Azure Service Bus Integration
Monitor queue and topic message counts, dead-letter queue depth, and throughput across your Azure Service Bus namespaces. Get predictive backlog alerts and AI root cause analysis before dead-letter spikes become incidents.
How It Works
Deploy ARM Template
Deploy the TigerOps ARM template to your Azure subscription. It provisions an Azure Monitor Diagnostic Setting on your Service Bus namespace and configures an Event Hub to forward metrics to the TigerOps ingest endpoint in real time.
Configure Azure Monitor Metric Streams
The ARM template creates an Azure Monitor data collection rule targeting your Service Bus namespaces. Metrics stream via Event Hub with sub-minute latency using Azure Monitor Metric Streams — no agents, no polling.
Set Dead-Letter Alert Thresholds
Define alert thresholds for DeadletteredMessageCount, ActiveMessageCount, and IncomingMessages per queue or topic subscription. TigerOps fires predictive alerts when dead-letter growth rate indicates a consumer processing failure.
Correlate with App Services & Functions
TigerOps automatically links Service Bus queue depth with Azure Function trigger execution counts, App Service CPU saturation, and outbound connection pool exhaustion — full context for every incident.
What You Get Out of the Box
Active Message Count Tracking
Per-queue and per-topic-subscription ActiveMessageCount with historical trend analysis. TigerOps alerts when message accumulation rate predicts a backlog breach and forecasts time-to-threshold.
Dead-Letter Queue Monitoring
Track DeadletteredMessageCount per queue and subscription with rate-of-change alerts. TigerOps detects dead-letter spikes that indicate consumer deserialization errors, poison messages, or max delivery count exhaustion.
Throughput & Message Rate Metrics
Monitor IncomingMessages, OutgoingMessages, IncomingBytes, and OutgoingBytes rates per namespace and entity. Identify throughput bottlenecks and correlate producer volume drops with consumer stalls.
Scheduled & Deferred Message Tracking
Track ScheduledMessageCount and per-entity message state distributions. Detect abnormal accumulation of deferred messages that can indicate stuck workflows or correlation filter misconfigurations.
Namespace Resource Utilization
Monitor CPU and memory utilization of Premium tier namespaces, connection counts, throttled requests, and server error rates. Alert when namespace resource saturation is about to impact message delivery.
AI Root Cause Analysis
When dead-letter counts spike, TigerOps AI correlates Azure Function execution failures, App Service exception traces, and Logic App run histories to surface the root cause without manual log trawling.
ARM Template for Azure Monitor Metric Streams
Deploy the TigerOps ARM template to stream Azure Service Bus metrics via Event Hub in minutes.
// Deploy via Azure CLI:
// az deployment group create \
// --resource-group <your-rg> \
// --template-file tigerops-servicebus-arm.json \
// --parameters tigerOpsApiKey=${TIGEROPS_API_KEY} \
// serviceBusNamespace=<your-namespace>
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"parameters": {
"tigerOpsApiKey": { "type": "securestring" },
"serviceBusNamespace": { "type": "string" }
},
"resources": [
{
"type": "Microsoft.EventHub/namespaces",
"name": "tigerops-metrics-eh",
"apiVersion": "2021-11-01",
"location": "[resourceGroup().location]",
"sku": { "name": "Standard", "tier": "Standard" }
},
{
"type": "Microsoft.Insights/diagnosticSettings",
"name": "tigerops-servicebus-diag",
"apiVersion": "2021-05-01-preview",
"scope": "[concat('Microsoft.ServiceBus/namespaces/', parameters('serviceBusNamespace'))]",
"properties": {
"eventHubAuthorizationRuleId": "[resourceId('Microsoft.EventHub/namespaces/authorizationRules', 'tigerops-metrics-eh', 'RootManageSharedAccessKey')]",
"metrics": [
{
"category": "AllMetrics",
"enabled": true,
"retentionPolicy": { "enabled": false, "days": 0 }
}
]
}
}
]
}
# TigerOps alert configuration (tigerops-alerts.yaml)
alerts:
- name: ServiceBus Dead-Letter Spike
metric: azure_servicebus_deadlettered_messages_count
condition: rate(5m) > 10
severity: critical
- name: ServiceBus Active Message Backlog
metric: azure_servicebus_active_messages_count
condition: value > 50000
severity: warning
- name: ServiceBus Throttled Requests
metric: azure_servicebus_throttled_requests_total
condition: rate(5m) > 5
severity: warningCommon Questions
What Azure permissions does TigerOps require for Service Bus monitoring?
TigerOps requires the Monitoring Reader built-in role on your Service Bus namespace or resource group. The ARM template also creates a service principal with the Azure Event Hubs Data Sender role on the Event Hub used for metric streaming. No write access to your Service Bus namespace is ever requested.
Can TigerOps monitor Service Bus across multiple Azure subscriptions?
Yes. TigerOps supports multi-subscription Azure monitoring. You can configure separate service principals per subscription or use an Azure Managed Identity with cross-subscription Monitoring Reader assignments. All namespaces are unified in a single TigerOps workspace with subscription and resource group labels.
How does TigerOps handle dead-letter queue alerting specifically?
TigerOps monitors DeadletteredMessageCount per entity and computes a dead-letter growth rate. You can configure alerts that fire on count thresholds, rate thresholds, or predictive growth. When a dead-letter alert fires, TigerOps surfaces the correlated consumer service error rate and exception traces in the same incident view.
Does TigerOps support Service Bus with geo-disaster recovery (Geo-DR) pairings?
Yes. TigerOps monitors both primary and secondary namespaces in a Geo-DR pairing independently. You can configure cross-namespace dashboards that show replication lag and entity synchronization state, and alert if the secondary namespace falls out of sync.
Can TigerOps alert when messages are about to expire from a queue?
TigerOps tracks message time-to-live (TTL) settings alongside ActiveMessageCount and can fire predictive expiry alerts when the oldest message age approaches the queue TTL. This prevents silent data loss in queues where consumers are slow or stuck without triggering the dead-letter path.
Stop Finding Dead-Letter Queues Full in the Morning
Predictive backlog alerts, dead-letter monitoring, and AI root cause analysis. Deploy with one ARM template in under 10 minutes.