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{ // handle the exception properly - in this case, we just // print the stack trace. JDBCUtil.printException ( e ); } finally { // release the JDBC resources in the finally clause. JRunstats.closeBenchmarkStatements( conn ); JDBCUtil.close( conn ); } } The _insertWithBind() method uses the PreparedStatement interface to execute the 10,000 inserts: private static void _insertWithBind( Connection conn ) throws SQLException { PreparedStatement pstmt = null; try { pstmt = conn.prepareStatement( "insert into t1(x) values( ) " ); for( int i=0; i < 10000; i++ ) { pstmt.setInt( 1, i ); pstmt.executeUpdate(); } } finally { // release JDBC-related resources in the finally clause. JDBCUtil.close( pstmt ); } } The _insertWithoutBind() method uses the Statement interface to execute the 10,000 inserts without using bind variables: private static void _insertWithoutBind( Connection conn ) throws SQLException { Statement stmt = null; try { stmt = conn.createStatement(); for( int i=0; i < 10000; i++ ) { stmt.executeUpdate( "insert into t1( x ) values( " + i + ")" ); } }
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finally { // release JDBC-related resources in the finally clause. JDBCUtil.close( stmt ); } } } The important thing to note in the DemoBind class is that in the case where we use bind variables, only one statement string is submitted to Oracle: insert into t1 ( x ) values ( ). Here, is the bind variable that is bound at runtime to the values 1, 2, . . . 10,000 in the loop. In the case where we don t use bind variables, Oracle sees a different insert statement for each value being inserted (insert into t1 ( x ) values ( 0 ) for a value of 0, insert into t1 ( x ) values ( 1 ) for a value of 1, and so on). Since Oracle has to parse each new query and generate a query execution plan, this approach doesn t scale well at all. In our example, Oracle has to process 10,000 spuriously different statements when we don t use bind variables. Let s run the program to see how these two approaches compare: URL:jdbc:oracle:thin:@(DESCRIPTION=(ADDRESS=(PROTOCOL=tcp)(PORT=1521) (HOST=rmenon-lap))(CONNECT_DATA=(SID=ora10g))) ------- Benchmark Results -------Results from RUNSTATS utility Run1 ran in 43 hsecs Run2 ran in 214 hsecs run 1 ran in 20.09% of the time As you can see, the approach that uses bind variables runs substantially faster. It completes in about 1/5 the time of the alternative that does not use bind variables. This in itself is a very good reason to use bind variables in your code. However, it is even more critical to understand the importance of differences in consumption of latches in the two approaches. You can see this from the next section of the JRunstatsreport: Name Run1 STAT...messages sent 3 STAT...calls to kcmgcs 5 <-- trimmed to conserve space --> STAT...session pga memory max 131,072 Run2 4 6 0 Diff 1 1 -131,072
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Run1 latches total versus runs -- difference and pct Run1 Run2 Diff Pct 16,883 126,673 109,790 13.33% The approach that uses bind variables consumes approximately 13% of the latches as compared to the alternative that does not use bind variables.
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Latches are lightweight serialization resources Oracle uses when accessing shared memory structures. While one session is parsing, Oracle will place latches on common resources in the shared pool so that no other sessions may modify them. The more latches there are in place, and the longer they are held, the longer the subsequent sessions have to wait in order to gain access to these resources. When approach 1 consumes 13% of the latches consumed by approach 2, as is true in the preceding example, the scalability of the system that uses approach 1 is substantially superior to the one that employs approach 2. In other words, as more and more users start using the system concurrently, the performance of the system employing approach 2 will degrade rapidly. To demonstrate this point in terms of its impact on elapsed time when multiple users are using the system, I ran the preceding program simultaneously for two, three, four, and five sessions separately. For example, to run it simultaneously in three sessions, I opened three windows on my PC and ran the program in each window simultaneously. The result is plotted in the graph shown in Figure 2-1.
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