Monitor Performance - Azure Analysis Services

Monitoring the performance of your Azure Analysis Services (AAS) models is crucial for ensuring a responsive and efficient user experience. This document outlines key metrics, tools, and best practices for monitoring AAS performance.

Key Performance Indicators (KPIs)

Focus on these critical metrics when evaluating the health and performance of your AAS instance:

• Query Latency: The time it takes for queries to return results. High latency can indicate inefficient queries, server resource bottlenecks, or network issues.
• Memory Usage: Monitor the amount of memory consumed by your AAS instance. Exceeding available memory can lead to performance degradation and instability.
• CPU Utilization: High CPU usage can signify intensive query processing, complex calculations, or insufficient server resources.
• Query Throughput: The number of queries processed per unit of time. This metric helps understand the overall load on the server.
• Cache Hit Ratio: The percentage of queries served from the cache. A low hit ratio suggests that data is frequently being re-read from the source, impacting performance.
• Connection Count: The number of active connections to your AAS instance. An unusually high number of connections might indicate connection leaks or excessive client activity.

Tools for Monitoring AAS Performance

Azure provides several tools to help you monitor and diagnose performance issues:

Azure Monitor

Azure Monitor is the foundational service for collecting, analyzing, and acting on telemetry from your Azure and on-premises environments. For Azure Analysis Services, it offers:

• Metrics: View predefined metrics like CPU usage, memory, query count, and latency directly in the Azure portal.
• Activity Log: Track administrative actions and operations on your AAS resource.
• Diagnostic Settings: Configure sending AAS logs and metrics to Log Analytics, Storage Accounts, or Event Hubs for advanced analysis.

Azure Log Analytics

When you send AAS diagnostic logs to Log Analytics, you can use Kusto Query Language (KQL) to perform deep analysis. This is invaluable for troubleshooting complex issues.

Here are some example KQL queries:

// Top 10 Slowest Queries by Duration
            AzureMetrics
            | where ResourceProvider == "MICROSOFT.ANALYSISSERVICES" and MetricName == "QueryDuration"
            | summarize avgDuration = avg(MetricValue) by OperationName
            | top 10 by avgDuration desc
            

// Queries with High Memory Usage
            AzureDiagnostics
            | where ResourceProvider == "MICROSOFT.ANALYSISSERVICES" and Category == "QueryLogs"
            | where MemoryUsedMs > 1000000000 // Example: filter for queries using more than 1GB of memory
            | project TimeGenerated, OperationName, ClientHost, DurationMs, MemoryUsedMs
            | order by MemoryUsedMs desc
            

SQL Server Management Studio (SSMS)

SSMS provides a rich interface for connecting to your AAS instance and performing performance-related tasks:

• Activity Monitor: View real-time queries, connections, and resource usage.
• DMVs (Dynamic Management Views): Query system views to get detailed information about server state, query execution, and resource consumption.

Example DMV for active queries:

SELECT * FROM sys.dm_exec_requests
            

SQL Server Profiler

While deprecated for SQL Server, SQL Server Profiler can still be used to trace AAS events. It allows you to capture and analyze individual query executions, including their performance characteristics.

Best Practices for Performance Monitoring

1. Set Up Alerts: Configure alerts in Azure Monitor for critical metrics (e.g., high CPU, low cache hit ratio, excessive query duration). This proactive approach helps you address issues before they impact users.
2. Regularly Review Logs: Periodically analyze your AAS diagnostic logs in Log Analytics to identify patterns, recurring issues, and potential optimization opportunities.
3. Monitor Query Performance: Pay close attention to query execution times. Identify and optimize long-running or resource-intensive queries. Use tools like SSMS and SQL Profiler to analyze query plans.
4. Understand Your Workload: Know what your typical user queries look like. This understanding helps in identifying anomalies and setting appropriate performance benchmarks.
5. Capacity Planning: Use historical performance data to forecast future resource needs and scale your AAS instance accordingly.
6. Test Changes: When making changes to your models or queries, monitor performance closely to ensure they have the desired positive impact and don't introduce regressions.

Common Performance Bottlenecks and Solutions

Inefficient Queries

• Problem: Queries that perform excessive calculations, scan large amounts of data, or use inefficient DAX/MDX expressions.
• Solution: Optimize DAX/MDX, review query plans, consider denormalization or pre-aggregation in the data source.

Model Design Issues

• Problem: Large fact tables, complex relationships, redundant calculations, or unoptimized measures.
• Solution: Refactor the model, simplify relationships, use calculated columns judiciously, and implement measures efficiently.

Resource Constraints

• Problem: The AAS instance is not sized appropriately for the workload, leading to high CPU, memory pressure, or slow query execution.
• Solution: Scale up (increase CPU/memory) or scale out (add more replicas for read-heavy workloads) your AAS instance.

Cache Invalidation

• Problem: Frequent full cache invalidations or a low cache hit ratio means queries are constantly hitting the data source.
• Solution: Optimize query patterns to maximize cache usage, ensure data refreshes are efficient.