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You can assign default values to columns. When you insert a new row, you can omit a value for any column with a default value. The database will automatically supply the default value for that column. If you don t explicitly define a default value for a column, the column value defaults to null. For example, if you set the 20 as the default value of the DEPT_NO column in the employees table, Oracle will insert the value 20 for the DEPT_NO column, even though that column doesn t have a value when new data in inserted.

We use the setObject() method of the PreparedStatement interface to insert the object after creating the PreparedStatement object: String insertStmt = "insert into address_table values( )"; pstmt = connection.prepareStatement ( insertStmt ); pstmt.setObject (1, myAddress ); int rows = pstmt.executeUpdate(); System.out.println ( "Inserted " + rows + " row(s) " ); connection.commit(); } finally { JDBCUtil.close( pstmt ); } }

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In an Oracle Database 11g release database, you can use virtual columns in a table. A virtual column is a column that is derived by evaluating an expression based on one or more of the actual columns of the table or by evaluating a SQL or PL/SQL function. Unlike normal columns, the virtual column data is not stored permanently on disk. The database computes the virtual column values when you query it, by dynamically evaluating an expression on other columns or by computing a function. You can use virtual columns in both DDL and DML statements. You can defined indexes on them and collect statistics on them as well.

You can cancel the workload replay in midstream by doing this: SQL> exec dbms_workload_replay.cancel_replay();

Once the database completes the workload replay, you can analyze the replay report to find out about any errors and performance differences, as well as possible data anomalies between the original workload and the replayed workload. Here s how you d get a replay report by executing the GET_REPLAY_INFO function: declare cap_id rep_id rep_rpt begin cap_id := number; number; clob;

We update an object by selecting it into memory, changing the in-memory Java object as required, and using the Java object to update the object value in the database. An important point to note is that when we select an object in memory, we don t have exclusive access to it, because in Oracle reads don t block writes. So it is possible that the changes we make are overridden by someone else who happens to be simultaneously updating the same set of rows using the same technique. This phenomenon is called lost updates. We will revisit and resolve this issue in detail in 16. To avoid lost updates, we need to lock any selected rows. We do that by using the for update nowait clause in our select statement. This tells Oracle to either lock the row for us or fail with an exception if the lock is already held by someone else updating the same row. The method _demoUpdate() illustrates this concept: private static void _demoUpdate( Connection connection ) throws SQLException { As a first step, we need to select the object using the concepts discussed in the section Selecting Objects earlier. The only major difference is that the select statement has a phrase for update nowait at the end to indicate that we want to try and lock any rows that get selected: PreparedStatement pstmt = null; PreparedStatement pstmt1 = null; ResultSet rset = null; try { MyAddress myAddress = null; String selectStmt = "select value(a) from address_table a"+ " where line1 = for update nowait"; pstmt = connection.prepareStatement ( selectStmt ); pstmt.setString( 1, "145 Apt # 7" ); rset = pstmt.executeQuery();

dbms_workload_replay.get_replay_info (dir => 'mytestdir'); select max(id) into rep_id from dba_workload_replays where capture_id = cap_id; rep_rpt := dbms_workload_replay.report( replay_id => rep_id, format => dbms_workload_replay.type_text);

end; / The REPLAY_REPORT function produces the following text report: Error Data (% of total captured actions) New errors: 12.3% Not reproduced old errors: 1.0% Mutated errors: 2.0% Data Divergence Percentage of row count diffs: 7.0% Average magnitude of difference (% of captured): 4.0% Percentage of diffs because of error (% of diffs): 20.0% Result checksums were generated for 10% of all actions(% of checKSUMS) Percentage of failed checksums: 0.0% Percentage of failed checksums on same row count: 0.0% Replay Specific Performance Metrics Total time deficit (-)/speed up (+): -32 min

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