Although there is no native incremental load capability, you can do incremental loads by adding a filter on the source data. Filters only work if the source has the capability of providing information on the last successful load. This means the source should have a timestamp field that indicates when a particular source was modified.
If your source data has such a timestamp, then you can use the following filters:
modifiedDate > $LastRunDate
modifiedTime > $LastRunTime
$LastRunTime filters are correctly updated at runtime
to reflect the date/time of the last successful job run. Both
$LastRunTime are in GMT, they cannot be reconfigured. `
You can use filters on any other column of any datatype, if the filter provides a way to identify the modified rows. Even if the source does not have this capability, you may still be able to do an incremental load.
As long as a primary key exists on the target ThoughtSpot table, the data will
be loaded using an
upsert. This means that for a given row being loaded, if
the primary key already exists in the target table, that row is updated with the
data in the new row. If a row with that primary key does not exist, the new row
will be inserted. This is how ThoughtSpot handles data loads into a table that
already contains rows, and it is the equivalent to an incremental load.
If you are relying on the
upsert method, you can run a
command in ThoughtSpot SQL Command Line (TQL) to reduce the size of the table at
intervals (usually weekly).
Another technique that can effectively free up space would be to run a post-job script that deletes data older than a particular date. This keeps the data fresh and ensures that old data, which may not be valuable, is not taking up memory.