Cross-Workspace Queries in Azure Monitor

When your data spans multiple Log Analytics workspaces, cross-workspace queries provide unified visibility. This is essential for enterprise environments with distributed monitoring architectures.

Understanding Cross-Workspace Queries

Cross-workspace queries allow you to:

• Query data from multiple workspaces in a single query
• Create unified dashboards across environments
• Correlate events across clusters
• Build centralized alerting

Basic Syntax

Using workspace() Function

// Query another workspace by name
workspace("production-workspace").ContainerLog
| where TimeGenerated > ago(1h)
| limit 100

// Query by workspace ID
workspace("12345678-1234-1234-1234-123456789abc").ContainerLog
| where TimeGenerated > ago(1h)
| limit 100

// Query by resource ID
workspace("/subscriptions/{sub}/resourcegroups/{rg}/providers/microsoft.operationalinsights/workspaces/{name}").ContainerLog
| where TimeGenerated > ago(1h)

Union Across Workspaces

// Combine data from multiple workspaces
union
    workspace("dev-workspace").ContainerLog,
    workspace("staging-workspace").ContainerLog,
    workspace("prod-workspace").ContainerLog
| where TimeGenerated > ago(1h)
| where LogEntry contains "error"
| summarize count() by bin(TimeGenerated, 5m)
| render timechart

Practical Examples

Cross-Cluster Pod Status

let devPods = workspace("dev-logs").KubePodInventory
| extend Cluster = "Development";
let stagingPods = workspace("staging-logs").KubePodInventory
| extend Cluster = "Staging";
let prodPods = workspace("prod-logs").KubePodInventory
| extend Cluster = "Production";

union devPods, stagingPods, prodPods
| where TimeGenerated > ago(1h)
| summarize
    Running = countif(PodStatus == "Running"),
    Pending = countif(PodStatus == "Pending"),
    Failed = countif(PodStatus == "Failed")
    by Cluster
| order by Cluster asc

Unified Error Dashboard

let getErrors = (ws: string) {
    workspace(ws).ContainerLog
    | where TimeGenerated > ago(24h)
    | where LogEntry contains "error" or LogEntry contains "exception"
    | extend Workspace = ws
};

union
    getErrors("dev-logs"),
    getErrors("staging-logs"),
    getErrors("prod-logs")
| summarize ErrorCount = count() by bin(TimeGenerated, 1h), Workspace
| render timechart

Cross-Environment Latency Comparison

let getLatency = (ws: string, env: string) {
    workspace(ws).Perf
    | where ObjectName == "K8SContainer"
    | where CounterName == "cpuUsageNanoCores"
    | extend Environment = env
};

union
    getLatency("dev-logs", "Dev"),
    getLatency("staging-logs", "Staging"),
    getLatency("prod-logs", "Production")
| summarize AvgCPU = avg(CounterValue) by bin(TimeGenerated, 5m), Environment
| render timechart

Using app() for Application Insights

Query Application Insights alongside Log Analytics:

// Combine Log Analytics with App Insights
let containerErrors = workspace("prod-logs").ContainerLog
| where LogEntry contains "error"
| project TimeGenerated, Source = "Container", Message = LogEntry;

let appErrors = app("prod-app-insights").exceptions
| project TimeGenerated, Source = "AppInsights", Message = outerMessage;

union containerErrors, appErrors
| where TimeGenerated > ago(1h)
| order by TimeGenerated desc

Resource-Centric Queries

Query across all workspaces in a resource group:

// Query all workspaces in resource scope
resources
| where type == "microsoft.operationalinsights/workspaces"
| project workspaceId = tostring(id)
| mv-expand workspaceId to typeof(string)
| evaluate workspace_centric_query(
    ContainerLog
    | where TimeGenerated > ago(1h)
    | summarize count() by Computer
)

Performance Optimization

Limit Time Ranges

// Always specify time filter first
workspace("prod-logs").ContainerLog
| where TimeGenerated > ago(1h)  // Filter early
| where LogEntry contains "error"
| summarize count()

Use Materialized Views

Create summarized data for faster cross-workspace queries:

// Create a function that pre-aggregates data
.create function with (folder = "Views") CrossClusterSummary() {
    union
        workspace("dev-logs").KubePodInventory | extend Cluster = "Dev",
        workspace("prod-logs").KubePodInventory | extend Cluster = "Prod"
    | where TimeGenerated > ago(1h)
    | summarize PodCount = count() by Cluster, PodStatus, bin(TimeGenerated, 5m)
}

Parallel Execution

Structure queries to enable parallel execution:

// Each workspace query runs in parallel
let devData = workspace("dev-logs").ContainerLog | where TimeGenerated > ago(1h) | summarize DevCount = count();
let prodData = workspace("prod-logs").ContainerLog | where TimeGenerated > ago(1h) | summarize ProdCount = count();

devData | extend ProdCount = toscalar(prodData | project ProdCount)

Cross-Workspace Alerting

Creating Multi-Workspace Alerts

{
  "type": "Microsoft.Insights/scheduledQueryRules",
  "apiVersion": "2021-08-01",
  "name": "cross-cluster-alert",
  "location": "eastus",
  "properties": {
    "displayName": "Cross-Cluster Error Alert",
    "severity": 2,
    "enabled": true,
    "evaluationFrequency": "PT5M",
    "scopes": [
      "/subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.OperationalInsights/workspaces/central-logs"
    ],
    "targetResourceTypes": [
      "Microsoft.OperationalInsights/workspaces"
    ],
    "windowSize": "PT15M",
    "criteria": {
      "allOf": [
        {
          "query": "union workspace('dev-logs').ContainerLog, workspace('prod-logs').ContainerLog | where LogEntry contains 'critical' | summarize count()",
          "timeAggregation": "Count",
          "operator": "GreaterThan",
          "threshold": 10
        }
      ]
    }
  }
}

Cross-Workspace Dashboard

Create an Azure Workbook with cross-workspace data:

{
  "version": "Notebook/1.0",
  "items": [
    {
      "type": 3,
      "content": {
        "version": "KqlItem/1.0",
        "query": "union workspace('dev-logs').KubePodInventory, workspace('staging-logs').KubePodInventory, workspace('prod-logs').KubePodInventory | where TimeGenerated > ago(1h) | summarize count() by PodStatus | render piechart",
        "size": 1,
        "title": "Pod Status Across All Clusters"
      }
    }
  ]
}

Limitations and Considerations

1. Query limits - Maximum 100 workspaces per query
2. Performance - Cross-workspace queries are slower
3. Permissions - Need read access to all workspaces
4. Region - Best performance when workspaces are in same region
5. Costs - Each workspace processes the query independently

Best Practices

1. Filter early - Apply time and other filters before union
2. Project needed columns - Reduce data transfer
3. Use summarize - Aggregate before combining results
4. Cache common queries - Use functions for repeated patterns
5. Monitor query performance - Track query durations

Conclusion

Cross-workspace queries unlock unified visibility across your distributed Log Analytics architecture. By understanding the syntax and optimization techniques, you can build powerful enterprise-wide monitoring solutions.

Tomorrow, we’ll explore Azure Data Explorer integration for advanced analytics on your operational data.