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Figure 9-5. Optimal redo log configuration You would place redo log group 1 with members A and B onto disk 1. You would place redo log group 2 with members C and D onto disk 2. If you have groups 3, 4, and so on, they ll go onto the odd and respectively even groups of disks. The effect of this is that LGWR, when the database is currently using group 1, will write to disks 1 and 3 simultaneously. When this group fills up, LGWR will move to disks 2 and 4. When they fill up, LGWR will go back to disks 1 and 3. Meanwhile, ARCH will be processing the full online redo logs and writing them to disks 5 and 6, the big disks. The net effect is neither ARCH nor LGWR is ever reading a disk being written to, or writing to a disk being read from, so there is no contention (see Figure 9-6).

nop break ldarg.0 ldarg.1 ldarg.2 ldarg.3 ldloc.0 ldloc.1 ldloc.2 ldloc.3 stloc.0 stloc.1 stloc.2 stloc.3 ldarg.s ldarga.s starg.s ldloc.s ldloca.s stloc.s ldnull ldc.i4.m1, ldc.i4.M1 ldc.i4.0 ldc.i4.1 ldc.i4.2 ldc.i4.3 ldc.i4.4 ldc.i4.5 ldc.i4.6 ldc.i4.7 ldc.i4.8 ldc.i4.s ldc.i4 ldc.i8 ldc.r4 ldc.r8 dup

multiple subpools, each protected by its own latch. This permits increased scalability for applications that do not use bind variables, but it does not make the latching problem go away by any means.

The script then collects all notification output files and sorts them by the age of the passwords. Then all transient files are removed. The final report will list any warnings in order of importance; for example, users without a password will appear first.

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Executing SQL statements without bind variables is very much like compiling a subroutine before each and every method call. Imagine shipping Java source code to your customers where, before calling a method in a class, they had to invoke the Java compiler, compile the class, run the method, and then throw away the bytecode. The next time they wanted to execute the exact same method, they would do the same thing: compile it, run it, and throw it away. You would never consider doing this in your application. You should never consider doing this in your database, either. As it was, on this particular project, reworking the existing code to use bind variables was the best course of action. The resulting code ran orders of magnitude faster and increased many times the number of simultaneous users that the system could support. However, it came at a high price in terms of time and effort. It is not that using bind variables is difficult or error-prone, it s just that the developers did not do it initially and thus were forced to go back and revisit virtually all of the code and change it. They would not have paid this high price if they had understood that it was vital to use bind variables in their application from day one.

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Concurrency control is one area where databases differentiate themselves It is an area that sets a database apart from a file system and databases apart from each other It is vital that your database application work correctly under concurrent access conditions, and yet this is something people fail to test time and time again Techniques that work well if everything happens consecutively do not work so well when everyone does them simultaneously If you don t have a good grasp of how your particular database implements concurrency control mechanisms, then you will Corrupt the integrity of your data Have applications run slower than they should with a small number of users Decrease your ability to scale to a large number of users Notice I don t say, You might or You run the risk of Rather, you will invariably do these things without proper concurrency control without even realizing it.

cat $REPORT | mail -s "$ENVIRONMENT password aging report" $ADMIN_EMAIL mv $REPORT $ARCHIVE/report.$backdate

Without correct concurrency control, you will corrupt the integrity of your database because something that works in isolation will not work as you expect in a multiuser situation Your application will run slower than it should because it will end up waiting for resources You ll lose your ability to scale because of locking and contention issues As the queues to access a resource get longer, the wait times get longer and longer An analogy here would be a backup at a tollbooth If cars arrive in an orderly, predictable fashion, one after the other, there will never be a backup If many cars arrive simultaneously, lines start to form Furthermore, the waiting time does not increase in line with the number of cars at the booth.

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