| title | description | author | ms.author | ms.reviewer | ms.service | ms.subservice | ms.topic | ms.date | ms.custom |
|---|---|---|---|---|---|---|---|---|---|
Switch activity in Azure Data Factory |
The Switch activity allows you to control the processing flow based on a condition. |
chez-charlie |
chez |
jburchel |
data-factory |
orchestration |
conceptual |
06/23/2021 |
devx-track-azurepowershell |
[!INCLUDEappliesto-adf-asa-md]
The Switch activity provides the same functionality that a switch statement provides in programming languages. It evaluates a set of activities corresponding to a case that matches the condition evaluation.
To use a Switch activity in a pipeline, complete the following steps:
- Search for Switch in the pipeline Activities pane, and add a Switch activity to the pipeline canvas.
- Select the Switch activity on the canvas if it is not already selected, and its Activities tab, to edit its details.
- Enter an expression for the Switch to evaluate. This can be any combination of dynamic expressions, functions, system variables, or outputs from other activities.
- Select Add case to add additional cases. If no case matches, the Default case activity will be used.
- Enter the value for the new case.
- Select the Edit button to add activities that will be executed when the expression evaluates to the matched case.
:::image type="content" source="media/control-flow-switch-activity/switch-activity-ui.png" alt-text="Shows the UI for a Switch activity with numbered indications of each step to configure it.":::
{
"name": "<Name of the activity>",
"type": "Switch",
"typeProperties": {
"expression": {
"value": "<expression that evaluates to some string value>",
"type": "Expression"
},
"cases": [
{
"value": "<string value that matches expression evaluation>",
"activities": [
{
"<Activity 1 definition>"
},
{
"<Activity 2 definition>"
},
{
"<Activity N definition>"
}
]
}
],
"defaultActivities": [
{
"<Activity 1 definition>"
},
{
"<Activity 2 definition>"
},
{
"<Activity N definition>"
}
]
}
}| Property | Description | Allowed values | Required |
|---|---|---|---|
| name | Name of the switch activity. | String | Yes |
| type | Must be set to Switch* | String | Yes |
| expression | Expression that must evaluate to string value | Expression with result type string | Yes |
| cases | Set of cases that contain a value and a set of activities to execute when the value matches the expression evaluation. Must provide at least one case. There's a max limit of 25 cases. | Array of Case Objects | Yes |
| defaultActivities | Set of activities that are executed when the expression evaluation isn't satisfied. | Array of Activities | Yes |
The pipeline in this example copies data from an input folder to an output folder. The output folder is determined by the value of pipeline parameter: routeSelection.
Note
This section provides JSON definitions and sample PowerShell commands to run the pipeline. For a walkthrough with step-by-step instructions to create a Data Factory pipeline by using Azure PowerShell and JSON definitions, see tutorial: create a data factory by using Azure PowerShell.
{
"name": "Adfv2QuickStartPipeline",
"properties": {
"activities": [
{
"name": "MySwitch",
"type": "Switch",
"typeProperties": {
"expression": {
"value": "@pipeline().parameters.routeSelection",
"type": "Expression"
},
"cases": [
{
"value": "1",
"activities": [
{
"name": "CopyFromBlobToBlob1",
"type": "Copy",
"inputs": [
{
"referenceName": "BlobDataset",
"parameters": {
"path": "@pipeline().parameters.inputPath"
},
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "BlobDataset",
"parameters": {
"path": "@pipeline().parameters.outputPath1",
},
"type": "DatasetReference"
}
],
"typeProperties": {
"source": {
"type": "BlobSource"
},
"sink": {
"type": "BlobSink"
}
}
}
]
},
{
"value": "2",
"activities": [
{
"name": "CopyFromBlobToBlob2",
"type": "Copy",
"inputs": [
{
"referenceName": "BlobDataset",
"parameters": {
"path": "@pipeline().parameters.inputPath",
},
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "BlobDataset",
"parameters": {
"path": "@pipeline().parameters.outputPath2",
},
"type": "DatasetReference"
}
],
"typeProperties": {
"source": {
"type": "BlobSource"
},
"sink": {
"type": "BlobSink"
}
}
}
]
},
{
"value": "3",
"activities": [
{
"name": "CopyFromBlobToBlob3",
"type": "Copy",
"inputs": [
{
"referenceName": "BlobDataset",
"parameters": {
"path": "@pipeline().parameters.inputPath",
},
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "BlobDataset",
"parameters": {
"path": "@pipeline().parameters.outputPath3",
},
"type": "DatasetReference"
}
],
"typeProperties": {
"source": {
"type": "BlobSource"
},
"sink": {
"type": "BlobSink"
}
}
}
]
},
],
"defaultActivities": []
}
}
],
"parameters": {
"inputPath": {
"type": "String"
},
"outputPath1": {
"type": "String"
},
"outputPath2": {
"type": "String"
},
"outputPath3": {
"type": "String"
},
"routeSelection": {
"type": "String"
}
}
}
}
{
"name": "AzureStorageLinkedService",
"properties": {
"type": "AzureStorage",
"typeProperties": {
"connectionString": "DefaultEndpointsProtocol=https;AccountName=<Azure Storage account name>;AccountKey=<Azure Storage account key>"
}
}
}The pipeline sets the folderPath to the value of either outputPath1 or outputPath2 parameter of the pipeline.
{
"name": "BlobDataset",
"properties": {
"type": "AzureBlob",
"typeProperties": {
"folderPath": {
"value": "@{dataset().path}",
"type": "Expression"
}
},
"linkedServiceName": {
"referenceName": "AzureStorageLinkedService",
"type": "LinkedServiceReference"
},
"parameters": {
"path": {
"type": "String"
}
}
}
}{
"inputPath": "adftutorial/input",
"outputPath1": "adftutorial/outputCase1",
"outputPath2": "adftutorial/outputCase2",
"outputPath2": "adftutorial/outputCase3",
"routeSelection": "1"
}[!INCLUDE updated-for-az]
These commands assume that you've saved the JSON files into the folder: C:\ADF.
Connect-AzAccount
Select-AzSubscription "<Your subscription name>"
$resourceGroupName = "<Resource Group Name>"
$dataFactoryName = "<Data Factory Name. Must be globally unique>";
Remove-AzDataFactoryV2 $dataFactoryName -ResourceGroupName $resourceGroupName -force
Set-AzDataFactoryV2 -ResourceGroupName $resourceGroupName -Location "East US" -Name $dataFactoryName
Set-AzDataFactoryV2LinkedService -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -Name "AzureStorageLinkedService" -DefinitionFile "C:\ADF\AzureStorageLinkedService.json"
Set-AzDataFactoryV2Dataset -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -Name "BlobDataset" -DefinitionFile "C:\ADF\BlobDataset.json"
Set-AzDataFactoryV2Pipeline -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -Name "Adfv2QuickStartPipeline" -DefinitionFile "C:\ADF\Adfv2QuickStartPipeline.json"
$runId = Invoke-AzDataFactoryV2Pipeline -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -PipelineName "Adfv2QuickStartPipeline" -ParameterFile C:\ADF\PipelineParameters.json
while ($True) {
$run = Get-AzDataFactoryV2PipelineRun -ResourceGroupName $resourceGroupName -DataFactoryName $DataFactoryName -PipelineRunId $runId
if ($run) {
if ($run.Status -ne 'InProgress') {
Write-Host "Pipeline run finished. The status is: " $run.Status -foregroundcolor "Yellow"
$run
break
}
Write-Host "Pipeline is running...status: InProgress" -foregroundcolor "Yellow"
}
Start-Sleep -Seconds 30
}
Write-Host "Activity run details:" -foregroundcolor "Yellow"
$result = Get-AzDataFactoryV2ActivityRun -DataFactoryName $dataFactoryName -ResourceGroupName $resourceGroupName -PipelineRunId $runId -RunStartedAfter (Get-Date).AddMinutes(-30) -RunStartedBefore (Get-Date).AddMinutes(30)
$result
Write-Host "Activity 'Output' section:" -foregroundcolor "Yellow"
$result.Output -join "`r`n"
Write-Host "\nActivity 'Error' section:" -foregroundcolor "Yellow"
$result.Error -join "`r`n"See other control flow activities supported by Data Factory: