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So, how can we measure the cost associated with column level encryption You ll not only want to measure the additional CPU associated with the encryption operation, but also the increased REDO generation as well since we know that an encrypted column will be larger and hence will generate more REDO in general. Additionally (and this is not something I can show you how to measure), you will need to have a really good understanding of how the data is used in your application. While the following will show you how to measure a percentage difference between non-encrypted and encrypted data, a simple percentage number would be meaningless. Suppose you measured that the overhead of encrypting the CREDIT_CARD# in the above example was 10 percent. What does that mean Does it mean you will need 10 percent more CPU resources after encrypting than before No, it doesn t, for the reason that very few transactions in your application will incur this extra 10 percent. So you need a metric that measures the overhead of encryption coupled with an understanding of how the encrypted data is used. In short, anyone that says Column encryption adds X percent overhead is only telling a very small part of the story. They are, in general, overstating the impact, because, in general, not every transaction will encounter this overhead. Typically, most transactions will not encounter the encrypt/decrypt processing.

From this section, you can easily create a PFILE to be converted into a new SPFILE using the CREATE SPFILE command.

Let s see how we can measure the impact. We ll use the following tables in this example: the STAGE table is used so that we measure the impact of encryption, not the impact of running a relatively complex query against the view ALL_OBJECTS, and table T is used to measure the impact of column level encryption:

Connection around() throws SQLException : obtainConnection() && cflow(transactedOperation()) { if (_connection == null) { _connection = proceed(); _connection.setAutoCommit(false); } return _connection; }

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ops$tkyte%ORA11GR2> create table stage 2 as 3 select object_name 4 from all_objects; Table created. ops$tkyte%ORA11GR2> create table t 2 ( non_encrypted varchar2(30), 3 encrypted varchar2(30) ENCRYPT 4 ); Table created. Our goal is to measure redo generation and CPU utilization of various SQL operations against these columns. To that end, we ll use a small stored procedure that can capture that utilization for us. Our procedure will use dynamic SQL (so we ll be using AUTHID CURRENT_USER to avoid SQL Injection security issues) and measure the amount of redo generated and CPU used. It starts by truncating the table we are using, if necessary. Then our procedure sets about getting the starting redo/cpu metrics, performing an operation, committing (without the PL/SQL optimization we discussed in 11 Indexes ), and printing out the totals for each of redo and CPU: ops$tkyte%ORA11GR2> create or replace 2 procedure do_sql( p_sql in varchar2, 3 p_truncate in boolean default true ) 4 authid current_user 5 as 6 l_start_cpu number; 7 l_start_redo number; 8 l_total_redo number; 9 begin 10 if (p_truncate) 11 then 12 execute immediate 'truncate table t'; 13 end if; 14 15 dbms_output.put_line( p_sql ); 16 17 l_start_cpu := dbms_utility.get_cpu_time; 18 l_start_redo := get_stat_val( 'redo size' ); 19 20 execute immediate p_sql; 21 commit work write batch wait; 22 23 dbms_output.put_line 24 ( (dbms_utility.get_cpu_time-l_start_cpu) || ' cpu hsecs' ); 25 26 l_total_redo := 27 round((get_stat_val('redo size')-l_start_redo)/1024/1024,1); 28 dbms_output.put_line 29 ( to_char(l_total_redo,'999,999,999.9') || ' mbytes redo' ); 30 end; 31 / Procedure created.

In this section, we covered all of the basics of managing Oracle initialization parameters and parameter files. We looked at how to set parameters, view parameter values, and have those settings persist across database restarts. We explored the two types of database parameter files: legacy PFILEs (simple text files) and SPFILEs. For all existing databases, using SPFILEs is recommended for the ease of administration and clarity they bring to the table. The ability to have a single source of parameter truth for the database, coupled with the ability of the ALTER SYSTEM command to persist the parameter values, make SPFILEs a compelling feature. I started using them the instant they became available and haven t looked back.

public static class TransactionException Exception indicating a failed transaction extends RuntimeException { public TransactionException(Exception cause) { super(cause); } } private static aspect SoftenSQLException { declare soft : java.sql.SQLException : (call(void Connection.rollback()) || call(void Connection.close())) && within(JDBCTransactionAspect); }

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