Informatica Reject File - How to Identify rejection reason

Informatica Reject File - How to Identify rejection reason

When we run a session, the integration service may create a reject file for each target instance in the mapping to store the target reject record. With the help of the Session Log and Reject File we can identify the cause of data rejection in the session. Eliminating the cause of rejection will lead to rejection free loads in the subsequent session runs. If the Informatica Writer or the Target Database rejects data due to any valid reason the integration service logs the rejected records into the reject file. Every time we run the session the integration service appends the rejected records to the reject file.

Working with Informatica Bad Files or Reject Files

By default, the Integration service creates the reject files or bad files in the $PMBadFileDir process variable directory. It writes the entire reject record row in the bad file although the problem may be in any one of the Columns.

The reject files have a default naming convention like [target_instance_name].bad . If we open the reject file in an editor, we will see comma separated values having some tags/ indicator and some data values. We will see two types of Indicators in the reject file. One is the Row Indicator and the other is the 

Column Indicator.

For reading the bad file the best method is to copy the contents of the bad file and saving the same as a CSV (Comma Separated Value) file. Opening the csv file will give an excel sheet type look and feel. The first most column in the reject file is the Row Indicator, that determines whether the row was destined for insert, update, delete or reject. It is basically a flag that determines the Update Strategy for the data row. When the Commit Type of the session is configured as User-defined the row indicator indicates whether the transaction was rolled back due to a non-fatal error, or if the committed transaction was in a failed

 target connection group.

List of Values of Row Indicators:

Row Indicator	Indicator Significance	Rejected By
0	Insert	Writer or target
1	Update	Writer or target
2	Delete	Writer or target
3	Reject	Writer
4	Rolled-back insert	Writer
5	Rolled-back update	Writer
6	Rolled-back delete	Writer
7	Committed insert	Writer
8	Committed update	Writer
9	Committed delete	Writer

Now comes the Column Data values followed by their Column Indicators, that determines the data quality of the corresponding Column.

List of Values of Column Indicators:

Column Indicator	Type of data	Writer Treats As
D	Valid data or Good Data.	Writer passes it to the target database. The  target accepts it unless a database error  occurs, such as finding a duplicate key while inserting.
O	Overflowed Numeric Data.	Numeric data exceeded the specified precision  or scale for the column. Bad data, if you  configured the mapping target to reject  overflow or truncated data.
N	Null Value.	The column contains a null value. Good data. Writer passes it to the target, which rejects  it if the target database does not accept  null values.
T	Truncated String Data.	String data exceeded a specified precision  for the column, so the Integration Service truncated it. Bad data, if you configured the mapping target to reject overflow or  truncated data.

Also to be noted that the second column contains column indicator 

flag value 'D' which signifies that the Row Indicator is valid.

Now let us see how Data in a Bad File looks like:

0,D,7,D,John,D,5000.375,O,,N,BrickLand Road Singapore,T