Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/14086
Let me share my 2 cents here.
`Truncate Table` is very risky. It is fast since RDBMS does not log the
individual row deletes. That means, we are unable to roll it back in most
RDBMS. The behaviors are different in different RDBMS vendors. For example,
1.
[PostgreSQL](https://www.postgresql.org/docs/9.1/static/sql-truncate.html)
could truncate all its descendant tables at default, unless we specify `ONLY`
in the statement.
2. [DB2
z](https://www.ibm.com/support/knowledgecenter/SSEPEK_10.0.0/sqlref/src/tpc/db2z_sql_truncate.html)
can roll it back but the table is still in a truncated state
3. Oracle 11 documents this caution at the very beginning:
http://docs.oracle.com/cd/B28359_01/server.111/b28286/statements_10007.htm
Unable to roll it back. (Note, [Oracle 12
](ttps://docs.oracle.com/database/121/SQLRF/statements_10007.htm#SQLRF01707)
added a new clause CASCADE. By default, the descendant table will not be
dropped.)
Thus, I am not confident to add such a mode or option into Spark SQL. This
command is used very rarely in production systems. DBAs should do it manually,
instead of using the Spark SQL interface.
If the community still thinks we should integrate it into Spark SQL, I am
also fine. After a quick review, I think the implementation misses the error
handling. For example, when users try to use `truncate` option in the other
`SaveMode`.
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