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the ISBN as the unique identifier. To convert the ISBN into the internal identifier, the search engine was used extensively as a palliative. It would have been better to expose the ISBN in the process and hide the internal identifier for machine consumption, and this is what the employees of this company ended up doing.
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Choosing a strategy: the first step on a long road
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Choosing one or several strategies is only half the work though, and implementing them efficiently can become fairly challenging depending on the underlying technology used. In most Java applications, both simple text-box searches and detailed screen searches are implemented using the request technology provided by the data store. The data store being usually a relational database management system, an SQL query is built from the query elements provided by the user (after a more or less sophisticated filtering and adjustment algorithm). Unfortunately, data source query technologies often do not match user-centric search needs. This is particularly true in the case of relational databases.
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Pitfalls of search engines in relational databases
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SQL (Structured Query Language) is a fantastic tool for retrieving information. It
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especially shines when it comes to restricting columns to particular values or ranges of values and expressing data aggregation. But is it the right tool to use to find information based on user input To answer this question, let s look at an example and see the kind of input a user can provide and how an SQL-based search engine would deal with it. A user is looking for a book at her favorite online store. The online store uses a relational database to store the books catalog. The search engine is entirely based on SQL technology. The search box on the upper right is ready to receive the user s request:
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"a book about persisting objects with ybernate in Java"
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A relational database groups information into tables, each table having one or several columns. A simple version of the website could be represented by the following model:
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A Book table containing a title and a description An Author table containing a first name and a last name A relation between books and their authors
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Thanks to this example, we ll be able to uncover typical problems arising on the way to building an SQL-based search engine. While this list is by no mean complete, we ll face the following problems:
Writing complex queries because the information is spread across several tables Converting the search query to search words individually Keeping the search engine efficient by eliminating meaningless words (those that are either too common or not relevant)
Pitfalls of search engines in relational databases
Finding efficient ways to search a given word as opposed to a column value Returning results matching words from the same root Returning results matching synonymous words Recovering from user typos and other approximations Returning the most useful information first
Let s now dive into some details and start with the query complexity problem.
Query information spread across several tables
Where should we look for the search information our user has requested Realistically, title, description, first name, and last name potentially contain the information the user could base her search on. The first problem comes to light: The SQL-based search engine needs to look for several columns and tables, potentially joining them and leading to somewhat complex queries. The more columns the search engine targets, the more complex the SQL query or queries will be.
select book.id from Book book left join book.authors author where book.title = OR book.description = OR author.firstname = OR author.lastname =
This is often one area where search engines limit the user in order to keep queries relatively simple (to generate) and efficient (to execute). Note that this query doesn t take into account in how many columns a given word is found, but it seems that this information could be important (more on this later).
Searching words, not columns
Our search engine now looks for the user-provided sentence across different columns. It s very unlikely that any of the columns contains the complete following phrase: a book about persisting objects with ybernate in Java. Searching each individual word sounds like a better strategy. This leads to the second problem: A phrase needs to be split into several words. While this could sound like a trivial matter, do you actually know how to split a Chinese sentence into words After a little Java preprocessing, the SQL-based search engine now has access to a list of words that can be searched for: a, about, book, ybernate, in, Java, persisting, objects, with.
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