Amazon ECS and Cortex Integration

Input and output integration overview

The Amazon ECS Input Plugin enables Telegraf to gather metrics from AWS ECS containers, providing detailed insights into container performance and resource usage.

This plugin enables Telegraf to send metrics to Cortex using the Prometheus remote write protocol, allowing seamless ingestion into Cortex’s scalable, multi-tenant time series storage.

Integration details

Amazon ECS

The Amazon ECS plugin for Telegraf is designed to collect metrics from ECS (Elastic Container Service) tasks running on AWS Fargate or EC2 instances. By utilizing the ECS metadata and stats API endpoints (v2 and v3), it fetches real-time information about container performance and health within a task. This plugin operates within the same task as the inspected workload, ensuring seamless access to metadata and statistics. Notably, it incorporates ECS-specific features that distinguish it from the Docker input plugin, such as handling unique ECS metadata formats and statistics. Users can include or exclude specific containers and adjust which container states to monitor, along with defining tag options for ECS labels. This flexibility allows for a tailored monitoring experience that aligns with the specific needs of an ECS environment, thereby enhancing observability and control over containerized applications.

Cortex

With Telegraf’s HTTP output plugin and the prometheusremotewrite data format you can send metrics directly to Cortex, a horizontally scalable, long-term storage backend for Prometheus. Cortex supports multi-tenancy and accepts remote write requests using the Prometheus protobuf format. By using Telegraf as the collection agent and Remote Write as the transport mechanism, organizations can extend observability into sources not natively supported by Prometheus—such as Windows hosts, SNMP-enabled devices, or custom application metrics—while leveraging Cortex’s high-availability and long-retention capabilities.

Configuration

Amazon ECS

[[inputs.ecs]]
  # endpoint_url = ""
  # container_name_include = []
  # container_name_exclude = []
  # container_status_include = []
  # container_status_exclude = []
  ecs_label_include = [ "com.amazonaws.ecs.*" ]
  ecs_label_exclude = []
  # timeout = "5s"

[[inputs.ecs]]
  endpoint_url = "http://169.254.170.2"
  # container_name_include = []
  # container_name_exclude = []
  # container_status_include = []
  # container_status_exclude = []
  ecs_label_include = [ "com.amazonaws.ecs.*" ]
  ecs_label_exclude = []
  # timeout = "5s"

Cortex

[[outputs.http]]
  ## Cortex Remote Write endpoint
  url = "http://cortex.example.com/api/v1/push"

  ## Use POST to send data
  method = "POST"

  ## Send metrics using Prometheus remote write format
  data_format = "prometheusremotewrite"

  ## Optional HTTP headers for authentication
  # [outputs.http.headers]
  #   X-Scope-OrgID = "your-tenant-id"
  #   Authorization = "Bearer YOUR_API_TOKEN"

  ## Optional TLS configuration
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

  ## Request timeout
  timeout = "10s"

Input and output integration examples

Amazon ECS

1. Dynamic Container Monitoring: Use the Amazon ECS plugin to monitor container health dynamically within an autoscaling ECS architecture. As new containers spin up or down, the plugin will automatically adjust the metrics it collects, ensuring that each container’s performance data is captured efficiently without manual configuration.

2. Custom Resource Allocation Alerts: Implement the ECS plugin to establish thresholds for resource usage per container. By integrating with notification systems, teams can receive alerts when a container’s CPU or memory usage exceeds predefined limits, enabling proactive resource management and maintaining application performance.

3. Cost-Optimization Dashboard: Leverage the metrics gathered from the ECS plugin to create a dashboard that visualizes resource usage and costs associated with each container. This insight allows organizations to identify underutilized resources, optimizing costs associated with their container infrastructure, thus driving financial efficiency in cloud operations.

4. Advanced Container Security Monitoring: Utilize this plugin in conjunction with security tools to monitor ECS container metrics for anomalies. By continuously analyzing usage patterns, any sudden spikes or irregular behaviors can be detected, prompting automated security responses and maintaining system integrity.

Cortex

1. Unified Multi-Tenant Monitoring: Use Telegraf to collect metrics from different teams or environments and push them to Cortex with separate X-Scope-OrgID headers. This enables isolated data ingestion and querying per tenant, ideal for managed services and platform teams.

2. Extending Prometheus Coverage to Edge Devices: Deploy Telegraf on edge or IoT devices to collect system metrics and send them to a centralized Cortex cluster. This approach ensures consistent observability even for environments without local Prometheus scrapers.

3. Global Service Observability with Federated Tenants: Aggregate metrics from global infrastructure by configuring Telegraf agents to push data into regional Cortex clusters, each tagged with tenant identifiers. Cortex handles deduplication and centralized access across regions.

4. Custom App Telemetry Pipeline: Collect app-specific telemetry via Telegraf’s exec or http input plugins and forward it to Cortex. This allows DevOps teams to monitor app-specific KPIs in a scalable, query-efficient format while keeping metrics logically grouped by tenant or service.

Feedback

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