Prometheus has become the go-to monitoring system in the cloud native ecosystem, prized for its powerful query language and efficient time series database. However, native Prometheus deployments face challenges when it comes to long-term storage and scaling beyond single clusters. Its local storage is limited by disk space and retention configurations, making it unsuitable for extended historical data or global observability needs.

This is where Thanos, a CNCF graduated project, enters the picture. Thanos extends Prometheus by adding a scalable, durable layer that supports long-term storage, global querying, and high availability. In this post, we’ll dive deep into the architecture, components, and best practices for scaling Prometheus with Thanos.

Understanding Thanos Architecture

At its core, Thanos acts as a transparent layer on top of Prometheus, enabling it to scale horizontally and store data cheaply in object storage backends like S3, GCS, or Azure Blob Storage.

Key components include:

  • Sidecar: Deployed alongside each Prometheus instance, the sidecar uploads TSDB blocks to object storage and serves Prometheus metrics via a gRPC API.
  • Store Gateway: Reads historical data blocks from object storage and serves them for queries.
  • Query: Provides a global query layer, federating data across multiple Prometheus instances and store gateways.
  • Compactor: Optimizes and compacts data blocks in object storage to reduce storage costs and improve query performance.
  • Ruler: Evaluates Prometheus recording and alerting rules at scale, integrated with the Thanos query layer.

This modular design allows teams to scale Prometheus horizontally across clusters and regions while maintaining a unified query interface.

Setting Up Thanos Sidecar for Prometheus

The first step in scaling is deploying the Thanos Sidecar alongside each Prometheus server. The sidecar is responsible for continuously uploading Prometheus’ TSDB blocks to your chosen object storage.

Key configuration tips:

  • Ensure the sidecar has access to both the Prometheus data directory and the object storage credentials.
  • Use flags like --tsdb.path pointed to Prometheus’ data directory and --objstore.config-file to specify your bucket configuration.
  • The sidecar also exposes a gRPC endpoint that the Thanos Query component will consume.

This setup guarantees durability of metrics data beyond local disk failures and enables long-term retention policies beyond what Prometheus alone supports.

Leveraging Object Storage for Cost Effective Long Term Storage

One of Thanos’ biggest advantages is offloading data to cloud native object storage. Supported backends include:

  • Amazon S3
  • Google Cloud Storage
  • Azure Blob Storage
  • MinIO and other S3-compatible stores

Benefits include:

  • Cheap, infinitely scalable storage for historical metrics.
  • Separation of compute (Prometheus) and storage layers.
  • Ability to retain data for months or years without impacting Prometheus performance.

When configuring object storage, pay attention to:

  • Bucket lifecycle policies for automatic data tiering
  • Network bandwidth and latency to minimize upload bottlenecks
  • Security policies such as encryption and access control

Global Querying Across Multiple Clusters

With multiple Prometheus instances deployed across environments or Kubernetes clusters, querying becomes complex. The Thanos Query component solves this by federating queries across all sidecars and store gateways.

Highlights:

  • Single unified PromQL endpoint for all metrics.
  • Caching and partial response support for efficient queries.
  • Label deduplication to handle HA Prometheus setups.

To scale querying:

  • Deploy multiple Thanos Query replicas behind a load balancer.
  • Use store API to connect to all sidecars and store gateways.
  • Optimize query timeouts and concurrency based on load.

This architecture enables global observability and troubleshooting across distributed systems from a single pane of glass.

Best Practices and Performance Considerations

Scaling Prometheus with Thanos is powerful but requires careful tuning:

  • Retention policies: Balance local Prometheus retention with sidecar upload frequency.
  • Compactor tuning: Schedule compactions during off-peak hours to optimize storage.
  • Resource allocation: Allocate sufficient CPU and memory for sidecars and query components to handle query loads.
  • Network optimization: Ensure high bandwidth and low latency connectivity between components and object storage.
  • Security: Use TLS and authentication for all inter-component communication.

Additionally, monitor Thanos components themselves to detect bottlenecks or failures early.

Conclusion

Integrating Thanos with Prometheus is a robust solution to overcome native limitations related to long-term storage and global querying. By leveraging object storage and a modular architecture, Thanos enables scalable, durable, and highly available monitoring for cloud native environments.

For intermediate to advanced users aiming to build production-grade observability platforms, Thanos provides the tools to scale Prometheus horizontally and query metrics globally with consistency and reliability. Implementing best practices around configuration, storage, and network will unlock the full potential of your monitoring stack.

Start scaling your Prometheus deployment with Thanos today and ensure your metrics data is always available, durable, and queryable at scale.