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Filtering the noise
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Not all words seem equal, though; book, ybernate, Java, persisting, and objects seem relevant to the search, whereas a, about, in, and with are more noise and return results completely unrelated to the spirit of the search. The notion of a noisy word is fairly relative. First of all, it depends on the language, but it also depends on the domain on which a search is applied. For an online book store, book might be considered a noisy word. As a rule of thumb, a word can be considered noisy if it s very common in the data and hence not discriminatory (a, the, or, and the like) or if it s not meaningful for the search (book in a bookstore). You ve now discovered yet another bump in the holy
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quest of SQL-based search engines: A word-filtering solution needs to be in place to make the question more selective.
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Find by words...fast
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Restricted to the list of meaningful query words, the SQL search engine can look for each word in each column. Searching for a word inside the value of a column can be a complex and costly operation in SQL. The SQL like operator is used in conjunction with the wild card character % (for example, select ... from ... where title like %persisting% ...). And unfortunately for our search engine, this operation can be fairly expensive; you ll understand why in a minute. To verify if a table row matches title like '%persisting%', a database has two main solutions:
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Walk through each row and do the comparison; this is called a table scan, and it can be a fairly expensive operation, especially when the table is big. Use an index.
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An index is a data structure that makes searching by the value of a column much more efficient by ordering the index data by column value (see figure 1.4). To return the results of the query select * from Book book where book.title = 'Alice's adventures in Wonderland', the database can use the index to find out which rows match. This operation is fairly efficient because the title column values are ordered alphabetically. The database will look in the index in a roughly similar way to how you would look in a dictionary to find words starting with A, followed by l, then by i. This operation is called an index seek. The index structure is used to find matching information very quickly. Note that the query select * from Book book where book.title like 'Alice%' can use the same technique because the index structure is very efficient in finding values that start with a given string. Now let s look at the original search engine s query,
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Figure 1.4 A typical index structure in a database. Row IDs can be quickly found by title column value, thanks to the structure.
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Pitfalls of search engines in relational databases
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where title like %persisting% . The database cannot reuse the dictionary trick here because the column value might not start with persisting. Sometimes the database will use the index, reading every single entry in it, and see which entry has the word persisting somewhere in the key; this operation is called an index scan. While faster than a table scan (the index is more compact), this operation is in essence similar to the table scan and thus often slow. Because the search engine needs to find a word inside a column value, our search engine query is reduced to using either the table scan or the index scan technique and suffers from their poor performance.
Searching words with the same root and meaning
After identifying all the previous problems, we end up with a slow, complex-to-implement SQL-based search engine. And we need to apply complex analysis to the human query before morphing it into an SQL query. Unfortunately, we re still far from the end of our journey; the perfect search engine is not there yet. One of the fundamental problems still present is that words provided by the user may not match letter to letter the words in our data. Our search user certainly expects the search engine to return books containing not only persisting but also persist, persistence, persisted, and any word whose root is persist. The process used to identify a root from a word (called a stem) is named the stemming process. Expectations might even go further; why not consider persist and all of its synonyms Save and store are both valid synonyms of persist. It would be nice if the search engine returned books containing the word save when the query is asking for persist. This is a new category of problems that would force us to modify our data structure to cope with them. A possible implementation could involve an additional data structure to store the stem and synonyms for each word, but this would involve a significant additional amount of work.
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