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The straightforward solution, frequently used to implement searching, consists of using LIKE in the WHERE clause of the SELECT statement. Let s take a look at a simple example that will return the products that have the word war somewhere in their description: SELECT name FROM product WHERE description LIKE '%war%' The LIKE operator matches parts of strings, and the percent wildcard (%) is used to specify any string of zero or more characters. That s why in the previous example, the pattern %war% matches all records whose description column has the word war somewhere in it. This search is case-insensitive. If you want to retrieve all the products that contain the word war somewhere in the product s name or description, the query will look like this: SELECT name FROM product WHERE description LIKE '%war%' OR name LIKE '%war%'; This method of searching has three important drawbacks: Speed: Because we need to search for text somewhere inside the description and name fields, the entire database must be searched on each query. This can significantly slow down the overall performance of HatShop when database searches are performed, especially if you have a large number of products in the database. Quality of search results: This method doesn t make it easy for you to implement various advanced features, such as returning the matching products sorted by search relevance. Advanced search features: These include searching using the Boolean operators (AND, OR) and searching for inflected forms of words, such as plurals and various verb tenses, or words located in close proximity. So how can you do better searches that implement these features If you have a large database that needs to be searched frequently, how can you search this database without killing your server The answer is using PostgreSQL s tsearch2 module.

Add some watches so you can see the variable s values change as the program executes 14 Sit back and think how helpful this tool can be in your future At this point, you should now have three tools in your troubleshooting arsenal: put_line(), table package DEBUG_TS, and a real live debugger! With these tools, you should be able to troubleshoot any logic problem, but what about performance.

If you have a long-running or large stored procedure, how do you know where in its code it s slow You can get this information with runtime profiling, our next topic.

MODE([Column1], [Column2],[Column3])

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tsearch2 is the search module we ll be using to implement our site s search feature. This module ships with PostgreSQL, and it allows performing advanced searches of your database by using special search indexes. Read Appendix A for installation instructions. There are two aspects to consider when building the data tier part of a catalog search feature: Preparing the database to be searched Using SQL to search the database

Profiling in this context is about collecting runtime statistics from your PL/SQL program units in order to determine which lines of code are consuming the most resources, usually in a unit of time. Armed with these statistics, you can quickly see what part of your PL/SQL program is taking the most time to execute, so you can concentrate your efforts on improving performance on those lines of code that are poor performers. In days of yore, you had to write your own profiling package in order to collect runtime statistics. Then you had to sprinkle your code with calls to your profiling package in order to collect the statistics. In fact, I did just that, creating a profiling package with architecture similar to the debug logging package. Nowadays, you have access to a built-in profiling package, SYS.DBMS_PROFILER, which can hook itself into your session and profile your PL/SQL code line by line, without you needing to add many lines of code to your listings. Here, we ll look at the three tables where DBMS_PROFILER stores its collected statistics, the package s methods, and an example of its use. Then we ll finish up this section by having you profile your own code.

In our scenario, the table that we ll use for searches is product, because that s what our visitors will be looking for To make the table searchable using the tsearch2 module, you need to prepare the table to be searched in three steps: 1 Add a new field to the product table, which will hold the search vectors A search vector is a string that contains a prepared (searchable) version of the data you want to be searched (such as product names and descriptions) In our case, the command will look like this (don t execute it now, we ll take care of this using an exercise): ALTER TABLE product ADD COLUMN search_vector tsvector; 2 Update the product table by adding a gist index on the newly added field.

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