Hi, We have a legacy process of scraping a MySQL Database. The Spark job uses the DataFrame API and MySQL JDBC driver to read the tables and save them as JSON files. One table has DateTime columns that contain values invalid for java.sql.Timestamp so it's throwing the exception: java.sql.SQLException: Value '0000-00-00 00:00:00' can not be represented as java.sql.Timestamp
Unfortunately, I can't edit the values in the table to make them valid. There doesn't seem to be a way to specify row level exception handling in the DataFrame API. Is there a way to handle this that would scale for hundreds of tables? Any help is appreciated. Anthony
