cchighman commented on pull request #28841:

   > Please take a look at how Kafka data source options apply with both batch 
and streaming query. The semantic of the option should be applied differently.
   > `startingOffsetsByTimestamp`, `startingOffsets`, 
`endingOffsetsByTimestamp`, `endingOffsets`
   > If we are not fully sure about how to do it, let's only apply the option 
to batch query, and file an issue to address for the streaming query.
   > Btw, that said, I prefer to have lower bound + upper bound instead of only 
lower bound, as commented earlier on reviewing.
   Hmm, I'm wondering if this isn't a different feature.  The goal of this 
feature is to begin reading from a file data source with files that have a 
particular modified date.  It's key value is really with having the ability to 
_start_ at a particular _physical_ location.
   With a Kafka data source, you're exclusively dealing with an event stream 
where event sourcing patterns leveraging offsets are at play.  I wonder though 
if structured streaming always implied an event source, particularly when 
streaming from a file source?
   For example, _modifiedDateFilter_ applies specifically to a point in time 
when you begin structured streaming on a file data source.  You would not have 
an offset yet in _availableOffsets_.  The offset use case would imply you are 
restarting an existing, checkpointed stream.
   When using an offset with a Kafka data source, some write has occurred by 
which a written checkpoint exists.  With the file data source for files that 
have not yet been read or written, I'm curious how I would apply offset bounds 
in this way.  I was thinking I would have to be reading from a data source that 
had used structured streaming with checkpointing in order for the offset to 
exist (committed).  
   Does this make sense?  It seems like once you've written a checkpoint while 
writing to a stream from the readStream dataframe that's loading files, you 
would have clear context to apply offset-based semantics.

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