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Both TKPROF and ESQLTRCPROF report CPU usage. Often high CPU usage is associated with SQL statements that access thousands or millions of blocks in the buffer cache. If this is the case, a poor execution plan, which includes unnecessary full scans, has an inappropriate join order, or uses the wrong index, might be the culprit. SQL statements with high CPU usage must be identified and their execution plans checked. TKPROF provides the sort options fchela and exeela, which may be combined. If you suspect that a SELECT statement is the most expensive statement in a trace file, use sort=fchela,exeela. Otherwise use sort=exeela,fchela. TKRPOF cannot sort by total elapsed time, which comprises elapsed time from the three stages parse, execute, and fetch. Furthermore it does not consider inter database call wait events attributed to a cursor when sorting. ESQLTRCPROF always sorts by total elapsed time and considers in between database call wait events such as SQL*Net message from/to client while ignoring think time. This makes sure that a SQL statement that causes many round-trips and thus incurs a lot of network latency is ranked as more expensive than a statement that is responsible for the same amount of elapsed time within database calls, but does not accumulate as much network latency.

>>> hash(42) 42 >>> hash("Hello, world!") -1886531940

Iterates through errors Checks if errors exist within the bean, model, or request Displays a message Displays the value of a field for a bean that has data binding

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This is the mechanism that is used in dictionaries, which are implemented using so-called hash tables. Sets are implemented using the same mechanism. The important thing is that the hash value can be constructed in essentially constant time (it s constant with respect to the hash table size but linear as a function of the size of the object being hashed). If the array that is used behind the scenes is large enough, accessing it using a hash value is also (1) in the average case. (The worst-case behavior is (n), unless we know the values beforehand and can write a custom hash function. Still, hashing is extremely efficient in practice.) What this means to us is that accessing elements of a dict or set can be assumed to take constant (expected) time, which makes them highly useful building blocks for more complex structures and algorithms.

Before the actual performance optimization may begin, a way to reproduce the problem at hand must be found. It is crucial to reproduce the problematic code path as closely as possible. Depending on the kind of performance problem, to reproduce an issue, the following factors may need to be identical between the original environment and the test environment: Hardware capability Operating system and release ORACLE DBMS release Initialization parameters (documented and hidden), especially optimizer parameters Database object statistics (a.k.a. optimizer statistics) Database block size Bind variables and bind data types in SQL statements which are optimized Stored outlines SQL profiles (Oracle10g and later releases only) When creating database objects, make sure you create them with the same DDL in tablespaces with the same block size as the original application. The package DBMS METADATA may be used to extract DDL for database objects. When reproducing SQL statements with bind variables, it s important to use bind variables with the same data type as the original statement. Level 12 SQL trace files contain bind variable values as well as bind variable data types in the BINDS section (see 24). SQL*Plus or PL/SQL variables may be used to reproduce the bind data types. Last but not least, make sure no other users are running stress tests or other resource-intensive programs on your test system, since this may lead to contradictory results of your tests.

Now that you have the layout, let s make the topbar functional. You want the topbar to provide the user the ability to log in. Once the user has logged in, the topbar should display the username and provide the ability for the user to log out. When the user selects the Login link, he or she should be presented with a Login form.

One of the most intuitive ways of implementing graphs is using adjacency lists. Basically, for each node, we can access a list (or set or other container or iterable) of its neighbors. Let s take the simplest way of implementing this, assuming we have n nodes, numbered 0 n 1.

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