Cortex is an open-source, horizontally scalable, and multi-tenant solution for storing and querying Prometheus metrics at long-term. It addresses Prometheus' inherent limitations in handling large-scale and long-term storage requirements, making it suitable for enterprise-level monitoring and observability.

• **Horizontal Scalability:** Cortex is designed to scale horizontally across multiple machines, enabling it to handle massive amounts of metrics data and query loads.
• **High Availability:** It employs replication and other fault-tolerance mechanisms to ensure high availability and data durability, even in the face of failures.
• **Multi-Tenancy:** Cortex supports multiple independent Prometheus instances (tenants) within a single cluster, providing isolation and resource management capabilities.
• **Long-Term Storage:** It integrates with various storage backends, such as Amazon S3, Google Cloud Storage, and Azure Blob Storage, allowing you to retain metrics data for extended periods.
• **PromQL Support:** Cortex provides a compatible PromQL (Prometheus Query Language) interface, enabling you to use the same familiar query language to analyze both real-time and historical metrics data.
• **Ruler:** Cortex includes a ruler component that allows you to define and evaluate recording and alerting rules across multiple Prometheus instances.

• **Scalability:** Cortex addresses Prometheus's limitations in handling large-scale and long-term storage, enabling you to scale your monitoring infrastructure horizontally.
• **High Availability:** Its multi-tenancy and fault-tolerance features ensure that your monitoring system remains available and responsive even under heavy load or in the event of failures.
• **Long-Term Metrics Retention:** Cortex enables you to store and query historical metrics data for months or even years, facilitating long-term analysis and trend identification.
• **Unified View:** It provides a global view of metrics across multiple Prometheus instances, allowing you to query and analyze data from different sources in a centralized manner.
• **Cost Efficiency:** Cortex's support for cloud-based object storage can potentially reduce the cost of long-term metrics storage compared to traditional on-premises solutions.

While Cortex configuration primarily involves YAML files and command-line options, here's a simplified example of a Cortex configuration for a single-binary mode deployment:

```yaml server:

 http_listen_port: 9009

distributor:

 pool_size: 100

ingester:

 lifecycler:
   ring:
     kvstore:
       store: inmemory
     replication_factor: 1

querier:

 query_store:
   grpc_client_config:
     grpc_timeout: 10s

ruler:

 enable_api: true
 rule_store:
   type: inmemory

storage:

 backend: inmemory

This configuration sets up a basic Cortex instance using in-memory storage for all components, suitable for development and testing purposes.

• **Cortex Official Website:** s://cortexmetrics.io/(https://cortexmetrics.io/)
• **Cortex GitHub Repository:** s://github.com/cortexproject/cortex(https://github.com/cortexproject/cortex)
• **Cortex Documentation:** s://cortexmetrics.io/docs/(https://cortexmetrics.io/docs/)