Hi everyone, This thread is a follow-up to a discussion that we started in the DSv2 community sync last week.
The problem I’m trying to solve is that the format I’m using DSv2 to integrate supports schema evolution. Specifically, adding a new optional column so that rows without that column get a default value (null for Iceberg). The current validation rule for an append in DSv2 fails a write if it is missing a column, so adding a column to an existing table will cause currently-scheduled jobs that insert data to start failing. Clearly, schema evolution shouldn't break existing jobs that produce valid data. To fix this problem, I suggested option 1: adding a way for Spark to check whether to fail when an optional column is missing. Other contributors in the sync thought that Spark should go with option 2: Spark’s schema should have defaults and Spark should handle filling in defaults the same way across all sources, like other databases. I think we agree that option 2 would be ideal. The problem is that it is very hard to implement. A source might manage data stored in millions of immutable Parquet files, so adding a default value isn’t possible. Spark would need to fill in defaults for files written before the column was added at read time (it could fill in defaults in new files at write time). Filling in defaults at read time would require Spark to fill in defaults for only some of the files in a scan, so Spark would need different handling for each task depending on the schema of that task. Tasks would also be required to produce a consistent schema, so a file without the new column couldn’t be combined into a task with a file that has the new column. This adds quite a bit of complexity. Other sources may not need Spark to fill in the default at all. A JDBC source would be capable of filling in the default values itself, so Spark would need some way to communicate the default to that source. If the source had a different policy for default values (write time instead of read time, for example) then behavior could still be inconsistent. I think that this complexity probably isn’t worth consistency in default values across sources, if that is even achievable. In the sync we thought it was a good idea to send this out to the larger group to discuss. Please reply with comments! rb -- Ryan Blue Software Engineer Netflix
