Azure Storage Queue and Thanos 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 sends metrics from Telegraf to Thanos using the Prometheus remote write protocol over HTTP, allowing efficient and scalable ingestion into Thanos Receive components.

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.

Thanos

Telegraf’s HTTP plugin can send metrics directly to Thanos via its Remote Write-compatible Receive component. By setting the data format to prometheusremotewrite, Telegraf can serialize metrics into the same protobuf-based format used by native Prometheus clients. This setup enables high-throughput, low-latency metric ingestion into Thanos, facilitating centralized observability at scale. It is particularly useful in hybrid environments where Telegraf is collecting metrics from systems outside Prometheus’ native reach, such as SNMP devices, Windows hosts, or custom apps, and streams them directly to Thanos for long-term storage and global querying.

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

Thanos

[[outputs.http]]
  ## Thanos Receive endpoint for remote write
  url = "http://thanos-receive.example.com/api/v1/receive"

  ## HTTP method
  method = "POST"

  ## Data format set to Prometheus remote write
  data_format = "prometheusremotewrite"

  ## Optional headers (authorization, etc.)
  # [outputs.http.headers]
  #   Authorization = "Bearer YOUR_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.

Thanos

1. Agentless Cloud Monitoring: Deploy Telegraf agents across cloud VMs to collect system and application metrics, then stream them directly into Thanos using Remote Write. This provides centralized observability without requiring Prometheus nodes at each location.

2. Scalable Windows Host Monitoring: Use Telegraf on Windows machines to collect OS-level metrics and send them via Remote Write to Thanos Receive. This enables observability across heterogeneous environments with native Prometheus support only on Linux.

3. Cross-Region Metrics Federation: Telegraf agents in multiple geographic regions can push data to region-local Thanos Receivers using this plugin. From there, Thanos can deduplicate and query metrics globally, reducing latency and network egress costs.

4. Integrating Third-Party Data into Thanos: Collect metrics from custom telemetry sources such as REST APIs or proprietary logs using Telegraf inputs and forward them to Thanos via Remote Write. This brings non-native data into a Prometheus-compatible, long-term analytics pipeline.

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