How to Scale Prometheus with Thanos for Long-Term Data

Prometheus is an open-source system developed by SoundCloud. It is widely recognized for its service monitoring and time series data storage capabilities. However, when it comes to long-term data retention and scalability, Thanos emerges as a powerful companion to Prometheus.

In this blog post, we will explore how to effectively scale Prometheus using Thanos while retaining data for extended periods.

Understanding Thanos

Thanos is a collection of components designed to enhance Prometheus by offering high availability and limitless storage capacity. Seamlessly integrating with existing Prometheus deployments, it leverages Prometheus 2.0's efficient storage format.

Let's briefly discuss the key features of Thanos:

1. Seamless Integration: Thanos seamlessly integrates with existing Prometheus deployments, taking advantage of Prometheus 2.0's efficient storage format.
2. Object Storage for Historical Data: Thanos stores historical metric data in object storage, ensuring scalability and durability for long-term data retention.
3. Rapid Query Response Times: Thanos maintains fast query response times, even when handling large volumes of data, enabling efficient data analysis and troubleshooting.
4. Global Query View: It provides a centralized, global query view that spans all connected Prometheus installations. This feature merges real-time data from high-availability Prometheus pairs, offering a comprehensive view of metrics across multiple instances.
5. Prometheus High Availability: Thanos ensures high availability for Prometheus by aggregating data from multiple instances and enabling centralized querying and monitoring.
6. Long-Term Metrics Retention: Retaining metrics for an extended period becomes effortless, allowing for historical analysis and trend identification.
7. Easy Backup: Thanos simplifies the backup process for metrics, ensuring data resilience and easy recovery in the event of failures.
8. Cross-Cluster Scalability: It enables seamless scaling across clusters, allowing you to handle increasing volumes of data without sacrificing performance.
9. Cost-Effective Data Access: Thanos provides affordable data access, allowing efficient storage and retrieval of metrics without incurring excessive costs.

These features make Thanos a powerful companion to Prometheus, enhancing its scalability, reliability, and long-term data management capabilities.

Scaling Prometheus Metrics with Thanos: Step-by-Step Guide

Thanos plays a crucial role in addressing the challenges faced when scaling Prometheus metrics, serving as a highly available setup with long-term storage capabilities.

It offers solutions for:

1) Storage:

a. Thanos Sidecar: The Sidecar component of Thanos solves memory-related issues by facilitating the seamless uploading of metrics as object storage on popular providers like S3, Swift, Azure, etc.

Sidecar Benefits:

The Sidecar component becomes invaluable in case of an outage, allowing retrieval of historical data from backups stored in the cloud. This ensures data integrity and prevents loss during unexpected events.

2) Basic Thanos Query:

Thanos Query is responsible for aggregating and deduplicating metrics in the basic Thanos setup. a. Thanos Query utilizes the Prometheus HTTP API to query data within a Thanos cluster using PromQL. b. It integrates with the StoreAPI to query underlying objects and retrieve results. c. The Thanos querier is fully stateless and horizontally scalable, designed to handle large volumes of queries.

3) Scaling Thanos Query:

To accommodate multiple Kubernetes clusters and Prometheus instances, multiple Thanos Query nodes are deployed to aggregate subsets of Sidecar and Prometheus instances. a. Thanos Query nodes can be aggregated, allowing a single node to handle multiple instances of Thanos Query nodes. b. Thanos Query automatically deduplicates metrics, ensuring accurate and consistent results across multiple clusters.

4) Querying Prometheus Metrics Across Clusters:

The head Thanos Query node efficiently handles the deduplication of metrics using high-performance algorithms. a. This setup simplifies the querying process by providing a single node to query all metrics. b. It ensures redundancy and high availability, enabling queries against any cluster and minimizing data loss during downtime or service failures.

By implementing Thanos in Prometheus scaling, developers can achieve horizontal scalability, seamless storage integration, efficient querying, and redundancy across multiple clusters, resulting in reliable and scalable metric monitoring

LOGIQ.AI: Scaling Prometheus with Thanos Made Easy

LOGIQ.AI offers a comprehensive solution for scaling Prometheus using Thanos. With the LOGIQ Stack, you can simplify configuration and management, achieve unified storage for observability data, leverage a scalable platform for ingesting data, and optimize data storage based on your specific needs.

Moreover, LOGIQ enhances efficiency, reduces complexity, and ensures reliability in scaling Prometheus metrics with Thanos.

Simplified Configuration and Management:

LOGIQ eliminates the complexities involved in configuring and managing Prometheus and Thanos manually. Instead, you can leverage the LOGIQ Stack, which provides a user-friendly interface and intuitive controls for easy setup and maintenance. This saves time and resources that would otherwise be spent on manual configuration and troubleshooting.

Unified Storage for Observability Data:

LOGIQ seamlessly integrates with Prometheus/Thanos remote write functionality, enabling you to store logs, metrics, and traces in a centralized object store. This approach simplifies data management and ensures that all your observability data is stored in a single, scalable platform. By consolidating data storage, LOGIQ enhances efficiency and reduces the complexity of managing multiple storage systems.

Scalable Platform for Ingesting Observability Data:

LOGIQ provides a scalable platform specifically designed for ingesting observability and machine data. With its robust architecture, the LOGIQ Stack can handle high volumes of data generated by Prometheus and other data sources without compromising performance or stability. This scalability ensures that your Prometheus deployment can accommodate increasing data loads as your infrastructure grows.

Optimized Data Storage:

LOGIQ offers granular control over the data you store, allowing you to optimize storage based on your specific requirements. With configurable retention policies and intelligent data lifecycle management, LOGIQ enables you to strike a balance between storage costs and the duration of data retention. This flexibility ensures efficient data storage management while meeting compliance requirements and retaining critical data for analysis and troubleshooting.

Conclusion

By combining Prometheus and Thanos, developers can achieve horizontal scalability, seamless storage integration, efficient querying, and redundancy across multiple clusters, enabling reliable and scalable metric monitoring. Thanos, with its high availability and centralized view capabilities, proves invaluable for effectively leveraging Prometheus in a large-scale production setting.

LOGIQ.AI further simplifies the process by providing a comprehensive platform for organizing, storing, and managing observability data, allowing you to harness the full potential of Prometheus and Thanos.