In ADF i have a copy task that copies data from JSON to Delimited text, i get the result as
A | B | C
"name"|False|"description"
Json record is like
{"A":"name","B":"false","C":"description"}
Excepted result is as below
A | B | C
"name"|false|"description"
The bool value have to be in lowercase in the resulting Delimited text file, what am i missing?
I can reproduce this. The reason is you are converting the string to the ADF dataytpe "Boolean" which for some reason renders the values in Proper case.
Do you really have a receiving process which is case-sensitive? If you need to maintain the case of the source value simply remove the mapping, ie
If you do need some kind of custom mapping, then simply change the mapping data type to String and not Boolean.
UPDATE after new JSON provided
OK, so your first json sample has the "false" value in quotes so is treated as a string. In your second example, your json "true" is not in quotes so is a genuine json boolean value. ADF is auto-detecting this at run time and it seems like it can not be over-ridden as far as I can tell. Happy to be corrected. As an alternative, consider altering your original json to a string, as per you original example OR copying the file to Blob Store or Azure Data Lake, runniing some transform on it (eg Databricks) and then outputting the file. Alternately consider Mapping Data Flows.
Related
I am trying to parametrize the Column Delimiter field in CSV dataset in Azure Data Factory.
(https://i.stack.imgur.com/JGkD5.png)
This unfortunately doesn't work when I pass a special character as a parameter.
When I hardcode the special character in the column delimiter field all works as expected.
This works
However, when I have \u0006 as a parameter in SQL DB (varchar(10) type)
(https://i.stack.imgur.com/GyTdr.png)
and I pass it in the pipeline
(https://i.stack.imgur.com/CUeRf.png)
The Copy Data activity doesn't detect this special character as a delimiter.
My guess is that when I use a parameter it passes \u0006 as a string, but I can't find anywhere how to bypass that.
I tried to pass \u0006 in column delimiter as a dynamic content. It didn't consider that as column delimiter. All data are shown as a single column.
Therefore, I tried to pass equivalent symbol of \u0006 ACK() as a dynamic value to that column delimiter and it worked. I tried to convert the \u0006 into the special character using SQL script. Below are the steps to do this.
File delimiters are stored in a table.
To convert this column into equivalent characters, \u is removed from the column and the resultant hexadecimal value is converted into an integer. Then nchar() function is used to the integer data.
select nchar(cast(right(file_delimiter,4) as int)) as file_delimiter from t5
The above SQL query is used in Lookup activity in ADF.
When this value is passed as a dynamic content to column delimiter to that dataset, values are properly delimited.
Once pipeline is run, data is copied successfully.
I have a simple pipeline that loads data from a csv file to an Azure SQL db.
I have added a data flow where I have ensured all schema matches the SQL table. I have a specific field which contains numbers with leading zeros. The data type in the source - projection is set to string. The field is mapped to the SQL sink showing as string data-type. The field in SQL has nvarchar(50) data-type.
Once the pipeline is run, all the leading zeros are lost and the field appears to be treated as decimal:
Original data: 0012345
Inserted data: 12345.0
The CSV data shown in the data preview is showing correctly, however for some reason it loses its formatting during insert.
Any ideas how I can get it to insert correctly?
I had repro’d in my lab and was able to load as expected. Please see the below repro details.
Source file (CSV file):
Sink table (SQL table):
ADF:
Connect the data flow source to the CSV source file. As my file is in text format, all the source columns in the projection are in a string.
Source data preview:
Connect sink to Azure SQL database to load the data to the destination table.
Data in Azure SQL database table.
Note: You can all add derived columns before sink to convert the value to string as the sink data type is a string.
Thank you very much for your response.
As per your post the DF dataflow appears to be working correctly. I have finally discovered an issue with the transformation - I have an Azure batch service which runs a python script, which does a basic transformation and saves the output to a csv file.
Interestingly, when I preview the data in the dataflow, it looks as expected. However, the values stored in SQL are not.
For the sake of others having a similar issue, my existing python script used to convert a 'float' datatype column to string-type. Upon conversion, it used to retain 1 decimal number but as all of my numbers are integers, they were ending up with .0.
The solution was to convert values to integer and then to string:
df['col_name'] = df['col_name'].astype('Int64').astype('str')
I am reading a fixed-width file in mapping Data Flow and loading it to the table. I want to validate the fields, datatype, lengths of the field that I am extracting in the Derived column using substring.
How to Achieve this in ADF
Use a Conditional Split and add a condition for each property of the field that you wish to test for. For data type checking, we literally just landed new isInteger(), isString() ... functions today. The docs are still in the printing press, but you'll find them in the expression builder. For length use length().
I've created a dataflow task in azure data factory and used derived column transformation. One of the source derived column value is '678396' which is extracted through Substring function and datatype "String" by default. I want to convert it into "Integer" because my target column datatype is "Integer".
I've to converted the column in this expression:
ToInteger(Substring(Column_1,1,8))
Please help me with correct expression.
Kind regards,
Rakesh
You don't need to build the expression. If you column data are all like int string "678396", or the output of Substring(Column_1,1,8) are int String
Data Factory can convert the int string to integer data type directly from source to sink. We don't need convert again.
Make sure you set column mapping correctly in sink settings. All things would works well.
Update:
This my csv dataset:
You can choose the Quote character to singe quote, then could solve the problem. See the source data preview in Copy active and Data Flow:
Copy active source:
Data Flow overview:
In data flow, we will get the alert like you said comment, we could ignore it and debug the data flow directly:
HTH.
you don't even need to substruct quotes '', as ToInteger function can convert numbers as string type
When reading a decimal number from JSON data files using a Mapping Data Flow, the decimal digits are truncated.
[Source data]
{
"value": 1123456789.12345678912345678912
}
In Data Factory, the source dataset is configured with no schema. The Mapping Data Flow projection defines a decimal data type with sufficient precision and scale.
[Mapping Data Flow script]
source(output(
value as decimal(35,20)
),
...
However, when viewing the value in the 'Data preview' window, or reviewing pipeline output, the value is truncated.
[Output]
1123456789.12345670000000000000
This issue doesn't occur with other file formats, such as delimited text.
When previewing the data from the source dataset, the decimal digits are truncated in the same way. This occurs whether or not a schema is set. If a schema is set, the data type is number rather than decimal since it's JSON. Mozilla Developer Network documentation calls out the varied number of decimal digits supported by browsers, so I wonder if this is down to the JSON parser being used.
Is this expected behaviour? Can Data Factory be configured to support a the full number of decimal places when working with JSON? Unfortunately this is calling into question whether it's viable to perform aggregate calculations in Data Factory.
I've created a same test as yours and got the same result as follows:
After I changed the soure data, I put double quotes on the value:
Then I use a toDecimal(Value,35,20) to convert the string type to decimal type:
It seems work well. So we can get conclusion:
Don't let ADF do default data type conversion, it will truncate the length of the value.
This issue doesn't occur with other file formats, such as delimited text. Because the default value is the string type.
Its common issue with JSON parser, FloatParseHandling setting is available in .net library but not in ADF.
FloatParseHandling can set to Decimal while parsing the file through .net.
Until the setting is made available in ADF need to try the workaround - using quotes ["] at both end to make the value string and get it converted after loading.