I have a Copy-Data task where I am adding an additional column called "Id" and its value is #guid(). Problem is that for every row it is importing, the Guid value is always the same and the destination/sink throws a primary key violation.
Additional column definition
The copy activity will copy the same guid() for all rows if you use additional column.
To get Unique guid() for Each row, you can follow the demonstration below.
First Give your source data to lookup activity and give its output to a ForEach activity.
This is my source data in csv format for sample, give this to lookup.
source.csv:
name
"Rakesh"
"Laddu"
"Virat"
"John"
Use another dummy dataset and give any one value to it. Use this in copy activity.
Dummy.csv:
name
"Rakesh"
ForEach activity:
Inside ForEach use Copy activity and give the dummy dataset. Create additional columns and give our source data(#item().name) and #guid().
Copy activity:
Now in sink give your database dataset. Here for sample, I have used Azure SQL database table.
Go to mapping of copy activity and click on import Schemas.Give any string value for it to import the schemas of source (Here dummy schema) and sink.
After the above, you will get like this, in this give the additional columns we created to the database columns.
Pipeline Execution:
After Executing the pipeline, you can get the desired output with Unique rows.
Output:
Related
A question concerning Azure Data Factory.
I need to persist the iterator value from a lookup activity (an Id column from a sql table) to my sink together with other values.
How to do that?
I thought that I could just reference the iterator value as #{item().id} as source and a destination column name from from my sql table sink. That doesn’t seems to work. The resulting value in the destination column is NULL.
I have used 2 look up activities, one for id values and the other for remaining values. Now, to combine and insert these values to sink table, I have used the following:
The ids look up activity output is as following:
I have one more column to combine with above id values. The following is the look up output for that:
I have given the following dynamic content as the items value in for each as following:
#range(0,length(activity('ids').output.value))
Inside for each activity, I have given the following script activity query to insert data as required into sink table:
insert into t1 values(#{activity('ids').output.value[item()].id},'#{activity('remaining rows').output.value[item()].gname}')
The data would be inserted successfully and the following is the reference image of the same:
Forum Newbie...
I want to utilise the ADF Copy function, to carry out incremental table extracts from one Azure DB to another. Every table in the database that I need all have the same 2 relevant fields i.e. date1, date2. For Watermark comparison purposes, I need to use isnull(date1,date2), but unsure how to do this, i.e. I am not sure how I can add this consistent derived value to the Source as an additional field that can perhaps be added via the Query or Stored Procedure Option on the source, to utilise the #item().source.schema and #item().source.table values that have already been generated as parameters..?
You can use the query option in the Copy data activity source and add a new column in the query itself to get the results of isnull(date1,date2) and include the parameter values to get the table name instead of hardcoding them as shown below.
In source, select Query option under Use query and add dynamic content to concat() select statement with parameter values.
#concat('select *, isnull(date1,date2) as final_dt from ',pipeline().parameters.schema,'.',pipeline().parameters.table)
Sink table data output:
I have an Azure Data factory trigger that is fired off when a file is placed in blob storage, this trigger will start pipeline execution and pass the file name to the data flow activity. I would like to make sure that all the column names from the header row in the file are in the sink table. There is an identity column in the sink table that should not be in the comparison. Not sure how to tackle this task, I've read about the 'derived column' activity, is that the route I should take?
You can select or filter which columns reside in sink dataset or table by using "Field mapping". You can optionally use "derived columns" transformation, however in the "sink transformation" you will have this by default and is set to "Auto mapping". Here you can add or remove which columns are written to sink.
In the below example the column "id" can be assumed as similar to "Identity" column in your table. Assuming all the files have same columns:
Once you have modified as per your need, you can confirm the same from the "inspect" tab before run.
Strategy:
Use two ADF pipelines, one to get a list of all files and another one to process each file copying its content to a specific SQL table.
Setup:
I’ve created 4 CSV files, following the pattern you need: “[CustomerID][TableName][FileID].csv” and 4 SQL tables, one for each type of file.
A_inventory_0001.csv: inventory records for customer A, to be
inserted into the SQL table “A_Inventory”.
A_sales_0003.csv: sales
records for customer A, to be inserted into the SQL table “A_Sales”.
B_inventory_0002.csv: inventory records for customer B, to be
inserted into the SQL table “B_Inventory”.
B_sales_0004.csv: sales
records for customer B, to be inserted into the SQL table “B_Sales”
Linked Services
In Azure Data Factory, the following linked services were create using Key Vault (Key Vault is optional).
Datasets
The following datasets were created. Note we have created some parameters to allow the pipeline to specify the source file and the destination SQL table.
The dataset “AzureSQLTable” has a parameter to specify the name of the destination SQL table.
The dataset “DelimitedTextFile” has a parameter to specify the name of the source CSV file.
The dataset “DelimitedTextFiles” has no parameter because it will be used to list all files from source folder.
Pipelines
The first pipeline “Get Files” will get the list of CSV files from source folder (Get Metadata activity), and then, for each file, call the second pipeline passing the CSV file name as a parameter.
Inside the foreach loop, there is a call to the second pipeline “Process File” passing the file name as a parameter.
The second pipeline has a parameter “pFileName” to receive the name of the file to be processed and a variable to calculate the name of the destination table based on the file name.
The first activity is to use a split in the file name to extract the parts we need to compose the destination table name.
In the expression bellow we are splitting the file name using the “__” separator and then using the first and second parts to compose the destination table name.
#concat(string(split(pipeline().parameters.pFileName, '_')[0]),'_',string(split(pipeline().parameters.pFileName, '_')[10]))
The second activity will then copy the file from the source “pFileName” to the desnation table “vTableName” using dynamic mapping, ie not adding specific column names as this will be dynamic.
The files I used in this example and the ADF code are available here:
https://github.com/diegoeick/stack-overflow/tree/main/69340699
I hope this will resolve your issue.
In case you still need to save the CustomerID and FileID in the database tables, you can use the dynamic mapping and use the available parameters (filename) and create a json with the dynamic mapping in the mapping tab of your copy activity. You can find more details here: https://learn.microsoft.com/en-us/azure/data-factory/copy-activity-schema-and-type-mapping#parameterize-mapping
I have a lot of JSON files in Blob Storage and what I would like to do is to load the JSON files via Data factoryV2 into SQL Data Warehouse. I would like the filename in a column for each JSON file. I know how to
do this in SSIS but I am not sure how to replicate this in Data Factory.
e.g File Name: CornerShop.csv as CornerShop in the filename column in SQL Data Warehouse
Firstly,please see the limitation in the copy activity column mapping:
Source data store query result does not have a column name that is
specified in the input dataset "structure" section.
Sink data store (if with pre-defined schema) does not have a column
name that is specified in the output dataset "structure" section.
Either fewer columns or more columns in the "structure" of sink
dataset than specified in the mapping.
Duplicate mapping.
So,i don't think you could do the data transfer plus file name at one time.My idea is:
1.First use a GetMetadata activity. It should get the filepaths of each file you want to copy. Use the "Child Items" in the Field list.
2.On success of GetMetaData activity, do ForEach activity. For the ForEach activity's Items, pass the list of filepaths.
3.Inside the ForEach activity's Activities, place the Copy activity. Reference the iterated item by #item() or #item().name on the blob storage source file name.
4.Meanwhile,configure the filename as a parameter into stored procedure. In the stored procedure, merge the filename into fileName column.
I have an ADF Copy Data flow and I'm getting the following error at runtime:
My source is defined as follows:
In my data set, the column is defined as shown below:
As you can see from the second image, the column IsLiftStation is defined in the source. Any idea why ADF cannot find the column?
I've had the same error. You can solve this by either selecting all columns (*) in the source and then mapping those you want to the sink schema, or by 'clearing' the mapping in which case the ADF Copy component will auto map to columns in the sink schema (best if columns have the same names in source and sink). Either of these approaches works.
Unfortunately, clicking the import schema button in the mapping tab doesn't work. It does produce the correct column mappings based on the columns in the source query but I still get the original error 'the column could not be located in the actual source' after doing this mapping.
could you check that is there a column named 'ae_type_id' in your schema? If that's the case, could you remove that column and try again? The columns in the schema must be aligned with columns in the query.
The issue is caused by an incomplete schema in one of the data sources. My solution is:
Step through the data flow selecting the first schema, Import projection
Go to the flow and Data Preview
Repeat for each step.
In my case, there were trailing commas in one of the CSV files. This caused automated column names to be created in the import allowing me to fix the data file.