Edit

Share via

Configure autoscale settings for a Stream Analytics job by using the CI/CD tool

Streaming units (SUs) represent the computing resources that are allocated to run an Azure Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated to your job.

The autoscale feature dynamically adjusts SUs based on your rule definitions. You can configure autoscale settings for your Stream Analytics job in the Azure portal or by using the Stream Analytics continuous integration and continuous delivery (CI/CD) tool on your local machine.

This article explains how you can use the Stream Analytics CI/CD tool to configure autoscale settings for Stream Analytics jobs. If you want to learn more about autoscaling jobs in the Azure portal, see Autoscale streaming units (preview).

The Stream Analytics CI/CD tool allows you to specify the maximum number of streaming units and configure a set of rules for autoscaling your jobs. Then it determines whether to add SUs (to handle increases in load) or reduce the number of SUs (when computing resources are sitting idle).

Here's an example of an autoscale setting:

  • If the maximum number of SUs is set to 12, increase SUs when the average SU utilization of the job over the last 2 minutes goes above 75 percent.

Prerequisites

To complete the steps in this article, you need either:

  • A Stream Analytics project on the local machine. Follow this guide to create one.
  • A running Stream Analytics job in Azure.

Configure autoscale settings

Scenario 1: Configure settings for a local Stream Analytics project

If you have a working Stream Analytics project on the local machine, follow these steps to configure autoscale settings:

  1. Open your Stream Analytics project in Visual Studio Code.

  2. On the Terminal panel, run the following command to install the Stream Analytics CI/CD tool:

    npm install -g azure-streamanalytics-cicd

    Here's the list of supported commands for azure-streamanalytics-cicd:

    Command Description
    build Generate a standard Azure Resource Manager template (ARM template) for a Stream Analytics project in Visual Studio Code.
    localrun Run locally for a Stream Analytics project in Visual Studio Code.
    test Test for a Stream Analytics project in Visual Studio Code.
    addtestcase Add test cases for a Stream Analytics project in Visual Studio Code.
    autoscale Generate an ARM template file for an autoscale setting.
    help Display more information on a specific command.

  3. Build the project:

    azure-streamanalytics-cicd build --v2 --project ./asaproj.json --outputPath ./Deploy

    If you build the project successfully, two JSON files are created under the Deploy folder. One is the ARM template file, and the other is the parameter file.

    Screenshot that shows the files generated after building a project.

    Note

    We highly recommend that you use the --v2 option for the updated ARM template schema. The updated schema has fewer parameters yet retains the same functionality as the previous version.

    The old ARM template will be deprecated in the future. After that, only templates that were created via build --v2 will receive updates or bug fixes.

  4. Configure the autoscale setting. Add parameter keys and values by using the azure-streamanalytics-cicd autoscale command.

    Parameter key Value Example
    capacity Maximum SUs (1/3, 2/3, 1, up to 66 SU V2s) 2
    metrics Metrics used for autoscale rules ProcessCPUUsagePercentage ResourceUtilization
    targetJobName Project name ClickStream-Filter
    outputPath Output path for ARM templates ./Deploy

    Here's an example:

    azure-streamanalytics-cicd autoscale --capacity 2 --metrics ProcessCPUUsagePercentage ResourceUtilization --targetJobName ClickStream-Filter --outputPath ./Deploy

    If you configure the autoscale setting successfully, two JSON files are created under the Deploy folder. One is the ARM template file, and the other is the parameter file.

    Screenshot that shows autoscale files generated after configuration of autoscale.

    The following table lists the metrics that you can use to define autoscale rules:

    Metric Name in REST API Unit Aggregation Dimensions Time Grains DS Export
    Failed Function Requests

    Failed Function Requests    		 AMLCalloutFailedRequests Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Function Events

    Function Events    		 AMLCalloutInputEvents Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Function Requests

    Function Requests    		 AMLCalloutRequests Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Data Conversion Errors

    Data Conversion Errors    		 ConversionErrors Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Input Deserialization Errors

    Input Deserialization Errors    		 DeserializationError Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Out of order Events

    Out of order Events    		 DroppedOrAdjustedEvents Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Early Input Events

    Early Input Events    		 EarlyInputEvents Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Runtime Errors

    Runtime Errors    		 Errors Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Input Event Bytes

    Input Event Bytes    		 InputEventBytes Bytes Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Input Events

    Input Events    		 InputEvents Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Backlogged Input Events

    Backlogged Input Events    		 InputEventsSourcesBacklogged Count Average, Maximum, Minimum LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Input Sources Received

    Input Sources Received    		 InputEventsSourcesPerSecond Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Late Input Events

    Late Input Events    		 LateInputEvents Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Output Events

    Output Events    		 OutputEvents Count Total (Sum) LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    Watermark Delay

    Watermark Delay    		 OutputWatermarkDelaySeconds Seconds Average, Maximum, Minimum LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    CPU % Utilization

    CPU % Utilization    		 ProcessCPUUsagePercentage Percent Average, Maximum, Minimum LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes
    SU (Memory) % Utilization

    SU (Memory) % Utilization    		 ResourceUtilization Percent Average, Maximum, Minimum LogicalName, PartitionId, ProcessorInstance, NodeName PT1M Yes

    The default value for all metric thresholds is 70. If you want to set the metric threshold to another number, open the *.AutoscaleSettingTemplate.parameters.json file and change the Threshold value.

    Screenshot that shows how to set the metric threshold in a parameter file.

    To learn more about defining autoscale rules, see Understand autoscale settings.

  5. Deploy to Azure.

    1. Connect to your Azure account:

      # Connect to Azure
Connect-AzAccount

# Set the Azure subscription
Set-AzContext [SubscriptionID/SubscriptionName]

    2. Deploy your Stream Analytics project:

      $templateFile = ".\Deploy\ClickStream-Filter.JobTemplate.json"
$parameterFile = ".\Deploy\ClickStream-Filter.JobTemplate.parameters.json"
New-AzResourceGroupDeployment `
  -Name devenvironment `
  -ResourceGroupName myResourceGroupDev `
  -TemplateFile $templateFile `
  -TemplateParameterFile $parameterFile

    3. Deploy your autoscale settings:

      $templateFile = ".\Deploy\ClickStream-Filter.AutoscaleSettingTemplate.json"
$parameterFile = ".\Deploy\ClickStream-Filter.AutoscaleSettingTemplate.parameters.json"
New-AzResourceGroupDeployment `
  -Name devenvironment `
  -ResourceGroupName myResourceGroupDev `
  -TemplateFile $templateFile `
  -TemplateParameterFile $parameterFile

After you deploy your project successfully, you can view the autoscale settings in the Azure portal.

Scenario 2: Configure settings for a running Stream Analytics job in Azure

If you have a Stream Analytics job running in Azure, you can use the Stream Analytics CI/CD tool in PowerShell to configure autoscale settings.

Run the following command. Replace $jobResourceId with the resource ID of your Stream Analytics job.

azure-streamanalytics-cicd autoscale --capacity 2 --metrics ProcessCPUUsagePercentage ResourceUtilization --targetJobResourceId $jobResourceId --outputPath ./Deploy

If you configure the settings successfully, ARM template and parameter files are created in the current directory.

Then you can deploy the autoscale settings to Azure by following the deployment steps in scenario 1.

Get help

For more information about autoscale settings, run this command in PowerShell:

azure-streamanalytics-cicd autoscale --help

If you have any problems with the Stream Analytics CI/CD tool, you can report them in GitHub.

  • Last updated on