I have two separate Data Factories on my Azure Subscription, lets call them DF-A and the other DF-B
In Data Factory DF-A I have a pipeline and when this has completed, I would like the Pipeline on DF-B to run; how would I achieve this?
Thanks
In Logic app designer, you can create two pipeline run steps to trigger the two pipelines in different Data Factory running.
It is more easier by using logic apps to achieve this.
create a Recurrence trigger to schedule the executions and two Azure Data Factory operations to trigger the pipeline running.
In the Azure Data Factory operations, select Create a pipeline run Action.
The summary is here:
While it's possible, it's much more complicated than one pipeline executing another from within the same Azure Data Factory.
In DF-A create a pipeline called ExecuteExternalPipeline copying the following JSON into the Code tab:
{
"name": "ExecuteExternalPipeline",
"properties": {
"description": "Executes an ADF pipeline in a different ADF",
"activities": [
{
"name": "StartPipelineThenWait",
"description": "Calls the ADF REST API to start a pipeline in another ADF running using the MSI of this current ADF. Then it waits on a webhook callback",
"type": "WebHook",
"dependsOn": [],
"userProperties": [],
"typeProperties": {
"url": {
"value": "#concat(\n 'https://management.azure.com/subscriptions/',\n pipeline().parameters.SubscriptionID,\n '/resourceGroups/',pipeline().parameters.ResourceGroup,\n '/providers/Microsoft.DataFactory/factories/',\n pipeline().parameters.DataFactory,\n '/pipelines/',\n pipeline().parameters.Pipeline,\n '/createRun?api-version=2018-06-01'\n)",
"type": "Expression"
},
"method": "POST",
"body": {
"value": "#json(\n concat(\n '{\n \"InputFileName\": \"', pipeline().parameters.InputFileName, '\"\n }'\n )\n)\n",
"type": "Expression"
},
"timeout": "20:00:00",
"authentication": {
"type": "MSI",
"resource": "https://management.azure.com"
}
}
},
{
"name": "ThrowErrorIfFailure",
"type": "IfCondition",
"dependsOn": [
{
"activity": "StartPipelineThenWait",
"dependencyConditions": [
"Succeeded"
]
}
],
"userProperties": [],
"typeProperties": {
"expression": {
"value": "#if(equals(activity('StartPipelineThenWait').status,'success'),true,json('throw an error!'))",
"type": "Expression"
}
}
}
],
"parameters": {
"SubscriptionID": {
"type": "string",
"defaultValue": "12345abcd-468e-472a-9761-9da416b14c0d"
},
"ResourceGroup": {
"type": "string",
"defaultValue": "DF-B-RG"
},
"DataFactory": {
"type": "string",
"defaultValue": "DF-B"
},
"Pipeline": {
"type": "string",
"defaultValue": "ChildPipeline"
},
"InputFileName": {
"type": "string",
"defaultValue": "File1.txt"
}
},
"annotations": []
}
}
Then create ChildPipeline in DF-B with the following code:
{
"name": "ChildPipeline",
"properties": {
"activities": [
{
"name": "DoYourLogicHere",
"description": "",
"type": "WebActivity",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"userProperties": [],
"typeProperties": {
"url": {
"value": "https://google.com",
"type": "Expression"
},
"method": "GET"
}
},
{
"name": "CallbackSuccess",
"description": "Do not remove this activity. It notifies the process which executed this pipeline that the pipeline is complete.",
"type": "WebActivity",
"dependsOn": [
{
"activity": "DoYourLogicHere",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"userProperties": [],
"typeProperties": {
"url": {
"value": "#pipeline().parameters.callBackUri",
"type": "Expression"
},
"method": "POST",
"body": {
"value": "#json(concat('{\"status\": \"success\", \"pipelineRunId\": \"',pipeline().RunId,'\"}'))",
"type": "Expression"
}
}
},
{
"name": "CallbackFail",
"description": "Do not remove this activity. It notifies the process which executed this pipeline that the pipeline is complete.",
"type": "WebActivity",
"dependsOn": [
{
"activity": "DoYourLogicHere",
"dependencyConditions": [
"Failed"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"userProperties": [],
"typeProperties": {
"url": {
"value": "#pipeline().parameters.callBackUri",
"type": "Expression"
},
"method": "POST",
"body": {
"value": "#json(concat('{\"status\": \"failure\", \"pipelineRunId\": \"',pipeline().RunId,'\"}'))",
"type": "Expression"
}
}
}
],
"parameters": {
"callBackUri": {
"type": "string",
"defaultValue": "https://google.com"
},
"InputFileName": {
"type": "string",
"defaultValue": "File1.txt"
}
},
"annotations": []
}
}
Replace the DoYourLogicHere activity with your own activities but leave the two callback activities.
Then you need to find the MSI (see the Properties tab of your DF-A in the Azure Portal) for DF-A and make it a Data Factory Contributor on DF-B so that it can execute the pipeline in the other ADF.
Related
I am trying to use Azure Data Factory to read data from a FHIR server and transform the results into newline delimited JSON (ndjson) files in Azure Blob storage. Specifically, if you query a FHIR server, you might get something like:
{
"resourceType": "Bundle",
"id": "som-id",
"type": "searchset",
"link": [
{
"relation": "next",
"url": "https://fhirserver/?ct=token"
},
{
"relation": "self",
"url": "https://fhirserver/"
}
],
"entry": [
{
"fullUrl": "https://fhirserver/Organization/1234",
"resource": {
"resourceType": "Organization",
"id": "1234",
// More fields
},
{
"fullUrl": "https://fhirserver/Organization/456",
"resource": {
"resourceType": "Organization",
"id": "456",
// More fields
},
// More resources
]
}
Basically a bundle of resources. I would like to transform that into a newline delimited (aka ndjson) file where each line is just the json for a resource:
{"resourceType": "Organization", "id": "1234", // More fields }
{"resourceType": "Organization", "id": "456", // More fields }
// More lines with resources
I am able to get the REST connector set up and it can query the FHIR server (including pagination), but no matter what I try I cannot seem to generate the ouput I want. I set up an Azure Blob storage dataset:
{
"name": "AzureBlob1",
"properties": {
"linkedServiceName": {
"referenceName": "AzureBlobStorage1",
"type": "LinkedServiceReference"
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "JsonFormat",
"filePattern": "setOfObjects"
},
"fileName": "myout.json",
"folderPath": "outfhirfromadf"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
And configure a copy activity:
{
"name": "pipeline1",
"properties": {
"activities": [
{
"name": "Copy Data1",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "RestSource",
"httpRequestTimeout": "00:01:40",
"requestInterval": "00.00:00:00.010"
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"translator": {
"type": "TabularTranslator",
"schemaMapping": {
"resource": "resource"
},
"collectionReference": "$.entry"
}
},
"inputs": [
{
"referenceName": "FHIRSource",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "AzureBlob1",
"type": "DatasetReference"
}
]
}
]
},
"type": "Microsoft.DataFactory/factories/pipelines"
}
But at the end (in spite of configuring the schema mapping), it the end result in the blob is always just the original bundle returned from the server. If I configure the output blob as being a comma delimited text, I can extract fields and create a flattened tabular view, but that is not really what I want.
Any suggestions would be much appreciated.
So I sort of found a solution. If I do the original step of converting where the bundles are simply dumped in the JSON file and then do a nother conversion from the JSON file to what I pretend to be a text file into another blob, I can get the njson file created.
Basically, define another blob dataset:
{
"name": "AzureBlob2",
"properties": {
"linkedServiceName": {
"referenceName": "AzureBlobStorage1",
"type": "LinkedServiceReference"
},
"type": "AzureBlob",
"structure": [
{
"name": "Prop_0",
"type": "String"
}
],
"typeProperties": {
"format": {
"type": "TextFormat",
"columnDelimiter": ",",
"rowDelimiter": "",
"quoteChar": "",
"nullValue": "\\N",
"encodingName": null,
"treatEmptyAsNull": true,
"skipLineCount": 0,
"firstRowAsHeader": false
},
"fileName": "myout.json",
"folderPath": "adfjsonout2"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
Note that this one TextFormat and also note that the quoteChar is blank. If I then add another Copy Activity:
{
"name": "pipeline1",
"properties": {
"activities": [
{
"name": "Copy Data1",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "RestSource",
"httpRequestTimeout": "00:01:40",
"requestInterval": "00.00:00:00.010"
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"translator": {
"type": "TabularTranslator",
"schemaMapping": {
"['resource']": "resource"
},
"collectionReference": "$.entry"
}
},
"inputs": [
{
"referenceName": "FHIRSource",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "AzureBlob1",
"type": "DatasetReference"
}
]
},
{
"name": "Copy Data2",
"type": "Copy",
"dependsOn": [
{
"activity": "Copy Data1",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "BlobSource",
"recursive": true
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"translator": {
"type": "TabularTranslator",
"columnMappings": {
"resource": "Prop_0"
}
}
},
"inputs": [
{
"referenceName": "AzureBlob1",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "AzureBlob2",
"type": "DatasetReference"
}
]
}
]
},
"type": "Microsoft.DataFactory/factories/pipelines"
}
Then it all works out. It is not ideal in that I now have two copies of the data in blobs, but one can easily be deleted, I suppose.
I would still love to hear about it if somebody has a one-step solution.
As briefly discussed in the comment, the Copy Activity does not provide much functionality aside from mapping data. As stated in the documentation, the Copy activity does the following operations:
Reads data from a source data store.
Performs serialization/deserialization, compression/decompression, column mapping, etc. It does these operations based on the
configurations of the input dataset, output dataset, and Copy
Activity.
Writes data to the sink/destination data store.
It does not look like that the Copy Activity does anything else aside from efficiently copying stuff around.
What I found out to be working was to use Databrick.
Here are the steps:
Add a Databricks account to your subscription;
Go to the Databricks page by clicking the authoring button;
Create a notebook;
Write the script (Scala, Python or .Net was recently announced).
The script would the following:
Read the data from the Blob storage;
Filter out & transform the data as needed;
Write the data back to a Blob storage;
You can test your script from there and, once ready, you can go back to your pipeline and create a Notebook activity that will point to your notebook containing the script.
I struggled coding in Scala but it was worth it :)
For anyone finding this post in the future you can just can use the $export api call to accomplish this. Note that you have to have a storage account linked to your Fhir server.
https://build.fhir.org/ig/HL7/bulk-data/export.html#endpoint---system-level-export
with Azure Data Factory V2, through the portal
https://adf.azure.com
I created a Pipeline for incremental copying of data from multiple tables, from one Azure SQL database to another Azure SQL database.
To create it, I have adapted the following example to my needs:
Incrementally load data from multiple tables
Following is the json file related to the pipeline created:
{
"name": "IncrementalCopyPipeline",
"properties": {
"activities": [
{
"name": "IterateSQLTables",
"type": "ForEach",
"typeProperties": {
"items": {
"value": "#pipeline().parameters.tableList",
"type": "Expression"
},
"activities": [
{
"name": "LookupOldWaterMarkActivity",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "SqlSource",
"sqlReaderQuery": {
"value": "select * \nfrom watermarktable \nwhere TableName = '#{item().TABLE_NAME}'",
"type": "Expression"
}
},
"dataset": {
"referenceName": "WatermarkDataset",
"type": "DatasetReference"
}
}
},
{
"name": "LookupNewWaterMarkActivity",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "SqlSource",
"sqlReaderQuery": {
"value": "select MAX(#{item().WaterMark_Column}) as NewWatermarkvalue \nfrom #{item().TABLE_NAME}",
"type": "Expression"
}
},
"dataset": {
"referenceName": "SourceDataset",
"type": "DatasetReference"
}
}
},
{
"name": "IncrementalCopyActivity",
"type": "Copy",
"dependsOn": [
{
"activity": "LookupNewWaterMarkActivity",
"dependencyConditions": [
"Succeeded"
]
},
{
"activity": "LookupOldWaterMarkActivity",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "SqlSource",
"sqlReaderQuery": {
"value": "select * from #{item().TABLE_NAME} \nwhere #{item().WaterMark_Column} > '#{activity('LookupOldWaterMarkActivity').output.firstRow.WatermarkValue}' and #{item().WaterMark_Column} <= '#{activity('LookupNewWaterMarkActivity').output.firstRow.NewWatermarkvalue}'",
"type": "Expression"
}
},
"sink": {
"type": "SqlSink",
"writeBatchSize": 10000,
"sqlWriterStoredProcedureName": {
"value": "#{item().StoredProcedureNameForMergeOperation}",
"type": "Expression"
},
"sqlWriterTableType": {
"value": "#{item().TableType}",
"type": "Expression"
}
},
"enableStaging": false,
"dataIntegrationUnits": 0
},
"inputs": [
{
"referenceName": "SourceDataset",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "SinkDataset",
"type": "DatasetReference",
"parameters": {
"SinkTableName": "#{item().TABLE_NAME}"
}
}
]
},
{
"name": "StoredProceduretoWriteWatermarkActivity",
"type": "SqlServerStoredProcedure",
"dependsOn": [
{
"activity": "IncrementalCopyActivity",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"storedProcedureName": "[dbo].[sp_write_watermark]",
"storedProcedureParameters": {
"LastModifiedtime": {
"value": {
"value": "#{activity('LookupNewWaterMarkActivity').output.firstRow.NewWatermarkvalue}",
"type": "Expression"
},
"type": "DateTime"
},
"TableName": {
"value": {
"value": "#{activity('LookupOldWaterMarkActivity').output.firstRow.TableName}",
"type": "Expression"
},
"type": "String"
}
}
},
"linkedServiceName": {
"referenceName": "SqlServerLinkedService_dest",
"type": "LinkedServiceReference"
}
}
]
}
}
],
"parameters": {
"tableList": {
"type": "Object",
"defaultValue": [
{
"TABLE_NAME": "customer_table",
"WaterMark_Column": "LastModifytime",
"TableType": "DataTypeforCustomerTable",
"StoredProcedureNameForMergeOperation": "sp_upsert_customer_table"
},
{
"TABLE_NAME": "project_table",
"WaterMark_Column": "Creationtime",
"TableType": "DataTypeforProjectTable",
"StoredProcedureNameForMergeOperation": "sp_upsert_project_table"
}
]
}
}
}
}
In my table I have a column that distinguishes between different companies and so I would like to add another parameter to this pipeline. I have a table like this:
NAME LASTMODIFY COMPANY
John 2015-01-01 00:00:00.000 1
Mike 2016-02-02 01:23:00.000 2
Andy 2017-03-04 05:16:00.000 3
Annie 2018-09-08 00:00:00.000 1
Someone would know how to insert a parameter into the pipeline in order to specify which company to copy and which one to not copy?
Does any suggestion? Thanks in advance to everyone!
Not exactly clear on what you're asking, so apologies if I am missing the mark, but:
Copy allows for a stored procedure that you can use to potentially solve your problem. Take a look at this example: https://learn.microsoft.com/en-us/azure/data-factory/connector-sql-server#invoking-stored-procedure-for-sql-sink
It uses a Stored Procedure to MERGE performing an UPDATE or INSERT dependent on the JOIN matching. It also allows for parameters to be passed.
So if you are trying to COPY only certain cases based on a parameter, the MERGE join may help.
I am currently trying to set up this pipeline on Azure Data Factory V2 (as you can see in the picture attached). In summary this ERP system will export in a monthly basis this report (CSV file with actual and forecast data) and this will be saved in a blob container. As soon as this file CSV is saved, an event trigger should activate this stored procedure that will - in turn - erase all actual data from my fact table in Azure SQL as this gets replaced every month.
Once actual data is deleted, the pipeline would have subsequently a copy activity that would - in turn - copy the CSV report (actuals + forecast) to same fact table in Azure SQL. Once the copy activity is finished, the HTTP logic APP would delete that new CSV file from the blob container. This workflow would be a recurrent event to be carried out very month.
So far I have been able to run these 3 x activities independently. However, when I join them in the same pipeline, I have had some parameters errors when trying to "publish all". Therefore I am not sure whether I need to have the same parameters for each activity in the pipeline?
The JSON code for my pipeline is the following:
{
"name": "TM1_pipeline",
"properties": {
"activities": [
{
"name": "Copy Data1",
"type": "Copy",
"dependsOn": [
{
"activity": "Stored Procedure1",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "BlobSource",
"recursive": false
},
"sink": {
"type": "SqlSink",
"writeBatchSize": 10000
},
"enableStaging": false,
"dataIntegrationUnits": 0
},
"inputs": [
{
"referenceName": "SourceDataset_e7y",
"type": "DatasetReference",
"parameters": {
"copyFolder": {
"value": "#pipeline().parameters.sourceFolder",
"type": "Expression"
},
"copyFile": {
"value": "#pipeline().parameters.sourceFile",
"type": "Expression"
}
}
}
],
"outputs": [
{
"referenceName": "DestinationDataset_e7y",
"type": "DatasetReference"
}
]
},
{
"name": "Stored Procedure1",
"type": "SqlServerStoredProcedure",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"storedProcedureName": "[dbo].[test_sp]"
},
"linkedServiceName": {
"referenceName": "AzureSqlDatabase",
"type": "LinkedServiceReference"
}
},
{
"name": "Web1",
"type": "WebActivity",
"dependsOn": [
{
"activity": "Copy Data1",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"url": "...",
"method": "POST",
"body": {
"value": "#pipeline().parameters.BlobName",
"type": "Expression"
}
}
}
],
"parameters": {
"sourceFolder": {
"type": "String",
"defaultValue": "#pipeline().parameters.sourceFolder"
},
"sourceFile": {
"type": "String",
"defaultValue": "#pipeline().parameters.sourceFile"
},
"BlobName": {
"type": "String",
"defaultValue": {
"blobname": "source-csv/test.csv"
}
}
}
},
"type": "Microsoft.DataFactory/factories/pipelines"
}
Please follow this doc to configure you blob event trigger and pass the right value to your parameters.
So we've got a factory with ~400 datasets and ~200 pipelines and it's getting unwieldy. Focusing on copying from sql source to blob sink. Since we are copying to blob the schema has no impact. I'd like to have one dataset for each source, one dataset for each blob account and one pipeline for each combination of source/blob account, dynamically feeding it the config from a lookup.
We've successfully developed a pipeline that uses dummy datasets for source and sink. It works if you feed it a query, container name and folder name.
{
"name": "pipeline1",
"properties": {
"activities": [
{
"name": "DynamicCopy",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "SqlSource",
"sqlReaderQuery": "select 1 a"
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"dataIntegrationUnits": 0
},
"inputs": [
{
"referenceName": "AzureSql",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "AzureBlob",
"type": "DatasetReference",
"parameters": {
"container": "raw-test",
"folder": "test"
}
}
]
}
]
}
}
When we put a lookup before it and wrap it in a foreach, it stops working. With the not so helpful
"errorCode": "400",
"message": "Activity failed because an inner activity failed",
"failureType": "UserError",
"target": "ForEach"
The lookup stored procedure [dbo].[adfdynamic] is not actually referred to in the foreach yet:
create proc adfdynamic as
select 'raw-test' container, 'test_a' folder, 'select 1 a, 2 b'
UNION ALL
select 'raw-test' container, 'test_b' folder, 'select 3 c, 2 d'
So what I desired behaviour is:
one blob in raw-test#..myblob.../test_a/out.dsv with content {'a,b','1,2'}
one blob in raw-test#..myblob.../test_b/out.dsv with content {'c,d','3,2'}
sql dataset:
{
"name": "AzureSql",
"properties": {
"linkedServiceName": {
"referenceName": "Dest",
"type": "LinkedServiceReference"
},
"type": "AzureSqlTable",
"structure": [
{
"name": "CustomerKey",
"type": "Int32"
},
{
"name": "Name",
"type": "String"
}
],
"typeProperties": {
"tableName": "[dbo].[DimCustomer]"
}
}
}
blob dataset:
{
"name": "AzureBlob",
"properties": {
"linkedServiceName": {
"referenceName": "AzureStorage1",
"type": "LinkedServiceReference"
},
"parameters": {
"container": {
"type": "String"
},
"folder": {
"type": "String"
}
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "TextFormat",
"columnDelimiter": ",",
"treatEmptyAsNull": false,
"skipLineCount": 0,
"firstRowAsHeader": false
},
"fileName": {
"value": "#{dataset().folder}/out.dsv",
"type": "Expression"
},
"folderPath": {
"value": "#dataset().container",
"type": "Expression"
}
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
and the non-working dynamic pipeline:
{
"name": "Copy",
"properties": {
"activities": [
{
"name": "ForEach",
"type": "ForEach",
"dependsOn": [
{
"activity": "Lookup",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "#activity('Lookup').output.value",
"type": "Expression"
},
"activities": [
{
"name": "Copy",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "SqlSource",
"sqlReaderQuery": {
"value": "select 1 a, 2 b from dest",
"type": "Expression"
}
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"dataIntegrationUnits": 0
},
"inputs": [
{
"referenceName": "AzureSql",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "AzureBlob",
"type": "DatasetReference",
"parameters": {
"container": {
"value": "raw-test",
"type": "Expression"
},
"folder": {
"value": "folder",
"type": "Expression"
}
}
}
]
}
]
}
},
{
"name": "Lookup",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
}
}
]
}
}
Apologies about the formatting. too much code in one message?
In you lookup activity, please check whether your firstRowOnly property. Is it false or True? By default, it is true.
In the UI, you could set a breakpoint to debug your lookup activity. Then you could see whether the output is what you want.
Not exactly an answer to your question, but something I did to make life simpler was to create a Dataset called GenericBlob. This had 2 parameters container and path.
This may help simplify what you're doing. I too used to have 20 blob datasets, now I have one ... (this is assuming the blobs are in the same storage account).
I'm trying to backup my Cosmos Db storage using Azure Data Factory(v2). In general, it's doing its job, but I want to have each doc in Cosmos collection to correspond new json file in blobs storage.
With next copying params i'm able to copy all docs in collection into 1 file in azure blob storage:
{
"name": "ForEach_mih",
"type": "ForEach",
"typeProperties": {
"items": {
"value": "#pipeline().parameters.cw_items",
"type": "Expression"
},
"activities": [
{
"name": "Copy_mih",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"userProperties": [
{
"name": "Source",
"value": "#{item().source.collectionName}"
},
{
"name": "Destination",
"value": "cosmos-backup-v2/#{item().destination.fileName}"
}
],
"typeProperties": {
"source": {
"type": "DocumentDbCollectionSource",
"nestingSeparator": "."
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"enableSkipIncompatibleRow": true,
"redirectIncompatibleRowSettings": {
"linkedServiceName": {
"referenceName": "Clear_Test_BlobStorage",
"type": "LinkedServiceReference"
},
"path": "cosmos-backup-logs"
},
"cloudDataMovementUnits": 0
},
"inputs": [
{
"referenceName": "SourceDataset_mih",
"type": "DatasetReference",
"parameters": {
"cw_collectionName": "#item().source.collectionName"
}
}
],
"outputs": [
{
"referenceName": "DestinationDataset_mih",
"type": "DatasetReference",
"parameters": {
"cw_fileName": "#item().destination.fileName"
}
}
]
}
]
}
}
How I can copy each cosmos doc to separate file and give it name the as {PartitionId}-{docId}?
UPD
Source set code:
{
"name": "ClustersData",
"properties": {
"linkedServiceName": {
"referenceName": "Clear_Test_CosmosDb",
"type": "LinkedServiceReference"
},
"type": "DocumentDbCollection",
"typeProperties": {
"collectionName": "directory-clusters"
}
}
}
Destination set code:
{
"name": "OutputClusters",
"properties": {
"linkedServiceName": {
"referenceName": "Clear_Test_BlobStorage",
"type": "LinkedServiceReference"
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "JsonFormat",
"filePattern": "arrayOfObjects"
},
"fileName": "",
"folderPath": "cosmos-backup-logs"
}
}
}
Pipeline code:
{
"name": "copy-clsts",
"properties": {
"activities": [
{
"name": "LookupClst",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "DocumentDbCollectionSource",
"nestingSeparator": "."
},
"dataset": {
"referenceName": "ClustersData",
"type": "DatasetReference"
},
"firstRowOnly": false
}
},
{
"name": "ForEachClst",
"type": "ForEach",
"dependsOn": [
{
"activity": "LookupClst",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "#activity('LookupClst').output.value",
"type": "Expression"
},
"batchCount": 8,
"activities": [
{
"name": "CpyClst",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "DocumentDbCollectionSource",
"query": "select #{item()}",
"nestingSeparator": "."
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"enableSkipIncompatibleRow": true,
"cloudDataMovementUnits": 0
},
"inputs": [
{
"referenceName": "ClustersData",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "OutputClusters",
"type": "DatasetReference"
}
]
}
]
}
}
]
}
}
Example of doc in input collection (all the same format):
{
"$type": "Entities.ADCluster",
"DisplayName": "TESTNetBIOS",
"OrgId": "9b679d2a-42c5-4c9a-a2e2-3ce63c1c3506",
"ClusterId": "ab2a242d-f1a5-62ed-b420-31b52e958586",
"AllowLdapLifeCycleSynchronization": true,
"DirectoryServers": [
{
"$type": "Entities.DirectoryServer",
"AddressId": "e6a8edbb-ad56-4135-94af-fab50b774256",
"Port": 389,
"Host": "192.168.342.234"
}
],
"DomainNames": [
"TESTNetBIOS"
],
"BaseDn": null,
"UseSsl": false,
"RepositoryType": 1,
"DirectoryCustomizations": null,
"_etag": "\"140046f2-0000-0000-0000-5ac63a180000\"",
"LastUpdateTime": "2018-04-05T15:00:40.243Z",
"id": "ab2a242d-f1a5-62ed-b420-31b52e958586",
"PartitionKey": "directory-clusters-9b679d2a-42c5-4c9a-a2e2-3ce63c1c3506",
"_rid": "kpvxLAs6gkmsCQAAAAAAAA==",
"_self": "dbs/kvpxAA==/colls/kpvxLAs6gkk=/docs/kvpxALs6kgmsCQAAAAAAAA==/",
"_attachments": "attachments/",
"_ts": 1522940440
}
Since your cosmosdb has array and ADF doesn't support serialize array for cosmos db, this is the workaround I can provide.
First, export all your document to json files with export json as-is (to blob or adls or file systems, any file storage). I think you already knows how to do it. In this way, each collection will have a json file.
Second, handle each json file, to exact each row in the file to a single file.
I only provide pipeline for step 2. You could use execute pipeline activity to chain step 1 and step 2. And you could even handle all the collections in step 2 with a foreach activity.
Pipeline json
{
"name": "pipeline27",
"properties": {
"activities": [
{
"name": "Lookup1",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "BlobSource",
"recursive": true
},
"dataset": {
"referenceName": "AzureBlob7",
"type": "DatasetReference"
},
"firstRowOnly": false
}
},
{
"name": "ForEach1",
"type": "ForEach",
"dependsOn": [
{
"activity": "Lookup1",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "#activity('Lookup1').output.value",
"type": "Expression"
},
"activities": [
{
"name": "Copy1",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "DocumentDbCollectionSource",
"query": {
"value": "select #{item()}",
"type": "Expression"
},
"nestingSeparator": "."
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false,
"cloudDataMovementUnits": 0
},
"inputs": [
{
"referenceName": "DocumentDbCollection1",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "AzureBlob6",
"type": "DatasetReference",
"parameters": {
"id": {
"value": "#item().id",
"type": "Expression"
},
"PartitionKey": {
"value": "#item().PartitionKey",
"type": "Expression"
}
}
}
]
}
]
}
}
]
},
"type": "Microsoft.DataFactory/factories/pipelines"
}
dataset json for lookup
{
"name": "AzureBlob7",
"properties": {
"linkedServiceName": {
"referenceName": "bloblinkedservice",
"type": "LinkedServiceReference"
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "JsonFormat",
"filePattern": "arrayOfObjects"
},
"fileName": "cosmos.json",
"folderPath": "aaa"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
Source dataset for copy. Actually, this dataset has no use. Just want to use it to host the query (select #{item()}
{
"name": "DocumentDbCollection1",
"properties": {
"linkedServiceName": {
"referenceName": "CosmosDB-r8c",
"type": "LinkedServiceReference"
},
"type": "DocumentDbCollection",
"typeProperties": {
"collectionName": "test"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
Destination dataset. With two parameters, it also addressed your file name request.
{
"name": "AzureBlob6",
"properties": {
"linkedServiceName": {
"referenceName": "AzureStorage-eastus",
"type": "LinkedServiceReference"
},
"parameters": {
"id": {
"type": "String"
},
"PartitionKey": {
"type": "String"
}
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "JsonFormat",
"filePattern": "setOfObjects"
},
"fileName": {
"value": "#{dataset().PartitionKey}-#{dataset().id}.json",
"type": "Expression"
},
"folderPath": "aaacosmos"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
please also note the limitation of Lookup activity:
The following data sources are supported for lookup. The maximum number of rows can be returned by Lookup activity is 5000, and up to 2MB in size. And currently the max duration for Lookup activity before timeout is one hour.
Have you considered implementing this in a different way using Azure Functions? ADF is designed for moving data in bulk from one place to another and only generates a single file per collection.
You could consider having an Azure Function that is triggered when documents are added / updated in your collection and have the Azure Function output the document to blob storage. This should scale well and would be relatively easy to implement.
Just take one collection as an example.
And inside the foreach:
And your lookup and copy activity source dataset reference the same cosmosdb dataset.
If you want to copy your 5 collections, you could put this pipeline into an execute activity. And the master pipeline of the execute activity has a foreach activity.
I also struggled a bit with this, especially getting around the size limits of the Lookup activity, since we have a LOT of data to migrate. I ended up creating a JSON file with a list of timestamps to query the Cosmos data with, then for each of those, getting the document IDs in that range, and then for each of those, getting the full document data and saving it to a path such as PartitionKey/DocumentID. Here's the pipelines I created:
LookupTimestamps - loops through each timestamp range from a times.json file, and for each timestamp, executes the ExportFromCosmos pipeline
{
"name": "LookupTimestamps",
"properties": {
"activities": [
{
"name": "LookupTimestamps",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "BlobSource",
"recursive": false
},
"dataset": {
"referenceName": "BlobStorageTimestamps",
"type": "DatasetReference"
},
"firstRowOnly": false
}
},
{
"name": "ForEachTimestamp",
"type": "ForEach",
"dependsOn": [
{
"activity": "LookupTimestamps",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "#activity('LookupTimestamps').output.value",
"type": "Expression"
},
"isSequential": false,
"activities": [
{
"name": "Execute Pipeline1",
"type": "ExecutePipeline",
"typeProperties": {
"pipeline": {
"referenceName": "ExportFromCosmos",
"type": "PipelineReference"
},
"waitOnCompletion": true,
"parameters": {
"From": {
"value": "#{item().From}",
"type": "Expression"
},
"To": {
"value": "#{item().To}",
"type": "Expression"
}
}
}
}
]
}
}
]
},
"type": "Microsoft.DataFactory/factories/pipelines"
}
ExportFromCosmos - nested pipeline that's executed from the above pipeline. This is to get around the fact you can't have nested ForEach activities.
{
"name": "ExportFromCosmos",
"properties": {
"activities": [
{
"name": "LookupDocuments",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "DocumentDbCollectionSource",
"query": {
"value": "select c.id, c.partitionKey from c where c._ts >= #{pipeline().parameters.from} and c._ts <= #{pipeline().parameters.to} order by c._ts desc",
"type": "Expression"
},
"nestingSeparator": "."
},
"dataset": {
"referenceName": "CosmosDb",
"type": "DatasetReference"
},
"firstRowOnly": false
}
},
{
"name": "ForEachDocument",
"type": "ForEach",
"dependsOn": [
{
"activity": "LookupDocuments",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "#activity('LookupDocuments').output.value",
"type": "Expression"
},
"activities": [
{
"name": "Copy1",
"type": "Copy",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false,
"secureInput": false
},
"typeProperties": {
"source": {
"type": "DocumentDbCollectionSource",
"query": {
"value": "select * from c where c.id = \"#{item().id}\" and c.partitionKey = \"#{item().partitionKey}\"",
"type": "Expression"
},
"nestingSeparator": "."
},
"sink": {
"type": "BlobSink"
},
"enableStaging": false
},
"inputs": [
{
"referenceName": "CosmosDb",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "BlobStorageDocuments",
"type": "DatasetReference",
"parameters": {
"id": {
"value": "#item().id",
"type": "Expression"
},
"partitionKey": {
"value": "#item().partitionKey",
"type": "Expression"
}
}
}
]
}
]
}
}
],
"parameters": {
"from": {
"type": "int"
},
"to": {
"type": "int"
}
}
}
}
BlobStorageTimestamps - dataset for the times.json file
{
"name": "BlobStorageTimestamps",
"properties": {
"linkedServiceName": {
"referenceName": "AzureBlobStorage1",
"type": "LinkedServiceReference"
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "JsonFormat",
"filePattern": "arrayOfObjects"
},
"fileName": "times.json",
"folderPath": "mycollection"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
BlobStorageDocuments - dataset for where the documents will be saved
{
"name": "BlobStorageDocuments",
"properties": {
"linkedServiceName": {
"referenceName": "AzureBlobStorage1",
"type": "LinkedServiceReference"
},
"parameters": {
"id": {
"type": "string"
},
"partitionKey": {
"type": "string"
}
},
"type": "AzureBlob",
"typeProperties": {
"format": {
"type": "JsonFormat",
"filePattern": "arrayOfObjects"
},
"fileName": {
"value": "#{dataset().partitionKey}/#{dataset().id}.json",
"type": "Expression"
},
"folderPath": "mycollection"
}
},
"type": "Microsoft.DataFactory/factories/datasets"
}
The times.json file it just a list of epoch times and looks like this:
[{
"From": 1556150400,
"To": 1556236799
},
{
"From": 1556236800,
"To": 1556323199
}]