Docker and Cortex Integration

Input and output integration overview

<p>The Docker input plugin allows you to collect metrics from your Docker containers using the Docker Engine API, facilitating enhanced visibility and monitoring of containerized applications.</p>

<p>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.</p>

Integration details

Docker

<p>The Docker input plugin for Telegraf gathers valuable metrics from the Docker Engine API, providing insights into running containers. This plugin utilizes the Official Docker Client to interface with the Engine API, allowing users to monitor various container states, resource allocations, and performance metrics. With options for filtering containers by names and states, along with customizable tags and labels, this plugin supports flexibility in monitoring containerized applications in diverse environments, whether on local systems or within orchestration platforms like Kubernetes. Additionally, it addresses security considerations by requiring permissions for accessing Docker’s daemon and emphasizes proper configuration when deploying within containerized environments.</p>

Cortex

<p>With Telegraf’s HTTP output plugin and the <code>prometheusremotewrite</code> 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.</p>

Configuration

Docker

Cortex

Input and output integration examples

Docker

<ol> <li> <p><strong>Monitoring the Performance of Containerized Applications</strong>: Use the Docker input plugin in order to track the CPU, memory, disk I/O, and network activity of applications running in Docker containers. By collecting these metrics, DevOps teams can proactively manage resource allocation, troubleshoot performance bottlenecks, and ensure optimal application performance across different environments.</p> </li> <li> <p><strong>Integrating with Kubernetes</strong>: Leverage this plugin to gather metrics from Docker containers orchestrated by Kubernetes. By filtering out unnecessary Kubernetes labels and focusing on key metrics, teams can streamline their monitoring solutions and create dashboards that provide insights into the overall health of microservices running within the Kubernetes cluster.</p> </li> <li> <p><strong>Capacity Planning and Resource Optimization</strong>: Use the metrics collected by the Docker input plugin to perform capacity planning for Docker deployments. Analyzing usage patterns helps identify underutilized resources and over-provisioned containers, guiding decisions on scaling up or down based on actual usage trends.</p> </li> <li> <p><strong>Automated Alerting for Container Anomalies</strong>: Set up alerting rules based on the metrics collected through the Docker plugin to notify teams of unusual spikes in resource usage or service disruptions. This proactive monitoring approach helps maintain service reliability and optimize the performance of containerized applications.</p> </li> </ol>

Cortex

<ol> <li> <p><strong>Unified Multi-Tenant Monitoring</strong>: Use Telegraf to collect metrics from different teams or environments and push them to Cortex with separate <code>X-Scope-OrgID</code> headers. This enables isolated data ingestion and querying per tenant, ideal for managed services and platform teams.</p> </li> <li> <p><strong>Extending Prometheus Coverage to Edge Devices</strong>: 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.</p> </li> <li> <p><strong>Global Service Observability with Federated Tenants</strong>: 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.</p> </li> <li> <p><strong>Custom App Telemetry Pipeline</strong>: Collect app-specific telemetry via Telegraf’s <code>exec</code> or <code>http</code> 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.</p> </li> </ol>

Feedback

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