The MATCH() function performs a natural language search for a string against a text collection. A collection is a set of one or more columns included in a FULLTEXT index. The search string is given as the argument to AGAINST(). The search is performed in case-insensitive fashion. For every row in the table, MATCH() returns a relevance value, that is, a similarity measure between the search string and the text in that row in the columns named in the MATCH() list.

When MATCH() is used in a WHERE clause, as in the preceding example, the rows returned are automatically sorted with the highest relevance first. Relevance values are non-negative floating-point numbers. Zero relevance means no similarity. Relevance is computed based on the number of words in the row, the number of unique words in that row, the total number of words in the collection, and the number of documents (rows) that contain a particular word.

For natural-language full-text searches, it is a requirement that the columns named in the MATCH() function be the same columns included in some FULLTEXT index in your table. For the preceding query, note that the columns named in the MATCH() function (title and body) are the same as those named in the definition of the article table’s FULLTEXT index. If you wanted to search the title or body separately, you would need to create FULLTEXT indexes for each column

Ryan’s solution is interesting, but I can’t help but wonder about performance. While using SQL for the logic is a little more ninja, I can’t help but feeling it will be slower, especially on large tables. Have you done any performance testing on this Ryan?

Dave, your way should work also.