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You can find complementary algorithms for any operation that is characterized by two facets or phases. There are many examples, but here are a few: Compression/decompression Encryption/decryption Writer/readers Producer/consumers Start/stop operations Open/close operations Complementary algorithms are logically conjoined like Siamese twins. They are separate parts of a single logical task. If you change one, you re probably going to have to change the other. Complementary algorithms are designed in terms of each other, so they are intimately coupled, in a logic sense. One is generally useless without the other. Listing 1-1 shows two C# classes, T1 and T2, that contain complementary algorithms. Listing 1-1. An Example of Complementary Writer and Reader Algorithms public class T1 { public void WriteMessages(StreamWriter theWriter, ArrayList theMessages) { // write all the messages foreach (string s in theMessages) theWriter.WriteLine(s); } } public class T2 { public void ReadMessages(StreamReader theReader, ArrayList theMessages) { // read all the messages while (true) { string s = theReader.ReadLine(); if (s == null) break; // end of file theMessages.Add(s); } } } The method T2.ReadMessages contains an algorithm that is designed to read not just any stream, but streams containing a list of string messages written using the algorithm in T1.WriteMessages. You can say that the algorithm in T2.ReadMessages is the complement of the one in T1.WriteMessages. Note that the relationship is the complement of is not commutative. If T2 is the complement of T1, you can t conclude that T1 is the complement of T2. For example, the reader algorithm in this example logically completes the writer algorithm, so the reader is the complement of the writer. The writer is not the complement of the reader, because the writer does not logically complete any operation begun by the reader.

The basic rationale behind the ADDM is to reduce a key database metric called DB time, which is the total time (in microseconds) the database spends actually processing users requests. DB time includes the total amount of time spent on actual database calls (at the user level) and it ignores time spent on background processes. DB time includes both the wait time and processing time (CPU time), but doesn t include the idle time incurred by your processes. For example, if you spend an hour connected to the database and you re idle for 58 of those minutes, the DB time is only 2 minutes. If a problem contributes to inappropriate or excessive DB time, ADDM automatically flags it as an issue needing attention. If there is a problem in your system, but it doesn t contribute significantly to the DB time, ADDM will simply ignore it. Thus, the ADDM is focused on the single mantra: reduce DB time. The ADDM aims to increase the throughput of your database, thereby serving more users with the same amount of resources.

The ADDM analyzes the AWR snapshots every hour by default, comes up with performance recommendations, and ranks them in terms of the expected benefit of implementing the various actions. These are some of the key problems that the ADDM diagnoses: Expensive SQL statements I/O performance issues Locking and concurrency issues Excessive parsing Resource bottlenecks, including memory and CPU bottlenecks Undersized memory allocation Connection management issues, such as excessive logon/logoff activity

When you are beset by a severe performance problem, look at the ADDM reports first, to get a good diagnosis of the problem. ADDM ignores the nonproblem areas and focuses on the truly significant causes affecting performance. The ADDM report contains the following: Expert problem diagnosis Emphasis on the root cause of the problem rather than on the symptoms A ranking of the effects of the problems Recommendations ranked according to their benefit Unlike running some complex SQL scripts, the ADDM report has very little overhead associated with it, since its raw material is already saved in the AWR. The ADDM uses sophisticated time-model statistics in Oracle Database 11g that are highly effective in determining where the database spends the most time. These new time-model statistics enable Oracle to focus on only the most critical performance problems. If a problem exceeds the threshold for the key DB time metric, the ADDM tags it as a top performance problem; otherwise, it leaves it alone as a nonproblem area. Let s look at these new time-model statistics in the following section.

Delegates support multicasting and can be defined for methods with any signature. In particular, you can use delegates with methods that have a return value. You can set the return value only with unicast delegates, which are delegates whose invocation list has only a single method reference.