Prometheus has become a cornerstone in the world of monitoring and observability, offering a powerful and flexible platform for collecting and querying metrics. However, as organizations scale, they often encounter limitations with Prometheus’s local storage, such as retention constraints and high availability challenges. This is where Cortex comes into play. Cortex is an open-source, horizontally scalable, and highly available multi-tenant long-term storage for Prometheus. In this article, we’ll explore how to write data from Prometheus to Cortex, enabling you to leverage the strengths of both systems.
Understanding Cortex
Cortex is designed to provide a scalable and durable storage backend for Prometheus metrics. It allows you to store data for longer periods and query it efficiently, even as your data volume grows. Cortex achieves this by using a microservices architecture that can be deployed on Kubernetes, making it a perfect fit for cloud-native environments.
Key Features of Cortex
- Scalability: Cortex can scale horizontally, allowing you to handle large volumes of data.
- Multi-tenancy: Supports multiple tenants, making it suitable for environments where multiple teams or customers need isolated data.
- High Availability: Provides redundancy and failover capabilities to ensure data availability.
- Long-term Storage: Allows you to store metrics for extended periods, beyond the typical retention limits of Prometheus.
Setting Up Cortex
Before you can write data from Prometheus to Cortex, you need to set up a Cortex cluster. Cortex can be deployed using various methods, including Kubernetes, Docker, or directly on virtual machines. For simplicity, we’ll focus on deploying Cortex on Kubernetes using Helm, a popular package manager for Kubernetes.
Prerequisites
- A Kubernetes cluster
- Helm installed and configured
Deploying Cortex with Helm
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Add the Cortex Helm Repository:
helm repo add cortex-helm https://cortexproject.github.io/cortex-helm-chart helm repo update -
Install Cortex:
helm install cortex cortex-helm/cortex
This command will deploy Cortex with default configurations. You can customize the deployment by modifying the Helm values file.
Configuring Prometheus to Write to Cortex
Once Cortex is up and running, the next step is to configure Prometheus to send its data to Cortex. This is done by setting up a remote write configuration in Prometheus.
Prometheus Configuration
Edit your Prometheus configuration file (prometheus.yml) to include the following remote write configuration:
remote_write:
- url: "http://<cortex-ingester-service>:<port>/api/v1/push"
Replace <cortex-ingester-service> and <port> with the appropriate service name and port for your Cortex ingester. This configuration tells Prometheus to send its metrics to Cortex for long-term storage.
Querying Data from Cortex
With data being written to Cortex, you can now query it using the same PromQL queries you use with Prometheus. Cortex provides a compatible API, allowing you to use tools like Grafana to visualize your metrics.
Setting Up Grafana
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Add Cortex as a Data Source:
- In Grafana, navigate to Configuration > Data Sources.
- Click Add data source and select Prometheus.
- Enter the URL of your Cortex query frontend service.
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Create Dashboards:
- Use Grafana’s dashboard capabilities to create visualizations based on the metrics stored in Cortex.
Conclusion
By integrating Prometheus with Cortex, you can overcome the limitations of Prometheus’s local storage and achieve a scalable, highly available, and long-term storage solution for your metrics. This setup not only enhances your monitoring capabilities but also ensures that you can retain and analyze historical data effectively.
Cortex, with its open-source nature and robust features, is an excellent choice for organizations looking to scale their observability stack. As you continue to grow, Cortex will provide the flexibility and reliability needed to maintain a comprehensive view of your systems.