This document guides you through strategies and best practices for scaling the performance of your Azure Analysis Services servers to meet the demands of your data solutions.
Azure Analysis Services offers vertical scaling by adjusting the Service Level (tier) and size of your server. This impacts compute and memory resources available to your models. Horizontal scaling is achieved through features like read-scale replicas for query workloads.
The primary method for scaling is by choosing the appropriate tier and size for your Analysis Services server. Tiers offer different levels of performance and features:
Each size within a tier provides a specific amount of memory and processing power. You can scale up or down by changing the tier or size through the Azure portal or using Azure Resource Manager templates.
For query-intensive workloads, you can create read-scale replicas. These replicas offload read queries from the primary server, improving overall query throughput and reducing latency for users. Read-scale replicas share the same model data as the primary and are automatically synchronized.
Key benefits of read-scale replicas:
You can configure read-scale replicas in the Azure portal under the "Scale" blade of your Analysis Services resource. The number of replicas you can create depends on your chosen tier and size.
Beyond scaling server resources, optimizing your Analysis Services models is crucial for achieving peak performance.
Monitor your queries to identify performance bottlenecks. Tools like SQL Server Management Studio (SSMS) and Azure Data Studio can be used to analyze query execution plans and identify slow-running queries.
Consider caching strategies and optimal query patterns to leverage the Analysis Services engine effectively.
If you notice that query performance is degrading as more users access your models, consider implementing read-scale replicas. This is a cost-effective way to handle increased read traffic without necessarily scaling up the primary server's tier.
If your models are becoming very large, or if complex calculations (especially during refreshes) are taking too long, you might need to scale vertically by moving to a higher tier or a larger size within your current tier. Ensure you have sufficient memory for your models and processes.
For frequent data refreshes, ensure your data sources can keep up and that your tabular model processing is optimized. Scaling the Analysis Services server vertically can help speed up data processing if it's the bottleneck.
Regularly monitor your Azure Analysis Services server's performance using Azure Monitor. Key metrics to track include:
Start with a smaller tier and size, monitor performance, and scale up as needed. This approach helps optimize costs while ensuring your service meets performance requirements.
When scaling up or down, plan for a brief period of unavailability. While Azure Analysis Services aims for minimal disruption, it's good practice to perform scaling operations during off-peak hours.
Scaling Azure Analysis Services involves a combination of vertical scaling (tiers and sizes), horizontal scaling (read-scale replicas), and diligent performance tuning of your tabular models and queries. By understanding these options and monitoring your server's performance, you can ensure your data solutions remain responsive and efficient.