I would like to create job:
SourceFile -> Transformer -> Lookup connected to DB connector (Lookup Sparse) -> Peek
SourceFile contains dates (string format). In Transformer I've added InputDate with input dates, SourceT and TargetT with some hardcoded parameters. I would like to call
select Function(to_date(Value1, 'YYYY-MM-DD HH:MI:SS'), Value2, Value3) from sysibm.sysdummy1
where values: Value1,2,3 are the transferred from Transformer (Value1=Inputdate, Value2=SourceT and Value3=TargetT ) to DB connector and results of query are transferred to Peek. I can do this using DB2 stored procedure but at this moment it is not possible. I would like to call the function as many times as I have InputDate values in source file.
I tried do this using lookup sparse but still does not work.
Anybody can help me?
Related
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 have columns like a int, b varchar,c timestamp in my table x (redshift) and am trying to load those three columns into another table b(redshift) by using a mapping m1, in that am using odbc connection's.
Issue is am able to load the data for all the columns except date fields (c timestamp) whether those are from src or expression.
In place of date null values are storing.
Mapping ran successful without any issue/warning.
Note: am using odbc connection's because I need to call stored procedure in Post sql.
thanks for your response.
As I modified the lookup transformation then I resolved the issue.
In lookup mapping I just modified the multiple matches to return all rows then the date fields are getting loaded from source/expression transformation to target.
I need a data factory that will:
check an Azure blob container for csv files
for each csv file
insert a row into an Azure Sql table, giving filename as a column value
There's just a single csv file in the blob container and this file contains five rows.
So far I have the following actions:
Within the for-each action I have a copy action. I did give this a source of a dynamic dataset which had a filename set as a parameter from #Item().name. However, as a result 5 rows were inserted into the target table whereas I was expecting just one.
The for-each loop executes just once but I don't know to use a data source that is variable(s) holding the filename and timestamp?
You are headed in the right direction, but within the For each you just need a Stored Procedure Activity that will insert the FileName (and whatever other metadata you have available) into Azure DB Table.
Like this:
Here is an example of the stored procedure in the DB:
CREATE Procedure Log.PopulateFileLog (#FileName varchar(100))
INSERT INTO Log.CvsRxFileLog
select
#FileName as FileName,
getdate() as ETL_Timestamp
EDIT:
You could also execute the insert directly with a Lookup Activity within the For Each like so:
EDIT 2
This will show how to do it without a for each
NOTE: This is the most cost effective method, especially when dealing with hundred or thousands of files on a recurring basis!!!
1st, Copy the output Json Array from your lookup/get metadata activity using a Copy Data activity with a Source of Azure SQLDB and Sink of Blob Storage CSV file
-------SOURCE:
-------SINK:
2nd, Create another Copy Data Activity with a Source of Blob Storage Json file, and a Sink of Azure SQLDB
---------SOURCE:
---------SINK:
---------MAPPING:
In essence, you save the entire json Output to a file in Blob, you then copy that file using a json file type to azure db. This way you have 3 activities to run even if you are trying to insert from a dataset that has 500 items in it.
Of course there is always more than one way to do things, but I don't think you need a For Each activity for this task. Activities like Lookup, Get Metadata and Filter output their results as JSON which can be passed around. This JSON can contain one or many items and can be passed to a Stored Procedure. An example pattern:
This is the sort of ELT pattern common with early ADF gen 2 (prior to Mapping Data Flows) which makes use of resources already in use in your architecture. You should remember that you are charged by the activity executions in ADF (eg multiple iteration in an unnecessary For Each loop) and that generally compute in Azure is expensive and storage is cheap, so think about this when implementing patterns in ADF. If you build the pattern above you have two types of compute: the compute behind your Azure SQL DB and the Azure Integration Runtime, so two types of compute. If you add a Data Flow to that, you will have a third type of compute operating concurrently to the other two, so personally I only add these under certain conditions.
An example implementation of the above pattern:
Note the expression I am passing into my example logging proc:
#string(activity('Filter1').output.Value)
Data Flows is perfectly fine if you want a low-code approach and do not have compute resource already available to do this processing. In your case you already have an Azure SQL DB which is quite capable with JSON processing, eg via the OPENJSON, JSON_VALUE and JSON_QUERY functions.
You mention not wanting to deploy additional code which I understand, but then where did your original SQL table come from? If you are absolutely against deploying additional code, you could simply call the sp_executesql stored proc via the Stored Proc activity, use a dynamic SQL statement which inserts your record, something like this:
#concat( 'INSERT INTO dbo.myLog ( logRecord ) SELECT ''', activity('Filter1').output, ''' ')
Shred the JSON either in your stored proc or later, eg
SELECT y.[key] AS name, y.[value] AS [fileName]
FROM dbo.myLog
CROSS APPLY OPENJSON( logRecord ) x
CROSS APPLY OPENJSON( x.[value] ) y
WHERE logId = 16
AND y.[key] = 'name';
I have an azure pipeline that moves data from one point to another in parquet files. I need to join some data from a Postgresql database that is in an AWS tenancy by a unique ID. I am using a dataflow to create the unique ID I need from two separate columns using a concatenate. I am trying to create where clause e.g.
select * from tablename where unique_id in ('id1','id2',id3'...)
I can do a lookup query to the database, but I can't figure out how to create the list of IDs in a parameter that I can use in the select statement out of the dataflow output. I tried using a set variable and was going to put that into a for-each, but the set variable doesn't like the output of the dataflow (object instead of array). "The variable 'xxx' of type 'Array' cannot be initialized or updated with value of type 'Object'. The variable 'xxx' only supports values of types 'Array'." I've used a flatten to try to transform to array, but I think the sync operation is putting it back into JSON?
What a workable approach to getting the IDs into a string that I can put into a lookup query?
Some notes:
The parquet file has a small number of unique IDs compared to the total unique IDs in the database.
If this were an azure postgresql I could just use a join in the dataflow to do the join, but the generic postgresql driver isn't available in dataflows. I can't copy the entire database over to Azure just to do the join and I need the dataflow in Azure for non-technical reasons.
Edit:
For clarity sake, I am trying to replace local python code that does the following:
query = "select * from mytable where id_number in "
df = pd.read_parquet("input_file.parquet")
df['id_number'] = df.country_code + df.id
df_other_data = pd.read_sql(conn, query + str(tuple(df.id_number))
I'd like to replace this locally executing code with ADF. In the ADF process, I have to replace the transformation of the IDs which seems easy enough if a couple of different ways. Once I have the IDs in the proper format in a column in a dataset, I can't figure out how to query a database that isn't supported by Data Flow and restrict it to only the IDs I need so I don't bring down the entire database.
Due to variables of ADF only can store simple type. So we can define an Array type paramter in ADF and set default value. Paramters of ADF support any type of elements including complex JSON structure.
For example:
Define a json array:
[{"name": "Steve","id": "001","tt_1": 0,"tt_2": 4,"tt3_": 1},{"name": "Tom","id": "002","tt_1": 10,"tt_2": 8,"tt3_": 1}]
Define an Array type paramter and set its default value:
So we will not get any error.
I have the following talend job where each is an API call to get the currency exchange rate.
The fields within each Tmap are:
- FrommCurr
- ToCurr (Each Tmap has a hardcoded string value "EUR", "USD", "CAD")
- Date
- DateTime
I would like to combine all of these into a single table before inserting. what is the object that will let me combine them? Or should I insert each single TMap into the database? The database, I will be inserting into is MSSQL.
As long As all the final tMap components produce exactly the same schema , you can join the output into a single stream of rows with tUnite .