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Wednesday 16 January 2013

What if table already exists during import



TABLE_EXISTS_ACTION TABLE_EXISTS_ACTION=[SKIP | APPEND | TRUNCATE | REPLACE]

The possible values have the following effects:

· SKIP leaves the table as is and moves on to the next object. This is not a valid option if the CONTENT parameter   is set to DATA_ONLY.
·   APPEND loads rows from the source and leaves existing rows unchanged.
·   TRUNCATE deletes existing rows and then loads rows from the source.
·   REPLACE drops the existing table and then creates and loads it from the source.
This is not a valid option if the CONTENT parameter is set to DATA_ONLY.


The following considerations apply when you are using these options:

·  When you use TRUNCATE or REPLACE, ensure that rows in the affected tables are not targets of any referential constraints.
· When you use SKIP, APPEND, or TRUNCATE, existing table-dependent objects in the source, such as indexes, grants, triggers, and constraints, are not modified. For REPLACE, the dependent objects are dropped and re-created from the source, if they were not explicitly or implicitly excluded (using EXCLUDE) and they exist in the source dump file or system.
· When you use APPEND or TRUNCATE, checks are made to ensure that rows from the source are compatible with the existing table before performing any action. If the existing table has active constraints and triggers, then it is loaded using the external tables access method. If any row violates an active constraint, then the load fails and no data is loaded. You can override this behavior by specifying DATA_OPTIONS=SKIP_CONSTRAINT_ERRORS on the Import command line. If you have data that must be loaded, but may cause constraint violations, then consider disabling the constraints, loading the data, and then deleting the problem rows before reenabling the constraints.
· When you use APPEND, the data is always loaded into new space; existing space, even if available, is not reused. For this reason, you may want to compress your data after the load.

When Data Pump detects that the source table and target table do not match (the two tables do not have the same number of columns or the target table has a column name that is not present in the source table), it compares column names between the two tables. If the tables have at least one column in common, then the data for the common
columns is imported into the table (assuming the datatypes are compatible).

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