However, for scenarios that don’t require the robust search features found in dedicated search engines, some general-purpose database management systems offer their own full-text search capabilities. There are multiple specialized open-source tools for such applications in use, with ElasticSearch being an especially popular choice. This level of flexibility in matching text to a search query is typical for full-text search engines that specialize in searching textual data. In this case, you may not find exactly what you were looking for since most popular websites with coffee recipes may not contain the exact phrase “coffee recipe.” If you were to enter that phrase into a real search engine, though, you might find pages with titles like “Great Coffee Drinks (with Recipes!)” or “Coffee Shop Drinks and Treats You Can Make at Home.” In these examples, the word “coffee” is present but the titles contain another form of the word “recipe” or exclude it entirely. Imagine you typed “coffee recipe” into a web search engine but it only returned pages that contained that exact phrase. However, these methods fall short when it comes to filtering against fields containing rich textual data. MongoDB queries that filter data by searching for exact matches, using greater-than or less-than comparisons, or by using regular expressions will work well enough in many situations. The author selected the Open Internet/Free Speech Fund to receive a donation as part of the Write for DOnations program.
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