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In exceptional cases, the Oracle DBMS may decide to execute the view query from the data dictionary, populate a temporary table with the results, and then use the temporary table as a base table for the query entered. This happens only if the Oracle DBMS is not able to merge the view definition with the query entered, or if the Oracle optimizer determines that using a temporary table is a good idea. In the regular approach, as outlined in the preceding five steps, steps 2 and 3 are the only additional overhead. One of the main advantages of this approach is that you can benefit optimally from indexes on the underlying base tables. For example, suppose you enter the following query against the AVG_EVALUATIONS view: SQL> select * 2 from avg_evaluations 3 where avg_eval >= 4 This query is transformed internally into the statement shown in Listing 10-20. Notice that the WHERE clause is translated into a HAVING clause, and the asterisk (*) in the SELECT clause is expanded to the appropriate list of column expressions. Listing 10-20. Rewritten Query Against the REGISTRATIONS Table SQL> 2 3 4 5 select , from group having r.course avg(r.evaluation) as avg_eval registrations r by r.course avg(r.evaluation) >= 4;

sub url_serialised { my ($self, $url, $format) = @_; $format ||= 'json'; $self->json_data( $self->api->semanticproxy($url, output => $format) ); } # provides the data from url_serialised in a perl data structure sub url_as_data { my ($self, $url) = @_; my $format = 'json'; my $j = JSON::Any->new; $self->url_serialised($url); return $j->jsonToObj( $self->json_data ); } # send it some text and a content-type and return the requested format sub text_serialised { my ($self, $txt, $args) = @_; $args->{format} ||='application/json'; $args->{content_type} ||= 'text/txt'; return $self->api->enlighten($txt, %$args); } # as text_serialised but as a perl data structure sub text_as_data { my ($self, $txt, $args) = @_; $args->{content_type} ||= 'text/txt'; delete $args->{format} if $args->{format}; my $json = $self->text_serialised($txt, %$args); my $j = JSON::Any->new; return $j->jsonToObj($json); } 1; Now we can fire up the text server and make a request for http://localhost:3000/ url= http://devcatalystframeworkorg/snow_white.

Selections in this group of options control the number of copies of each image that will print: Number of copies: You can use the text field s up and down arrows to specify the number of copies that will print. Collate: If the destination printer supports collation, this check box will force printed output to be collated. The first set of pages (or documents) will print, then the second set, and so on. If this option isn t selected, the first image will print n (as set in the Number of copies field) times, then the second image, and so on.

COURSE AVG_EVAL ------ -------JAV 4.125 OAU 4.5 XML 4.5 SQL> Especially when dealing with larger tables, the performance overhead of using views is normally negligible. If you start defining views on views on views, the performance overhead may become more significant. And, in case you don t trust the performance overhead, you can always use diagnostic tools such as SQL *Plus or SQL Developer AUTOTRACE (see 7, Section 7.6) to check execution plans and statistics.

If the web service is working properly, we should receive a JSON data structure back with the content of the file and a representation of all the named entities that could be found in the file The data structure output from the OpenCalais web service is quite complicated, and a successful response will start with the string {"doc":{"info":{"document":" If that doesn t happen, make sure you used a correct API key and be persistent in case the web service is having problems In fact, the data structure returned by OpenCalais isn t too useful by itself, and it s likely that you ll want to start adding a variety of methods to the MyCalais::Processor class (or more likely, refactor the preceding code to be more closely aligned to your goals) Next we ll extend this web service to create our own web service.

There are several advantages to using factored subqueries. First, they can make development easier by isolating each query (as I will show in Listing 9-18). Second, they make the code clearer. Using the previous example would look as follows: select ... from (select ... from (select ... from (select ... from ...) v3 ) v2 ) v1 When there is a problem with the query, it can be difficult to locate the actual problem. By using subquery factoring, you can create the subquery as a standalone query, then make it a factored subquery using WITH, SELECT * from it to check for completeness, and add in additional predicates, data transformations, exclude columns, and so on. If this query is also meant to be a factored subquery, you can then name it and SELECT * from it to check for completeness, add in additional predicates, and so on. Listing 9-18 shows how a statement using a factored subquery can be developed using a 3 step process. Each step in Listing 9-18 is executed separately. Listing 9-18. WITH Clause Development Example select , from group by x.deptno avg(x.msal) avg_sal employees x x.deptno; x.deptno avg(x.msal) avg_sal employees x x.deptno)