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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. word pdf 417 How to Encode a Tab or Function in a PDF417 in Microsoft Word ...
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Generating and creating specification-compatible PDF-417 barcodes in Microsoft Word documents directly. Download free trial package and view tutorial ... 10 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 word aflame upc, birt ean 13, birt upc-a, word code 39, birt data matrix, birt barcode font word pdf 417 PDF417 in Microsoft Office Automation | FAQs | PDF417 Barcode ...
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To manually place a single PDF417 barcode into a Word document, use these instructions for Word 2007 and Word 2010. Also, you can ... 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. For it to be truly useful, we would configure our OpenSSH server to allow forwarding only particular ports and setup keys to allow passwordless forwarding for authorized individuals. While beyond the scope of this text, this technique is a well-documented method of configuration using the authorized_keys file and its configuration syntax. gist is the engine that performs the actual searches, and it is an implementation of the Berkeley Generalized Search Tree (find more details about gist at http://gistcsberkeley edu/) The command for adding a gist index on the product table is CREATE INDEX idx_search_vector ON product USING gist(search_vector); 3 Populate the search_vector field of product with the search vectors These search vectors are lists of the words to be searchable for each product For HatShop, we ll consider the words that appear in the product s name and the product s description, giving more relevance to those appearing in the name This way, if more products match a particular search string, those with matches in the name will be shown at the top of the results list. word pdf 417 PDF417 Barcode Fonts - Barcode Resource
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