Effective monitoring is crucial for understanding the health, performance, and usage patterns of your Azure Analysis Services (AAS) resources. This document outlines the key metrics, tools, and best practices for monitoring your AAS instances.
Key Monitoring Metrics
Azure Analysis Services provides a rich set of metrics that can be accessed through Azure Monitor. These metrics help you identify potential issues and optimize resource utilization.
Performance Metrics
- CPU Usage: Indicates the overall CPU load on the Analysis Services engine. High CPU usage may suggest a need for scaling up or optimizing queries.
- Memory Usage: Tracks the amount of memory consumed by the Analysis Services engine. Monitor for memory pressure.
- Query Latency: Measures the average time it takes for queries to complete. High latency can impact user experience.
- Cache Hit Ratio: Represents the percentage of data retrieved from the cache versus from the source. A higher hit ratio indicates more efficient data retrieval.
- Active Connections: Shows the number of concurrent connections to the Analysis Services instance.
- Concurrent Queries: Displays the number of queries being executed simultaneously.
Resource Metrics
- Data Read/Write Operations: Tracks the volume of data being read from and written to storage.
- Partition Operations: Monitors the success and duration of partition refresh operations.
Availability Metrics
- Uptime: The percentage of time the service is available.
Monitoring Tools and Services
Azure Monitor
Azure Monitor is the central hub for monitoring your Azure resources. It provides:
- Metrics Explorer: Visualize and analyze metrics in near real-time.
- Activity Log: Track control-plane operations on your AAS resource, such as scaling or configuration changes.
- Diagnostic Settings: Configure diagnostic logs to be sent to Log Analytics, Storage Accounts, or Event Hubs for deeper analysis.
- Alerts: Set up automated notifications when specific metric thresholds are breached.
Log Analytics
By routing AAS diagnostic logs to Log Analytics, you can perform powerful Kusto Query Language (KQL) queries to analyze:
- Query performance details
- Errors and exceptions
- Long-running queries
- Resource utilization trends
Tip: Configure diagnostic settings to send Engine and ServiceLog logs to Log Analytics for comprehensive troubleshooting.
Performance Tuning Advisor
While not a direct monitoring tool, the Performance Tuning Advisor in Azure portal can analyze your models and provide recommendations for performance improvements, which indirectly aids in monitoring by identifying potential bottlenecks.
Setting Up Alerts
Proactive alerting is key to maintaining service health. Consider setting up alerts for the following scenarios:
- High CPU or Memory Usage: Alert when resource utilization exceeds predefined thresholds (e.g., 80%).
- Increased Query Latency: Notify when average query latency surpasses an acceptable limit.
- Low Cache Hit Ratio: Alert if the cache hit ratio drops significantly, indicating potential query optimization needs.
- Failed Partition Refresh: Get immediate notification of any partition refresh failures.
- High Number of Active Connections/Concurrent Queries: Alert if the instance is nearing capacity.
Best Practices for Monitoring
- Establish Baselines: Understand the normal operating performance of your AAS instance during peak and off-peak hours.
- Regularly Review Metrics: Don't just rely on alerts. Periodically review key metrics to identify trends and potential issues before they become critical.
- Correlate Metrics with Events: When performance degradation occurs, correlate AAS metrics with other Azure service metrics (e.g., data source performance, network throughput) and known application events.
- Optimize Queries: Poorly written queries are a common cause of performance issues. Use tools like SQL Server Management Studio (SSMS) or Azure Data Studio to analyze and optimize query performance.
- Monitor Partition Refresh Times: Ensure that your data refresh processes are completing within acceptable windows.
- Stay Updated: Keep track of Azure Analysis Services service updates and new monitoring features.
Example KQL Query for Long-Running Queries
The following Kusto Query Language (KQL) query, when run in Log Analytics, can help identify queries that are taking longer than a specified duration:
AzureDiagnostics
| where ResourceProvider == "MICROSOFT.ANALYSISSERVICES" and Category == "Engine"
| where OperationName == "Execute"
| extend Duration = todouble(parse_xml(Properties).Duration)
| where Duration > 300000 // Filter for queries longer than 5 minutes (300,000 milliseconds)
| project TimeGenerated, OperationName, Duration, ClientActivityId, ResultType, Properties
| order by Duration desc
This query helps you pinpoint specific queries that might be impacting performance and require optimization.
Next Steps
After reviewing this monitoring guide, consider:
- Configuring diagnostic settings for your AAS resource.
- Creating alert rules for critical metrics.
- Exploring Log Analytics for deeper query analysis.
- Familiarizing yourself with Azure Monitor's Metrics Explorer.