We have a requirement where the client will upload the file with set of rules understandable by the Abinitio. We have to read the rules from the file and need to apply on the dataset dynamically.
I have tried uploading the user rules into a table and joined with the data file to pull the required columns along with the filter conditions and purl the column name in the filter by expression but while executing I am getting too long for conversion error.
Two Options:
Use lookup file instead of joining with table(upload rules to a file instead of DB, this will reduce cost of connecting with table
You could also dump the ruleset in a regular file and try to do cat in Pset of the graph.
Related
Using CSV Data Set Config in JMeter works, but sometimes test stop in the middle
Now there are used records in CSV and if we re-run test it'll executed also used records
How can used records not be used in next run?
Theoretically I can write to CSV every usage, but it seems very IO consuming while running test
Is there a solution to mark used CSV records or able to execute only unused records next test run?
With the "normal" CSV Data Set Config it is not possible, the solutions are in:
Use Sample Variables property you will be able to write down "used" variables into JMeter's .jtl results file, this way you will know which variables have already been processed. If you want to write them into a separate file - go for Flexible File Writer. Once test stops abnormally you can use a diff tool to distinguish "used" and "not-used" data
Another option is going for HTTP Simple Table server, it is possible to invoke READ endpoint providing KEEP=FALSE parameter, this way "used" records will be removed from the CSV file. This approach also enables re-using the same CSV file in Distributed Testing mode without having to copy the CSV file to all slaves and collect used entries from them, also it protects from splitting the CSV file and using duplicate values in the test
I am implementing a pipeline to move csv files from one folder to another in a data lake. However, this should be done only if the csv files comply to some conditions regarding the delimiter, the strings that should be between quotes, no header, specific row delimiter...
At the moment, I am able to do the check by setting the connection conditions (rules) in the dataset and then analyzing the names and number of columns compared to what it is expected for each csv file.
But since I am using a get Metadata activity, I actually check the first row only and I have no guaranty that the rest of the rows comply to the conditions too (except for the "no header" condition).
In this case, which other robust and reasonable alternative do we have to check the complete file? knowing that the file could contain millions of rows and that the check could be done many times until the file is corrected and the conditions are met.
Thank you in advance.
As what the answer sais of your previous post, the Data Factory default file encoding is UTF-8. You also can check your COMPLETE CSV file via Azure Function, Azure Batch Service, a Databricks Notebook, a Synapse Notebook etc.
In Azure data factory, we only can use column pattern to check specify column content or all columns content.
For example:
This is my source csv files. I set the age column type as short.
In DerivedColumn1, I use column pattern and enter $$>29 to determine the value of the third column.
The debug value is as follows:
In Azure Data factory data flow, we only can do this.
I have 3 data files and 3 tables. Is there any way to enter the data from the data files to their respective tables using only a single control file using parameters.
I know that we can use multiple datafiles to insert data into single table but i want multiple datafiles to insert data into multiple tables.
I have not done it, but you can specify multiple files as long as they are the same layout by using multiple INFILE clauses. Then as long as there is some identifier in the record, you can use multiple INTO TABLE clauses, each with its own WITH clause. See the control file reference for more info. Please let us know how it works out.
I'm trying to move data from multiple csv files to a DB destination.For doing this I'm using a For Each Loop container that contains a Data Flow Task for shifting the data file-wise which consists of a merge join and a lookup transformation, the end result being that records with a match are inserted to the DB and non-matching records are transferred to a CSV destination.
The lookup transformation is used to lookup data in the file with pre-existing data in the DB and forward the No Match Output to a csv destination.
This works fine with files that have some non-matching data. However, with files wherein all data matches,I'm still getting an empty No-match file.I would like to avoid creating an empty file. I've tried using a conditional split based on the condition LEN(Column_Name) > 1 to work around but that doesn't work.
Would appreciate all the help I can get.
It's a little more work, but a work around I might suggest is to use the row count transformation in the no-match path. Then add a file system task at the end of your package and delete the CSV file if the row count equals 0.
I have an input csv file with columns eid,ename,designation. Next i use Lookup transformation, inside look up am using query like
select * from employee where ename=?
i need to pass parameter ? from csv file. That is ename which is in csv file has to be passed into the query using Lookup transformation.
Inside Lookup i have changed mode to Partial cache, and inside Advanced tab, i selected Modify the SQL Statement and placed my query, and clicke on paramters tab. But i don't know like how to pass the parameter.
you cant add parameters to your lookup query. If by adding the parameters your goal is to reduce the amount of data read from the database, you don't have to worry, the "partial cache" will do that for you.
Partial cache means that the lookup query is not executed on the validation phase (like the full cache option) and that rows are being added to the cache as they are being queried from the database one by one. So, if you have one million rows on your lookup cache and your query only have reference to 10 of those rows, your lookup will do 10 selects to your database and end up with 10 rows only.