Azure Analysis Services scale-out
With scale-out, client queries can be distributed among multiple query replicas in a query pool, reducing response times during high query workloads. You can also separate processing from the query pool, ensuring client queries are not adversely affected by processing operations. Scale-out can be configured in Azure portal or by using the Analysis Services REST API.
How it works
In a typical server deployment, one server serves as both processing server and query server. If the number of client queries against models on your server exceeds the Query Processing Units (QPU) for your server's plan, or model processing occurs at the same time as high query workloads, performance can decrease.
With scale-out, you can create a query pool with up to seven additional query replica resources (eight total, including your server). You can scale the number of query replicas to meet QPU demands at critical times and you can separate a processing server from the query pool at any time. All query replicas are created in the same region as your server.
Regardless of the number of query replicas you have in a query pool, processing workloads are not distributed among query replicas. A single server serves as the processing server. Query replicas serve only queries against the models synchronized between each query replica in the query pool.
When scaling out, new query replicas are added to the query pool incrementally. It can take up to five minutes for new query replica resources to be included in the query pool. When all new query replicas are up and running, new client connections are load balanced across all query pool resources. Existing client connections are not changed from the resource they are currently connected to. When scaling in, any existing client connections to a query pool resource that is being removed from the query pool are terminated. They are reconnected to a remaining query pool resource when the scale in operation has completed, which can take up to five minutes.
When processing models, after processing operations are completed, a synchronization must be performed between the processing server and the query replicas. When automating processing operations, it's important to configure a synchronization operation upon successful completion of processing operations. Synchronization can be performed manually in the portal, or by using PowerShell or REST API.
Separate processing from query pool
For maximum performance for both processing and query operations, you can choose to separate your processing server from the query pool. When separated, existing and new client connections are assigned to query replicas in the query pool only. If processing operations only take up a short amount of time, you can choose to separate your processing server from the query pool only for the amount of time it takes to perform processing and synchronization operations, and then include it back into the query pool.
Scale-out is available for servers in the Standard pricing tier. Each query replica is billed at the same rate as your server.
Scale-out does not increase the amount of available memory for your server. To increase memory, you need to upgrade your plan.
The number of query replicas you can configure are limited by the region your server is in. To learn more, see Availability by region.
Monitor QPU usage
To determine if scale-out for your server is necessary, monitor your server in Azure portal by using Metrics. If your QPU regularly maxes out, it means the number of queries against your models is exceeding the QPU limit for your plan. The Query pool job queue length metric also increases when the number of queries in the query thread pool queue exceeds available QPU. To learn more, see Monitor server metrics.
In Azure portal
In the portal, click Scale-out. Use the slider to select the number of query replica servers. The number of replicas you choose is in addition to your existing server.
In Separate the processing server from the querying pool, select yes to exclude your processing server from query servers. Client connections using the default connection string (without :rw) are redirected to replicas in the query pool.
Click Save to provision your new query replica servers.
Tabular models on your primary server are synchronized with the replica servers. When synchronization is complete, the query pool begins distributing incoming queries among the replica servers.
When you provision new query replicas, Azure Analysis Services automatically replicates your models across all replicas. You can also perform a manual synchronization by using the portal or REST API. When you process your models, you should perform a synchronization so updates are synchronized among your query replicas.
In Azure portal
In Overview > model > Synchronize model.
Use the sync operation.
Synchronize a model
Get sync status
Before using PowerShell, install or update the latest AzureRM module.
To set the number of query replicas, use Set-AzureRmAnalysisServicesServer. Specify the optional
To run sync, use Sync-AzureAnalysisServicesInstance.
On your server's Overview page, there are two server names. If you haven't yet configured scale-out for a server, both server names work the same. Once you configure scale-out for a server, you need to specify the appropriate server name depending on the connection type.
For end-user client connections like Power BI Desktop, Excel, and custom apps, use Server name.
For SSMS, SSDT, and connection strings in PowerShell, Azure Function apps, and AMO, use Management server name. The management server name includes a special
:rw (read-write) qualifier. All processing operations occur on the management server.
Issue: Users get error Cannot find server '<Name of the server>' instance in connection mode 'ReadOnly'.
Solution: When selecting the Separate the processing server from the querying pool option, client connections using the default connection string (without :rw) are redirected to query pool replicas. If replicas in the query pool are not yet online because synchronization has not yet been completed, redirected client connections can fail. To prevent failed connections, choose not to separate the processing server from the querying pool until a scale-out and synchronization operation are complete. You can use the Memory and QPU metrics to monitor synchronization status.