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
