I have two sample files in blob as sample1.csv and sample2.csv as below
data sample
SQL table name sample2, with column Name,id,last name,amount
Created a ADF flow without schema, it results as below
preview data
source settings are allow schema drift checked.
sink setting are auto mapping turned on. allow insert checked. table action none.
I have also tried setting a define schema in dataset, its result are same.
any help here?
my expected outcome would be data in sample1 will inserted null into the column "last name"
If I understand correctly, you said: "my expected outcome would be data in sample1 will inserted null into the column last name", you only need to add a derived column to you sample1.csv file.
You could follow my steps:
I create a sample1.csv file in Blob Storage and a sample2 table in my SQL database:
Using DerivedColumn to create new column last name with null value:
expression: toString(null())
Sink settings:
Run the pipeline and check the data in table:
Hope this helps.
You cannot mix schemas in the same source in the same data flow execution.
Schema Drift will handle changes to the schema on an execution-per-execution basis.
But if you are reading multiple different schemas from a folder, you will get non-deterministic results.
Instead, if you loop through those files in a pipeline ForEach one-by-one, data flow will be able to handle the evolving schema.
Related
I would like to load incremental data from data lake into on premise SQL, so that i created data flow do the necessary data transformation and cleaning the data.
after that i copied all the final data sink to staging data lake to stored CSV format.
I am facing two kind of issues here.
when ever i am trigger / debug to loading my dataset(data flow full activity ), the first time data loaded in CSV, if I load second time similar pipeline, the target data lake, the CSV file loaded empty data, which means, the column header loaded but i could not see the any value inside file.
coming to copy activity, which is connected to on premise SQL server, i am trying to load the data but if we trigger this pipeline again and again, the duplicate data loaded, i want to load only incremental or if updated data comes from data lake CSV file. how do we handle this.
Kindly suggest.
When we want to incrementally load our data to a database table, we need to use the Upsert option in copy data tool.
Upsert helps you to incrementally load the source data based on a key column (or columns). If the key column is already present in target table, it will update the rest of the column values, else it will insert the new key column with other values.
Look at following demonstration to understand how upsert works. I used azure SQL database as an example.
My initial table data:
create table player(id int, gname varchar(20), team varchar(10))
My source csv data (data I want to incrementally load):
I have taken an id which already exists in target table (id=1) and another which is new (id=4).
My copy data sink configuration:
Create/select dataset for the target table. Check the Upsert option as your write behavior and select a key column based on which upsert should happen.
Table after upsert using Copy data:
Now, after the upsert using copy data, the id=1 row should be updated and id=4 row should be inserted. The following is the final output achieved which is inline with expected output.
You can use the primary key in your target table (which is also present in your source csv) as the key column in Copy data sink configuration. Any other configuration (like source filter by last modified configuration) should not effect the process.
I've multiple csv files and multiple tables.
The table name is file name and column name is first row of csv file.
Now I want to add default value of empty string to the sink table.
Consider my scenario,
employee:
id int, name varchar, is_active bit NULL
employee.csv:
id|name|is_active
1|raja|
Now I'm trying to copy the csv data to PostgreSQL table its throwing error.
Expected result is default value if its empty value.
You can use NULLIF in PostgreSQL:
NULLIF(argument_1,argument_2);
The NULLIF function returns a null value if argument_1 equals to argument_2, otherwise it returns argument_1.
This way you can replace NULL value with some other value
If your error is related to Type mismatch then consider typecasting the column first
Thanks!
As per the issue, tried to repro the scenario and here is the following outcome which was successfully copied. You have to use
Source Dataset: employee.csv from Azure Blob Storage
Sink Dataset : Here, I have used the sink as Azure SQL DB for some limitations but as you have used PostgreSQL is almost similar.
Copy Activity Settings:
Under the mapping settings there will be type conversion, where you have to import schema else you can dynamically add
Output:
Alternative to use DataFlow - if you have multiple data fields, you need to use the derived column transformation to generate new columns in your data flow or to modify existing fields.
For more details, refer Derived column transformation in mapping data flow.
You can even refer to this Microsoft Q&A post for more insights: Copy Task failure because of conversion failure
I am trying to copy tables from one schema to another with the same Azure SQL db. So far, I have created a lookup pipeline and passed the parameters for the for each loop and copy activity. But my sink dataset is not taking the parameter value I have given under "table option" field rather it is taking the dummy table I chose when creating the sink dataset. Can someone tell how can I pass dynamic table name to a sink dataset?
I have given concat('dest_schema.STG_',#{item().table_name})} in the table option field.
To make the schema and table names dynamic, add Parameters to the Dataset:
Most important - do NOT import a schema. If you already have one defined in the Dataset, clear it. For this Dataset to be dynamic, you don't want improper schemas interfering with the process.
In the Copy activity, provide the values at runtime. These can be hardcoded, variables, parameters, or expressions, so very flexible.
If it's the same database, you can even use the same Dataset for both, just provide different values for the Source and Sink.
WARNING: If you use the "Auto-create table" option, the schema for the new table will define any character field as varchar(8000), which can cause serious performance problems.
MY OPINION:
While you can do this, one of my personal rules is to not cross the database boundary. If the Source and Sink are on the same SQL database, I would try to solve this problem with a Stored Procedure rather than a data factory.
I'm testing the data flow on my Azure Data Factory. I created Data Flow with the following details:
Source dataset linked service - from CSV files dataset from Blob storage
Sink linked service - Azure SQL database with pre-created table
My CSV files are quite simple as they contain only 2 columns (PARENT, CHILD). So, my table in SQL DB also have only 2 columns.
For the sink setting of my data flow, I have allowed insert data and leaving other options as default.
I have also mapped the 2 columns for input and output columns as per screenshot.
The pipeline with data flow ran successfully when I checked the result, I could see thqat 5732 rows were processed. Is this the correct way to check? As this is the first time I try this functionality in Azure Data Factory.
But, when I click on Data preview tab, they are all NULL value.
And; when I checked my Azure SQL DB in the table where I tried to insert the data from CSV files from Blob storage with selecting top 1000 rows from this table, I don't see any data.
Could you please let me know what I configured incorrectly on my Data Flow? Thank you very much in advance.
Here is the screenshot of ADF data flow source data, it does see the data on the right side as they are not NULL, but on the left side are all NULLs. I imagine that the right side are the data from the CSV from the source on the blob right? And the left side is the sink destination as the table is empty for now?
And here is the screenshot for the sink inspect input, I think this is correct as it reads the 2 columns correctly (Parent, Child), is it?
After adding Map drifted, to map "Parent" => "parent" and "Child" => "child"
I get this error message after running the pipeline.
When checking on sink data preview, I get this error message. It seems like there is incorrect mapping?
I rename the MapDrifted1 expression to "toString(byName('Parent1))" and Child1 as suggested.
The data flow executed successfully, however I still get NULL data in the sink SQL table.
Can you copy/paste the script behind your data flow design graph? Go to the ADF UI, open the data flow, then click the Script button on top right.
In your Source transformation, click on Data Preview to see the data. Make sure you are seeing your data, not NULLs. Also, look at the Inspect on the INPUT for your Sink, to see if ADF is reading additional columns.
Every morning, an automatic job creates a new table from an Avro file. In the afternoon, I would need to append some data to this table from a Query.
When trying to do so, I get the following error:
Error: Invalid schema update. Field chn has changed mode from REQUIRED to NULLABLE
I noticed that I can change the property of the field chn from REQUIRED to NULLABLE in the BigQuery Web UI and then it works fine, but I would have to do it manually everyday which is not what I am looking for.
Is there a way to "cast" the field as REQUIRED during the append query ?
Or during the first import from the Avro file, force the field to be NULLABLE and not REQUIRED ?
Thanks !
The feature that allows relaxing a field as part of a query or a load job will be available in production shortly. I will update this answer when it goes live (likely within a week).
Update: 08/25/2016
You can supply schemaUpdateOptions in load or query job configuration.
Multiple options can be provided.
It allows the schema of the destination table to be updated as a side effect of the load or query job. Schema update options are supported in two cases:
When writeDisposition is WRITE_APPEND
When writeDisposition is WRITE_TRUNCATE and the destination table is a partition of a table, specified by partition decorators
For non-partitioned tables, WRITE_TRUNCATE will always overwrite the schema.
The following values are supported:
ALLOW_FIELD_ADDITION: allow adding a nullable field to the schema
ALLOW_FIELD_RELAXATION: allow relaxing a required field in the original schema to nullable
NOTE: This doesn't currently work with schema auto-detection. We plan to support that soon.