Switch activity in Azure Data Factory
APPLIES TO: 
Azure Data Factory
Azure Synapse Analytics
Tip
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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.
Create a Switch activity with UI
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 isn't 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 more cases. If no case matches, the Default case activity is used.
- Enter the value for the new case.
- Select the Edit button to add activities that are executed when the expression evaluates to the matched case.
JSON syntax
{
"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>"
}
]
}
}
Type properties
| 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 |
Example
The pipeline in this example copies data from an input folder to an output folder. The pipeline parameter routeSelection determines the output folder.
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.
Pipeline with Switch activity (Adfv2QuickStartPipeline.json)
{
"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"
}
}
}
}
Azure Storage linked service (AzureStorageLinkedService.json)
{
"name": "AzureStorageLinkedService",
"properties": {
"type": "AzureStorage",
"typeProperties": {
"connectionString": "DefaultEndpointsProtocol=https;AccountName=<Azure Storage account name>;AccountKey=<Azure Storage account key>"
}
}
}
Parameterized Azure Blob dataset (BlobDataset.json)
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"
}
}
}
}
Pipeline parameter JSON (PipelineParameters.json)
{
"inputPath": "adftutorial/input",
"outputPath1": "adftutorial/outputCase1",
"outputPath2": "adftutorial/outputCase2",
"outputPath2": "adftutorial/outputCase3",
"routeSelection": "1"
}
PowerShell commands
Note
We recommend that you use the Azure Az PowerShell module to interact with Azure. To get started, see Install Azure PowerShell. To learn how to migrate to the Az PowerShell module, see Migrate Azure PowerShell from AzureRM to Az.
These commands assume you 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"
Related content
See other control flow activities supported by Data Factory: