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Improving performance is a vast subject. If we ignore the use of more powerful hardware, which might not even solve the problem, the following procedures may result in better performance: Parameter changes (e.g., DB CACHE SIZE, PARALLEL EXECUTION MESSAGE SIZE) Adding an index to avoid full table scans Dropping unnecessary indexes Partitioning of tables and/or indexes Materialized views Use of bind variables instead of literals in SQL statements Correction of bind data type mismatches Calculation of more accurate optimizer statistics with DBMS STATS Optimizer Dynamic Sampling Use of system statistics, given that the cost-based SQL optimizer chooses better execution plans than without them (DBMS STATS) Use of cached sequences instead of counters implemented with tables Adding hints to SQL statements (as a last resort to improve execution plans) Stored outlines Supplying hidden hints with stored outlines (see Metalink note 92202.1) SQL profiles (Oracle10g and subsequent releases) SQL plan management (requires Oracle11g) Use of array inserts or bulk load programming interfaces Reduction of network round-trips (e.g., with INSERT RETURNING) PL/SQL native compilation Changes in application coding to reduce contention, parse overhead, polling, and so forth Performance improvements should be documented with additional measurements. You should not rely on a single measurement, but instead take several measurements and calculate the average.

Note The login functionality you are building initially is simply to identify who is using the system. It s not

Note Nodes can be any objects, of course, or have arbitrary labels or names. Using integers in the range 0 n 1 can make many implementations easier, though, because the node numbers can easily be used as indices.

meant to provide a full, robust security system. Because the login you are constructing is temporary, adding authentication logic to all controller actions is out of scope at this time. In 7, you will add a fully functional security system.

By the time a substantial performance improvement has been achieved with the test case, it is important to extrapolate the effect of the improvement on the original application. This is to

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Each adjacency (or neighbor) list is then just a list of such numbers, and we can place the lists themselves into a main list of size n, indexable by the node numbers. Usually, the ordering of these lists is arbitrary, so we re really talking about using lists to implement adjacency sets. The term list in this context is primarily historical. In Python we re lucky enough to have a separate set type, which in many cases is a more natural choice. For an example that will be used to illustrate the various graph representations, see Figure 2-3. To begin with, assume that we have numbered the nodes (a = 0, b = 1, ). The graph can then be

Creating the login view requires you to create a GSP in the appropriate directory. The GSP contains a form that has a single selection field of usernames and a submit button. When the user submits the selection, the form invokes the handleLogin action on the UserController. Figure 5-7 illustrates the login view.

decide whether the tuning effort can be stopped or not. In case tests in the previous phase were run with instrumentation or SQL trace enabled, these should be switched off now. Measurement intrusion is undesirable when trying to get reliable figures to base a forecast on. After all, the original application usually runs without measurement intrusion through instrumentation or tracing. On the other hand, if the original application runs with instrumentation enabled, so should your test case.

represented in a straightforward manner, as shown in Listing 2-1. Just as a convenience, I have assigned the node numbers to variables with the same names as the node labels in the figure. You can, of course, just work with the numbers directly. Which adjacency list belongs to which node is indicated by the comments. If you want, take a minute to confirm that the representation does, indeed, correspond to the figure. Listing 2-1. A Straightforward Adjacency Set Representation a, b, c, d, e, f, g, h = range(8) N = [ {b, c, d, e, f}, # a {c, e}, # b {d}, # c {e}, # d {f}, # e {c, g, h}, # f {f, h}, # g {f, g} # h ]

Let s take a look at the Login link, <g:link controller="user" action="login"> Login</g:link>. When the user selects the Login link, the login action on the UserController is invoked. We ll explain the login action in the next section. Based upon convention, login.gsp should go in the grails-app/views/user directory. Listing 5-11 shows the contents of login.gsp.

Usually, changes are made in test and quality assurance systems first before they are allowed to go into production. If the root cause was inefficient SQL coding or application coding in general, it may take quite a while before the software manufacturer incorporates the necessary changes and releases a new version of the software. In addition to approval, changes that require reorganization of database objects need to wait for a sufficiently large maintenance window. If there is no budget for an extra-cost feature that was used in the improvement phase (e.g., partitioned tables), then it may be hard to get approval for the suggested changes. Furthermore it may require additional downtime to install the feature.

Listing 5-11. The Login View (login.gsp)

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