Azure Storage Queue and Cortex Integration

Input and output integration overview

This plugin gathers sizes of Azure Storage Queues, providing users with metrics that enhance observability and management of their storage resources.

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

Azure Storage Queue

The Azure Storage Queue plugin allows users to gather various metrics concerning the size and message age of Azure Storage Queues. This plugin connects to Azure Storage, requiring specific credentials and offers configurable options to enhance performance. By collecting metrics, users gain valuable insights into the performance of their storage queues, enabling them to monitor usage patterns, peak loads, and optimize storage management effectively. The integration with Azure’s storage infrastructure provides a straightforward way to monitor queue metrics, ensuring that users can react to changes promptly, maintaining the efficiency and reliability of their 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

Azure Storage Queue

[[inputs.azure_storage_queue]]
  ## Required Azure Storage Account name
  account_name = "mystorageaccount"

  ## Required Azure Storage Account access key
  account_key = "storageaccountaccesskey"

  ## Set to false to disable peeking age of oldest message (executes faster)
  # peek_oldest_message_age = true

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

Azure Storage Queue

1. Monitoring Queue Performance in Real-time: Use the Azure Storage Queue plugin to continuously track the size and age of messages in queues, providing operators with real-time insights. This information can help teams understand throughput and delays, enabling them to adjust processing rates or troubleshoot bottlenecks.

2. Dynamic Alerting Based on Queue Metrics: Integrate metrics from the Azure Storage Queue plugin into an alerting system. By defining thresholds for message age and queue size, organizations can automate notifications, ensuring they promptly address situations where queues become too long or messages are delayed, maintaining a healthy and responsive system environment.

3. Optimizing Cost Management: Leverage the insights from the Azure Storage Queue metrics to identify periods of inactivity and implement cost-saving measures by adjusting storage scales. By analyzing queue size trends, organizations can make informed decisions about resource allocation, effectively balancing performance needs with cost efficiency.

4. Enhancing Application Fault Tolerance: Use the age metrics of the oldest message to design smarter retry strategies within applications. In scenarios where message processing fails, understanding how long messages sit in the queue allows developers to fine-tune their error handling logic, enhancing the resilience and reliability of their applications.

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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