometheus instances to be centrally queried. However, federated queries in Prometheus can be resource-intensive and result in increased latency. Thanos provides a more efficient solution by introducing global queries. Thanos combines data from multiple Prometheus instances transparently, providing a unified view for querying across all the data without the need for expensive federation operations.

5. Data Deduplication: In Prometheus, if multiple instances scrape the same target, duplicate metrics can occur. Although Prometheus de-duplicates metrics during querying, the duplicate data is still stored, leading to increased storage requirements. Thanos tackles this issue by performing data deduplication during the compaction process, reducing storage costs by eliminating redundant metrics and storing only unique data.

6. Horizontal Scalability: Prometheus is designed to run as a vertical scaling solution, where a single instance can handle a certain volume of metrics data. As the data grows, additional instances need to be deployed in a sharded setup. Thanos, on the other hand, provides horizontal scalability out of the box. It allows for seamless scaling by adding more instances to the cluster, thus distributing the load and handling increased metrics ingestion and query traffic.

In summary, Prometheus excels in local data storage and efficient querying, while Thanos extends Prometheus by providing distributed storage, long-term retention, high availability, global querying, data deduplication, and horizontal scalability. Thanos is a powerful tool for organizations requiring scalable, fault-tolerant, and long-term storage and analysis of massive metrics data.